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Effects of Geographic Information Quality on Soil Erosion Prediction Dr. sc. ETH Karika Kunta Zurich, May 2009
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Page 1: Effects of Geographic Information Quality on Soil …...Bachelor of Engineering (Civil), Chiang Mai University, Thailand Born September 1, 1975 Citizen of Chiang Mai, Thailand Accepted

Effects of Geographic Information Quality

on Soil Erosion Prediction

Dr. sc. ETH Karika Kunta

Zurich, May 2009

Page 2: Effects of Geographic Information Quality on Soil …...Bachelor of Engineering (Civil), Chiang Mai University, Thailand Born September 1, 1975 Citizen of Chiang Mai, Thailand Accepted

This publication is an edited version of:

DISS. ETH Nr. 18136

Effects of Geographic Information Quality on Soil Erosion Prediction

A dissertation submitted to Swiss Federal Institute of Technology Zurich

for the degree of

Doctor of Sciences ETH ZURICH

Presented by

KARIKA KUNTA Master of Sciences (Environmental Engineering and Sustainable Infrastructure), KTH, Sweden

Bachelor of Engineering (Civil), Chiang Mai University, Thailand

Born September 1, 1975

Citizen of Chiang Mai, Thailand

Accepted on the recommendation of

Examiner: Prof. Dr. Alessandro Carosio, ETH Zurich Co-examiner: Associate Professor Dr. Maria Antonia Brovelli, Politecnico di Milano

Co-examiner: Prof. Dr. Alain Geiger, ETH Zurich

2009

IGP Mitteilungen Nr. 103 Karika Kunta © 2009 Institute of Geodesy and Photogrammetry Swiss Federal Institute of Technology Zurich ETH Hönggerberg CH-8093 Zürich All rights reserved ISBN 978-3-906467-84-9 ISSN 0252-9335

Page 3: Effects of Geographic Information Quality on Soil …...Bachelor of Engineering (Civil), Chiang Mai University, Thailand Born September 1, 1975 Citizen of Chiang Mai, Thailand Accepted

“All that we are is the result of what we have thought.

The mind is everything. What we think we become.”

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Vorwort Page 3

VorwortDie Technologie der Geoinformation bietet wirksame Werkzeuge für die Analyse undVisualisierung von Daten, die einen räumlichen Bezug aufweisen. Da Umweltprozesse in derRegel eine Raumkomponente besitzen, sind sie praktisch immer mit Rauminformationen ver-bunden. Die GIS-Technologie ist daher ein wichtiges Instrument für die Analyse und Verwal-tung von Umweltinformationen.

Die vorliegende Publikation von Karika Kunta analysiert ein Spezialgebiet, in welchem dieGeoinformation eine besonders grosse Rolle spielt: die Bodenerosion, ihre Ursachen, die quan-titativen Aspekte der Bodenverluste, die Modelle für eine Vorhersage der Erosionsphänomene.Alle diese Elemente haben offensichtlich einen Raumbezug und können von der Leistungs-fähigkeit der Geoinformationssysteme stark profitieren. Das Ergebnis ist die vorliegendeAbhandlung, die als Promotionsarbeit von der ETH Zürich im Mai 2009 angenommen wurde.

Karika Kunta interessierte sich von Anfang an für die Applikationen der GIS-Technologie:Datenanalyse, Datenverwaltung, -Akquisition und Darstellung, die zentrale Komponente einesmodernen GIS waren für sie von besonderer Bedeutung. Später verwendete sie diese leistungs-fähigen Werkzeuge im Bereich der Bodenerosion, um Analyseverfahren einsetzen und zuerproben, die bei der Vorhersage von Erosionsprozessen zur Anwendung kommen. DieAuswirkung der Qualität der geographischen Daten in den Berechnungsmodellen konnten soidentifiziert und Empfehlungen für den Aufbau von Geoinformationssystemen für die Land-wirtschaftspolitik entsprechend erarbeitet werden.

Die vorliegende Studie zeigt mit Hilfe von allgemeinen theoretischen Untersuchungen und aufGrund von Modellberechnungen, was von den heute bekannten Bodenerosionsmodellen zuerwarten ist und wie die geographischen Daten die Ergebnisse beeinflussen. Dies ist vongrosser Bedeutung, weil die Qualität der Geodaten oft sehr heterogen sein kann, und ihreAuswirkung nicht bekannt ist.

Die Autorin untersuchte zuerst die Bedürfnisse der Landwirtschaftspolitik, die Konsequenzender Bodenerosion auf die Produktivität und die mathematischen Modelle, die heute eingesetztwerden, um eine Vorhersage der Erosion mittels Simulationen angeben zu können. Ebenfallsbeschrieben sind die Faktoren, die die Erosionsphänomene beeinflussen: Niederschläge, Bode-neigenschaften, aktuelle Bodennutzung und Bodenbedeckung sowie die topographischenEigenschaften des Territoriums, welche die Erosionsprozesse überhaupt auslösen. Sie bes-chränkt aus verständlichen Gründen ihre Detailuntersuchungen auf diese letzte Komponenteder Erosionsmodelle, damit der Umfang der Arbeit in tragbaren Grenzen bleibt. Dabei werden

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Page 4 Vorwort

die Folgen der Geodatenqualität unter einer Vielfalt von Aspekten untersucht und die Zusam-menhänge zwischen den Komponenten bei der Anwendung von Bodenerosionsmodellen ver-ständlich beschrieben. Die Bedeutung der Geoinformation und der Geoinformationssysteme,die zurzeit für die Nutzung der Geoinformation zur Verfügung stehen, werden analysiert underläutert. Die Funktion der Datenmodelle und die Möglichkeit, eigene Operationen in GIS zudefinieren, werden am Beispiel der Bodenerosion illustriert.

Die Komponenten der heute verfügbaren Erosionsmodelle werden analysiert und praktischdurchführbare Berechnungsprozesse vorgeschlagen. Es werden auch Testregionen in der Sch-weiz und in Thailand festgelegt, in welchen durch Variantenvergleich die Sensibilität der Ver-fahren auf Änderungen in den Eingangsdaten bestimmt werden kann. Mit Hilfe derverfügbaren Testdaten zeigt die Autorin die Folgen der Qualitätsunterschiede bei Höhenmod-ellen (z.B. DTM-AV mit 2 m Maschenweite oder DHM 25 mit 25 m Maschenweite), ebenfallsuntersucht sie die Einflüsse des hydrographischen Netzes auf die Simulationen, indem siereelle Netze aus im Gelände genau erfassten topographischen Daten mit genäherten aus demHöhenmodell berechneten Wasserläufen vergleicht und sie in den Simulationsberechnungengegenüberstellt.

Später identifiziert Karika Kunta die kritischen Komponenten und untersucht die Einflüsse derheute verfügbaren Qualitätsniveaus auf die Erosionsvorhersagen. Einige weltweit verfügbareDatenbestände werden beschrieben und analysiert. Karika Kunta hat die Datenanforderungender Erosionsmodelle untersucht und schlägt durch die dazugehörenden Metadaten die pas-senden Inhalte für eine NGDI vor. Dabei erklärt sie, wie man zu den heute gültigen internation-alen Standards (ISO 19115) konform sein kann.

Die Autorin befasst sich auch mit den politisch- organisatorischen Voraussetzungen. Die Qual-ität der Daten ist von grosser Bedeutung, aber noch wichtiger sind ihre Zugänglichkeit undVerfügbarkeit, ohne welche kein praktischer Einsatz der Modelle möglich ist. Die Zugänglich-keit ist heute eine der Hauptfragen der nationalen Geodateninfrastrukturen. Aktuelle Themensind die Interoperabilität und die zentralen Geodatenkataloge, in welchen die Metadaten ver-waltet werden.

Karika Kunta hat die ihr gestellten Aufgaben erfolgreich gelöst. Die vorliegende Abhandlungbeweist, dass sie hochstehende wissenschaftliche Leistungen erbringen kann. Ich bedankemich bei ihr für die geleistete Arbeit und gratuliere ihr für die hier veröffentlichte Dissertation.

Alessandro Carosio Zürich, 4. Mai 2009

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Table of Contents Page 5

Table of Contents

Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

º·¤Ñ´ÂèÍ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.2 Motivation and problem statement of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.4 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.5 Basic definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Chapter 2 Soil Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.1 Soil Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.1.1 Soil Erosion Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.1.2 Principal soil erosion factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.2 Soil erosion models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362.2.1 Universal Soil Loss Equation (USLE) model . . . . . . . . . . . . . . . . . . . . . . . 372.2.2 Revised Soil Loss Equation (RUSLE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Chapter 3 Geographic Information System and Soil Erosion . . . . . . . . . . . 513.1 Geographic information system and soil erosion . . . . . . . . . . . . . . . . . . . . . . . . 52

3.1.1 Applications of GIS on soil erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.1.2 Development of data model in ArcGIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.1.3 ArcObjects in ArcGIS and soil erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543.1.4 Geographic Resources Analysis Support System with soil erosion . . . . . . . 553.1.5 Slope Length factor calculation with VBA . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.2 Soil GIS data sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583.2.1 Spatial Data Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583.2.2 Interoperability in GIS and standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

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Page 6 Table of Contents

3.3 Metadata on soil erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643.3.1 Development of metadata for National Spatial Data Infrastructure in Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653.3.2 Metadata on soil erosion and soil data in Europe . . . . . . . . . . . . . . . . . . . . . 663.3.3 Metadata standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Chapter 4 GIS Application for Soil Erosion Model . . . . . . . . . . . . . . . . . . . 694.1 GIS application on soil erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.1.1 Slope Length Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.1.2 Overall Slope Length calculation process . . . . . . . . . . . . . . . . . . . . . . . . . . 714.1.3 Iteration of accumulative Slope Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.1.4 Channelization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774.1.5 Conclusion of the calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.2 Study areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 794.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 794.2.2 Study Areas in Switzerland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804.2.3 Study area in Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.3 Application results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.3.1 Results in study areas of Switzerland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.3.2 Results in the Study Area of Chiang Rai province, Thailand . . . . . . . . . . . 984.3.3 The comparison of results in Thailand and Switzerland . . . . . . . . . . . . . . 103

4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Chapter 5 GIS Data Quality and Soil Erosion . . . . . . . . . . . . . . . . . . . . . . 1095.1 Different quality of GIS soil database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

5.1.1 World Soils and Terrain Digital Database . . . . . . . . . . . . . . . . . . . . . . . . . 1125.1.2 The Australian Soil Resource Information System . . . . . . . . . . . . . . . . . . 1155.1.3 Thailand soil information system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5.2 Soil GIS data sharing: Thai example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1185.2.1 Interoperability in GIS in Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

5.3 Web-based GIS soil data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1195.3.1 Water Erosion Prediction Project-Climate Assessment Tool . . . . . . . . . . 1205.3.2 Mapping services in the european soil portal . . . . . . . . . . . . . . . . . . . . . . . 121

Chapter 6 Metadata on Soil and Soil Erosion . . . . . . . . . . . . . . . . . . . . . . . 1236.1 Data model of soil erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

6.1.1 GIS data model for RUSLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1256.1.2 Required Data for RUSLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

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Table of Contents Page 7

6.2 Soil erosion metadata model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276.2.1 Soil erosion Required Metadata model . . . . . . . . . . . . . . . . . . . . . . . . . . . 1276.2.2 ISO 19115 conformity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

6.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Chapter 7 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1377.1 Summary of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

7.2 Outlooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

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List of FiguresList of Figures Page 9

Figure 1-1 Motivation of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figure 1-2 Study area in a catchment of Kleine Emme, Switzerland . . . . . . . . . . . . . . . . . . 23

Figure 1-3 Study area in Wieng Kaen district, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figure 1-4 Thesis structure overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 2-1 Soil erosion types(Engel, 2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Figure 2-2 Different slope shapes (modified from US Forest Service, 1979) . . . . . . . . . . . 35

Figure 2-3 Unit plot for USLE and RUSLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 2-4 Erosional forces in general . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Figure 2-5 Concept of RUSLE2 computer program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 3-1 Georelational data model (modified from Sarközy, 2001) . . . . . . . . . . . . . . . . . 53

Figure 3-2 Geodatabase data model (modified from www.telematica.com.pe/novedad22.htm,

2007). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Figure 3-3 Simple overview of ArcObjects (modified from ESRI, 2007b) . . . . . . . . . . . . . 55

Figure 3-4 An SDI hierarchy of SDIs at different levels (Rajabifard, 2001) . . . . . . . . . . . . 60

Figure 3-5 Components of NSDI (Federal Geographic Data Committee, 2008) . . . . . . . . . 62

Figure 3-6 Interoperability in GIS (ISO, 2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Figure 3-7 Integration of standards-based Web services (ESRI, 2003) . . . . . . . . . . . . . . . . 64

Figure 3-8 Metadata search engine in ThaiSDI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 4-1 Flowchart of LS calculation schema (modified from Kunta and Carosio, 2007) 72

Figure 4-2 Profile of a sink and depression in nature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 4-3 Steps of flow length calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 4-4 Flowchart of slope length accumulation iteration . . . . . . . . . . . . . . . . . . . . . . . . 76

Figure 4-5 Working environment of LS factor and of LS factor result sample . . . . . . . . . . 79

Figure 4-6 Location of study areas in Canton of Lucerne, Switzerland . . . . . . . . . . . . . . . . 82

Figure 4-7 Details of the two catchments in Kleine Emme river . . . . . . . . . . . . . . . . . . . . . 82

Figure 4-8 Location of Wieng Kaen district, Chiang Rai province in Thailand . . . . . . . . . . 85

Figure 4-9 Channels from Flow Accumulation Value method in Altmülibach . . . . . . . . . . 90

Figure 4-10 Flow Accumulations from Stream Burning method in Altmülibach . . . . . . . . 90

Figure 4-11 Channels from FAV method focusing on the flat area in Atlmülibach . . . . . . . 91

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Page 10 List of Figures

Figure 4-12 Section of the river in Altmülibach showing how resolution influence the channel

extractions: (a) Channels from DEM 25 (b) Channels from DEM 2 . . . . . . . . . 92

Figure 4-13 Locations of 5 compared river network nodes . . . . . . . . . . . . . . . . . . . . . . . . . 92

Figure 4-14 Channels predicted by the Flow Accumulation Value method in Staldenbach 93

Figure 4-15 Profile of slope from different DEM compared to the reality . . . . . . . . . . . . . . 96

Figure 4-16 Soil erosion risk map in Altmülibach from DEM 2 m . . . . . . . . . . . . . . . . . . . 97

Figure 4-17 Soil erosion risk map in Stadalbach from DEM 2 m . . . . . . . . . . . . . . . . . . . . . 97

Figure 4-18 10 Rain Stations in Chiang Rai province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Figure 4-19 Linear regression of estimated R factor from two methods . . . . . . . . . . . . . . 100

Figure 4-20 R factor surfaces resulting from spatial interpolation . . . . . . . . . . . . . . . . . . . 100

Figure 4-21 Channelization results in Wieng Kaen area and the compared nodes . . . . . . . 101

Figure 4-22 Soil erosion risk in Wieng Kaen area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Figure 5-1 The whole concept of chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Figure 5-2 General approach and data structure of SOTER

(modified from Weller and Stahr, 1995) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Figure 5-3 Soil level of ASRIS (modified from Australian department of the environment and

water resource, 2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Figure 5-4 The structure of Thailand soil information system

(Promburom and Ekasingh, 1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Figure 5-5 Example of data interoperability in a NSDI of Thailand . . . . . . . . . . . . . . . . . . 119

Figure 5-6 The relation of global warming and soil erosion in WEPPCAT . . . . . . . . . . . . 121

Figure 6-1 UML class diagram presenting the RUSLE GIS data model . . . . . . . . . . . . . . 125

Figure 6-2 UML class diagram of Soil erosion Required Metadata model . . . . . . . . . . . . 128

Figure 6-3 UML class diagram of metadata packages in ISO 19115 (ISO, 2003) . . . . . . . 130

Figure 6-4 Core metadata for geographic datasets (modified from ISO, 2003) . . . . . . . . . 131

Figure 6-5 Metadata community profile (ISO, 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Figure 6-6 UML diagram representing the extension information customized corresponding to

the model SRM. The additional elements in the profile are highlighted . . . . . . . 133

Figure 6-7 UML class diagram representing spatial resolution information in

ISO 19115 (ISO, 2003) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

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List of Tables Page 11

List of TablesTable 4-1 Summary of different GIS data sources applied . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Table 4-2 R factor and K factor for catchments in Switzerland . . . . . . . . . . . . . . . . . . . . . . 83

Table 4-3 Treated K factor values in Wieng Kaen area . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Table 4-4 Distances between generated channels and VRN using

the FAV method in Atlmülibach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Table 4-5 Soil erosion results from different cases in Altmülibach . . . . . . . . . . . . . . . . . . . 94

Table 4-6 Soil erosion results from different cases in Staldenbach . . . . . . . . . . . . . . . . . . . 94

Table 4-7 Estimated R Factor for the catchment in Wieng Kaen . . . . . . . . . . . . . . . . . . . . . 99

Table 4-8 Different distances between channels of DEM 30 and

DEM 90 m in Wieng Kaen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Table 4-9 Soil erosion results from different cases in Wieng Kaen area . . . . . . . . . . . . . . 103

Table 4-10 Results Comparison in Two Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Table 6-1 Recommended ISO 19115 metadata entities for the SRM model . . . . . . . . . . . 132

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Abstract Page 13

Abstract

Soil erosion is one of the most serious problems in the mountainous areas. Geographic Infor-mation Systems (GIS) are widely applied to predict soil erosion, as all factors on soil erosioncan be extracted by spatial analysis. Therefore, the quality of spatial data plays a great role onthe prediction and the most appropriated data should be used for input data to the model.

The purpose of this study is to evaluate the sensitivity of GIS data quality for the Revised Uni-versal Soil Loss Equation (RUSLE) model. Different quality of GIS data input for two catch-ments in Switzerland and a catchment in Thailand are applied to the calculation. Aprogrammed Visual Basic Application (VBA) extension on ArcGIS 9.2 and the geostatisticsanalysis are used for the calculation.

Moreover, the study aims to improve the soil erosion prediction, experienced from the study,using GIS technology. In order to achieve the aim, the study recommends different methods :the use of GIS database of different soil-scales, the soil GIS data sharing, the Web-based GISsoil data and the soil erosion metadata model.

From the study, the developed algorithm (VBA application) is implemented on ArcGIS 9.2Interface and has shown to be a good tool for the RUSLE model in the study areas. The resultsof the study present that in the heterogeneous slope area, the finer Digital Elevation Model(DEM) yields more accurate the soil erosion values. In contrast, in the flatter area, coarse DEMderives similar results to the finer ones. The finer DEMs are expensive, therefore it should beused as necessary.

Also, the channelization results using different methods, which combine DEM and a VectorRiver Network (VRN), are completed. The results show that the VRN is very effective to iden-tify the channels starting points. The study highly recommends to combine the VRN with theDEM for channelization in all cases.

Furthermore, the soil erosion metadata model is established conforming to the ISO 19115. It isfound that the basic GIS data (DEM, Vector River Network, etc.) can apply to ISO 19115, butspecific metadata (soil types, cropping types, etc.) is needed to identify the particular data.

Altogether, the GIS data transfer, the interoperability in GIS, a unique standard for soil classifi-cations, Spatial Data Infrastructures (SDI) and the soil erosion metadata model should be com-

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Page 14 Abstract

pleted for all soil data in order to share all data from different sources or organizations. Themethodologies will support all users to access the most appropriate GIS data and then obtainthe more accurate soil erosion.

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Zusammenfassung Page 15

Zusammenfassung

Bodenerosion ist eines der grössten Probleme in gebirgigen Regionen. Da alle Faktoren desBodenverlustes auf Daten beruhen, die in Geografischen Informationssystemen (GIS) ver-waltet, gespeichert und analysiert werden können, werden GIS für die Voraussage des Boden-verlustes verwendet. Bodenerosions-Modelle sind stark von der Qualität der verwendetenräumlichen Daten abhängig.

Das Ziel dieser Studie ist die Bestimmung des Einflusses der GIS-Datenqualität auf die Model-lierung der Bodenerosion und die Entwicklung eines Berechnungsverfahrens für die Bodener-osion auf der Basis von GIS mit dem „Revised Universal Soil Loss Equation“ (RUSLE)Modell. In den Untersuchungsgebieten der Zentralschweiz und in Nord-Thailand wurdenunterschiedliche Basisdaten verwendet. Um den Bodenverlust berechnen zu können, wurdeeine Visual Basic for Applications (VBA)-Anwendung in ArcGIS 9.2 entwickelt. Weiter wur-den geostatistische Analysen anhand der Anwendungsfälle in den erwähnten Gebieten realisi-ert.

Außerdem weist die Studie darauf hin, mit den daraus erzhielten Erfahrungen und mit Hilfeder GIS-Technologie die Bodenerosionsvorhersage zu verbessern. Um dieses Ziel zu erre-ichen, empfiehlt die Studie verschiedene Methoden: die Verwendung von GIS-Datenbankenunterschiedlicher Qualitätsstufen, die gemeinsame Nutzung von GIS Daten, die Verwendungvon webbasierten GIS Bodendaten und das Bodenerosionsmetadatenmodell.

Die entwickelten Algorithmen (VBA Anwendungen) wurden in die ArcGIS 9.2 Umgebungimplementiert und stellen in den untersuchten Gebieten gute Werkzeuge für das RUSLE Mod-ell dar. Das Resultat der Studie zeigt, dass in den heterogenen Hanggebieten das feinere digi-tale Höhenmodell (Digital Elevation Model DEM) exaktere Bodenerosionswerte liefert. ImGegensatz dazu erzielen grobmaschige DEM in flachen Gebieten im Vergleich zu den hochau-flösenden Modellen ähnlich gute Resultate. Feinere DEM sind teurer und sollten daher nurangewendet werden wenn sie wirklich nötig sind. Die erforderlichen hydrographischen Netzewurden durch Verwendung unterschiedlicher Methoden generiert. DEM wurden mit einemVector River Network (VRN) kombiniert. Die Studie empfiehlt, die Startpunkte der Gerinne inallen Fällen durch die Einbindung des VRN zu bestimmen.

Zudem wurde basierend auf ISO 19115 das Metadatenmodell für die Bodenerosion aufgestellt.Dabei stellte sich heraus, dass bei den Grundlagendaten (DEM, Gewäser usw.) ISO 19115

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Page 16 Zusammenfassung

anwendbar ist. Die spezifischen Metadaten der Bodenerosion (Bodentyp, -Bedeckung, -Nut-zung usw.) erfordern eine Erweiterung des ISO-Metamodells.

Um den Austausch zwischen den verschiedenen Datenquellen und Organisationen zu ermögli-chen, sollten für alle Bodendaten die Methoden des GIS-Datentranfers, die Interoperabilität inGIS, ein einheitlicher Standard für Bodenklassifizierung, eine räumliche Dateninfrastruktur(Spatial Data Infrastructure SDI) und das Bodenerosionsmetadatenmodell zur Verfügung ste-hen. In der Arbeit wird gezeigt, wie die Interoperabilität zwischen den Benutzern in allen ihrenFormen erreicht werden kann. Unter diesen Voraussetzungen wird eine präsizere Voraussageder Bodenerosion möglich sein, die alle wesentlichen Einflussfaktoren berücksichtigt.

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Thai Abstract Page 17

บทคัดยอ

ในบริเวณพื้นที่ภูเขา หรือพ้ืนที่ที่มีความลัดชัน การชะลางพังทลายของดินเปนปญหาที่สําคัญในพื้นที่เหลานี้ ปจจัยตางๆของการชะลางพังทลายของดินสามารถวิเคราะหโดยการวิเคราะหเชิงพื้นที ่ ระบบสารสนเทศภูมิศาสตร(GIS)จึงไดถูกนํามาประยุกตใชกับปญหาดังกลาวอยางแพรหลาย ดังนั้นคุณภาพของขอมูล GIS ที่นํามาใชจึงมีผลตอความถูกตองในการประเมินการชะลางพังทลายของดิน

วัตถุประสงคของการศึกษานี้ คือ การประเมินผลของขอมูล GIS ที่แตกตางกันตอโมเดลการชะลางพังทลายของดิน RUSLEโดยนําขอมูล GIS ที่มีคณุภาพแตกตางกันมาประยุกตหาปริมาณการชะลางพังทลายของดินบริเวณพื้นที่ลุมน้ําในประเทศสวิสเซอรแลนด และพื้นที่ลุมน้ําในประเทศไทย โดยมีการพัฒนาโปรแกรม Visual Basic Application (VBA) บนโปรแกรมทางดานสารสนเทศภูมิศาสตร ArcGIS 9.2 และการวิคราะหทางดานธรณีสถิติ เพื่อคําณวนการประเมินการชะลางพังทลายของดิน

นอกจากนี้การศึกษานี้ยังมีวัตถุประสงคเพื่อปรบัปรุงความถูกตองในการคํานวณการชะลางพังทลายของดิน โดยการปรบัปรุงคุณภาพขอมูล GIS และการคนหาขอมูล GIS เพื่อใหเปนไปตามวัตถุประสงควิทยานิพนธฉบับนี้จึงไดมีการศึกษาฐานขอมูล GIS ของดินในมาตราสวนที่แตกตางกัน เพื่อใหไดขอมูลดินที่เหมาะสมกับพื้นที่ในการศึกษาศึกษา การใชขอมูล GISรวมกัน(Interoperability in GIS) การใชขอมูล GIS ของดินบน(Web-beased soil data) อินเตอรเนทและการสรางโมเดลคําอธบิายขอมลู(Metadata) สําหรับการชะลางพังทลายของดิน

จากการศึกษาพบวาโปรแกรมที่พัฒนาขึ้นสามารถใชเปนเครื่องมือสําหรับโปรแกรม ArcGIS เพื่อหาปริมาณการชะลางพังทลายของดินตามโมเดล RUSLE ไดเปนอยางดี ผลจากการชะลางพังทลายของดินในบริเวณที่มีความลาดชันแตกตางกันโมเดลชั้นความสงู(Digital Elevation Model, DEM)ที่มีความละเอียดสงูใหผลที่มีความถูกตองสูงกวา DEM ที่มคีวามละเอียดตํ่า ในทางตรงกันขามบริเวณที่มีพ้ืนที่ที่มีควาลาดชันนอย DEM ทั้งสองใหผลของการชะลางพังทลายของดินที่ใกลเคียงกัน ซึ่งโดยปกติแลว DEM ที่มีความละเอียดสงูมีราคาสูง ดังนั้นควรนํามาใชเมื่อมีความจําเปน

นอกจากนี้การกําหนดเสนทางน้ําและจุดเริม่ของลําธาร(Channel)ในพื้นที่ศกึษาโดยใชขอมูลเสนทางน้ํา (Vector River Net-work, VRN) และDEM มารวมกัน ผลการศึกษาพบวาการระบุจุดเริ่มตนของลําธารโดยนํา VRN มาพิจารณารวมดวยนั้นไดผลที่มีประสทิธิภาพใกลเคียงกับเสนทางน้ําตามสภาพพื้นที่จรงิ ดังนั้นการศึกษานี้จึงแนะนําใหในทุกกรณีศกึษาจึงควรนําVRN มาพิจารณารวมกับ DEM เพื่อใหไดเสนทางน้ําทีม่ีความถูกตอง

ในการศึกษานี้ยังไดเสนอโมเดลขอมูลการอธิบายขอมูล GIS (Metadata model) สําหรับการชะลางพังทลายของดินทีส่อดคลองกับมาตรฐานอุตสาหกรรม 19 115 (มอก. 19 115) จากโมเดล Metadata ที่สรางขึ้น พบวา มอก. 19 115 สามารถอธิบายขอมูล GIS พ้ืนฐาน (เชน ขอมูลช้ันความสูง ขอมูลเสนทางแมน้ํา เปนตน) ไดอยางเพียงพอ สําหรับขอมูล GIS เฉพาะทางสําหรับการชะลางพังทลายของดินนั้น (เชน ชนิดของดิน ชนิดของพันธุพืชทีป่กคลุมพื้นที่ สถานีวัดปริมาณน้ําฝน ฯลฯ)ตองมีการใชสวนขยายจากมาตรฐานดังกลาว(MD_MetadataExtensionInfor mation) เพื่อชวยใหมีการอธิบายขอมูลไดชัดเจน และเฉพาะเจาะจงมากขึ้น

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Page 18 Thai Abstract

สําหรับการคํานวณหาปริมาณการชะลางพังทลายของดินสอดคลองตามสมการ RUSLE การศึกษานี้ไดพิจารณาใหมีการพัฒนาเกี่ยวกับดาน GIS ตางๆ ดังนี้ การถายเทขอมูล GIS (GIS data transfer) การทํางานรวมกันในดาน GIS (Interoperabilityin GIS) มาตรฐานเดียวในการแบงชั้นดิน โครงสรางพื้นฐานขอมูลภูมิสารสนเทศภูมิศาสตร(Spatial Data Infrastructure)และโมเดลขอมูลอธิบายขอมูลสารสนเทศ(Metadata model)สําหรับการชะลางพังทลายของดิน เพื่อจะสนับสนุนใหเกิดการใชขอมูลเกี่ยวกับดินรวมกันจากแหลงขอมูลที่แตกตางกัน เมื่อมีการนําแนวทางที่แนะนําในการศกึษานี้มาปฏิบัติแลวจะทําใหผูใชโมเดลสามารถเลือกใชขอมูลที่เหมาะสมกับพื้นที่อยางอิสระ ทั้งยังมีการใชขอมลูที่มอียูอยางมีประสิทธภิาพและสงผลใหการคํานวณปริมาณการชะลางพังทลายของดินนั้นมคีวามถูกตอง และแมนยํามากขึ้น

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Chapter 1: Introduction Page 19

Chapter 1Introduction

1.1 Background

Soil erosion is a naturally occurring process in high land. The agents of soil erosion are waterand wind. Since the interested areas are at risk of the soil erosion by water, therefore in thisstudy only soil erosion by water is considered. Soil erosion by water is the process of particledetaching and particle transporting by flowing water to downhill areas, see Section 2.1.

Soil deterioration and low quality of water, as a result of erosion and run off, often results in asevere global problem. Erosion rates worldwide range from a low of 0.001 - 2 ton/ha/year onrelatively flat land with grass and/or forest cover to rates ranging from 1 - 5 ton/ha/year inmountainous regions with normal vegetative cover (Patric, 2002). For example, in the UnitedStates soil loss is estimated to be 6.9 billion tons per year and China reports a 6 billion-ton lossper year (Wen, 1993). Also, in many parts of the Mediterranean region, erosion has reached astage of irreversibility and in some places erosion has practically ceased because there is nomore soil left (Van der Knijff et al., 1999). Consequently, there have been a number of studieson soil erosion by different organizations in this area.

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Page 20 Chapter 1: Introduction

Soil erosion diminishes soil quality and thereby reduces the productivity of natural, agricul-tural, and forest ecosystems (Pimental and Kounang, 1998). Therefore, soil erosion estimationhas been a popular topic in environmental studies for decades.

To predict the soil erosion, soil loss estimation is applied to soil erosion situations which it isimportant for land management and soil conservation. According to soil erosion theories, dif-ferent soil erosion models have been used in different areas around the world in order to pre-dict the soil loss.

In the study of Merritt et al., 2003 found that with the increasing powers of computers in thelast 20 to 30 years, there has been a rapid increase in the exploration of catchment erosion andsediment transport through the use of computer models. In addition, Geographic InformationSystem (GIS) have become a well known tool for spatial analysis for more than 40 years. GISis a tool to cr eate, stor e, organize, de monstrate and analyze spatial phenomena, forinstance soil erosion. All relevant erosion factors; rainfall and runoff factor, topography, landuse, soil types and agricultural practice patterns, can be converted in different GIS data formatsas they are spatial information. As a result, it is universal that GIS is applied to soil erosionanalysis as well as to other environmental problems.

Integrating GIS and soil erosion models, the quality of spatial data plays a great role in theresults, analysis and, importantly, decision making. The better quality data tends to providebetter results. The application of GIS-techniques can only be possible if the quality of requireddata (from available data or data collection) is known and can be examined. However, the dataacquisition is the most expensive and time-consuming step in GIS. Therefore, from an eco-nomic point of view, the optimum quality of GIS data, corresponding to specific require-ments, must be alternatively chosen.

In this study, the quality of GIS data on soil erosion prediction is the main interest. The qualityof GIS data is studied with case studies in Switzerland and in Thailand. The sensitivities ofGIS data for soil erosion are scrutinized by the developed calculation within GIS interface,especially Digital Elevation Models (DEM) and hydrology maps from various sources andareas.

Not only quality of GIS data is important in soil erosion models, but also obtaining data fromthe right source and fitting into proper model is important. According to this study, searchingfor the right data is one of the main problem for processing the model. In this regard, metadata,which describe spatial data in content, use, purpose, source and data quality, helps to find andaccess the right information together with the Spatial Data Infrastructure (SDI) components.Therefore, the proposed metadata model on soil erosion is completed, see Chapter 6. Further-

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Chapter 1: Introduction Page 21

more, the new trends for GIS applications, including interoperability in GIS, on soil erosionare discussed and some examples are given.

With a more accurate prediction of soil erosion, a better and more sustainable decision on soilmanagement is possible. This study will provide more certainty for the soil loss estimation.

1.2 Motivation and problem statement of thesis

As mentioned above, soil erosion is a critical environmental problem through out the world’sterrestrial ecosystems (Pimnetal, 2000). Effective tools are developed in order to estimate soilerosion more accurately, especially within GIS interface. As a result, the input for GIS mustobtain sufficient quality to contribute good r esult for the models. Th erefore, the qualityof GIS data exam ining is im portant f or t he m odel qualit y. Advance erosion predictionneeds the optimum GIS data quality corresponding to the erosion model.

Furthremore, searching soil data from different GIS and orgamizations by using metadatacould be less time consuming. Data from different sources need to be shared in a GIS. Thus,interoperability in terms of data access, complex data transformation and import/export capa-bilities need to be studied.

According to Figure 1-1 which presents the entire idea of the thesis motivation, the soil ero-sion prediction, with optimum quality GIS data, will lead to better soil conservation plan-ning. The metadata will facilitate to access to different sources of GIS data and also will assistthe users to make decision on data choosing.

In short, the main three motivations of thesis are;

• The examine for optimal data will assist choosing the most suitable of GIS data forsoil erosion models.

• The metadata on soil erosion will significantly help to search for the right data for themodel and from the right data sources. The metadata facilitates the user to know whether thedata is available or if the data meet sthe user’s requirements.

• The data from different sources can be integrated in the data input for the soil modelas well. To share and integrate data from different sources, data interoperability in GIS isrequired. Referring to these processes, when they succeed, there will be no limits to input thedata for the soil erosion model and for soil conservation plan. The interoperability can be sup-ported by Spatial Data Infrastrucure (SDI) and metadata is an important element in SDI.

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Page 22 Chapter 1: Introduction

Figure 1-1 Motivation of the thesis

After inputting the data to the soil erosion model, the soil conservation plan would then beintegrated with other types of factors, social, economic and other environmental aspects.Finally, the decision on land development based on the results from soil erosion model will becompleted.

Figure 1-1 presents above ideas. Different database can be accessed by different users throughthe metadata which can be collected as a database, so called metadatabase. The metadata canfacilitate the users to search the proper GIS data input to soil erosion model which is needed toexamine the quality of data to obtain the optimum quality. Accordingly, the soil erosion predic-tion leads to more appropriate soil conservation plan together with other factors, not only GISfactors.

Previous to the data input to the model, different users can acquire the metadata informationbefore asking for the data from different sources through the central metadatabase. With thisprocess, the users can make decision from which source the data meet their needs.

In the study, the examination for optimum quality GIS data is studied with ArcGIS interface indifferent study areas of Switzerland and Thailand. Fulfilling the examination process, threestudy areas in Switzerland and Thailand are processed by a soil erosion model with ArcGIS

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Chapter 1: Introduction Page 23

platform. Sharing data between different applications is recommended in Chapter 5. In Chapter6, the proposed metadata model of soil erosion is introduced according to the study experi-ences and as well to the national metadata standard, GM03 - Swiss Metadatamodel Standard(COGIS, 2005) and internationally, ISO 19115 (International Standard Organization, 2003).

In Switzerland, mountainous areas, which are at risk of soil erosion, cover almost the wholecountry. In early research references, Swiss erosion is related to soil loss in vineyards. Later,from the 1950s onwards, soil erosion by water was increasing in Swiss agriculture, and wasclearly visible in the areas directly affected (Weisshaidinger and Leser, 2006). The are in thecatchment of Kleine Emme River in Canton of Lucerne, central Switzerland, see Section 4.2.2,is in the Pre-alpine (north of Alpine) where faces a high risk of soil erosion (Weisshaidingerand Leser, 2006).

Due to the location, the rainfall and erosivity in the areas are high owing to the nature of therelief which increase the soil erosion risk in general. Besides, the soil erosion is encouraged bythe slope shape and soil type. Many parts of this area are covered by grassland which notice-ably moderates soil loss. Figure 1-2 shows the pasture in a small part of the area.

In northern Thailand, soil erosion is common and is one of the most critical problems in agri-culture. Not only areas around mountain regions, but also improperly cultivated tradition, forexample shifting cultivation (or swidden agriculture) and deforestation, of locals provoke ero-sion problems.

Figure 1-2 Study area in a catchment of Kleine Emme, Switzerland

Pasture in catchment of Kleine Emme River, Lucern Kanton, Switzerland

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Figure 1-3Study area in Wieng Kaen district, Thailand

The study has been carried out in Wieng Kaen district which is located in Chiang Rai Province,as shown in . Wieng Kaen district is in the catchment of Khong River and covers with intensiveagriculture. Due to the land use and topography, the area is prone to experience with soil lossrisk.

All case studies are shown in Chapter 4.

1.3 Objectives

The purpose of this study is to evaluate the sensitivity of GIS data quality on soil erosion andto develop a method to calculate soil erosion with GIS in a specific soil model. The resultsfrom different quality of GIS data inputs are examined and discussed.

The first objective is to examine differ ent quality data input and da ta input from differ-ent s ources for a specific soil er osion mode l; Revised Universal Soil Loss Equation(RUSLE). To accomplish these objectives, a programmed Visual Basic Application (VBA) inArcGIS is established and geostatistics analysis on case studies in Switzerland and in Thailandare achieved. In addition, the different hydrological data sources and different calculations areapplied in Swiss case studies.

The second objective is to study the new trends of GIS technologies for soil erosion appli-cation, in order to impr ove the qu ality of soil loss pr ediction. It involves data sharing,which includes GIS data transfer, interoperability in GIS, a unique standard for naturalresource classifications, and Spatial Data Infrastructure. Spatial Data Infrastructure, includingmetadata, should be offer all soil data in order to share data and update the data by different

Agriculture area changed from forest areas in Wieng Kaen, Chiang Rai Province, Thailand

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Chapter 1: Introduction Page 25

organizations. It is assumed that the developed methodology will facilitate the current soil ero-sion prediction.

1.4 Structure of the thesis

This thesis contains differnt chapters which are related to the concept of thesis, the literaturereviews, current studies, case studies, application results, result conclusion and discussions,analysis and synthesis, future trends and conclusion.

Chapter 1 explains about the background, thesis motivation and problem statement, the objec-tives and structure of the thesis in order to yield an overall picture. Making clear and confiningthesis contents, the basic definitions according to this dissertation are given at the end of thischapter.

In Chapter 2 and Chapter 3, the fundamental insights, current studies and literature reviews onsoil erosion are included, integration of GIS and soil erosion prediction, basic interoperabilityon GIS and Spatial Data Infrastructure. Chapter 2 focuses mainly on soil erosion backgroundand soil erosion models. Thereafter, Chapter 3 covers GIS study and the integration of GIS andsoil erosion.

According to Figure 1-4, Chapter 4 shows an application of RUSLE in the study areas in Swit-zerland and Thailand. The background of area, the different applications and the methods aredescribed. The results and the discussions for the case studies are also presented.

The analysis and synthesis of the study are introduced in the last three chapters; Chapter 5,Chapter 6 and Chapter 7. In Chapter 5, the GIS data quality from the application in Chapter 4is discussed with the author’s recommendations. The proposed metadata model on soil erosionis presented in Chapter 6, according to the results of the applications and to the metadata stan-dard. To conclude, Chapter 7 gives a general summary of the complete study. Also, the outlookand further studies related to this thesis are also identified in the last chapter.

The thesis structure overview is represented in Figure 1-4 making a clearer picture for the read-ers.

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Figure 1-4 Thesis structure overview

1.5 Basic definitions

In this section, various basic definitions of relevent terms related to this study are shown so asto understand the descriptions in the same way. The selected definitions according to the thesisare defined in this section.

Since the thesis deals substantially with soil erosion, the definition of it is given in the firstplace. There are a number of soil erosion definitions by different people. In general, soil ero-sion is caused by erosive agents such as water flow or wind. In this study, only erosion causedby water flow is considered. The definition of Morgan, 2005 has been applied all over the the-sis, as follow.

“Soil erosion is a two-phase process consisting of the detachment of individual soil particlesfrom the soil mass and then transport by erosive agents such as running water or wind. Whensufficient energy is no longer available to transport the particles, a third phase, deposition,occurs.” (Morgan, 2005)

Geographic Information System (GIS) plays an important role in this study. Therefore, GISdefinitions has been given in this section as well.

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Chapter 1: Introduction Page 27

Geographic Information System (GIS) is known as a tool to store, capture, manage, retrieve,analyze and visualize geographically referenced data. There have been many attempts todefine different GIS depending on background, experience and view of the definition givers.

Pickles, 1995, as well discussed that definitions of GIS are likely to change quickly as technol-ogy and applications develop further. A clear definition by Burrough, 1986 is given below.

“Geographic Information System is a set of tools for collecting, storing, retrieving, trans-forming, and displaying spatial data from the real world for a particular set of purposes.”

As mentioned in Section 1.1 and Section 1.2, to predict soil loss, it is greatly dependent on thequality of GIS data . In this study, high quality of GIS data is the data which contributesaccurate soil prediction and represents the condition of the interest areas properly. The bestquality of GIS data is suited for soil erosion prediction depending on the fitness to soil lossmodel, data availability and model users.

To improve the soil erosion prediction through GIS tools, one of the important idea in the the-sis is to integrate GIS data from different sources. Interoperability in GIS does not give oneclear definition, so different definitions are provided. The selected definitions of interoperabil-ity in GIS are shown according to this study concurrently. In the study, the definition from theOpensource GIS Consortium (OGC) is considered most suitable.

According to the OGC, 2001, the definition is given clearly and understandably; “Geographicinteroperability is the ability of information systems to

1) freely exchange all kinds of spatial information about the earth and about theobjects and phenomena on, above, and below the earth’s surface and

2) cooperatively, over networks, run software capable of manipulating such informa-tion.”

In Environmental System Research Institute (ESRI), 2003, it is suggested that in principle aGIS is not an end in itself, even so it must produce useful information products that can beshared among multiple users. Interoperability in GIS enables the integration of data betweenorganizations and across applications and industries, resulting in the generation and sharing ofmore useful information, and it also reduces redundancy of data.

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Moreover, a metadata model on soil erosion is introduced in Chapter 6. The metadata is one ofcomponents of a Spatial Data Infrastructure (SDI). Therefore, the definitions of SDI is givenbelow and as well as the metadata’s.

Spatial Data Infrastructure (SDI) is used to support the needs to integrate data from multiplesources, organizations, and formats by many organizations. SDI is a collaborative effortbetween public, private, and non-government organizations. Different definitions of SDI aregiven the understanding of different aspects of SDI. The variety of descriptions have resultedin a fragmation of the identities and nature of SDI (Chan et al., 2001).

In this thesis, the used SDI definition is in Douglas, 2004, “SDI is often used to denote the rel-evant base collection of technologies, policies and institutional arrangements that facilitate theavailability of and access to spatial data. The SDI provides a basis for spatial data discovery,evaluation, and application for users and providers within all levels of government, the com-mercial sector, the non-profit sector, academia and by citizens in general.”

The SDI development relates to both technical and organizational aspects. In the thesis, thetechnical aspect is more important than the organizational one.

SDI is also an enabling platform for the exchange, search and processing of spatial data setsand, thus supports interoperability (Najar, 2006). Due to the fact that each organization hasdeveloped its own GIS database, different individual standards are causing problems in poorsharing of data. In consequence, data are scattered and irretrievable. When an SDI is estab-lished, these problems should be solved.

A National Spatial Data Infrastructure (NSDI) comprises four core components; institutionalframework, technical standards, fundamental dataset, and clearing house networks (ANZLIC,1996). In SDIs, geospatial metadata are very important as they document the geospatialdatasets and facilitate access, dissemination, and use of those valuable assets. Metadata is akey part in clearing house networks.

Metadata, is often given the definition a data about data. Metadata facilitates many thingsbeyond enabling information discovery and access; it also informs about the appropriate use ofproducts and services. Metadata is applicable to anything, not just geographic information. InGIS aspect, it is used to describe how geospatial and attribute data was collected and pro-cessed into its final formats, as well as it is used in this study.

With metadata support, data producers can publish information about data, and data consum-ers can search for the data they need (ESRI, 2002). It is important to know if the geospatialdata will meet users’ needs.

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Chapter 1: Introduction Page 29

Again in Douglas, 2004, mentioned that metadata is the minimum amount of information thatneeds to be provided to convey to the inquirer the nature and content of the data resource. Itshould answer these “what, why, when, who, where and how” questions about geospatial data.

In order to search the metadata, the consistency in metadata content and style is neccesary toensure that comparisons can be made. Therefore, the metadata standards should be adopted ineach community. Without standardization, meaningful comparisons are more difficult to derivewithout reading and learning many metadata management styles. More details about metadataand metadata standards are given in Chapter 3.

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Chapter 2: Soil Erosion Page 31

Chapter 2

Soil Erosion

In this Chapter, principal understandings about soil erosion and soil erosion prediction modelsare provided according to the application in this thesis, see more details about application inChapter 4. These basics are mainly based on literature and relevant current research.

There is a number of literature according to this thesis. The first section, section 2.1, is aboutsoil erosion in general. Five water soil erosion types; splash, sheet, rill, gully and channelerosions, are described shortly in section 2.1.1 and more details on the factors related to soilerosion are defined in section 2.1.2, Principal Soil Erosion Factors.

In section 2.1.2, the idea of two erosion models; Universal Soil Loss Equation Model (USLE)and Revised Universal Soil Loss Equation Model (RUSLE) are given. The background, appli-cations and comparison of those two models are also explained. The advantages of RUSLE arediscussed while RUSLE is applied in this thesis to predict soil loss.

Thereafter, in Chapter 3 the integration of Geographic Information System (GIS) and soil ero-sion is described. Accordingly, in Chapter 4 details about the application of soil erosion modelin this study, including a slope length calculation with Visual Basic Application (VBA), rain-fall and runoff erosivity factor (R factor) interpolations, are given.

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2.1 Soil Erosion

The introduction of water soil erosion process is explained related to different literature andcurrent studies from different areas. Several important factors on soil erosion are also specifiedin this section.

2.1.1 Soil Erosion Types

As mentioned in the basic definition section, soil erosion is the process of detachment andtransport of soil particles by erosive agents. The eroded material is transported and depositedby water, wind, ice, gravity and other normal factors. In mountainous areas such as in Switzer-land, in the south of Italy, in the north of Thailand, erosion by water happens often and areserve in many points. In this study, these mountainous areas are focused. Therefore only soilerosion by water is studied throughout the thesis.

Basically, water erosion is a two-part-process; the loosening of soil particles caused largely byraindrop impact and the transporting of soil particles mostly by flowing water (Goldman et al.,1986). Raindrops segregate the soil particles from the earth’s mass. With the water flow, thesegregated soil particles move following the flow and then finally sediment. Sedimentation isthe process whereby the detached particles generated by erosion, are deposited elsewhere onthe land, in lakes or in rivers. Together, the two processes; erosion and sedimentation, result insoil being detached, carried away and eventually deposited elsewhere.

To erode the soil, the major types of water erosion are

• splash erosion

• sheet erosion

• rill erosion

• gully erosion

• channel erosion

All erosion type are characterized in brief below. represents the whole view of soil erosion bywater.

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The first stage of the water erosion processes when raindrop impact occurs, commonly termedsplash erosion. Splash erosion occurs when the soil is directly exposed by raindrop impactand the soil aggregates are broken up. Fine particles and organic matters are separated fromheavier soil particles. In the main, splash erosion is closely correlated directly to raindrop size.

When rainfall intensity becomes greater than the infiltration rate of the soil, sheet erosion willtake place. This erosion type is caused by shallow sheets of water flowing over the soil surface(Department of Primary Industries and Water, 2007 and Goldman et al., 1986). Thin layers ofthe topsoil are moved by the force of the runoff water, leaving the surface uniformly eroded.This erosion type accounts for great volumes of soil.

Later on, at low areas since the velocity and turbulence of water flow increases, the flowingwater concentrates deeper into soil surfaces. The energy of this concentrated flow is able toboth detach and transport soil particles. The soil surface is cut to small channels or rills whichresults in rill erosion, see . Both sheet and rill erosion occur on overland-flow areas and whenerosion becomes increasingly severe, rill erosion is assumed to begin. In addition, rill erosioncan often progress to gully erosion.

Gully erosion occurs when water flows in narrow channels during or immediately after heavyrains or melting snow. A gully is sufficiently deep that it would not be routinely recovered bytillage operations whereas rill erosion can be smoothed by ordinary farm tillage. The gully for-mation is a complex process that is not fully understood (Goldman, et al., 1986).

Figure 2-1 Soil erosion types(Engel, 2004)

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Not always does the gully erosion develop from rill erosion. Some gullies are formed whenrunoff cuts rills deeper and wider or when the flows from several rills come together and forma large channel. Gullies can enlarge in both uphill and downhill directions. Once it is estab-lished, gully erosion can be difficult to stop it from growing, and repair is costly.

Besides these four erosion types, channel erosion happens when the cover material or vegeta-tion is disturbed or when the volume or velocity of flow in a stream increases. The equilibriumof a stream changes and it causes streambank erosion, known as channel erosion (Goldman, etal., 1986). Common points where channel erosion occurs are at stream bends and at construc-tion points, for instance where a bridge crosses the river. Once more, eroded streambank is noteasy to recover from and is expensive.

2.1.2 Principal soil erosion factors

As introduced in the last section about different soil erosion types and the formation of theseerosions, there are several factors principally concerned which are

- climate,

- soil characteristic or soil type,

- topography and

- ground cover, including the crop management (Goldman et al., 1986).

All factors are related together directly and indirectly. These four factors are mainly composedin models of Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation(RUSLE) and as well as in other soil loss models.

Soil characteristics or soil type is associated to permeability, structure, organic matter contentand texture of soil. These properties represent how soil reacts to raindrops, runoffs and sedi-ment transport. When soil is high in silt or fine sand and low in clay or organic matter, it is gen-erally the most erodible (Mills et al., 1976, Virginia Soil and Water Conservation Commission,1980). On the other hand, the more organic content in soil, the less soil erosion occurs.

Related to the rainfall which is a climate factor, the rain intensity and raindrop size play mainroles effecting to soil loss. Rain is the driving force of erosion. Highly intense rainfall and largerain drops are significantly more erosive than short duration and small rain drops significantly.

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Not only is the direct effect on soil erosion caused by the rain, but also climate effects soil ero-sion indirectly. The season effects vegetation growth covering which is one of the most impor-tant factors on soil erosion. In extreme climate for instance in deserts or in cold climate area,ground can be uncovered in long period which is more potential to erode than the mild climate.

As specified earlier, ground cover is greatly significant in soil erosion. Vegetation acts as aprotective layer or buffer between the atmosphere and soil, and is associated with other factorsespecially farming, land use and climate.

In Morgan, 2005, it is explained that there are several effects on soil erosion which almost allcould be scaled down by vegetation cover. The vegetation absorbs the energy of rainfall,reduces the velocity of runoff, and helps to protect the land against mass movement. When thenatural covering is disturbed, re-establishing vegetation can be a difficult and expensive pro-cess. However, in many agricultural areas, new covering vegetation is provided to protect theland from erosion, such as Vetiver grass in Thailand.

The last principal factor of soil erosion is topography. Erosion would normally be expected toincrease with slope steepness and slope length increments as a result of respective increases invelocity and volume of surface runoff (Morgan, 2005). Long and steep slopes contributes largemomentum, thus, the energy of flow (erosion potential) is increased. High velocity runoff isprone to concentrate in narrow channels and produce rills and gullies (Goldman et al., 1986).

Figure 2-2 Different slope shapes (modified from US Forest Service, 1979)

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In addition, the shape of the slope effects substantially on soil erosion. Relevant to the momen-tum, at the foot of a slope will obtain greater momentum. Therefore, a convex slope magnifieserosion, while a concave one reduces the momentum (Morgan, 2005). Different slope shapesare shown in Figure 2-2.

Likewise, in Goldman et al., 1986 and Ritter, 2006 stated as well about outcomes of slope ori-entation. When slopes orientate or face more towards the sun, the soil surface tends to bewarmer and drier than other orientations. The dry and warm soil surface yields sparser vegeta-tion and so causes more soil erosion.

2.2 Soil erosion models

In the 1930s and 1940s with the need to evaluate different soil conservation practices, the stim-ulation for developing soil erosion models has begun. Different models based on knowledge oflocal conditions of climate, soils, topography, and land cover have been applied to measurelong-term soil erosion control, as described in section 2.2. Nevertheless, to complete the mod-els, meaningful data is required for the models.

Presently, soil erosion models differ remarkably from model to model. In Merritt et al., 2003,the different soil erosion models are reviewed. The conclusion represented that determining theappropriate model for an application requires consideration of the suitability of the model tolocal catchment conditions, model complexity, the accuracy and validity of the model, modelassumptions, the spatial and temporal variation, components of the model and the objectives ofthe model user(s). In general, ther e is no 'best' model for all a pplications. Every modelobtains shortcomings and benefits in various aspects. The most appropriate model to each casewill be chosen by the users.

Generally, the model can be considered in two main categories; empirical model and physicsbased model. In Harmon et al., 2001, cited that empirical soil erosion models were developedto answer relatively simple questions about mean rate of soil loss from the fields, in compari-son to physics-based models. Parameter values in empirical models may be obtained by cali-bration, but are more often transferred from calibration at experimental sites, for instanceUniversal Soil Loss Equation, USLE (Wischmeier and Smith, 1978) and Revised UniversalSoil Loss Equation, RUSLE (Renard et al., 1997).

Physics-based models are derived from mathematical equations to describe the processinvolved in the model, taking into account the laws of conservation of mass and energy (Mor-gan, 2005). The examples of physics-based models are Water Erosion Prediction Project,

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WEPP (Flanagan and Nearing, 1995), European Soil Erosion Model, EUSOREM (Morgan etal., 1998).

Furthermore, in Meritt at al., 2003, it is discussed about the complexity of each model type;physics-based models in particular are often over-parameterized, marking an excessive num-ber of parameters in a model. The over-parameter can lead to the problem that single parame-ters cannot be estimated independently (Beven, 2001).

The USLE and RUSLE are empirical models which are frequently used in preference to morecomplex models as they can be implemented in situations with limited data and parameterinputs. In regional scales, limited inputs are the main problem. Hence, prediction of sedimentdelivery at regional scales is commonly based on empirical methods (Meritt at al., 2003).

In this thesis, catchment scale and regional scale are applied as case studies. The empiricalmodels, RUSLE, is used throughout the study. As a result, in section 2.2.1 and section 2.2.2,the details of USLE and RUSLE are given.

2.2.1 Universal Soil Loss Equation (USLE) model

Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1965) is based on many yearsof data from about 1,000 small test plots from throughout the United States. Each test plot hassimilar characteristics, allowing the soil loss measurements to be combined into a predictivetool. The test plot or unit plot is shown in Figure 2-3.

USLE is an empirical model to estimate sheet and rill erosion from agricultural lands. Theequation is used widely within the United States and worldwide where soil erosion causesproblems. Since the 1970s USLE has been developed by the US. Department of Agricultureand is still undergoing evolutions with the development of various revisions (Laflen and Mold-enhauer, 2003).

USLE predicts the long term average annual rate of erosion on a field slope. The result fromthe equation represents only soil loss from sheet or rill erosion on a single slope and does notaccount for additional soil losses that might occur from gully, wind or tillage. This erosionmodel was created for use in selected cropping and management systems, but is also applicableto non-agricultural conditions such as construction sites. Alternative management and cropsystems may also be evaluated to determine the adequacy of conservation measures in farmplanning.

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Five major factors, which are explained afterwards, are used to calculate the soil loss for agiven site. Those factors are simply artimetrically multiplied and described as follows. Eachfactor is the numerical estimate of a specific condition that affects the severity of soil erosion.Therefore, the values obtained from the USLE more accurately represent long-term averages.

Commonly, the USLE is known as following equation.

(2.1)

Where A : the average annual soil loss (ton/ha/year)

R : rainfall and runoff erosivity factor (MJ mm ha/h/year)

K : soil erodibility factor (ton ha h/ha/MJ/mm)

L : slope length factor (dimensionless)

S : steepness factor (dimensionless)

C : cropping-management factor (dimensionless)

P : supporting practices factor (dimensionless)

Rainfall and Runoff Factor (R factor)

Rainfall and Runoff erosivity factor, R factor, represents the effect of energy and intensity ofrainfall (Kunta and Carosio, 2007). From Wischmeier and Smith, 1958, research data frommany sources indicate that when factors other than rainfall are held constant, soil loss fromcultivated field are directly proportional to a rainstorm parameter which is the total stormenergy (E) times the maximum 30-min. intensity (I30).

Wischmeier and Smith (1978) further defined R as the average of the annual summations ofstorm EI30 values, excluding storms of less than 12.7 mm total rainfall depth. The ‘E’ portionof this value represents the rainfall energy, and the ‘I30’ portion represents the maximum 30-

min rainfall intensity during the storm. This index has been widely tested, adopted, and used insome countries and regions, where rainfall is mainly characterized to be of moderate to highintensity, and runoff to be primarily infiltration process (Yin et al., 2007).

A R K L S C P⋅ ⋅ ⋅ ⋅ ⋅=

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The multiplied results of those two components aim to acquire rainfall erosion index over theperiod of evaluation. The greater of intensity will then lead to an increased duration of rainstorm thus amplify the erosion potential.

The general equation (Renard et al., 1997) to calculate R factor in USLE is show in Equation(2.2).

(2.2)

Where Ri : the rainfall and runoff erosivity factor of i th storm (MJ mm ha/h/year)

Ei : the total storm kinetic energy of the i th rainstorm (MJ/ha)

I(30)i: the max. 30 min rainfall intensity of the i th rainstorm (mm/h)

In section 2.2.2, the more details to calculate R factor for RUSLE is given.

To apply the USLE model, R factor will be calculated related on available data. Monthly rain-fall, daily rainfall and yearly rainfall are often treated to represent the data of each individualstorm which is not commonly available.

In the US., values of R factor have been computed from rainfall records and probability statis-tics. Nevertheless, the western part of the Rocky Mountains, which has irregular topography, isneeded more complicated calculations are needed (Goldman et al., 1986). Meanwhile inEurope, from the study of Van der Knijff et al., 2000, the R factor is calculated throughoutEurope.

The study mentioned that the required rainfall and rainfall intensity, used to estimate R factordirectly is not usually available for standard meteorological stations. Therefore, daily rainfalldata, which are stored in the Monitoring Agriculture with Remote Sensing (MARS) meteoro-logical database, is computed for long-term rainfall monthly and annual rainfall values in thestudy. Afterwards to compare R factor, two different equations, which are represented R fac-tor from north and south, are applied. The R factors are based on the latitude of the areaswhere in transition latitude, a continuous, fuzzy R factor between north and south was esti-mated.

As with all the examples above, data to retrieve R factor is not invariably obtainable. Diversi-fied versions of mathematical function of daily rainfall or of annual precipitation are usedinstead of direct EI30 sum.

Ri EiI 30( )i=

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Soil erodibility factor (K factor)

Soil erodibility factor (K factor) is related to the detachment and transport of soil particles byrainfall and runoff. In Wischmer and Smith, 1978, the unit plot is set. The Unit plot is a 72.6 ft.or 22.12 m long plot with not less than 6 ft. or 1.82 m width and with a 9% slope, and is contin-uously in a clean-tilled fallow condition with the tillage, presented inFigure 2-3.

Under this unit plot circumstance, factor of slope length (L factor), steepness (S factor), crop-ping management (C factor) and support practice (P factor) equal one. It means that those fac-tors have no influence on soil erosion on the unit plot, but soil erodibility factor, K and rainfalland runoff erosivity factors, R. Therefore, following Equation (2.1), K factor will be equiva-lent to ratio of average annual soil loss to R factor.

Soil texture is the principal factor affecting K factor, but soil structure (decrease as subsurfacesoil is more bigger than surface), organic matter (decrease as the top soils are removed) andpermeability (decrease with compaction with heavy equipment) also contribute (Goldman,1986). Value for K factor typically ranges between 0 and 0.7 in metric units, with high-sandand high-clay content soil having the lower K factor values and high-silt content soils havingthe higher values.

There are several methods to acquire the K values in the field site, but the monograph methodusing analysis of site soils appears to be the best predictive relationship (Renard et al., 1997).Using the monograph, the percentages of sand, very fine sand, silt and clay in the site arerequired. Therefore, the more carefully the site soils are characterized, the more accurate the Kvalues will be (Goldman et al., 1986). In other areas outside the U.S., other methods have beenintroduced. Römkens et al., 1986, initiated the regression analysis on a world-wide dataset ofall measured K-values and it has been applied for the study in Europe.

Figure 2-3 Unit plot for USLE and RUSLE

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Slope Length and Steepness Factors (LS factor)

In the USLE equation, the factors which serve as topographic conditions are slope length fac-tor (L factor) and slope steepness (S factor). Generally, the steeper and longer the slope, thehigher the risk for erosion.

In Wischemeier and Smith, 1978, slope length is defined as “the horizontal distance from theorigin of overland flow to the point where either (1) the slope gradient decreases enough thatdeposition begins (2) runoff becomes concentrated in a defined channel.” In USLE, slopelength factor and slope steepness factor derive from the ratio of soil loss per unit area on a siteto the Unit plot, as in Figure 2-3, with 22.13 m long and 9% slope. Following the equation,when it is in Unit plot, the L factor will be equal one.

Moreover, Wischmeier and Smith, 1978, found, as others had, that soil loss per unit area varied

as the mth power of slope length and it is represented in Equation (2.3). The mth power is theslope length exponent.

In order to obtain the L factor, the Equation (2.3) is introduced to calculate the factor fromslope length values, as specified.

(2.3)

Where L : slope length factor (dimensionless)

l : slope length (m)

22.13: the length of Unit plot (m)

: slope length exponent

The m values are applied corresponding to the slope, as below.

for s 1% m = 0.2

for 1% s < 3% m = 0.3

for 3% s < 5% m = 0.4

for s 5% m = 0.5

S l22.13-------------⎝ ⎠⎛ ⎞m

=

m

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Page 42 Chapter 2: Soil Erosion

Acquiring m values, there are a number of suggestions. In the above equation, it is suggested inWischmeier and Smith, 1978 that when the slope gradient is more than 5%, the slope lengthfactor for the USLE does not change in respect to slope steepness.

Slope steepness factor, S factor, reflects the influence of steepness of the slope on soil erosion.S factor is the proportion of soil erosion for a specific slope to erosion for a slope of 9% or inUnit plot (Laflen and Moldenhauer, 2003). The relation is given in Equation (2.4).

In Morgan, 2005 and Stone and Hilborn, 2000, there says that the S factor can obtained fromthe equation from Wichmeier and Smith, 1978, as shown below.

(2.4)

Where S : slope factor (dimensionless)

: slope (%)

6.613 : slope factor when slope is 9% as in Unit Plot

When s is 9%, as same as in Unit Plot, the S factor will be one.

Despite that USLE was revised in the USLE Handbook by Wischmeier and Smith, 1965, theslope length has been changed slightly. A new equation of S factor is recommended as theequation below:

(2.5)

Where S : slope factor (dimensionless)

: angle of slope (degree)

In Laflen and Moldenhauer, 2003, it is explained that this major change has occurred becauseerosional force is closer to sine function than tangent function, in Figure 2-4. The sine functionrepresents the erosional force better. Furthermore, the soil erosion prediction become morerealistic - the prediction rise much slower - when the sine function is used rather than the tan-gent of the slope, which is used in Equation (2.4).

S 0.43 0.30 s⋅ 0.043+ s2⋅+( )6.613

--------------------------------------------------------------------=

s

S 0.065 4.56 θsin⋅ 65.41 θsin( )2⋅+ +=

θ

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Chapter 2: Soil Erosion Page 43

Figure 2-4 Erosional forces in general

LS factor draws strongly on soil particle being transported by water. Thus, on steep slopes soilmovement increases dramatically. Contrary to this in flat areas, the runoff is slow and soil par-ticles do not move far from origin raindrop points (Goldman, 1968). The best way to determineLS factor, it is by pacing or measuring in the field area which it is extremely time and labourintensive for the large region.

Cropping-management factor (C factor)

Cropping management factor is defined as the ratio of soil loss from land with specific vegeta-tion to the corresponding soil loss from clean-tilled, continuous fallow (Wischemeier andSmith, 1978). The factor is contingent not only on the vegetation cover or management prac-tices, but also the growth stage when most erosive rain occurs needs to be considered. InUSLE, the C factor reduces the soil loss estimate according to the effectiveness of vegetationand mulch at preventing detachment and transport of soil particles.

To obtain C factor values, in the U.S. C factors relating to the vegetations can be found fromU.S. Department of Agriculture (USDA) publications. When the soil surface is bare, C is 1.0and C factor value of 0.1 is used if a complete cover of newly seeded annual grasses is wellestablished before the onset of rains. While a C factor value of 0.5 is an appropriate averagerepresenting little protection initially and more thorough protection when the grass is wellestablished (Goldman, 1986).

In other countries, the detailed information for computing the C factor is rare (Morgan, 2005).In Belgium, based on the USLE crop growth stages, the C factor was calculated for the maincrop rotation systems on arable farms, where are composed of 13 different crops, with particu-lar reference to those occurring on 40 farms on silt and silt loam soils. The calculation C factorindicated that the application of some rotation system might cause more erosion by runoff,

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Page 44 Chapter 2: Soil Erosion

therefore an appropriate rotation system can be selected with C factor criterion to reduce soilerosion risk (Gabriels et al., 2003).

Moreover, due to the development in remote sensing, among the principal concerns in theremote sensing of crops, forests, natural vegetation and soils are census taking, stress detec-tion, and change monitoring for agriculture and forestry, and entity detection and boundarydelineation for natural vegetation and for soils (Simonett, 1976). With the census taking, theidentification of crops and the determination of area can be specified by different satelliteimages.

Supporting practices factor (P factor)

P factor is defined as the ratio of soil loss with a given surface condition to soil loss with theploughing traces. The factor represents the conservation practices of soil. Supporting conserva-tion practices in the field will reduce the velocity of runoff and the tendency of runoff whichflow directly down a slope.

Accordingly, the P factor will reduce when there is well supporting mechanical practicesincluding contouring (tillage and planting on the contour, grass barriers strips), terracing andretention ditches. Also, in the study of Fu et al., 2006, the results showed that no-till practicehas significantly decreased the average cell soil and average cell sediment yield both in crop-land and the whole catchment area.

When there are no supporting practices in the area of interest, P factor is 1. Also, when there isno information regarding to the study areas, value of 1 is assigned to the model, meaning noland use influence. P factor values have been estimated in different studies. In the study ofAbidin et al., 1997, the use of the remote sensing application, a colour infrared interpretativekey is used to determine the combined land use management factor, C factor and P factor ofthe USLE.

USLE applications

Applying USLE, the data requirements are low compared with most other models such asannual rainfall, an estimate of soil erodibility, land cover information and topographic informa-tion, as described above.

During the 1970s, the USLE was widely used for estimating sheet and rill erosion in nationalassessments of soil erosion in the USA, intensively in the east of the Rocky Mountains. Notonly in the USA, have there been several attempts to apply the equation more widely, as exten-

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Chapter 2: Soil Erosion Page 45

sive data have been collected on R, K, C and P factor values (Morgan, 2005). Therefore, thereare a huge number of soil erosion estimations with USLE worldwide (e.g. Chavez, 2003; Dab-ral et al., 2008; Kim et al., 2005; Kitahara et al., 2000; Land Development Department, 2000;Van der Knijiff et al., 2000). Presently, the combination of GIS with USLE has been utilizedpopularly, as it will be presented in Chapter 3.

However, as in Merritt et al., 2003 and as in Morgan, 2005, there are limitations to the USLE,for instances a non event-based model, lack of data for required parameters outside the U.S.,ignorance of gully erosion and mass movement, etc. As a result, changes were made that arenow incorporated in the RUSLE (Renard et al., 1991). In the following section, the concepts ofRUSLE are described and as well as their application.

2.2.2 Revised Soil Loss Equation (RUSLE)

In section 2.2.1, USLE has been explained and discussed that USLE contains some limitations.The RUSLE is an updated version of USLE after workshops at Perdue in the early 1980s(Laflen and Moldenhauer, 2003). The revised equation maintains the basic form of USLE withthe extensive review of its database, analysis of data which is not previously included in theUSLE.

Although the original USLE has been retained in RUSLE, the technology for factor evaluationhas been altered and new data have been introduced, supporting the evaluation of the terms inspecified conditions.

In comparison with USLE, major improvements include time variation in soil erodibility,improved LS factor that incorporates recent science and a sub factor approach for computingthe cover and management factor (Bühlmann, 2006). The P factor has been better calculatedin more specified farming. More details are described as succeeding.

Rainfall and runoff factor (R factor)

To calculate R factor in RUSLE, it is basically the same as in USLE which is the product oftwo components, E and I30, being multiplied together. Equation has been applied for RUSLE

as well. To obtain an average measure of long-term rainfall erosivity according to the RUSLEmethodology requires high-resolution of rainfall measurements and an accurate computationof each storm rainfall and runoff factor (Diodata, 2005).

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From Renard et al., 1997, the sum of EI30 values of the whole year for different years is defines

as in Equation (2.6) and Equation (2.7).

(2.6)

(2.7)

Where Ri : the rainfall and runoff erosivity factor of i th rain storm(MJ mm/ha/h)

Ei : the total storm kinetic energy of the i th rainstorm (MJ/ha)

I(30)i: the max. 30 min rainfall intensity of the i th rainstorm (mm/h)

j : the number of erosive events for the n number of years

n : the number of year (year)

k : the temporal interval

m : the number of temporal intervals established for each storm event, er

er : kinetic energy of a storm for the r period (MJ /ha/mm)

: the volume of rainfall registered during the r period (mm)

When n = 1 the calculated R value is the rainfall erositivity for one specific year. When n ismore than, then the unit of R factor will be MJ mm/ha/h/year.

R 1n--- E I30( )k

k 1=

m

∑⎝ ⎠⎜ ⎟⎛ ⎞

j k=

n

∑⎝ ⎠⎜ ⎟⎛ ⎞

=

EI30 E( ) I30( ) erΔvrk 1=

m

∑ I30= =

Δvr

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Chapter 2: Soil Erosion Page 47

The kinetic energy is assessed in the RUSLE model following the approach of Brown and Fos-ter, 1987:

(2.8)

(2.9)

Where er : kinetic energy of a storm for the r period (MJ /ha/mm)

ir : the rainfall intensity for the r period (mm/h )

: the duration of the r period (min)

In USLE, the R factor has been applied for the calculation throughout the U.S. Meanwhile, inthe western part, where the rainfall is relatively uneven and the landscape is mountainous, theR factor values have been proven to provide poor estimations (Lane et al., 1992). Therefore, inRUSLE the new calculation has been completed, all of western U.S. and more refined smooth-ing and filling the gaps for the eastern United States. New R factor values in the west of theUS. are invented, as improved isoerodent map (isoerodent maps provide contour lines indicat-ing constant erosivity), which based on more than 1,200 gauge stations (USDA, 1993). More-over, some minor changes have been done in Eastern U.S.

Repeatedly, there is not enough data available to compute the R factor, so other parameters,such as the modified Fournier index (Fmod), are used instead. In this study, different methods

have been applied which will be explained later on in Chapter 4.

Soil erodibility factor (K factor)

K factor in RUSLE is a quantitative value according to the soil characteristics. However,unlike in USLE, K factor in RUSLE is varies seasonally. For example in Europe, the highestK factor is in spring time due to the soil being soft. During the winter or fall, the cold temper-ature forms frozen soil and the rainfall compacts the soil, therefore the K factor is low.

er 0.29 1 0.72 0.05ir–( )exp–[ ]=

Δtr

irΔVrΔtr---------=

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Page 48 Chapter 2: Soil Erosion

Slope length and steepness factors (LS factor)

The topographic factors or LS factor have been revised and algorithms developed to reflectthe ratio of rill to interrill erosion. The RUSLE more linear slope steepness relationship than inUSLE is applied. The new proposed equation in Renard, 1997 is as follows.

However, the slope length factor is as same as in Equation (2.3), but the slope length exponenthas been changed to Equation (2.10). The new m values increase continuously with the slopesteepness according to Renard, 1997.

(2.10)

(2.11)

Where S : Slope factor (dimentionless)

: Slope angle (degree)

: slope length exponent

: the ratio of rill erosion to interrill erosion

Slope length exponent or m value is related to the ratio of rill erosion (caused by flow) tointerrill erosion (principally caused by raindrop impact) (Renard, 1997).

Comparing the L factor from RUSLE to the one from USLE, when the slope is lesser than 9%,the USLE has a greater slope length factor than RUSLE (Lui et al., 2000). On the other hand,when it is greater than 9%, slope length factor of USLE is less than RUSLE due to the S factorof RUSLE is always increasing according to the steepness.

As well, the slope steepness factor, S factor has been changed from Equation (2.4) and Equa-tion (2.5), as specified.

(2.12)

(2.13)

m β1 β+( )

-----------------=

β θsin( ) 0.0896⁄( )3 θsin( )0.8 0.56+( )

----------------------------------------------=

θ

mm

β

β

S 16.8 θsin( ) 0.05–=

S 10.8 θsin( ) 0.03+=

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Chapter 2: Soil Erosion Page 49

Where S : Slope factor (dimentionless)

: Slope (%)

Equation (2.12) is applied when s > 9% and Equation (2.13) does when s 9%.

Fox and Bryan, 1999, stated that combining the two erosion processes into single data sets ledto the development of regression equations (e.g., USLE) that overestimated the effect of slopegradient on erosion rate for low slope gradients and short slopes. Moreover, complex slopescan be represented readily to provide a better approximation of the topographic effect inRUSLE.

Cover management factor (C factor)

The cover management factor, C factor, is used both in USLE and RUSLE to reflect the effectof cropping and management practices on soil erosion rates, and is the factor used most oftento compare the relative impacts of management options on conservation plans (Renard et al.,1997). In RUSLE, it tries to divide each year to shorter period, in order to be more precise fordifferent land cover according to the year period.

In practice, it is considerably dependent on the data availability. It can be completed with sea-sonal data, when the data is exist.

Supporting practices factor (P factor)

P factor has been expanded to consider conditions for rangelands, contouring, strip-croppingand terracing. RUSLE brings in a mixture of empirical and process-based erosion technologyto provide a better measure of the effect of contouring and strip-cropping on erosion (USDA,1993).

Moreover, the P factor can be calculated for terracing, buffer strips, strip copping, drainageand off-grade contouring (Renard, 1997).

RUSLE development and applications

As with USLE, RUSLE too has been used in every part of the world. In the study of Shi et al.,2003, RUSLE with GIS is used aiming to develop conservation-oriented catchment manage-ment strategies in the Wanjiaqiao catchment, the Three Gorge area, China. The model allowedfor easy assessment of soil erosion hazards under different crop and land management over theentire catchment. In a typical dry land agricultural catchment in south-eastern Washington,

s

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Page 50 Chapter 2: Soil Erosion

USA., RUSLE, Sediment Delivery Distributed (SEDD), and GIS were used to estimate theimpacts of no-till practice on soil erosion and sediment yield in Pataha Creek catchment.

Moreover, RUSLE has been developed to be calculated by computer. In 1993, RUSLE2 wasdeveloped in order to apply instead of USLE/RUSLE1 computer program by USDA - Agricul-tural Research Service. Later in year 2002, RUSLE2 had been accelerated to use since it wouldbe implemented in USDA-Natural Resources Conservation Service (USDA-NRCS) fieldoffices (USDA, 2008).

RUSLE2, like RUSLE, estimates average annual rill and interrill erosion based on site-specificconditions and aims to guide conservation and erosion control planning at the local field officelevel. It is computer-based technology that involves a computer program, mathematical equa-tions, and a database, which has a large collection of input data values (USDA, 2008).

In Foster, 2005, it is claimed that RUSLE2 program can be used under land use independentand can be applied wherever the conditions exist. Nevertheless, RUSLE2 database aids mainlythe area in the United States. The concept of RUSLE2 is shown in section Figure 2-5, below.

To acquire RUSLE2, the program can be downloaded from the website of RUSLE2 (http://fargo.nserl.purdue.edu/rusle2_dataweb/RUSLE2_Index.htm). Not only the program, but alsothe database for the program can be downloaded. As mentioned, the database is mainly for thearea in the U.S., not in other areas.

Therefore, in other areas RUSLE is not applicable but it can be used as a guideline for soil lossprediction in some aspects.

Figure 2-5 Concept of RUSLE2 computer program

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Chapter 3: Geographic Information System and Soil Erosion Page 51

Chapter 3Geographic InformationSystem and Soil Erosion

The previous chapter reviews the essential knowledge of soil erosion by water. The soil ero-sion can be analyzed with a spatial analysis. When combined a with GIS platform, soil modelscan be a significantly simpler and less time consuming method for estimating soil loss (Kuntaand Carosio, 2007). At present, most soil erosion studies have applied GIS to integrate, analyzeand visualize the data and the results to identify relationships, patterns and trends. With theseservices of GIS, the soil erosion can be treated with different GIS tools.

In this thesis, GIS is the main tool for examining the soil erosion problem. There are differentapproaches to answer the problems. Following sections in this chapter, the GIS, the GIS appli-cation on soil erosion, Spatial Data Infrastructure and metadata on soil erosion are introduced.This study has applied all given details to the case studies which are presented in Chapter 4. Inaddition, the general idea of spatial data infrastructure and metadata in GIS are given, as abackground to the metadata model, which is fit to the soil erosion model in Chapter 6.

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Page 52 Chapter 3: Geographic Information System and Soil Erosion

3.1 Geographic information system and soil erosion

3.1.1 Applications of GIS on soil erosion

With the recent increases in computing power, there has been a rapid increase in the explora-tion of catchment erosion and sediment transport through the use of computer models. GIS hasbeen a widely known tool for spatial analysis for decades. GIS is a tool to create, store, orga-nize, demonstrate and analyze spatial phenomena, such as soil erosion. All relevant erosionfactors can be converted in different GIS data formats as they are spatial information. As aresult, GIS is universally applied to soil erosion analysis as well as to other environmentalproblems.

A variety of GIS software is available, including ArcGIS, Geographic Resources AnalysisSupport System (GRASS), Idrisi, System for Automated Geoscientific Analyses (SAGA) andQuantum GIS (QGIS). These include both open sourced and licensed softwares. Each GISplatform has pros and cons which are dependent on the user and the available infrastructure. Inthe field of soil erosion, researchers have applied various GIS softwares to analyze the soil ero-sion problems (Richard, 2006, Dunn and Hickez, 1998, Flagen and Nearing, 1995, Kouli et al.,2008, Kunta and Carosio, 2007, Laflen, 2003, Land Development Department, 2000, Laure-ance, 2005, Mills, 1976, Raghunath, 2002).

In this study, ArcGIS platform is engaged through the whole thesis since it contains all therequired functions. Therefore, the development of the data model and the ArcObject in ArcGISare presented successively.

3.1.2 Development of data model in ArcGIS

ArcGIS is one of the most popular GIS softwares among GIS users. It has been widely used ina number of different researches, although it is a licensed software.

In ArcGIS 8.x which is produced by Environmental System Research Institute (ESRI), thegeodatabase data model and ArcObjects, a set of platform-independent software components,are introduced to the users. Geodatabase data model manages the data differently from the ear-lier geodata relational data model. The main difference between these two models is data man-agement. The geodatabase data model stores geographic and attribute data together in a singlesystem. Additionally, geographical data is stored in a geometry field. The georelational data

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Chapter 3: Geographic Information System and Soil Erosion Page 53

model stores spatial data and attribute data separately which are then joined by a commonidentifier (ESRI, 1999).

A geodatabase uses tables to store geographic data, which describe both the location and char-acteristics of spatial features as well as nongeographic data that are not referenced and candescribe only the attributes of spatial features. It is therefore important to distinguish differenttypes of tables. A table consists of rows and columns. Each row corresponds to a feature, andeach column or field represents an attribute. A table that contains geographic data, has a geom-etry field, which distinguishes the table from tables that contain only nongeographic data(Chang, 2005).

Simple concepts of both models are shown in Figure 3-1 and Figure 3-2.

Figure 3-1 shows a simple a georelational data model and the relationship of the parcel datawith the tables. Three different types of features as shown: a polygon attribute table, a polygonarc topology table and an arc table. An arc table defines each arc which is a border of an area ofinterest with a uniquely identified number. Accordingly, the arc table is joined to the polygonarc topology. The table represents each polygon composed of different defined arcs.

In contrast, a geodatabase data model is presented in Figure 3-1. Rules and behaviors definethe relationship between the geographic information and other thematic information. As shownin Figure 3-2, information about imagery, topology, surveys, networks, address and terrain arestored in the same database. The geodatabase data model supports many different types ofGIS data, all of which can be placed within its structure. The user must define the rules andbehaviors regarding the relationships between datasets, which is be saved in the database

Figure 3-1 Georelational data model (modified from Sarközy, 2001)

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Page 54 Chapter 3: Geographic Information System and Soil Erosion

Figure 3-2 Geodatabase data model (modified from www.telematica.com.pe/novedad22.htm, 2007)

In Chang, 2005, it is cited that another main difference characterizing these two data models isthe use of object-oriented technology. The Object-oriented technology treats a spatial fea-ture as an object and it groups spatial features of the same type into a class. A class can haveproperties and methods. A property describes a characteristic or attribute of an object. Amethod carries out an action by an object. ArcObject allows software developers to access dataand to perform tasks programmatically.

The trend in GIS use has shifted from the georelational data model toward a geodatabasedata model. This trend is mainly advantageous in terms of data management.

A geodatabase stores the data in one centralized location. Therefore, it is easy to manage andaccess the data. It is possible to use the geodatabase’s replication function, to easily share andduplicate the contents of a geodatabase with other users or other locations (ESRI, 2007c).Moreover, data entry and editing are more efficient. By storing GIS data in a geodatabase,there is a reduced chance of error being introduced into the datasets, where certain rules andconstraints are applied to the data. For data modeling, the geodatabase can model advancedspatial relationships since it can implement and model the spatial attributes of a feature class orbetween feature classes.

3.1.3 ArcObjects in ArcGIS and soil erosion

As mentioned in the previous section, ESRI introduced the geodatabase data model andArcObjects to the user in ArcGIS 8.x. Since the advent of ArcGIS 8.x, data management usesthe uses gedatabase data model instead of georealtional data model.

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ArcObjects can be used with the Geodatabase to develop a platform for an ArcGIS family ofapplications (ESRI, 2007). ArcObjects allows the user to customize applications to serve theuser particular needs through ArcMap, ArcCatalog or ArcScene interfaces. To customizeapplications, any Component Object Model (COM)-compliant development language can beapplied to ArcObjects, since ArcOjects is built using Microsoft’s COM technology. VisualBasic for Applications (VBA) is already embedded within ArcMap and ArcCatalog. ArcGISusers can customize and extend the functionality of the interface using Visual Basic for Appli-cations (VBA), the programming environment included with the software. Moreover, VisualBasic (VB) is an uncomplicated programming language. As a result, this study uses VBA.

The user can also use an external development environment to create a stand-alone COM inorder to customize applications by using other programming languages including Visual Basic,Visual C++ or Visual Java (University of Durham Information Technology Service, 2007).

Working with the ArcGIS interface, the customized application by VBA has been programmedto calculate soil erosion in ArcMap, focusing especially on slope length iterations. Detailsabout the customized VBA program according to RUSLE are described in the next chapter

Figure 3-3 shows the simple idea of ArcObjects.

3.1.4 Geographic Resources Analysis Support System with soil erosion

The Geographic Resources Analysis Support System (GRASS) has also been applied in anumber of different soil erosion analysis studies.

Figure 3-3 Simple overview of ArcObjects (modified from ESRI, 2007b)

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Page 56 Chapter 3: Geographic Information System and Soil Erosion

GRASS is a free, open source GIS software capable of handling GIS data. In 1982, GRASSwas developed with support from U.S. agencies, universities and private companies. In 2006,the Open Source Geospatial Foundation (OSGeo) was created and GRASS was included asone of its original projects (GRASS Development Team, 2008). At the moment, many usersapply GRASS for various applications and advanced GRASS users can develop different mod-ules corresponding to their interests.

As mentioned above, GRASS has also been applied to soil erosion investigations. In Raghu-nath, 2002, GRASS was used to prepare a potential erosion map, using Revised Universal SoilLoss Equation (RUSLE) for a river basin in Nepal. The results show that GRASS is a techni-cally sound and cost-effective tool for underdeveloping and developing countries where com-mercial software is generally not affordable.

Furthermore, the soil erosion modellings by GRASS are well established, for instance ArealNon-point Source catchment Environmental Response Simulation (ANSWERS), AGriculturalNon-Point Source (AGNPS).

Despite these benefits, GRASS is not applied in this study. Instead, ArcGIS is used, because itis well-understand and readily applied to the questions on hands.

3.1.5 Slope Length factor calculation with VBA

As mentioned earlier in section 2.2.1, the slope length and steepness (LS) factor is best mea-sured by pacing the field area. With GIS, Digital Elevation Models (DEMs) have been appliedto calculate the most effective LS factor.

There are several methods for computing slope length with GIS. Different GIS-based modelshave been used to calculate slope length from DEM. Two examples are the slope length solu-tions for GIS (Hickey, 2000) which the LS factor for RUSLE within the ArcInfo Grid (VanRemortel et al., 2001) and the LS factor for RUSLE using a C++ executable (Van Remortel etal., 2004).

In Dunn et al., 1998, it shows that the slope angles of the DEM need to be computed initially tocalculate the slope length factors. GIS can extract slope angles from DEM using different slopealgorithms, such as the quadratic surface algorithm in the ArcMap program on the maximumslope method available in IDRISI software. The comparison of slope angle algorithms showthat maximum downslope angle calculations retain the local variability in the original DEMwithout overestimating slopes (Dunn et al., 1998). In this study , the maximum downhillslope is used in the slope length calculation, see Chapter 4.

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Chapter 3: Geographic Information System and Soil Erosion Page 57

Hickey, 2000, and Van Remortel et al., 2001, have continued to study on the calculation of LSfactor. The studies have completed Arc Macro Language (AML) in ArcInfo program to LSfactor for USLE model and RUSLE model.

Later on, in Van Remortel, 2004, the new RUSLE-based LS factor has proposed with someadditions and modifications to the previous USLE AML code of Hickey, 2000. The RUSLE-based AML program has been tested in western USA and compared the results with theRUSLE handbook guidelines (Renard, 1997). The comparison suggests that LS factors gener-ated by the AML program are comparable to the ranges of LS values summarized in the litera-ture of McCool et al., 1997, although ground-truthing of the results has not yet been conducted(Van Remortel et al., 2001).

Computation of the LS factor for RUSLE through array-based slope processing of digital ele-vation data using a C++ executable has been developed by Van Remortel et al., 2004. More-over, the same algorithm in AML code has been rewritten with the American NationalStandard Institute C++ (ANSI C++) software. It was determined that the speed of computerruns could be significantly increased without sacrificing accuracy in the final results by per-forming that majority of the elevation data processing in a two-dimensional array frameworkoutside the ArcInfor environment (Van Remortel et al., 2004). Results of test runs using high-quality 10 and 30-m DEMs and demonstrated that a high-quality DEM input dataset is the keyelement for ensuring reliable LS-factor output data.

In the study of Cochrane et al., 2005, the effects of DEM resolution in the runoff and soil losspredictions by the Water Erosion Prediction Project (WEPP) are analyzed. The relationshipbetween resolution and its effect on slope and flowpath lengths and between resolution andrainfall event sizes are statistically studied. It found that a wide range of DEM resolutions canbe used for runoff and sediment yield simulations from the catchment outlet and hillslope.

However, if the accuracy of the catchment channel network, boundary, or hillslope shapes arecompromised, then the resolution is too coarse. If the user is interested in results of soil losswithin the hillslope profile, then finer resolutions are better, for modelling (Cochrane et al.,2005).

The degradation of DEM resolution showed that the average length and proportion of longerflowpaths increased as the resolution became coarser, and mean values of the predictions ofsediment yield increased, even though mean and maximum slopes decreased.

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In this thesis, the slope length calculation of Van Remortel, 2004, is applied by ArcObject pro-gramming. Different study areas, different resolutions of DEM and different sources of chan-nelization data are applied, see Chapter 4.

3.2 Soil GIS data sharing

It is apparent from earlier sections that soil erosion prediction with GIS is greatly dependent onGIS data. GIS data quality is examined in this thesis, as well as GIS data sharing is studiedsince GIS data used in soil er osion prediction is available for differ ent areas around theworld. Accordingly, data formats and data models also differ. As the use of GIS has spread,there has been a corresponding demand for connecting users and sharing GIS information onsoil data. The more data sources to choose from, the better results of the model are. In this sec-tion, GIS data sharing on soil is discussed, as it is one proposed answer to enhance the qualityof soil loss prediction through GIS data.

To fully r ealize the capability and benefits of geographic inform ation and GIS technol-ogy, GIS soi l data needs to be able to be shared and systems need to be inter operable(ESRI, 2003). Data interoperability is required in order to ensure to the smooth exchange ofsoil information among diverse users.

The major drawbacks of GIS interoperability are obscure semantics, the diversity of data setsand the heterogeneity of existing systems in terms of data modelling concepts, data encodingtechniques and storage structures. Current efforts to integrate geographic information adopt theidea of metadata structures as the key to information sharing and analysis (Ghosh and Paul,2006).

In this section, data sharing, metadata standard and interoperability in GIS and GIS soil dataare briefly described. The metadata model for soil erosion data is studied and designed for bet-ter soil erosion prediction in Chapter 6. Section 3.2.1 discusses the Spatial Data Infrastructurewhich strongly supports the access to GIS data between different organizations and agencies.Section 3.2.2 describes the interoperability of GIS and the relevant standards in general aregiven. In the last section of this chapter, introduction of metadata in GIS is introduced.

3.2.1 Spatial Data Infrastructure

As given the definition in Chapter 1, Spatial Data Infrastructur e (SDI) provides a basis ofspatial data discovery, evaluation, and application for users and providers within all level of

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the government, the commercial sector, the non-profit sector, academia and by citizens. It isused accordingly throughout the whole study.

Sharing GIS data between different organizations, in both vertical and horizontal dimensions,results in the development of the Spatial Data Infrastructure (SDI). As mentioned before, SDIenables platforms which facilitate the access to spatial data and comprise organizational, polit-ical, cultural and technical aspects. SDI encompasses the data sources, systems, linkages, pro-cesses, standards and institutional arrangements involved in delivering spatially-relatedinformation (both commercial and public) to the widest possible group of potential users.

The main driving forces of spatial data development for SDI presently are: the growing need ofimproving decision making and the increase of efficiency for the government and business sec-tor (Williamson, et al., 2003).

To succeed the SDI development, SDI composes with components are (Ezigbalike, 2001);

- Technology (hardware, software, networks, databases, technical implementationplans)

- Policies & Institutional Arrangements (governance, data privacy & security, datasharing, cost recovery)

- People (training, professional development, cooperation, outreach)

To develop SDI, it is important to work on four levels; global, regional, national and local. Thisfacilitates data sharing and the coordination of data activities within the vertical relationships(Najar, 2006). An SDI hierarchy of SDI at different levels is shown in Figure 3-4. In horizontalrelationships, it implies for example the cooperation between, similar political levels from dif-ferent geographical areas.

At the 2nd Global Spatial Data Infrastructure Conference in 1997, the multi-national GlobalSpatial Data Infrastructur e (GSDI) Steering Group defined the Global Spatial Data Infra-structure as: "… the policies, organizational remits, data, technologies, standards, deliverymechanisms, and financial and human resources necessary to ensure that those working at theglobal and regional scale are not impeded in meeting their objectives."

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Figure 3-4 An SDI hierarchy of SDIs at different levels (Rajabifard, 2001)

At the GSDI level, there is a steering committee to support a platform for the exchange of ideasand promotion of new results in the SDI environment.

The Regional Spatial Data Infrastructur e (RSDI) is related in regional level for instanceEuropean Commission (Infrastructure of Spatial Information in Europe (INSPIRE) Project) inEurope region, the Asia-Pacific SDI (APSDI) in Asia Pacific region.

The National Spatial Data Infr astructure (NSDI) is a mean of assembling geographic datanationwide to serve a variety of users (Federal Geographic Data Committee, 2008). NSDI canserve as the steward for a national geographic information strategy that includes a distributednetwork of technology, cross-organizations, partnerships, and the process and standardsneeded to facilitate data sharing. It is suggested that countries with an efficient NSDI will beable to contribute to the regional and global SDI initiatives. Main NSDI components in generalare shown in Figure 3-5.

According to Figure 3-5, the main NSDI components are as follows:

• Geodata - or Fundamental Geospatial Data Sets (FGDS). Good examples of suchdata sets are satellite imagery, topographical and cadastral data sets.

• Framework. The framework is a collaborative community based effort in whichcommonly need data themes are developed, maintained, and integrated by public and privateorganizations (Federal Geographic Data Committee, 2008). The framework is designed tofacilitate the production and use of geographic data, to reduce costs and improve service and todecision making.

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• Metadata. As explained previously, metadata represents the who, what, when, where,why and how of the data resource. A metadata record is a file of information such as XMLdocument which captures the basic characteristics of a data information resource.

Metadata helps the user determine which datasets are useful and allows for easyaccess to the data via a sear ch engine. Therefore, to make metadata more accessible to boththe human user and the machine, it is necessary to standardize the structure and context of theinformation. A metadata standard is simply a common set of terms and definitions that are pre-sented in a structured format. More details on metadata standards are given in Section 3.3.

• Standards. Standards facilitate the development, sharing, and use of geospatial data.As shown in Figure 3-5, standards relate to every NSDI components. By adhering to commonstandards different users can share data more readily.

There are several standardization efforts under way. At the international level there isthe effort being pursued the International Standard Organization through its ISO/TC211 initia-tive. Nationally, there is the Spatial Data Transfer Standard (SDTS) of the United States andthe Spatial Archive and Interchange Format (SAIF) of Canada.

• Clearinghouse. The clearinghouse is the mechanism that provides access to the meta-data and to the actual data sets. The Nation Spatial Data Infrastructure (NSDI) Clearinghouseis a group of “gateway” that send out search requests to participating servers on the Internet.Due to an Internet infrastructure for global data access that is fully functional, the metadata areconsiderably interesting to get information about data broadly(ESRI, 2002). However, sincethe clearinghouse must handle many different data sources, there is a need for standardizationof the access procedures, user interfaces and the metadata itself (Gupta, 2005).

• Partnership. The implicit agreement between administrative organizations on datause and application sharing requires a strong partnership. Institutions in government, industry,acedemia, societies and individuals are included in the partnership.

SDIs have become very important in determining the way in which spatial data is usedthroughout an organization, a nation and different regions of the world. By reducing duplica-tion and facilitating integration and development of new and innovative business applications,SDIs can produce significant human and resource savings and returns.

A number of countries have established NSDI including Thailand and Switzerland. Thus,when NSDI is complete and well established, the soil data at a national scale can be easilyaccessed. Overall, soil data in the country will be consistent and regularly updated.

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The state level of SDI is of importance especially in federal systems. It is characterized bymedium to large scale spatial data and is typically managed by state administration. The locallevel of SDI (LSDI) is often closely connected to the executing organizations of spatial dataand thus of high relevance to a functioning. Usually, LSDI follows the guidelines and frame-work of NSDI (Najar, 2006).

Figure 3-5 Components of NSDI (Federal Geographic Data Committee, 2008)

A SDI makes sense at the local, national, regional and global level where the overlap andduplication in the production of geographic information is paralleled by an insufficient flowsof geographic information between different stakeholders, a problem that is usually due to alack of standardization and harmonization of spatial data bases. Once the importance of pro-viding geographic information through an infrastructure similar to road and telecommunica-tion networks is recognized, it makes sense to ensure that a consistent Spatial DataInfrastructure is developed at all level (Douglas, 2004).

Mostly of spatial data are processed and analyzed in GIS. The GIS is an important tool to sup-port the SDI. Currently the web service on GIS is easy to access. In the future, the main pro-cessing and analysis of spatial data can be transferred to web services and web GIS which areoffered by SDI initiatives, online (Najar, 2006). GIS and SDI need metadata in order for theuser to find the desired data. There is a need for improved cataloging in order for users tosearch metadata through the internet.

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3.2.2 Interoperability in GIS and standards

Following the basic definition in Chapter 1, the interoperability of GIS is defined that aninteroperability enables the integration of data between organizations and across various disci-plines and industries which results in the generation and the sharing of information and reduc-tion of redundant data.

Figure 3-6 shows the interpolation of two systems. Two components X and Y interpolate if Xcan send requests for service R to Y, based on a mutual understanding of R by X and Y, and ifY can similarly return a mutually understandable response S to X. These two systems, X andY, are interoperable (ISO, 2005).

Figure 3-6 Interoperability in GIS (ISO, 2005)

Interoperability can solve problems of heterogeneity in GIS information including:

(i) syntactic

(ii) structural and

(iii) semantic heterogeneity.

Of these three variances, semantic heterogeneity is the most crucial problem. Syntacticalinteroperability ensures that there is a technical connection, i.e., that the data can be transferredbetween different systems. In the meantime, semantic interoperability promises that the con-tent is recognized in the same way in both systems and by the human user interacting with thesystems in a given context (OCG, 2002). Presently a number of studies are focused on semanticinteroperability on GIS in order to solve this problem (Ghosh and Manoj, 2006).

With the advancement of web service techno logy and standards of The Open GeospatialConsortium (OGC), the issues and complications associated with GIS applications beingtied to the spatial schema of softwar e are avoided. In addition, web services allow thedata owners to man age their data using th e best methods and form ats for their tools inwhatever database environment they choose. Each GIS software vendor can build and man-

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age their own GIS data and readily provide GIS services (data, maps, and geoprocessing) tolarger user groups (ESRI, 2003).

Figure 3-7 shows the relationship between web services, different GIS servers, web tier, XML/Simple Object Access Protocol (SOAP) and clients. The integration of GIS and Web servicessimply means that GIS can be more extensively implemented using the interoperable standardsof XML and SOAP. Users will be able to take data mapping, and geoprocessing services frommany servers and integrate them in a common environment. The web tier provides a standards-based framework for integration of geographic information services from multiple softwareproducers (ESRI, 2003).

In this study, an example of interoperability in Thailand is given by the author in Chapter 5.The example demonstrates the interoperability of the Land Development Project.

Figure 3-7 Integration of standards-based Web services (ESRI, 2003)

3.3 Metadata on soil erosion

Metadata is one of the primary SDI components. As described in Section 3.2.1, metadata isstandardized data format which describes the characteristics of the dataset including content,quality, condition, resolution, scale, time of collection, other instances it was collected, areas of

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coverage, ownership, and other characteristics of the actual data. Metadata allows users toquickly explore a dataset and determine if the data is useful for the their purposes.

Acquiring soil GIS data for soil erosion is complicated and time consuming work. The meta-data model for soil erosion used in this thesis will provide better way to gather the necessarydata from different sources. Therefore, the metadata model for soil erosion, as described in dif-ferent case studies in Chapter 4, is proposed (see details in Chapter 6). This metadata modelaims to assist the user in the search for the best data for a soil erosion model. The metadatamodel is based on the RUSLE equation.

3.3.1 Development of meta data for National Spatial Data Infrastructur e inThailand

As with other nations, the NSDI, or in Thailand referred to as The Thailand Spatial Data Infra-structure (ThaiSDI), has been established and implemented to solve not only technical prob-lems; duplications on data productions, poor sharing of data/ facilities / resources, scatteredand irretrievable data, no data standard available, but also administrative problems; limitedcooperation and collaboration between organizations, lack of expert and training personnel andunclear policies.

Therefore, the aims of ThaiSDI are to eliminate redundant GIS data collection and to manageGIS data network in Thailand to have better interoperability through the establishment of datacontent description and metadata standards (Tinachote and Teerasarn, 2006).

Figure 3-8 Metadata search engine in ThaiSDI

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Figure 3-8 shows the principle of the metadata search engine for ThaiSDI. Different users canaccess the metadata through the gateway and the gateway connects to different remote meta-data serving to exchange the metadata from different organizations.

GIS data in Thailand has been used for decades in more than 100 different organizations (Sila-pathong, 2007), especially in governmental organizations such as Department of Lands (DOL),Agriculture Land Reform Office, Land Development Department (LDD), Royal IrrigationDepartment (RID) etc. Among GIS data, which include agricultural area, land use, cadastreand water resource, soil data are important GIS data. Different soil data have been producedfrom different organizations. The settle up of ThaiSDI will support the organization to shareand to integrate the soil data.

Metadata for geospatial datasets are considered to be a major prerequisite for the developmentof ThaiSDI (Tinnachote, 2006). To Develop ThaiSDI, Metadata Edit Program has been devel-oped by the Geo-Informatics and Space Technology Development Agency (GISDA) under theproject of ThaiSDI Data Clearinghouse. The program aims to create, edit, update and searchMetadata by different conditions.

For “Spatial Data Clearinghouse”, the target is to establish a national geo-spatial one-stop ser-vice. Since 2005, different government agencies have been included in the project. They areresponsible for the Fundamental Geographic Data Set (FGDS) for various custodians, forinstance Land use layer by LDD, Hydrology layer by the Ministry of Natural Resource andEnvironment (MNRE) and RID.

The architecture of the ThaiSDI Clearinghouse includes the following agencies; Department ofProvincial Administration (DOPA), Ministry of Transport - Department of Highway (DOH),Department of Water Resource (DWR), Department of Public Works and Town-Country Plan-ning (DPT), MNRE, LDD, Royal Thai Survey Department (RTSD) and DOL. Every agencyneeds to generate metadata with the developed program. The agencies are grouped as nodesand gateways of the Clearinghouse.

ThaiSDI is still in the progress stage especially in the training and education of all the relevantusers. Online GIS Services and FGDS update are also required.

3.3.2 Metadata on soil erosion and soil data in Europe

Soil erosion data sets on the national scale in Europe are studied in Boardman, 2006. It isfound that in Europe, the data on soil erosion is rich but that datasets differ in spatial and tem-poral resolution and meaningfulness. In addition, the data are dispersed and language barriers

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often obstruct the region wide studies. In the study, metadata for national-scale soil erosiondatasets are comprised of five attributes: country name, type of erosion, method of calculation,resolution, scale and reference.

In summary, the common goal of these datasets is to comprehensively access the extent andmagnitude of soil erosion and to identify areas where more precise evaluations may be neces-sary to facilitate sustainable land use. The majority of national-scale data sets are based onUSLE-type modelling approaches and provides information on either the actual or potentialsoil erosion. It is recommended that in order to compare results, a common data classificationmethod should be agreed upon between nations.

The Defra Project SP08003 in United Kingdom (Price et al., 2006) shows that due to theincreasing recognition of the importance of soil and the need for strategies to protect if manyUK government agencies anticipate a greater need to access soil data in the future. The projectinvestigates the potential for developing descriptive soil metadata for integration to in theDefra SPIRE (Spatial Information REpository). In order to facilitate access to information, ametadata database will be produced to enable the attribute metadata collected by the project tobe easily queried.

According to the mentioned studies, it can be realized that soil metadata is highly in needs inEurope in national or international levels.

3.3.3 Metadata standards

Metadata can be produced by various organizations. The consistency and style in the metadataformat are recommended to ensure that the comparison of metadata between organizations isefficient and transparent. Without standardization, meaningful comparisons are more difficultto derive (Douglas, 2004).

An important standard in the United States is the Federal Geographic Data Committee (FGDC)Content S tandard for Digi tal Geospatial Metadata . International Organization for Stan-dardization (ISO) has created a spatial metadata standard, ISO 19115, which has been appliedwide world. The study areas considered in this thesis are in Thailand and Switzerland. Bothcountries have adopted ISO 19115. Moreover, in Switzerland there is another national standardfor spatial metadata GM03. However, the GM03 is based on the ISO 19115. To establish meta-data model, the metadata is compatible with ISO 19115 or the standards for the country wherethe model belongs to.

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Chapter 4GIS Application forSoil Erosion Model

The fundamental concepts of GIS and soil erosion were discussed in previous chapters. In thischapter, the GIS application on soil erosion model are presented: the calculation algorithm, theapplication results and a discussion of the results and conclusions. In order to test how thequality of different GIS data affects soil erosion prediction, the slope length calculation and theRevised Universal Soil Loss Equation (RUSLE) factors are calculated using different qualityof GIS data as inputs.

The soil erosion model is applied in several study areas, in Switzerland and in Thailand, wheredata is available and where soil erosion is a problem. The RUSLE factors are calculated by dif-ferent methods, for example the programmed VBA calculates LS factor automatically, rainfallfactor in Thailand is estimated and interpolated for the whole study area, etc.

Two small areas in the catchment of Kleine Emme river in Canton of Lucerne, Switzerland anda catchment in the north of Thailand, in Ngoa river, are applied for the VBA calculation. Sec-tion 4.2 explains the study areas in details and the GIS data sources used in each catchment.The results of the application are introduced in Section 4.3.

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Finally, the conclusions and remarks about the results in respect to GIS technology and soilprediction are given in Section 4.4. The sensitivity of GIS data affects the results of soil ero-sion significantly, as is discussed in this section.

The results of this chapter lead into, Chapter 5 and Chapter 6, in which the improvement ofGIS data quality, GIS data sharing, web-based GIS soil data and the establishment of metadatamodel for soil erosion prediction are discussed.

4.1 GIS application on soil erosion

ArcGIS version 9.2 is used for all applications in this thesis. Various factors of the RUSLEmodel are processed in GRID format and calculated in ArcGIS.

The Visual Basic Application (VBA) is programmed for slope length computation for eachstudy area. The algorithm is explained thoroughly in this section. To prepare the DEM withchannels, the channelization is determined by different two methods integrating with VectorRiver Network (VRN). The details about channelization are in Section 4.1.4.

4.1.1 Slope Length Calculation

As discussed in Section 2.2.2, slope length is one factor to calculate soil loss in the RUSLEequation. The calculation of slope length factors can be performed using Digital ElevationModel (DEM) data, as explained in Chapter 3. As in several studies, the slope length factorcalculations have been developed for soil erosion with GIS. For example, A series of ArcIn-

foTM Arc Marco Language (AML) scripts have been created that enable the production ofeither USLE - or RUSLE - based LS factor raster grids using a DEM input data set (VanRemortel et al., 2004).

In order to automatically calculate the LS factor using the ArcGIS platform, Visual BasicApplication (VBA) is programmed to calculate the LS factor with the equations in Renard etal., 1997 and developed from Van Remotel et al., 2004. This calculation is shown schemati-cally in Figure 4-1.

Following Hickey, 2000, two assumptions are made in the slope length calculation. The first isthat in areas of converging flow, the highest accumulative slope length takes precedence. Theassumption works when there is more than one inflow into a cell. Following the assumption,

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the highest flow accumulation is dominant and is chosen for the flow accumulation calcula-tion.

The second assumption concerns areas where deposition, not erosion, it is the dominant pro-cess. At some point, the flow velocity of water moving across the surface will decrease enoughthat sediment will be deposited rather than the energy of the flow enabling more sediment orerosion to be picked up. To define the deposition point, the cutoff slope angle is introduced byHickey, 2000.

The cutoff slope angle is defined as the change in slope angle from one cell to the next cellalong the flow direction (Hickey, 2000). When the change in slope angle between these twocells is more than the specific cutoff slope angle, the accumulative slope length is reset, mean-ing there is the deposition. The cutoff condition used in this study is explained in Section 4.1.3.

4.1.2 Overall Slope Length calculation process

As shown in Figure 4-1, the depressionless DEM input is required in the process to avoid rivernetwork interruptions. Even though true depressions or sinks, are rare in nature, depressionsoften occur in DEM (Hickey, 2000).

Sinks are areas where the ground is lower than surrounding areas. Accordingly, sink cells inDEM are cells what are lower than the neighbouring cells. Both sinks and peaks often haveerrors due to the resolution of the data or because the GIS software automatically rounds eleva-tions to the nearest integer value (ESRI, 2008). Sinks should be filled to ensure proper delinea-tion of basins and streams. If the sinks are not filled, a derived drainage network may be notcontinuous. To delineate catchments, the hydrologic analysis functions allow the user to iden-tify sinks and fill them. The results is a “depressionless elevation model” which determines thedirection of the flow. Therefore, the DEM applied in the model should be depressionless.

The profile of a sink and depression in nature is illustrated in Figure 4-2

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Figure 4-1 Flowchart of LS calculation schema (modified from Kunta and Carosio, 2007)

Figure 4-2 Profile of a sink and depression in nature

Fill

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Since the maximum downhill slope angle method retains the local variability present in theoriginal DEM without overestimating slopes, the maximum downhill slope of each cell can becalculated by 3x3 neighborhood cells (Hickey, 2000). The flow direction can be derived fromthe maximum downhill slope. This thesis adopts the maximum downhill slope angle method toobtain the slope for each cell.

When the maximum downhill slope of each cell is known, flow direction can be determined.The flow direction starts at the cell with the highest elevation cell or where is no inflow fromother cells. Flow will run in the direction of the cell with the greatest downhill slope in the 3x3neighborhood cells.

After obtaining the flow direction, the slope length of each cell can be computed using foursteps as follows.

- First two cases are considered: diagonal flow direction and cardinal flow direction.When the flow in the cell is in a diagonal direction, the flow length equals the diago-nal distance of the cell resolution. When the flow in the cell is in a cardinal direction,the flow length equals the cardinal distance of the cell resolution.

- Second, after acquiring the cell length from the flow direction in the previous step,the starting point will be considered. If a cell is the starting point of the flow path, theflow length will be half the slope length calculated from the first step. Because a start-ing cell has no inflow, it is assumed that the flow starts at the centre of the cell.

For non-starting point cases, the slope length of each cell is calculated in the samemanner as the slope length in the first step.

- The third step concerns the river network in the area. When a cell is considered achannel, the slope length equals zero. Because the channels collect the flow fromnumerous rills, they are generally considered to be slope-ending concentrated flowchannels (Institute of Water Research, 2002). Therefore, the slope length is for a chan-nel is zero.

- The final step aims to determine the accumulative slope length given the assignedflow direction. Following the flow direction, the cutoff between two cells is deter-mined. The cumulative slope length is reset and the new cumulative slope lengthstarts in the same flow line when the cut off between two continuing cells meets thecut-off conditions. The cut-off conditions are explained later.

Figure 4-3 illustrates the over all slope length calculations.

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4.1.3 Iteration of accumulative Slope Length

To obtain the cumulative slope length for each cell, an intensively iterative routine must beperformed. The routine starts only when the cell is the starting point of the flow. The slopelength of each cell is calculated iteratively. The collecting of cumulative slope lengths resets(reset to 0) under the following cut-off slope conditions and the cumulative slope length is cal-culated anew each time the following cut-off conditions:

- When the maximum downhill slope of a cell is equal 5% or greater and the max-imum slope of the cell, compared to that of the previous cell (which contribute theinflow), decreases by 50% or more.

- When the maximum downhill slope of a cell is less than 5% and the maximumslope of the cell, compar ed to that of the pr evious cell (which contribute theinflow), decreases by 70% or more.

These conditions are adopted from Van Remortel et al., 2001. Under these circumstances, thecumulative slope length is saved and reset to zero for the next cell. However, the flow stillcontinues to travel in the same flow path. The flow cumulative calculation starts to collectslope length until the end of the flow.

The intensive iteration calculates the cumulative slope length cell by cell under above circum-stances. The concept it is illustrated in Figure 4-4.

When a flow path ends, a new iteration with a new starting point begins. The flow ends underthese conditions:

• When the flow flows outside the study area

• When the flow becomes a channel

• When the flow reaches to the lowest point and does not flow to any other cell.

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Figure 4-3 Steps of flow length calculations

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Figure 4-4 Flowchart of slope length accumulation iteration

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4.1.4 Channelization

It is not easy to define where a channel begins within a watershed by Digital Elevation Model(DEM). Other GIS data, a Vector River Network, is integrated to define the channel startingpoints at the flow accumulation from DEM. In this study, where the VRN is available, it willbe used.

The flow accumulation represents the sum of cell through which the flow must travel beforereaching the end cell. In this study, this value is described as the Flow Accumulation Value(FAV).

Two methods are applied in order to determine channel networks in the study areas. The twomethods are as follow:

• The Flow Accumulation Value (FAV) is compared to the Vector River Network(VRN). This method integrates the VRN with the DEM by comparing the starting points ofchannels. The starting point in flow accumulation raster can be obtained from the VRN. Thechannels are determined from the flow direction and flow accumulation.

• Stream burning. This method also integrates between the VRN data and the DEMrasters. The stream burning method is explained in details in the next section.

The differences between these methods are discussed in Section 4.3.1.

Stream Burning

The stream burning method is a technique for modifying a DEM using a VRN, which isassumed to be correct, in order to produce a DEM that fitter reproduces existing hydrologicalpatterns (Paz et al., 2008). It has been applied in many different hydrological studies (Callowet al., 2007, Paz et al., 2008, Saunder, 1999). During the stream-burning process, a raster rivernetwork with the same spatial resolution as the DEM is generated from the VRN by an vectorto raster conversion and DEM values are artificially lowered at cells that are part of this rasterdrainage (DEM burning).

Stream burning corrects for the error incurred when a stream flows over areas which may beflat or exhibit upstream gradients due to survey anomalies. To apply this method, it is recom-mended in Saunders, 1999, that the scale of vector data must be similar to that of the DEM.The extensive pre-processing prior to the “burn-in” is taken place.

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In this study, a simple form of the stream burning procedure is adopted in which the elevationvalues of the DEM are lowered such that they coincide with these of the VRN, without any fur-ther treatment (Graham et al., 1999). However, the calculation loops need to be applied untilthe optimal lowering elevation value is found. The generated DEM from this value is used forslope length calculation.

The optimal value should be low enough to ensure that flow paths that intersect the stream net-work are forced to follow the burnt flow paths. When stream burning has been undertaken toodeeply, it can influence other factors in the area, such as the slope values.

The advantage of stream burning is that it improves the quality of extracted drainage networksis more visible in flat regions and with relatively low-quality DEMs, both in terms of spatialresolution and vertical accuracy (Paz et al., 2008). However, it has the disadvantage of alteringtopography locally in order to ensure consistency between the existing vector hydrology andthe DEM (Gruber and Peckham, 2009).

4.1.5 Conclusion of the calculation

When the slope length iterations are completed, the slope length factor (L factor) will be com-puted. As cited in, the L factor is derived from Equation (2.3), Equation (2.10) andEquation (2.11). The steepness factor (S factor) can be computed using Equation (2.12) andEquation (2.13).

Other factors in the RUSLE equation are also calculated using different methods. The detailsare described in Figure 4-5 following the area of study.

The programmed VBA runs on an ArcGIS platform as an extended function of the interface. Inorder to complete the program, a DEM of the area of interest is required. The sink functionmust be used before the DEM can be imported.

Different GRID files are used in the calculation process. The final result is a GRID file of LSfactor values, named “lsfactor”, which the user can apply to RUSLE automatically. The newlsfactor GRID file has the same resolution as the input GRID file.

Figure 4-5 illustrates the view of working environment of the VBA application in ArcGIS.

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Figure 4-5 Working environment of LS factor and of LS factor result sample

4.2 Study areas

4.2.1 Introduction

In this thesis, the VBA application is used with data from different catchments in Switzerlandand in Thailand. Two catchment located in the Canton of Lucerne, Switzerland and one catch-ment in Wieng Kaen in the north of Thailand are studied.

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All three study areas are mountainous and therefore prone to soil erosion. The two Swisscatchments are located in the pre-alpine Swiss Plateau (north of Alpine), where rainfall anderosivity are high due to the topography (Weisshaidinger and Leser, 2006). However, the pre-dominance of meadowland and of well-adapted crop rotation decreases the rate of soil erosionto some extent.

In contrast, Chiang Rai Province is an area considered to have high soil erosion risks since theintensive agriculture, swidden practices and the topography (Sang A-run et al., 2005, Yu et al.,1999).

To compare the effect of different sources of data on the RUSLE model, in this study differentcases in three catchments are applied, as details in Table 4-1. There are seven diverse cases asin the table.

4.2.2 Study Areas in Switzerland

The Swiss study areas are located in the Canton of Lucerne (region of Lucerne) where is incentral Switzerland. The study areas are in the watershed of the Kleine Emme River, a tribu-tary of the Reuss River (latitude 46° N, longitude 8° E). The Kleine Emme extends for 58 kmand drains a basin of 477 km². Figure 3-6, shows the location of The Kleine Emme catchmentin Switzerland.

The soil types in this area are Cambisol, Gleysol and Podzol (Weisshaidinger and Leser, 2006).The slope in this study area can be rather high and ranges from 2% to 125%. The highest peakin the area is the Napf peak at 1,408 m.AMSL.

Table 4-1 Summary of different GIS data sources applied

Area Channelization metod Data Source

Switzerland FAV (a) DEM 25 m calculated by FAV

(b) DEM 25 m origin from 2 m calculated by FAV

(c) DEM 2 m calculated by FAV

Stream Burning (d) DEM 25 m calculated by stream burning

(e) DEM 2 m calculated by stream burning

Thailand FAV (f) DEM 90 m calculated by FAV

(g) DEM 300 m calculated by FAV

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To test the program, two catchments of the Kleine Emme River have been chosen to run VBAextension on the ArcGIS version 9.2 platform based on the RUSLE. The RUSLE was origi-nally established for agricultural areas where two catchments are covered mainly by agricul-tural areas and meadowland. Previous studies have noted that the risk of soil erosion in this areis high since the area is mountainous (Prasuhn and Weisskopf, 2003,Weisshaidinger and Leser,2006). The DEM data from laser scanning (2 m resolution) is available only for areas lowerthan 2,000 m asl but these two catchments are not higher than 2,000 m AMSL.

The first catchment drains to the northeast and is located southwest of the Kleine Emme River,see Figure 4-7. A small stream, the Altmülibach, is the main watercourse in this area. It runsfrom a high elevation of the forest down through a sparsely populated agriculture area. The

total area of the catchment is approximately 1.3 km2. The elevation of the area is between 800and 1090 meters. Depending on the resolution of DEM, the elevations in the study area areslightly different. The land is mostly used for forestry and agriculture areas. The croppingmanagements of the agriculture mainly are a cross slope farming which it can reduce soil ero-sion rate up to 50 percent.

The second catchment is to the east of the Kleine Emme, as illustrated in Figure 4-7. Thecatchment located near Ebnet. A small creek, the Staldenbach, drains the catchment directionto north-west. The elevation of the Staldenbach basin ranges between 708 and 1006 m AMSL.The total area is about 0.9 km². Meadowland, forest and orchards predominantly cover theStaldenbach catchment.

R factor and K factor

Weisshaidinger and Leser, 2006, recommended values of the rainfall factor and the soil erod-ibility factor (K factor) for use in the Pre-alpine Swiss Plateau. The data is based on a meanannual precipitation of 1,000 - 2,000 mm per year and a dominant meadowland vegetationtype.

The mean values of the R factor and the K factors are shown in Table 4-2. These values areapplied to the model and to all resolutions. To apply to GIS, the GRID file format of two catch-ments areas are given with these mean values for the whole area in order to treat with otherfactors.

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Figure 4-6 Location of study areas in Canton of Lucerne, Switzerland

Figure 4-7 Details of the two catchments in Kleine Emme river

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Table 4-2 R factor and K factor for catchments in Switzerland

LS factor

The LS factor is one of the major focuses factors in this study. The LS factor is calculatedusing three different qualities of DEM data. In Switzerland, DEM data available from theSwiss Federal Office of Topography. The DEM data of the catchment has been purchased fromThe Swiss Federal Office of Topography which is DEM 25 m and DEM 2 m. The third DEMreproduced originally from DEM 2 to DEM 25.

The digital elevation model DEM 25 is a data set representing the three-dimensional form ofthe earth's surface. DEM 25 is derived from the height information of the Swiss National Mapat the scale of 1:25,000 (NM 25) (Swiss Federal Office of Topography, 2004). There are twotypes of DEM 25; a basis model and a matrix model. The DEM 25 basis model consists of thedigitized contour lines and spot heights of the NM 25. The DEM 25 matrix model is a heightmatrix with a 25-m grid corresponding to a mm grid superimposed on the NM 25. In this study,the DEM 25 matrix model has been applied for the DEM 25 m raster.

Because the DEM 25 is derived from the National Map 1:25 000, the DEM 25 reflects basi-cally NM 25 accuracy. Comparisons of “model heights” with photogrammetric measurementsshow an average error of 1.5 m for the Central Plateau and Jura, 2 m for the pre-Alps and theTicino, and 3 m for the Alps. Both study areas in Switzerland are in pre-Alps area.

The second set of DEMs is a cadastral survey digital terrain model DTM-AV-GRID with a 2 mresolution. Originally, DTM-AV consisted of a digital terrain model (surface of the groundbeing without vegetation and buildings) obtained in a direct way with points isolated on all theareas considered. The method of capture employed is the airborne laser scanning. The density

of a point is approximately 1 point per 2 m2 in open areas (Swiss Federal Office of Topogra-phy, 2007). The accuracy of DTM-AV is ±0.5 m.

The raster grid has been interpolated from the DTM-AV, so called DTM-AV-GRID. DTM-AV-GRID is available only for areas when elevation between 0 to 2,000 meters AMSL, since theneed of higher elevation is rare and DEM 2 is often used for the agricultural boundary where

Geographical Region R factor(MJ*mm/ha/h/year)

K factor (ton*ha*h/ha/MJ/mm)

Pre-alpine Swiss Plateau 1000 - > 1400 0.02 - 0.045

Mean values 1200 0.035

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most of is are lower than 2,000 m. Therefore, the study area must be in an area not higher than2,000 meters to have the DEM 2 to apply. Both DEM are provided in ASCII files which arethen converted to raster GRID format by ArcGIS.

In order to apply the DEM 25 m and the DEM 2 m to the VBA program, there are three casesof DEM to be applied; DEM 2 m, DEM 25 m and, for comparison, DEM 25 m produced fromDEM 2 m using the Nearest neighbor resampling technique.

For channelization, the hydrology map or VECTOR25 from the Swiss Federal Office ofTopography is used. The VECTOR25 is produced from the National Map scale 1: 25 000 (NM25) and it is the best fit for these study areas.

P factor and C factor

P factor represents the pattern of agriculture in the area of interest. The crop pattern derivesfrom the aerial photos and shapefile of land use data from the Swiss Federal Office of Topogra-phy. The pattern can be classified into two types; forest and agricultural areas with the rossslope supporting practice. P factor of the forest is 0.75 and P factor of the agricultural area withcross slope farming is 0.75.

The C factor describes the type of vegetation covered. Opened forest as a C factor of 0.023 andagricultural area a factor of 0.05 (Lopez et al., 1998). The vegetation covered the area of inter-est usually changes seasonally. Related to the RUSLE, the seasonal C factors should be appliedaccording to the seasons, but the seasonal data is not be able to reach. Therefore, the area fromaerial photos are applied to design the C factors.

Other materials and data

More shapefile from the Swiss Federal Office of Topography and Atlas of Switzerland pro-gram are applied in the area to obtain other useful data for the calculation such as the map ofpolitical boundaries.

4.2.3 Study area in Thailand

A large part of the total area of northern Thailand is covered with hills and mountains. As toldpreviously, the soil degradation is a big problems in this region, due to deforestation and swid-den agriculture.

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The study area is a catchment of the Mekong River in Wieng Kaen District, Chiang Rai Prov-ince in the north of Thailand, see Figure 4-8. The main watercourse in the area is Ngoa river. Itdrains throughout the area to the north where the Mekong rivet is. The catchment is located at

20° N, 105° E and covers about 470 km2. Agriculture in the areas include, for instance, differ-ent kinds of rice, orchard maize and swidden agriculture.

R factor estimation in Wieng Kaen

There are a number of different ways to estimate the rainfall and runoff factor for the RUSLEsoil erosion model. In this thesis, two methods are applied in order to find the best R factor val-ues for the study areas. The available data to calculate R factor in Thailand can be found in thisstudy, therefore R factor estimation from rainfall data has only been completed in the WiengKaen area, not in the Kleine Emme area, Switzerland.

Figure 4-8 Location of Wieng Kaen district, Chiang Rai province in Thailand

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From the previous studies, various methods are proposed in different areas particularly in thehighland of north Thailand (Land Development Department, 2000, Srichaichana, 2002). In theLand Development Department, 2000, the equation of Srikhajon at al., 1984 has been adoptedfor the whole country with USLE equation. The equation is presented in Equation (4.1).

(4.1)

Where; : R factor (MJ mm/ha/h/year)

: Average annual rainfall (mm)

Equation (4.1) is required the annual average rainfall.

When computing the R factor in the RUSLE, the seasonal period is taken into account. Thereare several proposed R factor estimations for different areas and different climates that usedaily or monthly rainfall data (Diodata, 2005, Kouli et al., 2008 de Santos Loureiro and deAzevedo Coutinho, 2001, Renard and Freimund, 1994). Since the storm data for maximalintensity in 30 min are rarely given at standard meteorological stations, a number of methodsare proposed for obtaining the R factor.

In this study, the Modified Fournier’s Index (MFI) is one of two methods to estimate the R fac-tor. In Arnoldus’s study (1977), it is used to estimate the R factor. Equation (4.2) shows how toderive the MFI.

(4.2)

Where MFI : Modified Fournier’s Index

p : average monthly rainfall (mm)

P : average total annual rainfall (mm)

R 0.4669 X( ) 12.1415–=

R

X

MFI

pi( )2

i 1=

12

∑P

-----------------------=

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Seasonal changes affects the MFI, unlike Equation (4.1) which consider than annual rainfall.Arnoldus, 1977, recommends using;

(4.3)

Where R : the erosivity factor (MJ mm/ha/h/year)

MFI : Modified Fournier’s Index

These three equations, Equation (4.1), Equation (4.2) and Equation (4.3), are used to calculatethe R factor in the Wieng Kaen catchment. The average values of both methods; Srikhajon atal., 1984 and Arnoldus, 1977, are used for R factor.

There is no rain station in the area of Wieng Kaen. In order to estimate the R factor for thecatchment in Wieng Kaen. According to available data from Royal Thai Meteorology Divi-sion, there are 10 rain stations around Chiang Rai Province. Thus, the R factor for the catch-ment in Wieng Kaen is estimated from these 10 rain stations. Figure 4-18 shows the locationsof rain stations included in the estimation.

To obtain the raster of the R factor throughout the study area, a spatial interpolation of theresulting values of the R factor has been completed in order to acquire the values for the wholearea. The Inverse Distance Weighted (IDW), global polynomial, local polynomial and radialbasis function (RBF) methods are used for comparing the results, see Section 4.3.2 for moredetails.

Data for total monthly rainfall and number of rainy days is obtained from the Royal Thai Mete-orology Divison (RTMD) for the period of 1987 to 2007 (20 years).

Table 4-3 Treated K factor values in Wieng Kaen area

Soil Types K factor

Clay 0.018

Clay with gravel or sedimented clay 0.024

Sandy Loam, Silty clay loam 0.027

Clay mixed with sand 0.033

Clay loam, sandy clay 0.035

Silt loam 0.049

R 4.17MFI( ) 152–=

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K factor

The K factor is obtained from a soil map from the Land Development Department. Soil typesin the Wieng Kaen are sandy loam, clay, sedimented clay and some rock areas. The K factorvalues used in the study are shown in Table 4-3.

LS factor

The slope length and steepness factors are extracted from DEM, as explained in Section 4.1.1.The DEM used for the LS factor is a SRTM product. An SRTM 3-arc-second product (90mresolution) is available for the entire world. The accuracy of it for SRTM 30 m resolution is 20m for a horizontal error and 16 m for a vertical error. Therefore, the SRTM 90 m resolutionused here is implied that it contains less accuracy than the one with 30 m resolution.

A 30 m resolution DEM is available from the Royal Thai Survey Department (RTSD) andfrom the National Image and Mapping Agency (NIMA) of the U.S. Geological Survey(USGS). The 30 m DEM is a Digital Terrain Elevation Data Level 2 (DTED2), a uniform grid-ded matrix of terrain elevation values with post spacing of one arc second or approximately 30meters. The DTED2 is the basic high resolution elevation data source for all military activitiesand systems that require landform, slope, elevation, and/or terrain roughness in a digital format(Pike, 2000). The DTED2 has, in general, global accuracy with ±16 m (Konecny, 2004).

The scale of the river maps and the VRN in Thailand available in Thailand is not comparableto DEM 30 and DEM 90. As the result, the channel starting points are defined by the methodof the FAV not the stream burning which needs to obtain comparable the VRN’s scale to theDEM’s scale.

P factor and C factor

Land use data from the Land Development Department (LDD) and aerial photos from the Goo-gle Map website (Google Inc., 2008) show the area of the Wieng Kaen covered by agriculturalland or mixed deciduous forests. The agriculture area had not applied any of protection crop-ping (cropping management). Most of the area is intensively flowed intensively. Under stan-dard fallow conditions, the annual P factor value is 1 due to a soil surface having no protection.Moreover, seasonal crop data is not available.

Intensive agriculture is practised throughout the area., as can be seen in the Google Map. Fur-thermore, there is no updated data of agricultural areas spatially in the area. For the C factor, a

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value 0.05 of the agricultural area is applied in all parts of the catchment area(Lopez et al.,1998).

4.3 Application results and discussions

The results from RUSLE model with VBA application in all study areas are discussed in thissection.

4.3.1 Results in study areas of Switzerland

Channelization results

Section 4.1 explains how channelization is determined for the Swiss catchments. In Table 4-1,different applied cases in the areas are shown.

To determine automatically channelization using the VBA program, a given constant minimalFlow Accumulation Value (FAV) must be given. With the constant value, the VBA programgenerates the channels related to the given value. However, the created channel is not a perfectfit to the VRN, which it is assumed to be corrected. For example, when the minimal FAV ishigh, fewer and shorter channels are produced than are presenting with VRN. In contrast, whenthe minimal FAV is low, more numerous and longer channels are generated. Channels concur-rence cannot automatically be derived from a single minimum FAV.

Avoiding mentioned problems, thus, the starting point of channels are adopted point by pointrelated to the VRN and flow accumulation values from the DEM. This process is used in FAVmethod. The following results fit the VRN best, but are not obtained automatically.

In comparison, the stream burning method tries to direct the flow accumulation to be consid-ered with VRN and therefore changes the flow direction and flow accumulation. However,forcing the flow to run following the artificial DEM, it can proceed only to some extent whichthe flow accumulation becomes constant. The optimal value to lower the raster cells to a valuethat coincides with the VRN is specified. The optimal lowering values are chosen by loopinguntil the flow accumulation from the artificial DEM is constant.

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The results from two methods using DEM 2 and DEM 25 in Altmülibach and Staldenbachshow that channels produced using the same method demonstrate the same trend in both reso-lutions, see Figure 4-9 and Figure 4-10. However, the channels (or flow accumulations) fromdifferent methods contribute to different channelization. The results from stream burning showa larger number of channels than as present in the VRN, as the new channels are created whenthe DEM values are lowered.

The stream burning method is especially useful in areas with flat terrain, such as in the southpart of the Altmülibach area. The channels produced by stream burning method are moreprominent than those produced using the FAV method.

Figure 4-9 Channels from Flow Accumulation Value method in Altmülibach

Figure 4-10 Flow Accumulations from Stream Burning method in Altmülibach

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In the north of the Altmülibach, the topography are more steep and irregular, therefore thechannels are more prominent than the southern area, see in Figure 4-9. In the south, the area isflat as is evident in Figure 4-11. In this case, it is not easy to identify the channel from theDEM. In Figure 4-11, the aerial photo and contour lines display how flat the area to the southis. The channels from both DEM resolution are not able to channelize properly by flow accu-mulation since the accumulations in the channel are not dominant enough to indicate the chan-nels. When the topology is steep, the DEM contributes better channel networks in bothmethods.

In addition to the flatness, canopy cover can also affect the DEM 2 m data. The DEM 2 m cap-tured the height by airborne laser scanning. In forrest areas, it is difficult to capture the eleva-tion of the area without a large decrease of uncertainty. As the result the area under thiscircumstance, the elevation can be not true. Anyway, the outcomes from DEM 25, which orig-inally is produced from VECTOR25, does not show the channel dominance at the same pointas DEM 2. It implies that both DEMs can not detect the channels when the topography is flat.

As is evident the aerial photos, channels are often obscured by forest as is the case in the area.DEM 2 is a product of airborne laser scanning. As canopy cover impedes the laser pulses,actual topography in these areas can be uncertain. Although the DEM 25 is originally gener-ated using the National Map 25, not the airborne laser scanning, the resolution is not fineenough to identify small channels in the flat areas.

Figure 4-11 Channels from FAV method focusing on the flat area in Atlmülibach

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Figure 4-12 Section of the river in Altmülibach showing how resolution influence the channel extractions: (a) Channels from DEM 25 (b) Channels from DEM 2

Figure 4-13 Locations of 5 compared river network nodes

Table 4-4 Distances between generated channels and VRN using the FAV method in Atlmülibach

DEMDifferent Distances Between Generated Channels and VRN (m)

Mean (m) SD

Node 1 Node 2 Node 3 Node 4 Node 5 Node 5

2 36.14 1.39 14.94 0.95 6.39 14.73 12.42 13.14

25 54.20 19.43 15.68 6.70 21.51 9.42 21.16 17.16

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Figure 4-12, Figure 4-13 and Table 4-4 show the differences between the channels from DEM2 and DEM 25 using FAV method. In Figure 4-12, the locations of river junctions from DEM2 and DEM 25 are represented. The river junctions or river nodes are located differentlydepending on the data sources (DEM or VRN) and the data resolution (DEM 2 or DEM 25).The junction nodes from DEM 2 is more precised to the VRN than the results of DEM 25.

In the Staldenbach area, the topography is relatively heterogeneous. The flow accumulationtherefore represents the channels in the area. Since there is only one river junction in this area,the distance from different DEM is not presented here.

Figure 4-14 illustrates the channels calculated from the flow accumulation which shows rela-tively concurring with the VRN. The river junction of DEM 25 is fitter to the VRN more thanthe channels from DEM 25. Due to in the steep slope area, the DEM 2 can represent the eleva-tion finer than the DEM 25.

In summary, the DEM 2 and DEM 25 result very similar channelization results. The DEM 2predicts the location of the river junctions slightly more accurate than the DEM 25, especiallyin uneven area. However, in flat or gentle sloping areas, the DEM 2 does not represent thechannel as well. In this case, the stream burning method will be preferable to use.

In all cases, the VRN is superior for predicting channels in the study areas that the VRNand the DEM be integrated in various ways to identify the channels.

Figure 4-14 Channels predicted by the Flow Accumulation Value method in Staldenbach

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Table 4-5 Soil erosion results from different cases in Altmülibach

Table 4-6 Soil erosion results from different cases in Staldenbach

Soil erosion results

The results from the RULSE model with the factors corresponding to Section 4.2.2 are shownand discussed in this section. The results from different GIS data sources (DEM, hydrologicaldata) are compared. The soil erosion predicted in each study area is presented in Table 4-5 andTable 4-6 using five different methodology/DEM resolution combinations.

As shown the tables above, soil erosion results obtained from the DEM 25 in both study areasgive higher mean annual soil erosion values than those calculated from DEM 2. The DEM 25,

Interested statistics

Stream Burning FAV

DEM 2 DEM 25 DEM 2 DEM 25 DEM 25 from DEM 2

Catchment area (ha) 78.15 78.06 78.15 78.06 80.31

Mean value (ton/ha/year) 3.84 18.14 3.82 16.67 8.94

Standard deviation 5.47 19.38 5.41 15.43 8.68

% soil erosion < 1(ton/ha/year) 25.56 10.49 26.09 9.44 17.74

% soil erosion 1 - 5 (ton/ha/year) 50.27 9.84 49.89 12.34 21.48

% soil erosion 5 - 10 (ton/ha/year) 14.00 22.50 13.95 22.66 25.44

% soil erosion 10 - 20 (ton/ha/year) 7.88 25.14 7.83 25.00 23.66

20 (ton/ha/year) < % soil erosion 2.29 32.05 2.23 30.58 11.67

Interested statistics

Stream Burning FAV

DEM 2 DEM 25 DEM 2 DEM 25 DEM 25 from DEM 2

Catchment area (ha) 83.19 83.94 83.19 83.94 84.06

Mean value (ton/ha/year) 2.68 11.86 2.64 11.88 9.99

Standard deviation 4.85 12.86 4.69 12.84 12.00

% soil erosion < 1(ton/ha/year) 49.54 11.61 49.93 11.39 10.92

% soil erosion 1 - 5 (ton/ha/year) 35.44 28.52 35.17 28.44 34.20

% soil erosion 5 - 10 (ton/ha/year) 9.70 19.66 9.69 19.88 21.93

% soil erosion 10 - 20 (ton/ha/year) 4.00 20.18 3.94 20.18 17.92

20 (ton/ha/year) < % soil erosion 1.32 20.03 1.27 20.10 15.02

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which is reproduced from DEM 2, contributes the mean values of soil erosion always lining upin between the values of DEM 2 and DEM 25.

The mean annual soil erosion obtained from DEM 25 is significantly higher than that cal-culated from DEM 2. For example, in Altmülibach the FAV method gives a value of 16.67ton/ha/year based on the DEM 25, compared to a value of 3.82 ton/ha/year using the DEM 2.In addition, the reproduced DEM 25 contributes a value of 8.94 ton/ha/year.

The slope length algorithm resets the slope length accumulation when the slope decreases fromcell to cell, regarding to the cutoff conditions, see in Section 4.1. As a result, the smooth slopeof a coarse DEM is reset lass often and, therefore, a finer resolution DEM predicts greater soilloss.

In Figure 4-15, an illustration of slope smoothing is presented. Profiles of slope in reality andin different resolutions are shown. In reality, slope changes with the topography of the area. Asan example, the profile of DEM 2 is comparatively closer to reality than that of DEM 25 whenthe topography is heterogeneous. However, when the area is relatively even, DEM 25 also rep-resents the topography well.

When the slope is smoother, the condition of the flow accumulation meets the cut-off lessoften. It results low LS factor values and, consequently, low annual soil loss. This circum-stance supports the idea that DEM 25 gives a longer slope length and suggests a higher the soilerosion rate.

The accuracy of DEM 2 is ±0.5 m, that of DEM 25 m is ±2m. Therefore the DEM 2 moreaccurately predicts soil erosion than the DEM 25. In addition, the results of Weisshaidingerand Leser, 2006, show that the measured erosion rate of the site with conventional plough till-age with uncovered soil, during the catch crop period, does not significant varied from theresults from the DEM 2. The soil erosion results of the DEM 2 has highly accurate and has rel-evant to the soil erosion results from other studies in the same area.

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Figure 4-15 Profile of slope from different DEM compared to the reality

The DEM 25, which is reproduced from DEM 2, contributes the mean values of soil erosionalways lining up in between the values of DEM 2 and DEM 25. As noted previously the accu-racy of DEM 2 is higher than of DEM 25, which implies that the results from the DEM 25from DEM 2 is more accurate that those of the original DEM 25. It also indicates that the near-est neighbor resampling technique represents the converted DEM from 2 m resolution to 25 mresolution properly.

The mean values of the results of both channelization methods are not significantly different.The biggest different result between two methods is from DEM 25 in Altmülibach. Due to theslope changes that occur as a result of the stream burning method, the slope factor is higherthan that of the original DEM 25. However, there is not a big difference in the results fromeither the stream burning or the FAV method, as show in Table 4-5 and Table 4-6.

According to Shreatha et al., 1996, when the soil erosion is in the range of 1- 5 ton/ha/year, thesoil erosion is slight. The results from the calculations confirm by presenting 50% of area is inclass soil erosion 1- 5 ton/ha/year in Altmülibach and 35.17% of area is in class soil erosion 1-5 ton/ha/year in Staldenbach. Therefore, both catchment are in the low level of soil erosion.

The soil erosion risk map from DEM 2 in both areas are illustrated in Figure 4-16 and Figure4-17.

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Figure 4-16 Soil erosion risk map in Altmülibach from DEM 2 m

Figure 4-17 Soil erosion risk map in Stadalbach from DEM 2 m

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4.3.2 Results in the Study Area of Chiang Rai province, Thailand

Channelization and soil erosion results in the Thai study area are shown in this section. Due tothe R factor being interpolated from 10 rainfall stations over a 20 year time span in Wien Kaenarea, the details and results of the interpolation are also presented.

R factor estimation

Since the R factor is based on rainfall data, monthly rainfall from 10 rainfall stations over a 20year period (1997 - 2007) are applied in the RUSLE model in this study, as mentioned in Sec-tion 4.2.3. Figure 4-18 illustrates the location of all stations in Chiang Rai Province, Thailand.However, the data is not complete for some years. As a result, not all rain stations contribute20 years of data to the R factor values. The number of applied years are shown in Table 4-18.

The Wieng Kaen catchment has no rain stations within its borders. As a result, it is necessaryto interpolate an R factor for the area. Base on the advised methodology, the Modified FournierIndex (MFI) is estimated for all rain stations. An R factor is calculated from the derived MFIusing Equation (4.3). An R factor fitting to Equation (4.1) has been established especially forThailand. The average values of these two method results are used for the R factor in ChiangRai, to make the R factor from MFI more appropriate for Thailand, average values of theresulted R factor are finally adopted as shown in Table 4-18 (column 6).

Both the R factor from the MFI and the R factor of Srikhajon are highly correlated. The linearregression of estimated factors is shown in Figure . The goodness-of-fit is 0.8861 which ishigh. Moreover, from the linear regression, it represents when the R factor value is high, theless correlation has located. Therefore, if the R factor is high, this relationship is not recom-mended to use.

The equation from linear regression analysis developed in this study can be applied to futuresoil erosion case in the area of Chiang Rai Province.

R factor surface interpolation

In order to verify the reliability of the estimations, the calculated rainfall factors are interpo-lated using Inverse Distance Weighted method in ArcGIS. The kriging interpolation methodcan not be applied to this data set because the data does not present normal distribution check-ing by Histrogram and QQ Plot tools and, additionally, the data set obtains a small number ofdata points to apply to the interpolation.

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Figure 4-18 10 Rain Stations in Chiang Rai province

Table 4-7Estimated R Factor for the catchment in Wieng Kaen

Rain Station

Length of record period

Average total annual rainfall

R factor (from MFI)

R factor (from Equation (4.1))

Average R factor

(year) (mm) MJ mm/h/ha/yr MJ mm/h/ha/yr MJ mm/h/ha/yr

Chiang Khong 20 1,881.38 1,011.8 830.46 938.73

Chiang Saen 20 1,647.04 861.23 737.66 809.04

Doi Tung Forest 16 1,873.11 1,066.72 1009.64 964.57

Mae Jan 16 1,272.21 767.15 667.67 721.19

Mae Sai 18 1,768.84 976.44 813.73 895.09

Mae Saruai 20 1,210.10 533.38 615.63 543.12

Pa Dad 17 1,429.12 660.44 618.04 657.78

Phan 20 1,357.80 652.02 621.82 636.92

Teng 19 1,750.23 993.12 797.30 899.08

Wiang Pa Pao 14 1,049.17 432.09 474.75 454.90

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Figure 4-19 Linear regression of estimated R factor from two methods

The Inverse Distance Weighted (IDW), global polynomial, local polynomial and radial basisfunction (RBF) methods are applied to the point data to compare the results. RBF method is aseries of exact interpolation techniques, that is, the surface must go through each measuredsample value. The best result from RBF (spline with tension) shows the best Root MeanSquare (RMS) value (value of 203.8). The results of interpolation are shown in Figure 4-18with a contour interval of 30 MJ mm/h/ha/year.

Figure 4-20 R factor surfaces resulting from spatial interpolation

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Figure 4-21 Channelization results in Wieng Kaen area and the compared nodes

Channelization Results

The Vector River Network (VRN) of Thailand is integrated with the Flow Accumulation Value(FAV) to obtain the channels in the study areas. Since the scale of the VRN does not fit to theDigital Elevation Model (DEM), stream burning method does not apply in Thai case study, butFAV method.

The channels derived from the FAV process for DEM 30 m and DEM 90 are shown in Figure4-21. Table 4-8 shows the different distances between channels generated from both DEMs.The results show apparently that the differences of the node positions at the upstream area arelower than at the downstream area. At the downstream area, the topography becomes flatter.As the result, channels are not dominant in DEM data. This is also true of the Altmülibachareas. Therefore, channels from different DEM sources do not match point-for-point, as theydo where channels are more prominent.

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Table 4-8 Different distances between channels of DEM 30 and DEM 90 m in Wieng Kaen

Soil erosion results

The different and the elevation range of DEM 30 is wider than that of DEM 90 (361 - 2,002 mand 632 - 1,999.6 m, respectively). As noted in Section 4.3.1, a finer DEM better representsvisualized the actual topography. The difference in accuracy between a fine resolution and acoarse resolution DEM can be seen in the comparison of the DEM 30 and DEM 90 for theWiesn Kaen area, Figure 4-21.

Table 4-9 shows the results of mean annual soil loss as well as the percentage of area in eachclasses of soil erosion risk. The results of soil risk estimated from DEM 30 are shown in Figure4-22 below.

The mean annual soil loss values of DEM 30 is smaller than the mean values of DEM 90, dueto the cutoff conditions explained in Section 4.3.1. The Section 4.3.1 explains that when theslope is smoother, the condition of the flow accumulation meets the cut-off less often. It resultslow LS factor values and, consequently, low annual soil loss.

The DEM 90 represents the channels well, but the soil erosion is significant more than theresult of DEM 30. As mentioned in Section 4.2.3, the accuracy of DEM 30 is higher than DEM90. The calculation from DEM 30 is therefore more accurate than DEM 90. Moreover, theresults of DEM 30 are comparable to the soil erosion rates in Chiang Rai province computed inother studies (Turkelbooma et al., 2008, Sang-Arun et al., 2005).

As shown in Table 4-9, the highest percentage (40.85%) of soil erosion areas using DEM 30estimated are in a class of 1 - 10 ton/ha/year, using the DEM 90, 31.52% of the total area in the

Node Distances (m) Node Distances

(m)Mean (m) SD

1 100.0 8 143.2

262.21 199.40

2 514.8 9 313.8

3 125.3 10 186.0

4 269.1 11 250.0

5 125.3 12 210.2

6 125.3 13 470.9

7 63.2 14 773.9

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same category. Shreatha et al., 1996, define a moderate level of soil erosion as 5 to 10 ton/ha/year. Thus, the results show that Wieng Kaen risks of soil erosion.

4.3.3 The comparison of results in Thailand and Switzerland

A summary of the results for the study areas in both Switzerland and Thailand are shown inTable 4-10. The table shows that the mean annual soil erosion in Thailand is significantlygreater than mean annual soil erosion in Switzerland. Although the average slope in Altmüli-bach is higher than that of the Wieng Kaen study area, the annual soil erosion value in WienKaen is higher. This is likely due to the fact that in Wieng Kaen, no soil erosion reducing bycrop management. In addition, crop type (rice, maize, forest, etc.) in the are has a higher C fac-tor values. More soil erosion therefore occurs in Wieng Kaen. As there is no crop managementplan in Wieng Kaen, despite intensive agriculture, a conservation plan is needed for the area.

In Thailand, a soil loss estimation of 1 - 10 ton/ha/year is predicted by both DEMs. In contrast,a maximum soil erosion rate of 1 - 5 ton/ha/year is predicted by only the high-resolution DEM2 for Switzerland. In Wieng Kaen, the lowland area is flatter than the Swiss catchment. There-fore, the Wieng Kaen catchment covers more flat areas than the Swiss one. Therefore, the dif-ferences of the results computed by different DEMs are not as big as in Switzerland due to theslope length iteration does not reset often.

Table 4-9 Soil erosion results from different cases in Wieng Kaen area

Statistics Wieng Kaen area

DEM 30 m DEM 90 m

Mean annual soil loss (ton/ha/year) 13.46 20.67

Standard Deviation 14.17 22.35

Area (km2) 476.26 475.24

% soil erosion < 1(ton/ha/year) 12.48 9.40

% soil erosion 1 - 10 (ton/ha/year) 40.85 31.52

% soil erosion 10 - 25 (ton/ha/year) 29.92 29.20

25 (ton/ha/year) < % soil erosion 16.74 29.89

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Figure 4-22 Soil erosion risk in Wieng Kaen area

Table 4-10 Results Comparison in Two Countries

Characteristics Switzerland Thailand

Location 46° N, 8° E 20° N, 105° E

Elevation (m AMSL) ~ 700 - 1090 ~ 360 - 2000

Main Crop glass land, forest rice field, orchard

P factor 0.75 1

C factor 0.023 0.05

R factor 1200 830 - 900

K factor for RUSLE 0.045 0.018 - 0.049

Mean annual soil ero-sion (ton/ha/year)

3.80/2.64from DEM 2 in Altmülibach/

Staldenbach

13.46/20.67from DEM 30 and 90

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4.4 Conclusion

The DEM comparison

For erosion risk maps, a finer DEM is more accurate once the catchment channel network,boundaries and hillslope shapes are compromised. In uneven areas, a finer resolution DEMshould be applied to avoid the interpolation of elevation due to large cell size (resolution).When the area is relatively flat and the hillslopes are not compromised, the coarse resolutioncan be used. As it can be seen in the lowland south of Altmülibach that both DEMs can notchannelize well. However, it is complicated to distinguish whether the hillslope is compro-mised. Therefore, it should be anyhow recommended by specialists for compromised hillslope.

In Thailand, finer DEM da ta will enhance the quality of soil erosion in estimations veryuneven areas. At the moment, laser scanning has been applied to only small parts of Thailandbecause it is costly to produce a DEM from laser scanning. However, it is recommended by theauthor the laser scanning be used whenever possible, particular in more topographically heter-ogeneous areas.

Channelization

The stream burning method predicts higher soil erosion values than the Flow AccumulationValues (FAV) method, as it uses higher slope estimations. Both methods integrate Vector RiverNetwork (VRN) data to define channels and channel starting points.

When there is no VRN, available channels can be identified but not where the starting pointsare. The aerial photos can be integrated in this case to examine the watercourses. Therefore, itis highly recommended to identify the starting points of channels by the integration withVRN. Flow accumulation can be checked by comparing the FAV to the VRN.

Factor estimations

Estimating all the factors for the RUSLE model is relatively complicated. This study hasadopted several factors from specific tables or previous studies due to the difficulties in access-ing the data. Also, different data formats are treated in this study which it makes data prepara-tion even more troublesome.

Both obsolete and unavailable data make it difficult to run the model. Therefore, this studystrongly supports the development of a simple universal method for accessing up-to-date

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GIS data. Up-to-date data and ease of access it will significantly improve the input dataand result in a better soil loss prediction.

Data sources and variety of Data

In Thailand, both data quality and data availability present significantly challenges. The data isnot always available to investigators outside the government administration. Furthermore, themeans by which the data was produced are not clear. In contrast, the data in Switzerland isreadily available through the office of topography and it is regularly updated.

However, updating GIS data in Thailand is not a simple project. The data are produced by sev-eral different organizations and area available in multiple formats. Finally, many versions ofthe same dataset exist.

To tackle this problem, more Spatial Data Infrastructure (for data creating, data editing,data updating and as well searching metadata) must be supported in GIS at every level.

Soil conservation planning

Using the soil loss prediction tool in GIS, a soil conservation plan can be established based onthe prediction results. Switzerland proposes several different types of soil-conservation policystrategies for long-term soil fertility. Prasuhn and Weisskopf, 2003, list these strategies:

- a well-balanced fertilizer budget;

- a suitable role for ecological compensation areas;

- well-adapted crop rotations;

- proper soil protection; and

- careful selection and application of pesticides.

Using GIS, a conservation plan could be created with more region-specific.

Through reorientation of Swiss agricultural policy, soil erosion should be minimized by con-servation tillage, buffer strips, adapted crop rotation, etc. Nevertheless, the projected 50%reduction of P input from fields to surface water is planned. In order to update the P factor, datasharing from different users or different organizations are needed to facilitate prompt data

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updating. Data sharing helps to improve the accuracy of soil erosion data, therefore improvessoil erosion prediction.

In Thailand, the Land Development Department plays a main role in resolving this problem.For decades, GIS has been the main tool in the Department for analyzing the problem, practis-ing the conservation plan and monitoring the results.

Thus, GIS is a significant and effective tool for solving the soil erosion problem in Thai-land and Switzerland. With the soil erosion risk analysis, it can help to access and estab-lish appropriate soil conservation implementation plan and database.

Concluding remarks

As mentioned above, different qualities of GIS data, the data are non-consistent data, differentdata classifications, not up-to-date data and non-interoperable data. In the study, the DEM andthe VRN from different sources had to be modified in order to be run in and in the ArcGISplatform. The modifications are completed in several ways and both raw data and GIS data aretreated. Several different GIS formats exist, but not all are compatible.

To overcome these problems, data sharing including GIS data transfer, interoperabilityin GIS, a unique standard for natural resource classification and a soil erosion metadatamodel should be developed to allow for data exchange and updating between differentorganizations.

In the next chapter, suggested solutions such as SDI, soil metadata and web-based GIS soildata are described in details.

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Chapter 5GIS Data Quality and

Soil Erosion

According to the results of the GIS applicat ions of RUSLE model in Chapter 4, the soilloss prediction depends consid erably on the quality of the GIS data, such as DEM, soiltype, hydrological data, aerial photos and rainfall data, etc. The results from the case stud-ies show that the finer Digital Elevation Models (DEM) and other finer spatial data yield con-siderably better results in heterogeneous areas. Contrary to this, when the area is relatively flat,coarse DEM derives comparatively similar results to the finer DEM. The finer DEM is moreexpensive, therefore it should be applied when they are necessary.

The conclusion in Chapter 4 represent that a finer DEM is more accurate once the catchmentchannel network, boundaries and hillslope are compromised. Besides, it is highly recom-mended to identify the starting points of channels by the integration with Vector River Net-work. It apparently shows when the GIS data quality has high accuracy, it increases the qualityof soil erosion prediction as well. To acquire those high quality and proper data, it is not easy.

In this chapter, the problems on GIS data quality from application experiences in this study arediscussed and the author proposes the various recommendations to improve soil erosion resultsthrough GIS data quality.

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During the RUSLE application, the found main problems are in summary:

- Data updating . The GIS data quality is often not up-to-date. Data has not beenupdated or it is unknown when it is updated, but there is no idea about how to acquire the infor-mation about data updating, especially in Thailand.

- Access restriction. In Switzerland, most of the data can be obtained from differentadministrations without restriction, but some are needed the permission to get the data. On theother hand, in Thailand most of the data are restricted only in use for the governmental admin-istration.

- Soil classification are not harmonized. The soil classifications of soil data are var-ied, therefore it is not easy to specify them correctly. Unique soil classifications should be usedfor everywhere or the references to compare different soil classifications should be cleary iden-tified.

- A large variety of data formats exist and s o they are not interperable. In thesoil erosion model, the data from different sources is used and some of it is not compatible toothers due to different formats. Moreover, required data are stored in different sources which itcan not be easily accessed or reached.

As the results, the methodology to improve the result of the model regarding to the GIS dataquality are focused. To overcome these problems, data sharing including GIS data trans-fer, interoperability in GIS, a unique standa rd for soil classifications and Spatial DataInfrastructure (SDI ) s hould be cr eated for al l s oil data in or der t o share all data andupdate the data for different organizations.

To obtain better data fr om various of data sour ces, the most economical way is to shar edata which is now on hand in any case, and is to adopt the most w ell-suited data for themodel. The methodologies will improve to obtain better GIS soil data for soil erosion predic-tion. The whole picture of all recommendations are shown in Figure 5-1.

Firstly, the Soils and Terrain Digital Database (SOTER) is explained as it is the solution toobtain standardized, compatible and credible soil database. The SOTER is a GIS that handlessoil data under the specific classification of The Food and Agricultural Organization (FAO) ofThe United Nations. With SOTER classification, the soil data will be sorted according to theFAO. With this, the soil data from different areas can be integrated or compared with eachother properly and simply. Moreover, the same soil classification is easy to apply to the meta-data model for soil erosion.

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Presently to set up any soil erosion model, different soil classification systems have beencoped in the same areas. The SOTER will be a way to improve this confused soil classifica-tion. The details are described below in Section .

The quality of soil erosion estimation will be more relevant to more alternative soil data. Sec-tion 5.2 explains in detail, modern GIS thematic and GIS interoperability for soil data. Anindependent GIS system will support to impr ove to access better data and shar e datamore widely. In this section, the examples of new GIS database in Thailand are given. Previ-ously established sharing and integration of soil GIS data is also shown later in this section.

The final section, Section 5.3, explains Web-based GIS on soil erosion as it is also up-to-datesource which people can access via the internet with no location distractions and it is greatlyefficient when visualizing and acquiring the data are needed. Furthermore, it has become alow-priced and effortless way to disseminate geospatial data and its processing tools. WaterErosion Prediction Project-Climate Assessment Tool (WEPPCAT) and SOil Map Internet Ser-vice (SOMIS) are good examples of the web-based GIS on soil erosion and are shown in Sec-tion 5.3.

Figure 5-1 The whole concept of chapter 5

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5.1 Different quality of GIS soil database

Available GIS soil data, which is accessible anywhere, will improve the quality of soil erosionprediction in the sense of time and economy, instead of doing more expensive field works. Thequality of soil database, or scale of it, depends on the producing organization. For the users, thequality or the scale, which is appropriate for their works, is the best for them. It is important toaccess to the most appropriate data.

However, different scales can provide various perspectives of soil information for the users tohave an overview and also detailed pictures of their area of interest. Also, an accessible soildatabase for every scales is valuable for soil erosion prediction.

As cited in Toy, 2002, regarding soil erosion globally and locally: soil erosion is an issue wherethe principle “think globally, act locally” is clearly appropriate. Think globally, because soilerosion is a common problem that has, does, and will continue to impact the global commu-nity. Act locally, because effective erosion control requires action at the hillslope, field, streamchannel, and upland catchment scales. Local acting should be supported by the governmentaccording to the national database.

Various soil database have been established with GIS within the last twenty years. The follow-ing information looks at the soil GIS database from different sources and diverse scales.

From the following soil database, the soil classifications are anyway varied from one databaseto another database. It is not a simply process to set up one classification for all countries butwhich is the best way for the data users to compare the soil data in everywhere.

5.1.1 World Soils and Terrain Digital Database

Existing data is not often used to its full potential because of poor accessibility. Generally, tra-ditional soil maps generally do not display the information that is required or it is presented ina format that cannot be used directly for a particular interpretation (ISRIC, 2004). Moreover,there has been intensive demands to obtain an international standard on soil data at both globaland national levels.

World Soils and Terrain Digital Database (SOTER) has been introduced and developed by theinitiative of the International Society of Soil Sciences (ISSS), the United Nations EnvironmentProgramme (UNEP), FAO and International Soil Reference and Information Center (ISRIC)(Van Engelen, 2000). Due to lack of standardized, compatible and credible soils database with

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appropriate scales, SOTER has been developed for the problems. It is a new technology hasbeen developed for database management. More new data has been produced after the comple-tion of FAO soil map in 1986.

SOTER is a program for storing and handling data related to soil and consists of a spatial andan attribute data component handled by GIS and database management software, respectively.It has been developed at scales ranging from 1:5 million to 1:500 000, depending largely on theneeds of the users.

SOTER is supported by different international organizations and it can be considered as aninternational standard. The unit was originally at the first was designed for small-scale maps(1:1 000 000), but it can be used on a wide range of scales (Weller, 2002). The overall objec-tives of SOTER are to

(i) make data on soil and terrain resources more accessible, not only for certain groupsof people,

(ii) answer queries more promptly

(iii) transform basic data into understandable information to a wide array of users(Van Engelen, 2000)

(iv) to provide harmonized norms for soil mapping, soil classification, soil analysisand interpretation of soil resources information.

Soil data in SOTER is classified with the terrain character and soil information integrally. Theunit of this integrated character is SOTER (SOil and TERrian) which in Van Engelen and Wen,1995 has suitably described “the identification of areas of land with distinctive, often repetitivepatterns of land form, lithology, surface form, slope parent material and soil”. As a result, eachSOTER unit thus represents one unique combination composed of terrain component and soilcomponent.

Figure 5-2 shows the general approach of SOTER and presents a data structure of SOTER andan example of a SOTER map. The general approach of SOTER is to review all the existingdata for example DEM, soil maps and remote sensing data, etc.

Following the SOTER procedure manual (Van Engelen and Wen, 1995), all data is rearrangedinto TERRAIN units (major landforms, general lithology), terrain components (slope, soil par-ent materials) and soil component (soil profiles). These unit arrangements will be geo-refer-enced to obtain map and SOTER database in different areas.

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Figure 5-2 General approach and data structure of SOTER (modified from Weller and Stahr, 1995)

Various studies have been accomplished to set up SOTER database in different areas, forinstance, in the Czech Republic at the scale 1:250 000 by Nemecek and Kozak, 2003 and in theNile Delta at scale 1:100 000 by Rahim, 2006. Until 2004, SOTER has been applied in Africa,Asia, Europe and South America (ISRIC, 2004). The database of the global SOTER containsdata from 50 countries.

In addition, SOTER data has been used for a wide range of applications, including assessmentsof impacts of soil degradation on food supply, soil vulnerability to pollution, the Amazonianland studies (Cochrane, T.T. and Cochrane, T.A., 2001), land use planning, suitability assess-ment for Banana in Hainan, China (Zhang et al., 2002).

However, there are limitations to SOTER especially on the scale changing; new subcompo-nents insert or new data setup for downscale, different unit summing for upscaling (Weller,2002). Weller has proposed the NewSOTER to help solving these difficulties. The NewS-OTER allows more flexibility to change soil and terrain maps in different scales, because foreach scale the landscape data structure has to be re-designed for the new resolution. Thearrangement of complex land patterns is not well represented because it requires a moredetailed hierarchy whereas large homogeneous land surfaces can be adequately described by asimple data structure (Weller, 2002). Moreover, soil descriptions are improved in the revisedversion.

Improving the data for SOTER, not only on data structure as in NewSOTER but Space ShuttleRadar Topographic Mission (SRTM) digital elevation data is now being used to derive the dif-

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ferent landform units and to generate terrain information. Contrary to this, soil attribute dataare largely derived from traditional field data. Under the main leading of Europeans Communi-ties, the SOTER Procedure Modification (SMP) Project has been initiated to incorporate theDEM in SOTER delineation. The new, quantitative method for creating SOTER terrain Unitpolygon system is introduces. This method will benefit SOTER database with consistency,homogeneity, limited data generalization problems, less edge-matching and less harmonizationproblems (Dobos, et al., 2005). It has been found that the development of a quantitative proce-dure is seen as a promising way to speed up the completion process of the global SOTER. Thismethod is used to develop the SOTER database for the states of the European Union.

Prospective benefits of SOTER database r elated to the above mention allow many moredifferent organizations to share the soil data from any source or any place. The updatingof database is improved as well.

SOTER database is supporting unique soil classifications for different scales of a soil databaseand access to soil data in everywhere. Soil prediction will benefit from the SOTER consider-ably.

5.1.2 The Australian Soil Resource Information System

Previously different Australian organizations produced the same data in the same area with dif-ferent resolutions for a national database. In 1998, The Australian Soil Resource InformationSystem (ASRIS) is set to created a national soils dataset co-operated with various agencies andorganizations. ASRIS is a national database of soil information, suitable for use on a nationalto large regional scale.

ASRIS provides online access to the best publicly available information on soil and landresources in a consistent format across Australia. It provides information at seven differentscales with resolutions from 30 km to 10 m (Australian Department of the Environment andWater Resource, 2007), as follows and in Figure 5-4. Details of the information in each scaleare provided below.

- The upper-three scales provide general descriptions of soil types, landforms andregolith across the continent.

- The lower scales provide more detailed information in regions where mapping iscomplete. Information relates to soil depth, water storage, permeability, fertility, car-bon and erodibility. Most soil information is recorded at five depths.

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- The lowest scale consists of a soil profile database with fully characterized sites thatare known to be representative of significant areas and environments.

The creation of the ASRIS database is a landmark achievement for Australian soil science(Johnston et al., 2003). The ASRIS is being released in stages. Since the beginning of 2008, theupper levels have been completed across the country. There will be a restricted coverage atlower levels. Data will also be available for approximately 10,000 representative profiles.However, outputs from ASRIS have already been tested. The ASRIS attributes have been usedto prepare comprehensive assessments of erosion by water and river nutrient transport, the cur-rent and projected extent of soil acidification and landscape productivity.

The ASRIS can be accessed through its website (http://www.asris.csiro.au/mapping/viewer.htm). In the website, soil database from all seven levels can be alternatively visualized,including climate, landcover, landscape and terrain.

The national standard for soil description and classification is absent. The ASRIS fulfilled thisgap. The standard would facilitate comparison of map data nationwide.

Figure 5-3 Soil level of ASRIS (modified from Australian department of the environment and water resource, 2007)

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5.1.3 Thailand soil information system

In Thailand, the same data is collected and stored in different places by different organizations.Consequently, redundant works and data are plentiful. “Thailand Soil Information System” hasbeen set up since 1996 around the country through the attempts of the Land DevelopmentDepartment (LDD). The spatial database was created by digitizing 1:50,000 soil group maps,administrative boundaries at a Tambon (sub-district level), main roads and streams. The data-base covers the northern, central, and north-eastern regions of Thailand (Promburom and Ekas-ingh, 1996). They are stored as Arc/INFO coverages and ArcView shape files. Despite theLDD being the main data collector and influence on the soil management in Thailand, the soildata standard and “Thailand Soil Information Systems” is not widely used outside the LDD.

Figure 5-4 illustrates the structure of Thailand Soil Information System from the LDD.

Data is produced and stored in the database of the Thailand Soil Information System, but it israrely updated. So there is no doubts that it has been not popular for users. Other smaller orga-nizations even establish the soil database by themselves and, of course, it covers only a smallpart of the country and is not compatible to others.

The LDD has also developed a number of related soil data, for example Soil Database (SoilView 2.0), Zoning of economic crops (AgZone 2.2), Soil erosion (ErosView 1.0) and Physicalland evaluation for farm pond (FarmPond 1.0), etc.

Figure 5-4 The structure of Thailand soil information system (Promburom and Ekasingh, 1996)

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5.2 Soil GIS data sharing: Thai example

GIS data applied to the soil erosion prediction is available for different areas around theworld. Not only are there different areas of data, but also data formats and data modelsare different. To fully r ealize the capa bility and benef its of geographic information andGIS technology , GIS soil data needs to be able to be s hared an d syst ems n eed t o beinteroperable (ESRI, 2003). The data interoperability requires to be ensured for the smoothexchange of soil information among diverse information sources to be ensured.

Section 3.2 discusses the Spatial Data Infrastructure (SDI) which strongly supports the accessto GIS data between different organizations and different agencies. Also, the interoperability inGIS is explained interoperability in GIS and the relevant standards in general. From the back-ground from those sections, Section 5.2.1 shows the example of the GIS interoperabilty inThailand focusing on the land development project.

5.2.1 Interoperability in GIS in Thailand

Making GIS interoperability clear, an example in Thailand is presented. A land developmentproject is expected to be set up in Chiang Mai province, Thailand. Different organizations arerelated in the project, not only at a horizontal level between each department; the land develop-ment department, the transportation department, the forestry department, but also at a verticallevel, such as Chiang Mai Province, Sankhamphang District, etc.

Since each province (district, sub-district) or each department have their own symbols to repre-sent the content on the map (soil type, land cover, road, etc.) without any data standards, itwould be difficult to integrate those data for the project. Moreover, in each organizationdiverse GIS software handles the data. Therefore, the data standards and interoperability areessential.

The concept of this example is shown in Figure 5-5. The interoperabilities can be completedthrough NSDI with various kinds of web services. National Spatial Data Infrastructure (NSDI)is a mean of assembling geographic data nationwide to serve a variety of users. In this exam-ple, all the governmental organizations involve in producing, maintaining and offering spatialdata sets. Different organizations with different scales, quality, format and different purposesof data are working together through the NSDI. The organizations in these systems can sharethe data with each other and with those who do not know about the existence of the data in theother organizations.

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Figure 5-5 Example of data interoperability in a NSDI of Thailand

From Figure 5-5, the NSDI is a center for data exchange in the land development project. Theaccess from different organizations can be done by different kinds of web services. The meta-data will strongly support the process of NSDI. Therefire, in Chapter 6, the soil erosion meta-data data model is designated.

5.3 Web-based GIS soil data

Due to GIS technology is also being used to share crucial information across organizationalboundaries via the Internet and web services. It is an alternative to access the soil data. Theweb-based GIS soil data is included in this section successively.

Advances to the Internet and related technologies have made it possible to share current GISinformation from its original source to remote users on a 24 hour basis. With this web-basedtool and web-based applications, the soil GIS data can be accessed freely and can be sharedwith different users and different systems. In this section, different web-based GIS applicationon soil data are introduced.

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5.3.1 Water Erosion Prediction Project-Climate Assessment Tool

Web Soil Survey, USA

In the United State of America, the soil data throughout the country can be visualized fromWeb Soil Survey (WSS) of The Natural Resource Conservation Service (NRCS) of The UnitedStates Department of Agriculture (USDA). Soil data and information produced by NationalCooperative Soil Survey is available on WSS. It is operated by NRCS and serves access to thelargest natural resources information system in the world (Soil Survey Staff, 2008).

Web Soil Survey (WSS) provides soil data and information produced by the National Cooper-ative Soil Survey. It is operated by the USDA Natural Resources Conservation Service(NRCS) and provides access to the largest natural resource information system in the world.NRCS has soil maps and data available online for more than 95 percent of the nation’s countiesand anticipates having 100 percent in the near future. The site is updated and maintained onlineas the single authoritative source of soil survey information.

GIS data to apply in soil erosion prediction is available from different sources around theworld. A good examples is the GIS data of soil in the US. by Natural Resource ConservationService (NRCS), as shown in the following website at http://www.cei.psu.edu/soiltool/sem-tool.html.

Water Erosion Prediction Project-Climate Assessment Tool

Water Erosion Prediction Project-Climate Assessment Tool (WEPPCAT) is a web-based ero-sion simulation tool that allows for the assessment of changes in erosion rates as a conse-quence of user-defined climate change scenarios. The tool is based on the USDA-ARS WaterErosion Prediction Project (WEPP) erosion model.

WEPPCAT is an interactive, iterative tool used for helping to understand and assess manage-ment practice (BMP) effectiveness under user-defined climate change scenarios. The purposeof this product is to provide interested users an online tool that can be used for assessing andmanaging the impacts of climate change on erosion rates for soils under agricultural manage-ment in the United States. WEPPCAT utilizes the capabilities of the WEPP model. It alsoincludes a new capability of independently modifying the intensity of rainfall events for anylocation's climate in the United States.

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Figure 5-6 The relation of global warming and soil erosion in WEPPCAT

In WEPPCAT, global warming is expected to lead to a more powerful hydrological cycle,including more total rainfall and more frequent high intensity rainfall events. Rainfall amountsand intensities increased on average in the United States during the 20th century and, accord-ing to climate change models, they are expected to continue to increase during the 21st century.These rainfall changes, along with expected changes in temperature, solar radiation, and atmo-spheric CO2 concentrations, will have significant impacts on soil erosion rates.

WEPPCAT is based on extensive amounts of U.S. data related to the model, but not outside theU.S. which is the main restriction of the tool.

WEPPCAT can be accessed via the website www.weppcat.net.

5.3.2 Mapping services in the european soil portal

The european soil portal offers a number of on-line applications and services to the public user.The soil portal has disseminated the information of the european soil database through variousapplications via internet (Panagos and Liedekerke, 2006). Not only different application, butalso different database are included in the soil portal. It can be found in the website of http://eusoils.jrc.it.

There are important applications-services which are presently available and related to the soildata, as following:

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• Soil Map Internet Service (SOMIS) is the most important web-base application thatallows the user to navigate the European Soil database and interact through map operations(Panagos and Liedekerke, 2006). This map server publishes all the maps derived fromattributes which are present in the Soil Geographical Database of Eurasia at scale 1:1,000,000and the Pedotransfer Rules Data Base, which are components of the European Soil Database.

• Multiscale EUropean Soil Information System (MEUSIS), an application allows theuser to navigate in the Alpine Reference Grid. It is a prototype of the Multi-scale EuropeanSoil Information System (MEUSIS).

• Pan European Soil Erosion Estimates (PESERA), an interactive application thatallows the user to navigate the soil erosion from PESERA.

• Soils of Eurasia. The user can select to see the soil types in the area of interestaccording to the World Reference Base for Soil Resource (WRB) group classification.

• Soil Profiles of Europe.

Among these applications, SOMIS is the most important web-based application. The applica-tions are built with free web mapping. The user can view and inspect soil data but cannot dow-load.

For the interoperability matters, the developed services are based on international standards aspromoted by the INSPIRE initiative. The mapping services have been extended so it is possi-ble to combine map layers from different servers which are based on internationally acceptedWeb Map Service (WMS) standards and specifications form the Open GIS Consortium(OGC).

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Chapter 6Metadata on Soil and

Soil Erosion

According to Chapter 4 and Chapter 5, to succeed the soil erosion prediction with RevisedUniversal Soil Loss Equations (RUSLE), different GIS data from different sources arerequired. To search for required data from proper organization, there are difficulties to acquirethe appropriate data or to obtain the right information of data, especially in Thailand wherethere are numerous data redundancies. Not only data redundancy, but the data updating is alsoquestionable. Unlike in Switzerland, the GIS data are well organized and relatively good qual-ity. However, the required data is not easily retrieved.

Due to these problems, the metadata in GIS are strongly supported by the author to be the solu-tion them. The metadata will facilitate to answer the questions; what, why, when, who, whereand how about geospatial data on soil erosion. Soil metadata will allow users to explore anddetermine if they need the data or if the da ta are useful for the soil erosion prediction inspecific study areas. Together with the better quality of GIS data, the soil erosion predictionwill be more accurate and less time consuming.

Due to the metadata is important, the study proposes a data model of Soil erosion RequiredMetadata (SRM). The model is based on the RUSLE soil erosion model, implied from the

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applications. To conform to the available standard, the applicability of the current metadatastandards, for example standard ISO 19 115, with respect to their use for describing soil ero-sion models, as a relatively new kind of data type, is addressed.

From RUSLE case studies, the GIS data model and required metadata on RUSLE are provided,see Section 6.1.1 and Section 6.2. This develops metadata model for soil erosion is compliantto ISO 19115. Section 6.2.2 describes how the proposed metadata model conforms to the stan-dard.

Due to the recently increased importance of soil and the need for strategies to protect soil,the government organizations, private agencies and farmers an ticipate a greater need toaccess soil data in the future. However, at the present, many offices are not aware of the soildata set availability. Soil metadata should further improve the ability of organizations andagencies to assess which soil datasets are fit for their purposes.

For example, if a user is interested in soil types, it would be helpful to have soil metadata thatprovide e.g. a list of existing soil types and a description of the area where informations aboutthe spatial distribution of these types are available, according to the specific standards of soilmetadata metadata. This would assist the user to decide whether a particular dataset could pro-vide useful information for their purposes, such as monitoring the change over time or provid-ing an overall picture of the recent situation.

6.1 Data model of soil erosion

To build Soil erosion Required Metadata model (SRM model), it is important to understand thedata model on soil erosion first. In this study, RUSLE is applied for different case studies, seeChapter 2 and Chapter 4. This proposed metadata model is constructed concerning to the situa-tions in Thailand and Switzerland, but it can be applied to other places in the future as well.

Section 6.1.1 characterizes the GIS data model on RUSLE. Afterwards, the required data in themodel are explained finally showing the standard conformity of the metadata model in Section6.2.2.

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6.1.1 GIS data model for RUSLE

Chapter 2 explains that the RUSLE model requires different factors to predict the annual soilerosion basically. From the applications, it shows various data sources are used to extract thesefactors. Figure 6-1 illustrates the RUSLE GIS data model from the study in UML class dia-gram.

The diagram shows the relation between the soil erosion class, which is a soil erosion result,and other data class. Some factors can directly be used as the RUSLE factor from the com-pared tables. Some factors are extracted from different data as shown in Figure 6-1, for exam-ple the catchment area is extracted from DEM. There are also some data, which are notmandatory to calculate soil erosion, represented with optional association (0..*), for examplethe aerial photos are used optionally when they are available.

The final product of the model is a rate of soil erosion in an interested area. The data from allapplied factors are needed to be known in this area. The regarded area specifies where the datashould be available and in which area the users need to look for the metadata.

Figure 6-1 UML class diagram presenting the RUSLE GIS data model

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6.1.2 Required Data for RUSLE

The required data for RUSLE play a main role in a metadata model establishment. FollowingFigure 6-1, the required GIS data involve:

• Soil type, landuse type and cr opping type. Soil type data and cropping type datacan be in vector or raster format according to the reality, but the landuse type data is usuallydistributed in vector data. As in Chapter 2, soil type, landuse type and cropping type informa-tion have to be provided by the areas of interest.

To establish metadata, it is necessary to know which type of soil type, landuse typeand cropping type exist in the study area and if these data exist. The example of area A isgiven. Area A is composed of two soil types: sandy clay and loamy sand, landuse types areagriculture and housing, and there are three cropping types: corn, forest and rice.

• Area of Interest. It is a area for which one would like to calculate the soil erosionrisk.

• Cropping management. Cropping management is a table of different values repre-senting to the different cropping management in the area. The cropping management connectsoptionally to each cropping type area. It appears in some agricultural areas. Therefore, not inall study area, where the cropping undergoes, has cropping management.

In the SRM model, the metadata of cropping management have to indicate whetherthere is information about cropping management in the area of interest. If there is, the metadatacan show where and which of cropping management are in the area.

• Rain Station. The rain stations in general are provided in vector form; the locationpoint of the rain station with basic information of each station. The metadata must determine ifthere are existing rain stations in the study area. Often, there is no rain station within the areaof interest. The interpolation from adjacent rain stations is needed in this case.

• Elevation. The elevation in this model is important for rain stations in this study. It isoptional for the rain station to obtain the stations’ elevations. The metadata require to show ifthe rain stations obtain the elevations.

• Aerial photos. They present a "bird's-eye" view of the earth and they are generally inimage files. Sometimes users need to ensure about some information on the earth’s surface.The aerial photos serve the needs in this reason. Creating the metadata model, it needs to be

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appointed which part of the study area is covered by existing aerial photos. Following the rela-tions in the model, it is optionally to obtain the aerial photos.

• Vector River Network (VRN). The river network data are given in the vector formatin different quality. The metadata is essential to identify if there is VRN in the interested areaand in which the quality or scale is.

• Channel. The channels in the RUSLE can be calculated from DEM and VRN, as isshown in Figure 6-1. When the DEM and the VRN are available, the channels can be acquired.Correspondingly, there is no need for channel metadata.

• Digital Elevation Model (DEM). The DEM consists of raster data providing the ele-vation according to geographical points. In the metadata, the users should know if the DEMcovers the whole interested area and with which resolution or data quality is.

6.2 Soil erosion metadata model

The aim of this section is to develop possible amendments for a current metadata specificationthat fulfills the requirements for the RUSLE model. As mentioned earlier, the author consid-ered mainly the standard ISO 19115 since the standard are widely used, including in Thailandand Switzerland. The individual elements of ISO 19115 standard were assessed critically underthe aspect of sufficiency for soil erosion.

Also the question was stated if any further metadata information is needed, which is presentlynot included in the ISO 19115 standard. As an outcome, a metadata data model on RUSLE willbe presented and proposals for further additions to the specifications will be discussed.

6.2.1 Soil erosion Required Metadata model

The required data for the RUSLE model in Section 6.1.2 are considered thoroughly and chosenas necessary to assign in the Soil erosion Required Metadata (SRM) model. Figure 6-2 illus-trates the model of SRM. In the model, there is one class of UML. Attributes of the UML classrepresent the required data to calculate soil erosion based on the RUSLE equation. The usersneed to gain the metadata of presented data in order to decide if the data are sufficient for thecalculation or if the data are appropriate to the calculation or where these data can be accessed,etc.

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Figure 6-2 UML class diagram of Soil erosion Required Metadata model

6.2.2 ISO 19115 conformity

The metadata consistency and style is recommended to ensure that the comparison of the meta-data from different organizations efficiently is possible and all users understand the same con-cepts. This metadata consistency can be succeeded by the metadata standard. Withoutstandardization, meaningful comparisons are more difficult to derive without reading andlearning many metadata management styles (Douglas, 2004). For that reason, the standardconformity is needed in order to create the SMR model.

The metadata standards are introduced in Chapter 3. In the United States and in some coun-tries, an important standard is the Federal Geographic Data Committee (FGDC) Content Stan-dard for Digital Geospatial metadata. Meanwhile, International Organization forStandardization (ISO) has created a spatial metadata standard, ISO 19115, which has beenapplied world wide. In Thailand, Geo-Informatics and Space Technology DevelopmentAgency (GISTDA), as a core organization for space and geoinformatics activities of the coun-try has developed the metadata Editor Program which is compliant to the ISO 19115. Also, inSwitzerland, even though they have their standard (GM 03), but the standard is mainly basedand compliant to the ISO 19115. Therefore, ISO 19115 is the main standard to apply for theproposed model in this study.

ISO 19115 defines more than 300 metadata elements (86 classes, 282 attributes and 56 rela-tions), most of which can be applied optionally (Sarafidis and Paraschakis, 2006). Accordingto the ISO 19115, it is cited that metadata shall be provided for geographic datasets and may,optionally, be provided for aggregations of datasets, features, and attributes of features. The

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metadata is composed of one or more metadata Section or UML Package containing one ormore metadata Entities (UML Classes) (ISO, 2003). Therefore in SRM Model, eight metadatacontent elements from the ISO 19115 standard are adopted, presented in Figure 6-3. Theadopted elements are highlighted. Some are mandatory and some are necessary for the model,but optional for the ISO 19115, see Figure 6-5.

The dataset in the ISO 19115 has been defined as a dataset which is located physically within alarger dataset. The dataset can be through limited by some constraint such as spatial extent orfeature type. Theoretically, a dataset may be as small as a single feature or feature attributecontained within a larger dataset (ISO, 2003). Therefore, the data, which are presented in theSRM model, can be defined as dataset. The datasets from the SRM model show in Figure 6-5concerning to the chosen ISO 19115 entities.

In order to identify most important parts of the metadata, the proposed metadata is structuredinto components of

• MD_Metadata entity. Metadata entity set information consists of the entityMD_Metadata, which is mandatory in ISO 19115. The entity contains both mandatory andoptional metadata elements, see Figure 6-3. The figure shows MD_Metadata entity is an aggre-gate of different entities.abstractly characterize and identify data (MD_Identification),

• their access (MD_Distribution) and production context (DQ_DataQuality),

• describe their content (MD_ContentInformation), extent (EX_Extent), reference sys-tem information (MD_ReferenceSystem) and additional information fit to the SRM model(MD_MetadataExtensionInformation).

Each dataset in SRM model are suggested by the author to hold different ISO 19115 elementsdepending on nature of each data. However, following ISO 19115 the metadata entity(class)set information, MD_Metadata, and Identification Information entity, MD_Identification, aremandatory. Other entities are optional. In the study, the mandatory entities and optional ele-ments are included in the SRM model as necessary. The uses of the entities are classified withdifferent datasets from the model, shown in Figure 6-3.

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Figure 6-3 UML class diagram of metadata packages in ISO 19115 (ISO, 2003)

Moreover, according to describe the metadata in the ISO 19115, it is essential that a basic min-imum number of metadata elements be maintained for a dataset which the metadata of SMRcontain. Therefore, the core metadata elements are required to identify a dataset. The optionalelements can explain a dataset more specific and allow the users to understand the geographicdata without any obscurity. Figure 6-4 illustrates the core metadata regarding to the ISO19115.

From SMR model, the conformity to the ISO 19115 of the SMR model examples are describedin the next sections. There are examples of the existing elements in ISO 19115 and the amend-ments for the non-exist elements regarding to the SMR model. The extension elements areused for soil type dataset, landuse dataset, cropping dataset and rain station dataset, but otherdatasets can be explained sufficiently by the ISO 19115 metadata profile.

Making clear picture of the metadata extension in ISO 19115 is shown in Figure 6-5. Metadatacommunity profile of the ISO 19115 can be developed by individual communities, nations or

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organizations. The community will select sets of mandatory metadata elements, which arealways needed that metadata elements reported.

The metadata community profile shows that the community profile must include the coremetadata components and may include some extended metadata, not necessary to include allmetadata components. As presented, the core metadata components are included in the com-prehensive metadata profile.

In this study, there are the mandatory elements, MD_Metadata and MD_Identification, andother elements are optional, see Figure 6-5.

Metadata extension information example: Soil types

To provide more descriptions of the metadata about soil type, metadata extension information(MD_MetadataExtensionInformation) allows the author to define more specific informationaccording to the SRM model. The addition of new metadata entities and an extended codelistare used to document a hierarchical classification-based soil type. Figure 6-6 shows these newentities for the SRM model regarding to the ISO 19115 entities.

Figure 6-4 Core metadata for geographic datasets (modified from ISO, 2003)

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Table 6-1 Recommended ISO 19115 metadata entities for the SRM model

Note: * : the elements is used, - : the elements is no used

Figure 6-5 Metadata community profile (ISO, 2003)

Dataset from SRM model

Elements from ISO 19115

MD

_Met

adat

a

MD

_Ide

ntifi

catio

n

MD

_Dis

tribu

tion

EX_E

xten

t

MD

_Con

tent

Info

rmat

ion

DQ

_Dat

aQua

lity

MD

_Met

adat

aExt

ensi

onIn

form

atio

n

MD

_Ref

eren

ceSy

stem

Soil type dataset * * * * * * * *

Landuse dataset * * * * * * * *

Cropping dataset * * * * * * * *

Elevation * * * * - - - *

DEM * * * * - * - *

Aerial photos * * * * - * - *

Vector River Network * * * * - * - *

Rain station dataset * * * * - - * -

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Chapter 6: Metadata on Soil and Soil Erosion Page 133

Figure 6-6 UML diagram representing the extension information customized corresponding to the model SRM. The additional elements in the profile are highlighted.

The metadata of soil type must identify the entity of soil classification system, and, the entityof either the soil type name or the soil abbreviation or both. These both entities are mandatoryin the model, see Figure 6-6. The new code list is established to be used for specify the soilclassification system.

Other specific factors for example landuse dataset, cropping dataset and rain station datasetcan be also completed in the same way respecting to the data characteristics. The new metadataentities are essential to be created depending on the needs to describe the data.

Basic metadata information example: Digital Elevation Model (DEM)

To identify the DEM in the metadata, the subclass of MD_Ideintification, calledMD_DataIdentification, is considered. One attribute in the subclass, SpaitalRepresentation-Type, can specify the spatial data type by the code list MD_SpatialRepresentationTypeCode.The code reflects how the geographic information presents, including grid, tin, textTable.

Moreover, it needs to be specified the spatial resolution of the DEM, for example the scale,grid spacing, etc. Subclass MD_DataIdentification contains one the attribute spatialResolutionwhich can be further specified by MD_Resolution class. The MD_Resolution class consists oftwo attributes: equivalentScale and distance. Distance holds information about the ground res-olution of vector data or the DEM resolution or raster resolution. EquivalentScale represents a

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Page 134 Chapter 6: Metadata on Soil and Soil Erosion

Figure 6-7 UML class diagram representing spatial resolution information in ISO 19115 (ISO, 2003)

level of detail expressed as the scale of a comparable hardcopy map or chart (ISO, 2003) whichit defines by the integer, see MD_RepresentativeFraction in Figure 6-7.

The DEM is greatly essential for the soil erosion prediction. Therefore, it is important that themetadata can represents well the DEM for the users. As explained above, the DEM can beidentified as a grid or a tin by MD_SpatialRepresentationTypeCode. However, the metadataneeds more extensions in order to describe more accurately which elevation information isused for the DEM creation.

6.3 Conclusion

Conclusion

The possibilities, the existences and the deficiencies of the present-day metadata in ISO 19115with respect to the RUSLE model are analyzed. It is found that several possible topics, whichare essential for RUSLE model, are missing. The soil erosion model is comparatively specific

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Chapter 6: Metadata on Soil and Soil Erosion Page 135

for ISO 19115 standard. Therefore, the author present an example of possible ways to add themissing elements to ISO 119115.

The results of SRM model conforming to the ISO 19115 show that the missing elements can beadded with the metadata extension elements. The missing metadata elements are for identify-ing the landuse dataset, the soil type dataset, the cropping dataset and the rain station dataset.However, it can be work-loaded to obtain all these missing metadata elements. It is also foundthat the basic data such as DEM, a Vector River Network, aerial photos, etc., can be describedby ISO 19115 metadata entirely.

In summary, the SRM model can be conformed to the ISO 19115 according to the mentionedimprovements, but it can be demanding works to complete them.

ISO 19115

As the ISO 19115 is studied in this chapter, the notice of it are represented. The author foundthat ISO 19115 Metadata is comprehensive. The ISO 19115 includes most of basic need aboutmetadata. Anyway, too specific or small community will require metadata extensions to meetthe their needs. In this case, there is a risk that extension mechanisms can be used to definenew metadata element even if there are standard mechanisms that fulfill the user’s require-ments.

Outlooks

In Thailand, as mentioned in Section 3.3.1, most organizations do not have metadata recordedin files compliant to ISO 19115. The process of ThaiSDI is at the beginning including themetadata development. Moreover, the language is one important problem for Thai metadatabecause many basic data are coding in Thai. This issue needs to be aware as in other non-English speaking countries when there is need to share the metadata internationally.

The Swiss profile GM03 uses the multilingualism package in the core metadata which makessense for a country with multiple official language like Switzerland or a large community likethe European Union (Najar, 2006). The ThaiSDI can implement the package “MultilingualismInformation” in their national metadata profile. Then the problem can be solved only if othercountries do have this implementation within their metadata profile besides.

Therefore, the future work will be necessary to validate the model in specific user communi-ties, such as Land Development Department of Thailand, and at different levels of SDI.

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Chapter 7: Conclusion and Discussion Page 137

Chapter 7Conclusion and Discussion

7.1 Summary of results

Following the thesis, the objectives of the study have been fulfilled:

• The different GIS data quality are compared with the RUSLE model. It shows thatthe different quality of GIS data derives significantly different soil erosion results. The properquality of GIS data should be chosen regarding to all the GIS data in the study and the scope ofthe area of interest, with appropriate water river.

In addition, the soil erosion results depend greatly on the quality of soil GIS data.Therefore the improvement of soil erosion prediction can be done through the GIS data in dif-ferent ways.

• The new trends of GIS technologies for soil erosion application are proposed in orderto improve the quality of soil erosion prediction by means of GIS data. The study suggests theuse of GIS database of different soil-scale, the soil GIS sharing, the web-based GIS soil dataand the soil erosion metadata model. The improvement of soil erosion, in this study using the

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Page 138 Chapter 7: Conclusion and Discussion

RUSLE model, through the GIS technology and GIS data is possible and practical. The pro-posed methodology will facilitate the current soil erosion prediction.

The following statements summarize the results of this thesis according to GIS and soil erosionaspects. This thesis tries to locate solutions and answers for the main problems found, seeChapter 5.

Recommended methodology and solutions as follows;

• Applying high resolution DEM to a soil erosion model where it is necessary, dueto the economic aspects.

The new fashion of DEM production by laser scanning can collect the details oftopography well. In this study, it has indicated that is be set up for the irregular topographyareas. However, this kind of DEM is relatively expensive. It is essential to consider where highresolution of DEM is needed. For example, a flat area would not need high resolution DEM toinput into a soil erosion model or a area where there is no interest in it.

• The soil metadata is highly recommended to be set up for the searching reasons.

The soil data in Thailand has been stored in different places by a number of variousorganizations. The data is very much redundant and varied in soil classifications and formats.Moreover, the recent increased profile of soil and need for planning to protect soil and othernatural resources, different organizations anticipate a greater need to access soil data in thefuture.Thus, soil metadata development is exceedingly advantageous to determine the fitnessfor purpose of each soil data set for soil model users.

In Chapter 6, the soil erosion required metadata model is provided to facilitate thesearching of the GIS data relevant to the soil erosion model and ISO 19115 standard.

• National Spati al Data Infrastructu re (N SDI) should be intr oduced to everycountries, supporting GIS data sharing and interpolation in GIS.

Due to SDI facilitates and coordinates to share and exchange soil data between dif-ferent organizations in vertical and horizontal dimension, the SDI is advantageous whenacquiring soil GIS data is possible. The settle up of NSDI with web-based integration will sup-port the organization to share and to integrate the soil data in the nation online. The easieraccess can be completed from where the internet access is feasible.

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Chapter 7: Conclusion and Discussion Page 139

• Different qualities (scales) on the soil database is useful when the decision mak-ing is related to the soil conservation.

With this wider view of soil data, it will be more effortless for soil erosion prediction,as the same classifications and a better overall view is given. Moreover, the appropriate scalecan be chosen from those different scale of soil data.

When the recommended solutions are fulfilled, it is highly anticipated that the above problemsfrom the study will be solved. However, to accomplish these solutions, it is complicated tocomplete not only in technique aspects, but the administration and human problems also.

7.2 Outlooks

It is strongly advocated that the soil GIS data should be developed as it is cited in this study.The GIS data and soil GIS data in Thailand still needs further development. However, in Swit-zerland, the GIS data are well completed and always updated with a good catalogue to searchfor the data. A more centralized GIS database (including soil data) should be concentrated onin Thailand. Nevertheless, in Thailand, there is already ThaiSDI and also Regional SpatialData Infrastructure (RSDI), but both are still in the process. Therefore, ThaiSDI and RSDI forThailand are crucial to continue and to be reinforced by governmental organizations.

The SDI at every level should be developed cooperatively, especially in global and regionalspatial infrastructure levels. Developing countries need the support and guidelines from moreexperienced countries. For this reason, Global Spatial Data Infrastructure (GSDI) is highly rel-evant. Since GSDI is also the host of the annual conference which is the platform for theexchange of ideas on SDI at every level and aspects. Accordingly, when these infras truc-tures are set, soil erosion prediction with GIS tools will achieve substantial benefits on theresults of the soil erosion model.

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Vita Page 151

Vita

Karika Kunta

Karika Kunta, daughter of Bunchong and Wattana Kunta, was born in Chiang Mai, Thailand inSeptember, 1975. She attended the primary school at Regina Choeli School in Chiang Mai.Later on, she finished the secondary at Satit Chiang Mai University School. Kunta continued todo her B.Eng. degree in Civil Engineering at Chiang Mai Universtiy finishing in the year 1997.After that, she started to work as a civil engineer in construction engineering company inBangkok.

In the middle of year 2000, she started her M.Sc. in Environmental Engineering and Sustain-able Infrastructure at KTH, Stockholm, Sweden on a Swedish government scholarship. After-wards her M.S. she returned to Thailand and worked as a lecturer at Mae Fah LuangUniversity, Chiang Rai, for environmental science and as a researcher for community projects.

Since March, 2004 Ms.Kunta continued her Ph.D. at Chair of Geographic Information Systemand Theory of Error, Swiss Federal Institute of Technology Zürich (ETH Zürich), Switzerland.Her Ph.D. has been completed at the beginning of year 2009 guided mainly by Prof. Dr.Alessandro Carosio and co-supervised by Prof. Dr. Maria Antonia Brovelli and Prof. Dr. AlainGeiger, which is presented in this dissertation. Later on Ms.Kunta will serve as a permanentresearcher at Land Development Department, Ministry of Agriculture & Cooperatives in Thai-land.

Contact: [email protected], [email protected]

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Page 155: Effects of Geographic Information Quality on Soil …...Bachelor of Engineering (Civil), Chiang Mai University, Thailand Born September 1, 1975 Citizen of Chiang Mai, Thailand Accepted

Acknowledgements Page 153

Acknowledgements

I would like to extend genuine gratitude to my professor, Prof. Dr. Alessandro Carosio, for his invaluable

supports and giving me opportunity to do this Ph.D. Not only academically, but his great advocates in

life aspects supported me to get through the study. His pushes and ideas are worthy for my works. It

would not have been successful without him. In addition, many thanks go to both of my co examiners;

Prof. Dr. Maria A. Brovelli of Politechnico di Milano for her big supports and useful suggestions and Prof.

Dr. Alain Geiger.

Thanks to Hans Rudolf Gnägi, the data model discussions with him were constructive for the thesis and

to my best traveler ever, Dr.Christine Najar, for reading parts of my work and gave some advices.

Distantly, big supports repeatedly are from my parents and my sister in Bangkok. My highest gratitude is

due to them. Alongside I express special thank to Paisarn Peeraparp, my husband, owing to his kind

patience and magnificent back-ups on my mind in these years.

Memorable thanks go to awesome Thai connections here. Great events, jokes and broad discussions

save me as home. The Thai Bonds include Sakorn Korman (& Beat), Supannee Wisetsap (& Emil), Puangphet

Meister (& Freddi), Naree Stamm, Dr.Sampun Thammachareon, Dr.Nathinee Pa-in Ruta, Dr.Bundit Panchaphong-

saphak, Jedsada Kerdsrilek, Dr.Komkrich Teunkum, Rachaneewan Khiaosa-Ard, Siwapon Pinkaew, Dr. Putthapoom

Lumjiaktase, Quanjai Rupitak, Dr.Wipit Senn, Nutcharee Pongnarisorn, especially my all time best freind, Nong

Aui -Nattharuthai shinawatra.

During difficult time as a foreign student in Zürich, some friends have made me lively everyday. I

appreciate genuinely sharing ideas and time with them; Dr. Rossella Nocera (with Mamma Francesca & Dr.

Ramon Egli) giving me Italian culture insights, Mio Kato - my super girl, Dr. Aleksej Majkic, Matthias Kistler,

Tan Ying, Dr. Li Pang and, particularly for his always fine amusements for me, Freddy Xavier Yugsi Molina.

Also, for long friendships since Stockholm time, I bestow thanks to Jürg Schneider and Florian Spicher.

In addition, I am thankful to the people at the Chair of GIS and Theory of Errors (2004 - 2009), spend-

ing coffee breaks, Mensa lunches (not the food itself, but companies) and “Skiwochenend” with them

were “Super”.

At last, my gratefulness goes to Land Development Department under Ministry of Agriculture and

Cooperatives, Thailand, who is primarily my financial supporter, and, additionally, goes to NIDECO

and ETH-Zürich who funded partly my Ph.D.

¦ÃÔ¡Ò ¤Ñ¹¸Ò «ÙÃÔ¤ ÁÕ¹Ò¤Á 2552 (Karika Kunta, Zurich, March 2009)


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