Detection and Analyses of Land-cover Change: A Case of Two ......ii This thesis, entitled DETECTION...

Detection and Analyses of Land-cover Change: A Case of Two Mindanao Provinces

with History of Forest Resource Utilization

Thesis by

Meriam M. Makinano

B.S. Geodetic Engineering

Submitted to the Graduate Division

College of Engineering

University of the Philippines Diliman

In Partial Fulfillment of the Requirements

For the Degree of Master of Science in

Remote Sensing

College of Engineering

University of the Philippines Diliman

Quezon City

May 2010

ii

This thesis, entitled DETECTION AND ANALYSES OF LAND-COVER

CHANGE: A CASE OF TWO MINDANAO PROVINCES WITH HISTORY OF

FOREST RESOURCE UTILIZATION, prepared and submitted by MERIAM M.

MAKINANO, in partial fulfillment of the requirements for the degree of MASTER OF

SCIENCE IN REMOTE SENSING is hereby accepted.

ENRICO C. PARINGIT, Dr. Eng.

Thesis Adviser

Accepted as partial fulfillment of the requirements for the degree of MASTER

OF SCIENCE IN REMOTE SENSING.

ROWENA CRISTINA L. GUEVARA, Ph.D.

Dean

iii

Acknowledgment

This thesis would not have been possible without the help of so many people. I

am heartily thankful to all of those who supported me in any respect during the

completion of this thesis.

I am sincerely thankful to my adviser, Dr. Enrico C. Paringit, whose

encouragement, guidance and support enabled me to develop a good understanding of my

thesis topic.

I am indebted to the Department of Science and Technology-Philippine Council

for Advanced Science and Research Development (DOST-PCASTRD) through its

Human Resources and Institution Development Division, for the financial support

extended during my graduate studies here in UP Diliman, and for financing this thesis.

I am also very thankful to Dr. Tolentino B. Moya and Prof. Florence A. Galeon

for accepting my invitation to be my thesis defense chairman and member, respectively.

Their insightful comments and suggestions helped me better understand my thesis and

these were very helpful in the improvement of the manuscript.

Acknowledgements are also extended to DENR-Caraga Region XIII and to the

Forest Management Bureau Main Office for providing the datasets needed in the

analysis.

To Dr. Edgar W. Ignacio (former President of the Northern Mindanao State

Institute of Science and Technology, now Caraga State University), thank you sir for

your encouragement for me to pursue graduate studies here in UP Diliman.

My deep appreciation is also extended to the Caraga State University (through our

President, Dr. Joanna B. Cuenca) and also to my CEIT family especially to Engr.

Jonathan M. Tiongson and Engr. Alexander T. Demetillo for all the support given to me.

Thank you also to Engr. Lorie Cris S. Asube for taking care of my responsibilities in the

CEIT while I’m away.

To Engr. Michelle V. Japitana, my ever loving and helpful friend, thank you Kay

for the companionship and for always being there for me.

To all my Research Groupmates at the Applied Geodesy and Space Technology

Laboratory namely: Cecil, Ate Beth, Nimol, Alex O., Rose, Ate Merlie, Mitch and Jene.

Thank you for all your comments and suggestions during my progress reports.

My special thanks to Engr. Alexander S. Caparas and Engr. Jessi Lin P. Ablao for

the friendship and for being so accommodating especially during my initial stays in UP

Diliman.

iv

Thank you also to Ma’am Lynn Serrano and all the staff of the Engineering

Graduate Office for all your assistance, especially during the preparations for my thesis

defense.

Thank you to Tatay, Gigie, Dodong, and most especially to Nanay for

understanding my absence during her time of illness. Thank you for your support and

believing that I can pursue graduate studies in UP Diliman. Kining tanan na akong

pagpaningkamot ay para sa inyo.

To my best friend and boyfriend, Engr. Jojene R. Santillan, thank you gá for

helping me in all aspects of my graduate studies, especially during the conduct of this

thesis. Thank you for being my second adviser, my proof reader, critique, and personal

assistant. Salamat kaayo gá sa tanan nimong pagpalangga sa ako.

And most of all, to our Almighty God for making all these things possible.

v

For My Dearest Mother,

Virgincita M. Makinano.

vi

Abstract

This study presents an integrated approach involving Remote Sensing (RS), Geographic

Information System (GIS) and statistical analysis to detect and analyze 25-year land-

use/land-cover change (LULCC) in the provinces of Agusan del Norte and Agusan del

Sur in Northeastern Mindanao, Philippines with history of forest resource utilization in

the context of limited land-cover information due to cloud contamination of RS images.

Using cloud and shadow masking algorithm and state-of-the-art RS image analysis

techniques provided by the Support Vector Machine classifier, highly accurate land-cover

maps were obtained from Landsat Multi-Spectral Scanner (MSS) and Enhanced Thematic

Mapper + (ETM+) images and used to detect land-cover transitions in the study area

from 1976-2001. The differences in deforestation and other land-cover change types in

the two provinces were then characterized and compared using GIS-based spatial analysis

techniques. The significance and magnitude of the relationship between the detected

deforestation and various georeferenced socio-economic and bio-physical factors were

determined through logistic regression analysis. Major results showed that the detected

changes in land-cover were found to be different in the Agusan provinces. Forest to

rangeland is the major land-cover change in Agusan del Norte from 1976 to 2001; in

Agusan del Sur, the two most prominent land-cover change types are the conversions of

rangeland to forest and of forest to palm trees. The results of GIS-based characterization

of deforestation and logistic regression analysis based on combined bio-physical and

socio-economic factors provided significant results as to what factors were associated

with deforestation in the Agusan provinces. For Agusan del Norte, the bio-physical

factors DISTRIV (distance to rivers) and ELEV (elevation) were found to be the most

positively and negatively related to deforestation, respectively. For Agusan del Sur,

DISTNEWBUILT (distance to new built-up areas) and ELEV are found to be the most

positively and negatively related to deforestation, respectively. With the identification of

the factors associated with deforestation, this study has provided a first step in controlling

forest loss which is very useful in comprehensive forest management planning and in

formulation of appropriate forest policy. This study is a significant contribution to

LULCC research by providing a series of techniques to understand deforestation and

relate it to bio-physical and socio-economic factors using an un-ideal dataset. An

important finding of this study is that it is possible to analyze deforestation using cloud

contaminated RS images. Local agencies in the Agusan provinces may use the land-cover

maps and statistics obtained in this study to further evaluate the process of deforestation

in these provinces in order to create and evaluate strategies that attempt to mitigate its

negative effects.

vii

Table of Contents

Acknowledgment ............................................................................................................... iii

Abstract .............................................................................................................................. vi

Table of Contents .............................................................................................................. vii

List of Figures .................................................................................................................... ix

List of Tables .................................................................................................................... xii

List of Abbreviations ....................................................................................................... xiv

Chapter 1. Introduction ........................................................................................................1

1.1 Background of the study ........................................................................................... 1

1.2 Objectives of the study.............................................................................................. 5

1.3 Research significance ................................................................................................ 5

Chapter 2. Review of Related Literature .............................................................................7

2.1 Drivers of land-use/land-cover change ..................................................................... 7

2.2 Deforestation and land-cover change in the Philippines......................................... 12

2.3 Land-cover change detection .................................................................................. 17

2.3.1 Review of RS change detection techniques ..................................................... 17

2.3.2 Post-classification change detection: review of classification methods .......... 19

2.3.3 Classification by Support Vector Machine ...................................................... 21

2.4 GIS in LULCC studies ............................................................................................ 23

2.5 Review of statistical methods in LULCC studies ................................................... 26

Chapter 3. The Study Area.................................................................................................34

3.1 Background ............................................................................................................. 34

3.2 The Province of Agusan del Norte.......................................................................... 34

3.3 The Province of Agusan del Sur ............................................................................. 36

3.4 Status of Forest Resources in the Agusan Provinces .............................................. 38

3.5 Forest License Agreements Issued in the Agusan Provinces.................................. 40

Chapter 4. Methodology ....................................................................................................45

4.1 Overview ................................................................................................................. 45

4.2 Remote sensing image analysis .............................................................................. 47

4.2.1 Landsat images................................................................................................. 47

4.2.2 Image geometric accuracy assessment............................................................. 50

4.2.3 Image pre-processing ....................................................................................... 54

4.2.4 Cloud and shadow masking ............................................................................. 58

4.2.5 Image classification and accuracy assessment ................................................. 59

4.2.6 Post-classification change detection ................................................................ 66

4.3 GIS spatial change analysis .................................................................................... 67

4.4 Statistical analysis of land-cover change ................................................................ 70

Chapter 5. Results and Discussions ...................................................................................73

5.1 Land-cover maps ..................................................................................................... 73

5.1.1 The 1976 land-cover map ................................................................................ 73

5.1.2 Accuracy of the 1976 land-cover map ............................................................. 79

viii

5.1.3 The 2001 land-cover map and accuracy .......................................................... 80

5.2 Land-cover change in the Agusan Provinces .......................................................... 85

5.3 Deforestation in the Agusan Provinces ................................................................... 92

5.4 Characterizing 25-year deforestation in the Agusan Provinces .............................. 95

5.5 Logistic regression analysis results ....................................................................... 104

5.5.1 Logistic regression based on bio-physical factors only ................................. 105

5.5.2 Logistic regression based on socio-economic factors only............................ 108

5.5.3 Logistic regression using combined socio-economic and bio-physical factors

................................................................................................................................. 112

5.5.4 Logistic regression analysis using new set of 5% sample ............................. 114

5.6 Characterization of “No Data” pixels ................................................................... 119

5.7 Summary of findings............................................................................................. 120

Chapter 6. Conclusions and Recommendations ...............................................................125

6.1 Conclusions ........................................................................................................... 125

6.2 Recommendations ................................................................................................. 127

References ........................................................................................................................128

Appendices .......................................................................................................................135

Appendix 1. Maps showing the location of retained and deforested areas. ................ 136

Appendix 2. Factor Maps ............................................................................................ 137

Appendix 3. Maps showing the location of retained forest and deforested areas with

CBFMA, CBRM, TLA, and IFMA. ........................................................................... 141

Appendix 4. Maps showing the distance to new roads of retained forest and deforested

areas. ........................................................................................................................... 142

Appendix 5. Maps showing the distance to river of retained forest and deforested areas.

..................................................................................................................................... 143

ix

List of Figures

Figure 1. Map showing the provinces of Agusan del Norte and Agusan del Sur. ...............3

Figure 2. Drivers of tropical deforestation [22],[10]. ..........................................................8

Figure 3. Deforestation trend in the Philippines from 1903-2001 [19]. ............................15

Figure 4. Map showing the municipalities and cities in the Agusan provinces .................35

Figure 5. Log Production of Agusan del Norte and Agusan del Sur from 1984-2001 ......38

Figure 6. Graph showing the timber processing plants and sawmills in the Agusan

Provinces .............................................................................................................39

Figure 7. Map showing the location of TLAs and IFMAs issued in the Agusan provinces.43

Figure 8. Map showing the location of CBFMAs and CBRMs issued in Agusan

Provinces. ............................................................................................................44

Figure 9. The three phases of the study’s methodology. ...................................................46

Figure 10. Process flow diagram of remotely-sensed image analysis ...............................47

Figure 11. The two Landsat images of the study area that were subjected to image

analysis to derive land-covers maps for the years 1976 and 2001. .....................49

Figure 12. Location of points used to determine the geometric accuracy of the 2001

Landsat image and the resulting RMSE vectors of the comparisons with

NAMRIA maps. The numerical values and the lines indicate the magnitude and

direction of the differences in coordinates (local RMSE), with the arrows

pointing to the “actual” (i.e., NAMRIA map) coordinates. .................................52


Landsat image and its co-registration with the 2001 Landsat image. Also shown

are the resulting RMSE vectors of the comparisons. The numerical values and

the lines indicate the magnitude and direction of the differences in coordinates,

with the arrows pointing to the “actual” (i.e., 2001 Landsat) coordinates. .........53

Figure 14. Flowchart of the simple cloud and shadow detection and masking technique

developed and applied in this study. ....................................................................60

Figure 15. The 1976 land-cover map of Agusan del Norte and Agusan del Sur resulting

from the classification of the April 17, 1976 Landsat MSS image using SVM.

All white areas within the provincial boundaries classified as “No Data” are

clouds and shadow pixels in the image. ..............................................................74

x

Figure 16. Bar charts showing the Producer’s (a) and User’s (b) Accuracies of land-cover

types in three SVM-classified land-cover maps for 1976. ..................................78


from the classification of the May 22, 2001 Landsat ETM+ image using SVM.

All white areas within the provincial boundaries classified as “No Data” are

clouds and shadow pixels in the image. ..............................................................81


types in the three land-cover maps. .....................................................................84

Figure 19. The 1976-2001 land-cover maps of Agusan del Norte province. Areas with

data comprise 66.98% (or 2044.67sq.km.) of the total land area of Agusan del

Norte. ...................................................................................................................86

Figure 20. The 1976-2001 land-cover maps of Agusan del Sur province. Areas with data

comprise 51.10% (or 4,133.82 sq. km.) of the total land area of Agusan del Sur.87

Figure 21. Land-cover change in Agusan del Norte province from 1976-2001 for cloud

free areas only. Upper and lower error bars represent errors of omission and

commission, respectively, of the land-cover classifications. ..............................88

Figure 22. Top 10 land-cover change types in Agusan del Norte province from 1976-

2001 for cloud-free areas only. Upper and lower error bars represent errors of

omission and commission, respectively, of the land-cover classifications .........89

Figure 23. Land-cover change in Agusan del Sur province from 1976-2001 for cloud-free

areas only. Upper and lower error bars represent errors of omission and

commission, respectively, of the land-cover classifications. ..............................90

Figure 24. Top 10 land-cover change types in Agusan del Sur province from 1976-2001

for cloud-free areas only. Upper and lower error bars represent errors of

omission and commission, respectively, of the land-cover classifications. ........91

Figure 25. Comparison of magnitude of forest cover area reduction by types of change. 93

Figure 26. Mean elevation of location of forest cover occurrences in Agusan del Norte

and Agusan del Sur. Error bars indicate 95% confidence interval of the mean. .96

Figure 27. Mean SLOPE of location of forest cover occurrences in Agusan del Norte and

Agusan del Sur. Error bars indicate 95% confidence interval of the mean. ........97

Figure 28. Mean DISTRIV of location of forest cover occurrences in Agusan del Norte

and Agusan del Sur. Error bars indicate 95% confidence interval of the mean. .98

Figure 29. Mean DISTNEWBUILT of location of forest cover occurrences in Agusan del

Norte and Agusan del Sur. Error bars indicate 95% confidence interval of the

mean. ...................................................................................................................99

Figure 30. Mean DISTNEWRD of location of forest cover occurrences in Agusan del


mean. .................................................................................................................100

xi

Figure 31. Mean DIST_TLA-IFMA of location of forest cover occurrences in Agusan del


mean. .................................................................................................................101

Figure 32. Mean DIST_CBFMA-CBRM of location of forest cover occurrences in

Agusan del Norte and Agusan del Sur. Error bars indicate 95% confidence

interval of the mean. ..........................................................................................103

Figure 33. Mean POPDENCHANGE of location of forest cover occurrences in Agusan

del Norte and Agusan del Sur. Error bars indicate 95% confidence interval of the

mean. .................................................................................................................104

Figure 34. Diagram for interpreting the logistic regression coefficients. ........................105

Figure 35. Graph showing β values indicating the magnitude of association of bio-

physical factors with deforestation. Error bar indicate standard error. .............106

Figure 36. Graph showing the magnitude of association of socio-economic factors with

deforestation. Error bars indicate +/- standard error. ........................................109

Figure 37. Graph showing the magnitude of association of the combined bio-physical and

socio-economic factors with deforestation. .......................................................113

Figure 38. Graph showing the comparison between the original and the new 5% samples

in Agusan del Norte. Error bars indicate +/- standard error. .............................116


in Agusan del Sur. Error bars indicate +/- standard error. .................................118

Figure 40. Graph showing the mean factor values of no data and with data pixels for

Agusan del Norte and Agusan del Sur. Error bars indicate +/- standard error. .120

xii

List of Tables

Table 1. Drivers of tropical deforestation presented by Geist and Lambin. ........................8

Table 2. List of Timber License Agreements (TLAs) issued in Agusan del Norte and

Agusan del Sur with date of TLA issuance and expiry, and area covered.

(Source: Yearly Forestry Statistics, DENR-FMB). .............................................42

Table 3. Characteristics of the Landsat images used in the study. ....................................48

Table 4. Values used for the calibration of the Landsat MSS image to radiance. .............55

Table 5. Values used for the calibration of the Landsat ETM+ image to radiance. ..........55

Table 6. Landsat MSS mean solar exoatmospheric spectral irradiances [87]. ..................56

Table 7. Landsat ETM+ mean solar exoatmospheric spectral irradiances [86]. ................57

Table 8. Values used for the computation of the surface reflectance. ...............................57

Table 9. Definitions of land-cover types used in this study. ..............................................61

Table 10. Image keys used in visual interpretations of the 1976 Landsat MSS image. ....62

Table 11. Image keys used in visual interpretations of the 2001 Landsat ETM+ image. ..63

Table 12. Number of pixels collected for image classifications and accuracy assessments.64

Table 13. Various combinations of input bands used in image classification ...................65

Table 14. Definitions of georeferenced bio-physical and socio-economic factors. ...........67

Table 15. The 5% samples used in logistic regression analysis. .......................................71

Table 16. Matrix of percent overall classification accuracies of 32 classified images (from

various band combinations of the1976 Landsat MSS image and image by-

products (Ground truth pixels = 2, 276) ..............................................................75

Table 17. Error matrix of the SVM-classified Landsat MSS reflectance bands with NDVI

and DEM (the source of the 1976 land-cover map of the study area). ................76

Table 18. Error matrix of the SVM-classified Landsat MSS reflectance bands with DEM.77

Table 19. Error matrix of the SVM-classified Landsat MSS reflectance bands with

simulated Red and Green bands and DEM. .........................................................77

Table 20. Summary of Producer’s and User’s Accuracies of 1976 land-cover types in

three SVM-classified land-cover maps. ..............................................................79

Table 21. Matrix of percent overall classification accuracies of 8 classified images from

various band combinations of the 2001 Landsat ETM+ image and DEM.

(Ground truth pixels= 6,581). ..............................................................................82

xiii

Table 22. Error matrix of the SVM-classified Landsat ETM+ reflectance bands with

normalized temperature and DEM (the source of the 2001 land-cover map of the

study area). ..........................................................................................................83


temperature band (normalized from 0 to 1). ........................................................83

Table 24. Error matrix of the Maximum likelihood-classified Landsat ETM+ reflectance

bands with temperature band (normalized form 0 to 1) and DEM (also

normalized from 0 to 1) .......................................................................................84

Table 25. Summary of the Producer’s and User’s Accuracies of land-cover types in three

derived land-cover maps. .....................................................................................85

Table 26. Forest cover change statistic (1976-2001) in the Agusan Provinces. ................93

Table 27. Binary logistic regression of FCOVER versus bio-physical factors for Agusan

del Norte and Agusan del Sur ............................................................................106

Table 28. Binary logistic regression of FCOVER versus socio-economic factors for

Agusan del Norte and Agusan del Sur ..............................................................109

Table 29. Binary logistic regression of FCOVER versus the combined bio-physical and

socio-economic factors for Agusan del Norte and Agusan del Sur ...................112

Table 30. Comparison between the β values for Agusan del Norte ................................115

Table 31. t-Test results for Agusan del Norte ..................................................................116

Table 32. Comparison between the β values for Agusan del Sur ...................................117

Table 33. t-Test results for Agusan del Sur .....................................................................118

xiv

List of Abbreviations

ADN Agusan del Norte

ADS Agusan del Sur

ANN Artificial Neural Network

CBFMA Community-Based Forest Management Agreement

CBRM Community-Based Resource Management

DENR Department of Environment and Natural Resources

DT Decision Tree

ETM+ Enhanced Thematic Mapper Plus

FMB Forest Management Bureau

GIS Geographic Information System

IFMA Integrated Forest Management Agreement

ITP Industrial Tree Plantation

LULCC Land-use/Land-cover Change

MLC Maximum Likelihood Classifier

MSS Multi-spectral Scanner

RBF Radial Basis Function

RS Remote Sensing

SVM Support Vector Machine

TLA Timber License Agreement

1

Chapter 1

Introduction

1.1 Background of the study

Understanding the drivers of land-use/land-cover change (LULCC) is a complex

issue and presently remains to be a very active area of research. LULCCs are the result of

the interplay between socio-economic, institutional and environmental factors [1]. The

causes attributed to LULCC are considered multivariate in nature, interrelated and differ

at local, regional as well as national scale and can be summed up as complex socio-

economic processes such that it is impossible to isolate a single cause [2]. It is because of

these complexities that questions of LULCC have constantly attracted interests among a

wide variety of researchers concerned with understanding the causes and consequences of

these changes [3]. Studying the dynamics of land-cover change is essential because it

could generate primary data on the location, type, and rate of land development and, in

turn, provide a basis for analyzing the impacts of these dynamics not only on socio-

economic processes but also on such environmental processes as energy flux, runoff,

erosion, air and water quality, and biodiversity.

In northeastern Mindanao, Philippines, the provinces of Agusan del Norte and

Agusan del Sur (Figure 1) have been widely known for its rich forest resource; hence,

2

making them the major timber producers in the whole country since the 1950s up to the

present. In fact, the two provinces belong to the so-called “Eastern Mindanao Corridor”

where 75 percent of the country’s timber extraction comes from [4]. The two provinces

have utilized their forest resources extensively resulting from the establishment of

logging and timber industries way back in the 1950s [5] that continue to operate until

this time by way of forest license agreements issued by the Philippine government to

private corporations and non-government organizations. These industries have

contributed greatly to the economy of both provinces and to the Philippines as a whole

[6]; however, they are often blamed for decades of rampant upland forest destruction and

significant changes in land-cover whose ecological aftermath continues to unfold in the

valleys below. In fact, logging in the primary watersheds of the two provinces between

the 1950s and 1970s has resulted in massive upland erosion and lowland siltation,

combined with rapid runoff and flooding [7]. In 1981, for example, heavy rains spilling

into the Agusan River were blocked by huge silt deposits near the mouth of the river,

causing a series of floods which killed hundreds and left thousands homeless [7],[8].

Recently, the same environmental impacts of deforestation and land-cover change are

still a common problem that environmentalists, watershed planners, and policy makers

face today in the Agusan Provinces [9].

3

Figure 1. Map showing the provinces of Agusan del Norte and Agusan del Sur.

While the logging industries may have direct connection to deforestation and

other types of land-cover changes in the Agusan provinces, the contributions of other

equally relevant factors associated with deforestation such as agricultural expansion,

wood extraction, expansion of infrastructure, population growth, economic and

4

technological factors, policy/institutional factor, land characteristics, bio-physical

environment, and government policy failures, among others [10] maybe overlooked.

Hence, there arises a necessity to ascertain what were the factors associated with

deforestation in these two provinces.

The roles of Remote Sensing (RS) and Geographic Information Systems (GIS)

have become significant recently in LULCC researches (e.g., [11-17]). Imageries from

satellite RS platforms provides valuable sources of land-cover and other information

related to topography, and surface conditions especially in areas which are difficult to

monitor and could be very expensive when using conventional techniques [11]. Despite

the high regard accorded to RS and GIS in LULCC studies, the studies of land-cover

change are hampered by lack of good RS images due to the presence of clouds and cloud

shadows, especially in tropical countries like the Philippines. Hence, the use of medium

resolution optical RS images (e.g., those provided by the Landsat satellite) for land-cover

change detection are often limited because of the presence of clouds and shadows that

prevents the derivation of land-cover characteristics from the images. The utilization of

RS and GIS technologies to understand the process of land-cover change especially

deforestation at a finer scale are limited in these areas. Furthermore, none of numerous

studies attempted to consider and take into account the case when RS images used for

deriving land-cover change are contaminated with clouds and cloud shadows. The use of

radar RS images that overcome weather obstacles may be a solution but the availability

of such images and their long-term and multi-temporal capabilities are often inadequate

for studies that requires immediate images.

5

1.2 Objectives of the study

This study is an attempt to detect and analyze deforestation and ascertain what

were the factors associated to it in an area with a history of forest resource utilization (the

Agusan Provinces) in the context of limited land-cover information due to cloud

contamination of RS images. Using an integrated approach involving Remote Sensing

(RS), Geographic Information System (GIS) and statistical analysis, 25-year land-cover

change in the Agusan Provinces was detected and analyzed. Specifically, this involved:

1. Detecting deforestation and other types of LULCC in the two provinces

through analysis of Landsat MSS and ETM+ images;

2. Characterizing and comparing the differences in deforestation in the two

provinces using GIS-based spatial analysis techniques; and

3. Determining, through logistic regression analysis, the significance and

magnitude of the relationship between the detected deforestation and

georeferenced socio-economic and bio-physical factors such as presence of

logging and timber industries, population growth, road infrastructures,

elevation, slope, soil quality and proximity to water resources.

1.3 Research significance

This study provides an integrated RS-GIS-Statistical Analysis approach in

understanding as to which factors were associated with deforestation in the Agusan

provinces. From a socioeconomic perspective, studying deforestation and other types of

6

land-cover change in the Agusan Provinces is important because it provides data that may

be used to explore relationships with potential causal mechanisms, thereby increasing our

understanding of the development process. Conversely, analyzing LULCC and

identifying its major drivers are important from a planning perspective because they

provide a means to create and evaluate strategies that attempt to mitigate its negative

effects [18].

Deforestation, a widely recognized problem in the study area [7],[9], is the major

reason behind flooding, acute water shortages, rapid soil erosion, siltation, and mudslides

that have proved to be costly not only to the environment and properties but also in

human lives [19]. In this context, identification of factors contributing to deforestation,

among other LULCC in the Agusan Provinces, is a first step in controlling forest loss

[20] and is necessary in comprehensive forest management planning and formulation of

appropriate forest policy [12]. Furthermore, results of this study can be utilized as a

temporal LULCC model for the provinces of Agusan del Norte and Agusan del Sur that

can help in quantifying the extent and nature of change and aid planning agencies in

developing sound and sustainable land-use practices.

7

Chapter 2

Review of Related Literature

This chapter presents a review of literatures relevant to the nature and scope of the

study. The review aims to provide a clearer understanding on the different processes and

drivers with LULCC. Studies on the LULCC in the Philippines are also presented. The

state of the art of the detection and analysis of the drivers of land-use/land-cover change

through RS, GIS and statistical analysis are discusses as well.

2.1 Drivers of land-use/land-cover change

Understanding the drivers of LULCC is a complex issue and presently remains to

be a very active area of research. Lesschen et al. [1] reported that LULCC are the result

of the interplay between socio-economic, institutional and environmental factors. The

most common form of LULCC is deforestation. This is probably due to the already

established knowledge that the process of deforestation is a first step in LULCC [21].

Geist & Lambin [22] presented a grouping of the drivers of tropical deforestation

(Table 1 and Figure 2). These are a complex set of actions and factors involved in

deforestation.

8

Table 1. Drivers of tropical deforestation presented by Geist and Lambin.

Cluster Major examples

Proximate causes Agricultural expansion

Wood extraction

Expansion of infrastructure

Underlying causes Demographic (population growth)

factors

Economic factor

Technological factor

Policy/institutional factor

Cultural or socio-political factors

Other factors (land characteristics, bio-

physical drivers and social trigger events)

Land characteristics

Bio-physical environment

Health and economic crisis

Government policy failures

Figure 2. Drivers of tropical deforestation [22],[10].

Proximate causes of deforestation are human activities at the local level, that

originate from intended land-use and that have direct impact on forest cover [22].

Examples of such causes are agricultural expansion, wood extraction and infrastructure

expansion. Underlying driving factors are fundamental social processes associated with

9

deforestation, such as human population dynamics or agricultural policies that underpin

the proximate causes, and which either operate at the local level or have indirect impacts

that are felt at the local level (e.g. national or global policies). These factors include: (1)

demographic, (2) economic, (3) technological, (4) policy and institutional and (5)

cultural.

The Other factors are defined as those factors that can also play an important role

in driving deforestation; these factors include pre-disposing environmental factors (e.g.,

land characteristics, including soil quality and topography), bio-physical drivers or

triggers (fires, droughts, floods and pest outbreaks) and social trigger events (e.g.

revolution, social disorder and economic shocks) [22].

The conceptual framework developed by Geist & Lambin [22] as presented in

Figure 2 is based on the analysis of 152 case studies of tropical forest cover loss in Asia.

According to Verbist et al. [21], this framework is probably the most comprehensive in

identifying which factors drives tropical forest decline but he asserted that it needs to be

mentioned that in most of these studies, deforestation has been regarded as a unilinear

process, whereby little or no attention has been given either to the land-cover types that

were replacing the forests, or to the factors driving that replacement.

Lambin et al. [23] highlighted the complexity of land-use/cover by stating that

land-cover changes do not always occur in a progressive and gradual way, but they may

show periods of rapid and abrupt change followed either by a quick recovery of

ecosystems or by a non-equilibrium trajectory. Such short-term changes are often caused

by the interaction of climatic and land-use factors (for example, periodic El Niño-driven

droughts lead to an increase in the forest’s susceptibility to fires).

10

Lambin et al. [23]’s study further indicates that slow and localized land-cover

conversion takes place against a background of high temporal frequency regional-scale

fluctuations in land-cover conditions caused by climatic variability, and it is often linked

through positive feedback with land-cover modifications. These multiple spatial and

temporal scales of change, with interactions between climate-driven and anthropogenic

changes, are a significant source of complexity in the assessment of land-cover changes.

Lambin et al. assessed that it is not surprising that the land-cover changes for which the

best data exist—deforestation, changes in the extent of cultivated lands, and

urbanization—are processes of conversion that are not strongly affected by inter-annual

climatic variability. By contrast, few quantitative data exist at the global scale for

processes of land-cover modification that are heavily influenced by inter-annual climatic

fluctuations, e.g., desertification, forest degradation and rangeland modifications.

The roles that the proximate and underlying factors play in the complex dynamics

of LULCC are described by Lambin et al. [23] as follows. In general, proximate causes

operate at the local level (e.g., individual farms, households, or communities). By

contrast, underlying causes may originate from the regional (districts, provinces, or

country) or even global levels, with complex interplays between levels of organization.

Underlying causes are often exogenous to the local communities managing land and are

thus uncontrollable by these communities. Only some local-scale factors are endogenous

to decision makers. An important system property associated with changes in land-use is

feedback that can either accentuate or amplify the speed, intensity, or mode of land

change, or constitute human mitigating forces, for example via institutional actions that

dampen, impede, or counteract factors or their impacts. Examples are the direct

11

regulation of access to land resources, market adjustments, or informal social regulations

(e.g., shared norms and values that give rise to shared land management practices).

According to Lambin et al. [23], land-use change is always caused by multiple

interacting factors originating from different levels of organization of the coupled human-

environment systems. Changes are generally driven by a combination of factors that work

gradually and factors that happen intermittently [24]. The mix of driving forces of land-

use change varies in time and space, according to specific human-environment

conditions. Driving forces can be slow variables, with long turnover times, which

determine the boundaries of sustainability and collectively govern the land use trajectory

(such as the spread of salinity in irrigation schemes or declining infant mortality), or fast

variables, with short turnover times (such as food aid or climatic variability associated

with El Niño oscillation) [23]. Summarizing a large number of case studies, Lambin et al.

[23] concluded that land-use change is driven by a combination of the following

fundamental high-level causes:

1. resource scarcity leading to an increase in the pressure of production on

resources,

2. changing opportunities created by markets,

3. outside policy intervention,

4. loss of adaptive capacity and increased vulnerability, and

5. changes in social organization, in resource access, and in attitudes.

Lambin et al. [23] explained that some of these fundamental causes are

experienced as constraints. They force local land managers into degradation, innovation,

or displacement pathways. The other causes are associated with the seizure of new

12

opportunities by land managers who seek to realize their diverse aspirations. Each of

these high-level causes can apply as slow evolutionary processes that change

incrementally at the timescale of decades or more, or as fast changes that are abrupt and

occur as perturbations that affect human-environment systems suddenly. Only a

combination of several causes, with synergetic interactions, is likely to drive a region into

a critical trajectory. Lambin et al. explained further that some of the fundamental causes

leading to land-use change are mostly endogenous, such as resource scarcity, increased

vulnerability and changes in social organization, even though they may be influenced by

exogenous factors as well.

2.2 Deforestation and land-cover change in the Philippines

In the Philippines, deforestation and forest degradation are the most important

land-use change processes [25],[26]. These processes are an important threat to the highly

rated biodiversity of the country. Only a small fraction of the natural forest that once

covered the country remains.

It has been reported in the vast literature of Philippine LULCC that the country

was 90% forested when the Spaniards conquered the islands in the middle of the

sixteenth century, decreasing to 70% by 1900 and approximately 23% by 1987 [25],[27-

29]. The establishment of plantations of export crops led to deforestation in the

nineteenth century while unrestricted forest harvesting caused enormous losses in the

post-war years. Of almost 15 million hectares of natural dipterocarp forest in 1950 only 4

million remained in 1992. A large part of these 4 million hectares is heavily logged-over

13

forest of varying quality [29],[25]. In the late 1970s the emphasis began to shift from

timber harvesting and utilization to the protection, rehabilitation, and development of

forestlands. Log production steadily decreased through prescribed annual allowable cuts

for each logging concession. From 1992 onward, logging became officially prohibited in

virgin forests, in areas over 1000 meters in elevation and in areas with slopes of 50% and

above. The conservation impact of this order was limited because by the time it was

issued, only a small portion of the Philippines’ remaining natural forest had not yet been

logged over. The log ban in virgin forests did not mean the end of corporate logging: it

simply led companies to transfer their attention to the secondary forests. In the mid 1990s

a political discussion was held concerning the implementation of a total log ban. This

total log ban was never implemented, but policies reducing logging in fragile areas have

become stronger by the years [27],[29],[25]. In spite of different policies that aim to

reduce logging recent commercial deforestation, illegal logging and agricultural

expansion pose an important threat to the remaining forest areas in the Philippines [26].

For the past four decades, a number of studies have been conducted to detect and

analyze LULCC in the Philippines. In particular, Kummer [30] presented a model of

deforestation in which logging and agriculture (both shifting and permanent) have been

identified as the two main agents of forest destruction for the post-war (late 1940’s to

1986) Philippines. He postulated that logging is primary responsible for converting the

primary forest to secondary forest and that agriculture activities then convert the

secondary forest to farmland. His postulation attributed conversion of tropical moist

forest to logger and forest farmer’s interaction, i.e. loggers log the forest and then leave,

while the farmers follow logging roads to new accessible forest areas for cultivation.

14

Kummer tested his model of deforestation using multiple regression cross-sectional

analysis, panel analysis, and path analysis. The results of these statistical analyses

indicate that absolute forest cover is negatively related to road and population density but

there is a positive relation between the actual deforestation from 1970 to 1980 and the

forest area in 1970, distance from Manila, change in agricultural area and logging quotas

in 1970. An important conclusion of Kummer's research is that studies of deforestation

which uses percentage forest cover as the dependent variable are of limited importance in

depicting the process of deforestation. Kummer further stated that deforestation in the

post-war Philippines is the result of the failure of the Philippine economy to provide jobs

and elite control of government which has concentrated the financial returns from logging

in the hands of concessionaires and their allies which means that deforestation in the

Philippines is amenable to policy intervention.

The findings of Kummer [30] was supported by the study conducted by Verburg

et al [26] in which he reported that land-cover change in the Philippines between 1970

and the early 1990s are generally caused by large-scale logging of the forest areas

followed by agriculture. This process was accompanied by road construction for logging

and non-logging purposes and by both internal population growth and migration. Logging

opened up the forests both by constructing roads into the forests and, at the same time, by

removing large amount of timber, facilitating the clearing of the remaining degraded

forests by subsistence migrant farmers.

Moya & Malayang III [19] presented a very good background on the rate and

extent of deforestation in the Philippine which was believed to have been covered

partially, if not wholly covered with forest vegetation at the start of 20th

century

15

amounting to 21 million hectares of forest or about 70% of the national land area of the

Philippines in 1903. But in 2001, only 5.1 million ha of forest cover remain intact which

is only 17% of the national total land area. Figure 3 shows the deforestation trend of the

Philippine forest between 1903 and 2001 according to Moya & Malayang III.

Figure 3. Deforestation trend in the Philippines from 1903-2001 [19].

Moya & Malayang III [19] further stated that the conversion of forest into

croplands has been the leading cause of deforestation in the tropics, especially in the

Philippines. Population growth, inequitable land distribution, and the expansion of export

agriculture have reduced cropland available for subsistence farming, forcing many

farmers to clear virgin forest to grow food.

The interference of the Philippine governments’ policy into deforestation cited by

Kummer [30] has been supported by the result of the study conducted by Moya &

Malayang III [19]. They stated that the main culprits for continued deforestation in the

Philippines are the unchecked illegal logging and government’s negligence to combat the

same resulting to devastation of the forests.

16

In consonance with Moya & Malayang III [19]’s study, Verburg & Veldkamp

[25] reported that agricultural expansion is the main cause of further degradation of the

Philippine forests. Citing earlier reports [31-34], Verburg & Veldkamp [25] stated that

the Philippines is an example of unchecked agricultural expansion in uplands, within a

policy setting that encourages it. The area devoted to upland agriculture in the Philippines

increased six-fold between 1960 and 1987, and coincided with a rapid decline in forest

cover. According to the authors, the main reasons for this enormous expansion in upland

agriculture are population growth, inadequate labor absorption and agricultural price

policies. The high rates of forest clearing in the uplands are driven, in part, by the efforts

of low-income farmers to secure subsistence [34]. The policy bias (through price and

technology policies) in favor of crops, such as corn and temperate vegetables, whose

cultivation is most strongly associated with upland agricultural lands, is another cause of

forest frontier expansion [25],[31].

Apan & Peterson [12] probed tropical deforestation in two municipalities (Abra

de Ilog and Mamburao) of Mindoro Occidental, a province south of Manila. Licensed

logging in the area began in the late 1960s and ended in 1983. In 1978, there were about

40 pasture lease agreement holders covering some 23,825 ha. The authors aimed to (1)

determine the significance and magnitude of the relationship between forest cover and

some georeferenced environmental factors (such as population, land-use, land-ownership,

geology, soil depth, soil fertility, distance from water resources, distance from road,

aspect, elevation and slope), (2) characterize and analyze the deforested lands using GIS-

based spatial analysis techniques, and (3) gain insights as to the causes of this

deforestation. The results of their statistical analysis using Pearson chi-square test

17

indicated that all the georeferenced environmental factors are significantly related to

forest cover. However, additional testing using Cramer’s V revealed that magnitude of

relationship for all variable ranges from weak to very weak. Those variables with very

low magnitude of association (almost no relation between factors) with forest cover

include population, distance to water and distance to road. One of the appealing results

that Apan and Peterson found was that accessibility factors (i.e., distance from road and

distance from water) were very weakly associated with forest cover. Deforested lands are

significantly present in both accessible and inaccessible areas (arbitrarily, > 4 km for

roads and > 1 km for rivers/creeks).

2.3 Land-cover change detection

2.3.1 Review of RS change detection techniques

Imageries from satellite RS platforms provides valuable sources of land-cover and

other information related to topography, and surface conditions especially in areas which

are difficult to monitor and could be very expensive when using conventional techniques

(e.g., ground-based mapping and aerial photography) [11]. In land-cover change

detection and analysis, one of the most interesting applications of RS concerns the

analysis of multi-temporal images for detecting land-cover changes [35]. This process

involves the comparison of two co-registered images acquired in the same geographical

area at two different times. In the vast literature on digital change detection, two main

approaches to the change-detection problem have been adopted for RS images: the pre-

18

classification approach and the post-classification comparison approach [36]. The former

is based on existing classification methods, which require the availability of a multi-

temporal ground-truth. The latter performs change detection by making a direct

comparison of the two multispectral images considered, without relying on any additional

information [35].

In general, pre-classification change detection techniques apply various

algorithms to multiple dates of satellite imagery to generate “change” vs. “no-change”

maps [36]. They are sets of image enhancement procedures where mathematical

combinations of satellite imagery from different dates are involved such as univariate

image differencing, image ratioing, image regression or principal components

transformation [37]. Thresholds are applied to the enhanced image to isolate the pixels

that have changed. These techniques locate changes but do not provide information on

the nature of change [38],[39],[37].

Post-classification comparison methods use separate classifications of images

acquired at different times to produce difference maps from which ‘‘from–to’’ change

information can be generated [40]. The objective of post classification change detection

is to achieve the best possible independent classification for each data set and then assess

any change as accurately as the data allow [13]. Post-classification approach exhibits

some important advantages over the pre-classification approach because of its capability

of explicitly recognizing the kind of land-cover transitions which occurred in the

investigated area and its ability to process multisensor/multisource images [41]. The post-

classification comparison approach also compensates for variation in atmospheric

conditions and vegetation phenology between dates since each classification is

19

independently produced and mapped [14],[37],[42],[41]. Factors that limit the application

of post-classification change detection techniques can include cost, consistency, and error

propagation [39]. One of the major requirements of using this approach is the availability

of ground truth information for the individual classification of the images taken at

different times [35]. As the land-cover transitions are usually detected by comparing the

thematic maps obtained by classifying independently the two considered images, the

accuracy yielded strongly depends on the errors present in the classification maps. For

this reason, in the context of the detection of land-cover transitions, it is of great

importance to develop effective classification approaches capable of achieving

classification accuracies as high as possible.

2.3.2 Post-classification change detection: review of classification

methods

The usual flow of analysis in detecting land-cover change using the post-

classification approach involves applying traditional supervised classification algorithms

such as the Maximum Likelihood Classifier [43] to each image (e.g., date 1 and date 2

images) in order to categorize each pixel in the image to a particular land-cover type. The

two-independently classified images are then compared pixel-by-pixel to determine the

type of change [14]. While the use of traditional classifiers, especially Maximum

Likelihood, has been effective in a number of post-classification comparison change

detection studies e.g., [44],[15],[45],[46], a major problem with it is the errors attributed

to misclassification caused by similarities in spectral responses of certain land-cover

classes [42]. Another limitation of maximum likelihood is its assumption of normal

20

distribution of class signatures. In some cases, the number of training samples to obtain

class signatures is actually limited and may not have normal distributions [47], which

make the Maximum Likelihood classifier can not get ideal result [48]. Some studies

addressed these problems by using a hybrid supervised–unsupervised training approach

with post-classification refinements [42], or reclassifying inaccurately classified or

“mixed” pixels using several filter algorithms [13] to improve the classification accuracy.

Others refrained from using the Maximum Likelihood classifier and instead resorted to

other means of classification such as decision tree rules [49], artificial neural networks

[16],[50],[51], and support vector machines [49],[52-54]. Decision trees in particular

offer advantages not provided by other approaches [49]. They are computationally fast

and make no statistical assumptions regarding the distribution of data. However, the

challenge to using decision trees lies in the determination of the “best” tree structure and

the decision boundaries [49]. Artificial neural networks (ANN), on the other hand, are

non-linear mapping structures based on the function of the human brain [16]. Advantages

of the ANN approach include ability to handle non-linear functions, to perform model-

free function estimation, to learn from data relationships that are not otherwise known

and, to generalize to unseen situations. In land-cover classification, ANNs can produce

the most accurate maps and could be resistant to training data deficiencies [50]. ANNs

avoid some of the problems of the Maximum Likelihood Classifier (i.e., the normal

distribution assumption) by adopting a non-parametric approach [54]. Paola &

Schowengerdt [55] compared Maximum Likelihood classifier with ANN and showed that

ANN is more robust to training site heterogeneity and the use of class labels for land use

that are mixtures of land cover spectral signatures. The differences between the two

21

algorithms may be viewed, in part, as the differences between nonparametric (neural

network) and parametric (maximum-likelihood) classifiers. Computationally, the back

propagation neural network is at a serious disadvantage to maximum-likelihood, taking

nearly an order of magnitude more computing time when implemented on a serial

workstation.

2.3.3 Classification by Support Vector Machine

The support vector machine (SVM) is a classification system derived from

statistical learning theory [56],[57]. It represents a group of theoretically superior

machine learning algorithms, and employs optimization algorithms to locate the optimal

boundaries between classes [54]. It separates the classes with a decision surface that

maximizes the margin between the classes. The surface is often called the optimal

hyperplane, and the data points closest to the hyperplane are called support vectors. The

support vectors are the critical elements of the training set. In practice, the SVM has been

applied to optical character recognition, handwritten digit recognition and text

categorization [56],[58]. These experiments found the SVM to be competitive with the

best available classification methods, including neural networks and decision tree

classifiers [54]. Recently, support vector machines are becoming popular for

classification of multispectral RS images [54],[52],[59],[53],[60]. SVM achieves a higher

level of classification accuracy than either the Maximum Likelihood Classifier or the

ANN classifier, and that the SVM can be used with small training datasets and high-

dimensional data [60]. The superior performance of the SVM was also demonstrated in

classifying hyperspectral images acquired from the Airborne Visible/Infrared Imaging

22

Spectrometer (AVIRIS) [61]. Nemmour & Chibani [53] introduced the use of SVM for

land cover change detection with an application for mapping urban extensions and

showed that SVMs have higher recognition rates compared to neural networks, hence

confirming their efficiency for land cover change detection.

A detailed assessment has been conducted by Huang et al. [54] with regards to the

relative performance of Maximum Likelihood classifier (MLC), Decision Tree Classifier

(DT), Neural Network Classifier (NNC) and SVM in land-cover classification from a

Landsat TM image. Of the four algorithms evaluated, the MLC had lower accuracies than

the three non-parametric algorithms. The SVM was more accurate than DT in 22 out of

24 training cases. It also gave higher accuracies than NNC when seven TM bands were

used in the classification. The higher accuracies of the SVM should be attributed to its

ability to locate an optimal separating hyperplane. Statistically, the optimal separating

hyperplane found by the SVM algorithm should be generalized to unseen samples with

fewer errors than any other separating hyperplane that might be found by other

classifiers. Generally, the absolute differences of classification accuracy were small

among the four classifiers. However, many of the differences were statistically

significant. In terms of algorithm stability, the SVM gave more stable overall accuracies

than the other three algorithms except when trained using 6% pixels with three variables.

Of the other three algorithms, DT gave slightly more stable overall accuracies than NNC

or the MLC, both of which gave overall accuracies in wide ranges. In terms of training

speed, the MLC and DTC were much faster than the SVM and NNC. While the training

speed of NNC depended on network structure, momentum rate, learning rate and

converging criteria, that of the SVM was affected by training data size, kernel parameter

23

setting and class separability. All four classifiers were affected by the selection of

training samples. It was not possible to determine the minimum number of samples for

sufficiently training an algorithm according to results from this experiment. However, the

initial trends of improved classification accuracies for all four classifiers as training data

size increased emphasize the necessity of having adequate training samples in land cover

classification. Feature selection is another factor affecting classification accuracy.

Substantial increases in accuracy were achieved when all six TM spectral bands and the

NDVI were used instead of only the red, NIR and the NDVI. The additional four TM

bands improved the discrimination between land classes. Improvements due to the

inclusion of the four TM bands exceeded those due to the use of better classification

algorithms or increased training data size, underlining the need to use as much

information as possible in deriving land cover classification from satellite images [54].

2.4 GIS in LULCC studies

Much LULCC researches have been devoted to the analysis of relations between

LULCC and socio economic and biophysical variables that act as the ‘driving factors’ of

change [24],[1]. The roles of GIS have become significant recently in this LULCC

researches.

GIS has been used extensively by Hietel et al. [62] to develop spatial-temporal

database of land cover and of environmental variables (such as elevation, slope, aspect,

available water capacity and soil texture, and structural variables such as patch size,

shape and distance) that are needed in investigating land-cover trajectory types, land-

cover transitions at individual time intervals and their relationships to the environmental

24

variables in Hesse, Germany. GIS aided in preparing the complex set of variables

required in their conduct of the statistical analyses such as the creation of “trajectories of

change” maps at eight time intervals, and to introduce these datasets to multivariate

statistical analysis through binary encoding into a presence/absence map layers.

Similarly, Apan & Peterson [12] used GIS capabilities to probe tropical

deforestation in Mindoro, Philippines. Specifically, they used GIS to store and analyze

forest-cover data from Landsat TM and thematic maps of georeferenced physical

variables (elevation, geology, slope, distance to road, distance to water, etc) and to further

process these datasets using Pearson’s chi square test, Cramer’s V calculations and

logistic regression analysis. They were able to show the utility and effectiveness of the

GIS environment, in tandem with statistical packages, to handle large datasets, to obtain

samples of almost unlimited number for a study area, and to analyze the relationship

between variables expressed as data layers. Data formatting for transfer to statistical

software was likewise unconstrained. However, they recognized that all the thematic

layers should be digitized and georeferenced with high accuracy to maximize the

effectiveness of GIS-coupled statistical analyses.

Helmer [63] used GIS to satisfy his LULCC study’s requirement of comparing

multitemporal land-cover maps of Puerto Rico derived from 1977-1978 aerial

photographs and 1991-1992 Landsat TM images in order to analyze patterns of land

development. He used GIS to (1) rasterize the polygon-level maps from aerial

photographs into 30-m cell size, (2) co-register it with the map from Landsat TM image,

(3) edit both maps to a comparable set of classes through overlays and class

generalizations, and (4) cross-tabulate the number of pixels of each class in 1977-78 that

25

changed or did not change to each 1991-92 class. In addition to these, he also used GIS to

create the large amount of datasets necessary for binomial logistic regression analysis of

land cover change, which included among others, distances to road, distance to nearest

urban area, distance to nearest road, elevation, slope, geology, and forest and urban patch

size.

Indeed, GIS plays a crucial role in LULCC research. From the three articles

reviewed above, it can be observed that GIS is being used as pre-processor of land-cover

information and related variables prior to statistical analysis. Some studies have coupled

GIS with RS to visualize and quantify land-cover changes [64] and some even used GIS

native functions to derive spatial patterns and statistics of land-cover change vis-à-vis sets

of environmental variables [65] and to supplement statistical change detection analysis

[66].

In Jung et. al [65]’s study, GIS was used extensively to investigate the

relationship between deforested area and spatial data (e.g., topography, road, and existing

protection area. The basic topographic information (altitude, slope, distance from access

roads, and distance from protected zones) were computationally derived from geographic

data of 30-m resolution. By performing an overlay analysis technique with the

topographic and spatial information, the relationship between these basic spatial factors

and deforestation distribution patterns on each polygon was analyzed. Consequently, they

were able to produce various graphs that depict the relationship between the spatial

factors and deforestation such as the correspondence of distance from the nearest access

roads and protected area borders with quantity of deforestation and with the frequency of

occurrence of deforestation.

26

Porter-Bolland et al. [66] used GIS buffering techniques to supplement their

change detection analysis of three satellite images to assess forest clearing for agricultural

use from two time periods (1988-2000 and 2000-2005) in La Montaña, Campeche,

Mexico. Buffers from specific variables (distance to roads, distance to settlements and

proximity to lowland flooded forest) were used to assess deforestation patterns and

identify potential variables that determine land use change. Through this buffer analysis,

they were able to establish the trend that (1) deforestation appears to be prominent near

lowland flooded forests on soil types identified by local people as suitable for agriculture,

particularly for pasture establishment., and (2) recent occurrence of deforestation in the

area is strongly associated with soil/vegetation characteristics, infrastructure development

and settlement locations while the proximate causes are related to agricultural expansion.

2.5 Review of statistical methods in LULCC studies

It has been stated in the earlier sections of this chapter that LULCC’s are the

result of the interplay between socio-economic, institutional and environmental factors.

Lesschen et al. [1] provide a comprehensive discussion on statistical methods for

analyzing the spatial dimension of changes in LULCC in relation to these factors. In their

report, the analysis of relations between land use and the socio-economic and biophysical

variables that act as the ‘driving forces’ of land use change is given great emphasis, i.e.,

to understand LULCC by identifying its proximate and underlying causes through

empirical data analysis. Two categories of empirical analysis techniques are presented

27

based on objective and data structure: (1) exploratory spatial data analysis, and (2)

regression analysis.

The main uses of exploratory data analysis techniques are related to data

reduction and structure detection [1]. These methods aim (i) to reduce the number of

variables; (ii) to describe the underlying structure between variables in the data; and (iii)

to classify variables into groups. Examples of these are factor analysis and principal

component analysis (PCA), which are applied as data reduction or structure detection

methods, and cluster analysis for classification. This is useful in LUCC analysis because

land use change is often assumed to be influenced by a large set of driving and

conditioning factors. PCA and factor analysis are suited to exploration of the structure of

interrelationships between these different driving factors. Furthermore, the methods can

also be used to characterize land use systems based on a number of indicators.

The study conducted by Veldkamp & Fresco [67] is an example of exploratory

data analysis wherein factor analysis was used to investigate land use and land cover in

Costa Rica at six different scales. Spatial distributions of potential biophysical and LUCC

drivers were statistically related to the distribution of pastures, arable lands, permanent

crops, and natural and secondary vegetation. The factor analysis demonstrated that factor

contributions and compositions change with scale, confirming spatial scale dependence in

the structure of the spatial data. The total variance in the data set could be described by

four significant factors for all scales, describing between 68% and 81% of the total

variance.

Lesschen et al. [1] provide caution on the use of factor analysis. All variables

should be quantitative at the interval or ratio level. Categorical data, e.g. ethnicity or soil

28

type, are not suitable for factor analysis. The data should have a bivariate normal

distribution for each pair of variables, and observations should be independent. Those

variables that do not show variability can be discarded. It is established on an a priori

basis that the variables with a coefficient of variation of less than 50% are normally not

considered [68]. Second, some variables may not be relevant to the typification required

for the purposes of a particular study and can therefore be discarded, even though the

typology obtained initially is consistent with observations. Thus one has to assess if the

information imparted by a variable is consistent with the research objectives. Third,

highly correlated variables can be eliminated, as an uncritical use of such variables.

Canonical correlation analysis is another exploratory data analysis technique. It is

a multivariate technique that has the same computational basis as factor analysis, but in

its concept and objectives it is closely related to multiple regression [1]. Multiple

regression is concerned with the relationship between a single dependent variable Y and a

set of predictor variables X1, X2, …, Xm. An extension of this concern is the

relationship(s) between a set of Y variables and a second set of X variables measured on

the same objects. These relationships may be investigated by finding linear combinations

of the X and Y variables that give the highest correlation between the two sets. Such

correlations are called canonical correlations and the linear combinations are called

canonical variables. In effect, the set of X variables is converted into a single new

variable and the set of Y variables into another single new variable. Then the correlation

between these new variables is determined [69]. This statistical method is particularly

appropriate when the dependent variables themselves are correlated with each other. In

29

such cases, canonical correlation analysis can uncover complex relationships that reflect

the structure between predictor and dependent variables.

Canonical correlation analysis was used by Hietel et al. [70] to identify key socio-

economic indicators of land-cover changes in Hesse, Germany. Canonical analysis was

used as an explorative process to reduce large set of socioeconomic variables and to

define a plausible land cover model. Correlation coefficients computed were used to

identify key socio-economic indicators of land-cover changes. The results showed that a

relatively high percentage of variance in land-cover data can be explained by socio-

economic factors. The types of land-cover changes can be characterized by combinations

of key socio-economic indicators.

Regression analysis falls in the second category of empirical analysis techniques

[1]. Regression analysis is used to investigate the association of a dependent variable with

one or more independent variables. In linear regression a straight line is used to represent

the association of the explanatory variables with the dependent variable. More complex

methods of regression exist, intended for different types of dependent variables and data

structures.

Linear regression is a method that estimates the coefficients of a linear equation,

involving one or more independent variables that best predict the value of the dependent

variable. Linear regression is a frequently used technique; however, in LUCC modelling,

this regression is less popular because linear regression can only be applied for

continuous dependent variables [1]. Instead logistic or multinomial regression is used,

because land use is normally expressed as a discrete variable. In linear regression

analysis, it is possible to test whether two variables (or transformed variables to allow for

30

non-linearity) are linearly related and to calculate the strength of the linear relationship if

the relationship between the variables can be described by an equation of the form Y = α

+ βX. Y is the variable being predicted (the dependent, criterion, outcome or endogenous

variable), X is a variable whose values are being used to predict Y (the independent,

exogenous or predictor variable), and α and β are population parameters to be estimated

[71].

The study of Weiss et al. [72] is an example study where linear regression was

used to assess the condition of rangelands in Saudi Arabia and evaluate the effects of

grazing. The coefficient of variation (COV) of the monthly normalized difference

vegetation index (NDVI) was used as a measure of vegetative biomass change. A higher

NDVI COV for a given pixel represented a greater change in vegetation biomass for that

area. The trend in COV values was assessed with linear regression over a 12-year period.

The COV regression line for each pixel reflects the overall long-term trend in the data. A

t-test of the value of the slope was performed to test whether the data used to compute the

regression line were statistically significant at a certain confidence level. Another

example of linear regression is the study of López et al. [73], who used linear regression

between urban growth and population growth for the prediction of urban expansion in

Morelia, Mexico.

Lesschen et al. [1] noted that linear regression for the analysis of multiple land

use types is only used when the land use data are represented as continuous values

instead of dichotomous. Such a representation is used in the case of a coarse spatial

resolution at which the data land use situation cannot adequately be presented by

31

dichotomous data. This is exemplified in the works of Verburg & Chen [74] and Wood &

Skole [75].

Logistic regression is useful for situations where the dependent variable has a

binary output, e.g. the presence or absence of a characteristic or outcome [1]. The method

is very appropriate in predicting the probability that a case will be classified into one as

opposed to the other of the two categories of the dependent variable. The odds that Y = 1,

written odds(Y=1), is the ratio of the probability that Y = 1 to the probability that Y ≠ 1.

The odds that Y = 1 is equal to P(Y=1) / [1– P(Y=1)]. Unlike P(Y=1), the odds has no

fixed maximum value, but like the probability, it has a minimum value of 0 [71].

Logistic regression is a very popular and widely used method in LULCC studies

[1]. Apan & Peterson [12] used logistic regression to probe tropical deforestation in

Mindoro, Philippines by determining the statistical significance of the relationships of

various georeferenced environmental variables to forest cover. Their results indicated

that, except from distance to road, all variables (elevation, slope, distance to water,

geology, land form, soil fertility, etc.) are statistically related to forest cover.

Serneels & Lambin [76] used logistic regression to identify how much

understanding of the driving forces of land use changes can be gained through a spatial

statistical analysis for the Mara ecosystem in Kenya. All explanatory variables suggested

by the conceptual model for the study area were introduced in the statistical mode and,

based on the full model information, they analyzed which variables contribute

significantly to the explanation of land use changes. Schneider & Pontius [77] used

logistic regression for modelling deforestation in the Ipswich watershed of

Massachusetts. Geoghegan et al. [78] used logistic regression to model tropical

32

deforestation and land use intensification in the southern Yucatán peninsular region, in

combination with household survey data on agricultural practices.

Verburg et al. [79] used logistic regression to analyze the factors determining land

use patterns in the Netherlands. The method was based on an extensive database,

including land use, biophysical, socio-economic, neighborhood and policy characteristics.

All data were aggregated to 500×500-meter grids covering the Netherlands. Historic and

recent land use changes were studied. The long-term effects of land use changes were

studied by analyzing current land use patterns. Many factors that are commonly used to

explain land use change patterns are endogenous at a long timescale, e.g. measures

indicating current accessibility. Therefore the assumption was made that long-term land

use change was mainly determined by biophysical factors. A binomial logit model was

compiled for each land use type:

Logit P = α + β1Xsoil + β2Xaltitude + β3Xdist-hist-town

The exp(β) values (odds ratio) for the logit models describing the land use pattern for the

main land use types in 1989 indicated that a very clear association exists between the pH

and the location of forest, which is mainly found on poor sandy soils. Model fit for forest

is good, while the independent variables for residential and industrial areas only explain a

small fraction of the spatial variability. The logit models indicated which factors were

important determinants of land use patterns in the Netherlands.

Lesschen et al. [1] noted that multicollinearity of dependent variables needs to be

accounted for in exploratory and regression analysis of LULCC data. Multicollinearity,

or the dependency between the explanatory variables, is an important issue to account for

in all multivariate methods. Collinearity arises when independent variables are correlated

33

with one another. It is suggested that data should be checked on multicollinearity before

any regression analysis [1]. Perfect collinearity means that an independent variable is a

perfect linear combination of the other independent variables. If each independent

variable in turn is treated as the dependent variable in a model with all of the other

independent variables as predictors, perfect collinearity would result in R2 = 1 for each of

the independent variables. When perfect collinearity exists, it is impossible to obtain a

unique estimate of the regression coefficients; any of an infinite number of possible

combinations of linear or logistic regression coefficients will work equally well [71].

Collinearity is easy to detect, but there are only few acceptable remedies for it [80].

Deleting a variable involved in collinearity runs the risk of omitted variable bias.

Methods to prevent multicollinearity include factor analysis, a priori correlation analysis

and stepwise regression [1].

34

Chapter 3

The Study Area

3.1 Background

The Provinces of Agusan del Norte and Agusan del Sur (Figure 4) are located in

the Caraga Region XIII in the northeastern part of Mindanao, Philippines. The two

provinces were previously part of one province, the Agusan province, not until the

issuance of Republic Act 4979 on June 17, 1967 separating it into two independent

provinces as they are now.

3.2 The Province of Agusan del Norte

The province of Agusan del Norte (ADN) is located in the northeastern part of

Mindanao. It is bounded on the north by Butuan Bay and Surigao del Norte, east by

Surigao del Sur; west by Misamis Oriental, and south by Agusan del Sur. The capital of

ADN is Butuan City until August 16, 2000 where the seat of provincial government was

transferred to Cabadbaran by virtue of RA 8811 and Butuan City became an

administratively independent city. The province has 10 municipalities namely:

Buenavista, Carmen, Jabonga, Kitcharao, La Nievas, Magallanes, Nasipit, Santiago,

35

Tubay, and Remedios T. Romualdez and 1 component city, City of Cabadbaran, which is

also the capital of the province. (Butuan City was integrated in Agusan del Norte in all

the analyses conducted in this study.)

Figure 4. Map showing the municipalities and cities in the Agusan provinces

ADN has a total land area of 259,052 hectares or 2,590.52 sq. km. where 25.72%

of it is classified as alienable and disposable while 74.28% are forestlands [81]. Land use

36

in Agusan del Norte is primarily for agriculture making it one of the country’s leading

rice producer. Other major products are coconut, corn, mango, bananas, palm oil,

vegetables, and prawns. The province also has 23 lumber and plywood production plants

operating in Butuan City, thus, the province continues to be a major timber producer.

Based on the May 1, 1975 census, Agusan del Norte (including Butuan City) had

a population of 300,735 which gives a population density of 116 persons per sq. km. As

of May 1, 2000 census, the province population already reach 551,503 with an annual

growth rate of 1.89% hence making it the 47th

most populous province in the Philippines.

Population density of the province in the year 2000 is 213 persons per sq. km. With a

span of 25-years, difference in population is 250,768 and the difference in population

density is 97 persons per sq. km. Climate in the province is moderate, having no definite

dry season. ADN is strategically located outside the typhoon belt area in the Philippines.

Rainfall is evenly distributed throughout the whole year. The terrain of ADN is generally

composed of lowlands plains.

3.3 The Province of Agusan del Sur

Agusan del Sur (ADS) is a landlocked province located south of Agusan del

Norte. It is bounded on the east by Surigao del Sur; south by Davao Oriental, Compostela

Valley and Davao, and west by Bukidnon. The province has 14 municipalities namely:

Bayugan, Bunawan, Esperanza, La Paz, Loreto, Prosperidad, Rosario, San Francisco, San

Luis, Santa Josefa, Sibagat, Talacogon, Trento and Veruela. The provincial capital is

Prosperidad.

37

ADS has a total land area of 896,550 hectares or 8,965.50 sq. km. making it the

seventh largest province in the country where 75.28% of it is forestland and the

remaining 24.72% is alienable and disposable. ADS is an elongated basin formation with

mountain ranges in the eastern and western sides forming a valley, and half of the

southern province is an area filled with swamps and lakes. Agusan del Sur falls under the

Type II climate classification system: no dry season with very pronounced wet season of

heavy precipitation. Maximum rainfall generally occurs from the months of December to

January. The average temperature is 27 °C.

Based on the May 1, 1975 census, Agusan del Sur had a population of 174,682

which gives a population density of 19 persons per sq. km. As of May 1, 2000 census, the

province population already reach 559,294 with an annual growth rate of 1.79% giving

the place the most populous province in the Caraga region and ranked 43rd

in the most

populous provinces in the Philippines. Population density of the province in 2000 is 62

persons per sq. km. With a span of 25-years, difference in population is 384,612 and

difference in population density of 43 persons per sq. km. Residents in ADS are mostly

engaged in forestry and agriculture with rice, corn, and fruits as the major agricultural

crops. Palm oil plantation, crude oil processing and coconut trees products are also

produced in ADS. The terrain of ADS is generally rugged with slope that extends up to

more than 50 percent.

38

3.4 Status of Forest Resources in the Agusan Provinces

Records of the Department of Environment and Natural Resources-Forest

Management Bureau (DENR-FMB) show that in the year 1984 to 1992, the log

production of the two Agusan provinces is relatively high (Figure 5). The log production

of Agusan del Norte is very high until year 1992. After year 1992, the log production of

Agusan del Norte was observed to decline rapidly. This may be due to the

implementation of the total log ban in year 1992. The province of Agusan del Sur also

experienced decline in log production after the implementation of the total log ban but the

province was slowly recovering and was observed that its log production increased

starting 1996. Unlike Agusan del Sur, Agusan del Norte was not able to recover from its

decline in log production after the implementation of the total log ban.

LOG PRODUCTION (1984-2001)

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Year

Vo

lum

e o

f P

ro

du

cti

on

(cu

.m.)

Agusan del Norte

Agusan del Sur

Figure 5. Log Production of Agusan del Norte and Agusan del Sur from 1984-2001

39

0

5

10

15

20

25

30

A DN ADS A DN ADS A DN ADS

1995 1998 2000

Year

Nu

mb

er

of

Pro

du

cti

on

Pla

nts Active Regular Sawmills

Existing Mini-Sawmills

Active Plywood Plants

Active Veneer Plants

Existing other Wood-Based

Plants

Figure 6. Graph showing the timber processing plants and sawmills in the Agusan

Provinces

Figure 6 shows the number of sawmills, plywood plants, veneer plants and other

wood-based plants existing in the two provinces. It can be noticed that there was an

abrupt increase in the establishment of mini-sawmills in Agusan del Norte from the year

1995 to 1998. From 7 sawmills in 1995, it increased to 25 in the year 1998 but slightly

goes down to 18 in the year 2000. No sawmill is present in Agusan del Sur from 1995 to

1998. Only 2 mini-sawmills was established in Agusan del Sur in 2000. Although

decreasing in number from year 1995 to year 2000, regular sawmills and plywood plants

are also found in Agusan del Norte. On the other hand, only 1 plywood plant was

established in Agusan del Sur 1995. Establishment of veneer plants however in Agusan

del Norte increased from 2 in year 1995 to 7 in year 1998. Establishment of other wood-

based plants started in 1998.

40

3.5 Forest License Agreements Issued in the Agusan Provinces

Due to the vast forest resource of the Agusan Provinces, several privately owned

companies and community-based organizations applied and where awarded with different

forest licenses in the Agusan Provinces. Forest licenses issued in the Agusan Provinces

include Timber License Agreement (TLA), Integrated Forest Management Agreement

(IFMA), Community-Based Forest Management Agreement (CBFMA), and Community-

Based Resource Management (CBRM).

Timber License Agreement (TLA) is an agreement between the government and

privately owned companies to explore and exploit the forest resource in the area. Since

the implementation of TLA in 1958, it consequently covered major part of the country’s

forest land resulting to denudation of the forest resources. In order to answer the

denudation problem caused by TLA, the establishment of Industrial Tree Plantation (ITP)

was implemented in September 9, 1981 under Executive Order No. 725. The areas

available were the open, denuded and inadequacy stocked residual natural forest areas

within the concession. It was renamed as Industrial Forest Management (IFM) since its

coverage expanded to allow planting of non-timber products. Likewise, the activities

under the program were expanded to include not just the industrial plantation

development and related activities but also the management and protection of the natural

forest. Industrial Forest Management Agreement was again renamed as Integrated Forest

Management Agreement (IFMA). An IFMA is a production sharing contract entered into

by and between the DENR and a qualified applicant wherein the DENR grants to the

latter the exclusive right to develop, manage, protect and utilize a specified area of

forestland and forest resources therein for a period of 25 years and may be renewed for

41

another 25-year period. Aside from the TLA which was subsequently replaced by IFMA,

another timber license introduced last July 19, 1995 was the Community-Based Forest

Management Agreement (CBFMA). Anchoring on the concept of "people first and

sustainable forestry will follow", CBFMA is a production sharing agreement between the

DENR and the participating people’s organization (POs) for a period of 25 years

renewable for another 25 years and shall provide tenurial security and incentives to

develop, utilize and manage specific portions of forest lands. Along with this, another

program introduced by the government is the Community-Based Resource Management.

CBRM is a $US50M project financed by the World Bank and the Philippine government,

designed to address the twin objectives of ameliorating rural poverty and resource

degradation through support for locally generated and implemented natural resource

management projects. The project aimed to strengthen the capacity of local communities

in forest, upland and near-shore areas, and that of Local Government Units (LGUs) to

plan and implement investments for community-initiated development projects to reduce

poverty and environmental degradation [82].

Table 2 shows the timber license agreements issued in the provinces of Agusan

del Norte and Sur. It can be observed that as of the year 1959 to 1983, there were a total

of 200,144 hectares issued with TLA in Agusan del Norte while during the same time

period, there were a total of 323,931 hectares issued with TLA in Agusan del Sur.

Looking at the percent area of the total TLA coverage in each province with respect to its

total land area, 77% of the total land area of Agusan del Norte is covered with TLA while

in Agusan del Sur only 36% of its total land area is covered with TLA. Based on these

statistics, it is evident that more TLAs were issued in Agusan del Norte than in Agusan

42

del Sur during the time period of 1959-1983. Figure 7 shows the location of the TLAs

and IFMAs issued in the Agusan provinces.

Table 2. List of Timber License Agreements (TLAs) issued in Agusan del Norte and

Agusan del Sur with date of TLA issuance and expiry, and area covered. (Source: Yearly

Forestry Statistics, DENR-FMB).

Name Date Issued Expiry Date Area Covered (ha.)

Agusan del Norte

Nasipit Lumber Corp. 4-Dec-1959 30-Jun-2007 98,310

Butuan Lumber Manufacturing Co. 14-Sep-1961 30-Jun-1986 12,109

Sibagat Timber Corp. 22-Feb-1973 30-Jun-1997 19,050

Adgawan Timber Inc. 24-Sep-1975 30-Jun-1985 9,175

Mainit Lumber and Dev. 5-Dec-1975 30-Jun-1997 27,870

Ventura Timber Corp. 11-May-1983 31-Mar-2008 33,630

Agusan del Sur

Bueno Industrial and Dev't. Corp 14-Sep-1961 3-Jun-1982 23,402

Grecan Co., Inc. 14-Aug-1970 30-Jun-1982 14,975

JCA Lumber and Plywood Ind. 8-Sep-1970 30-Jun-1990 12,940

CVC Lumber Ind. 8-Sep-1970 30-Jun-1990 30,295

Republic Timber Corp. 2-Aug-1972 30-Jun-1997 19,270

JJ Tirador Lumber Ind. 4-Feb-1974 3-Jun-1997 47,980

SPV Timber and Construction Inc.

19-Apr-1974 30-Jun-1974 37,160

Del Rosario and Sons Logging Ent. 25-Apr-1974 30-Jun-1984 14,470

Agusan Wood Ind. Inc. 16-Jul-1974 30-Jun-1998 60,390

Southern Agusan Timber Co. 27-Feb-1976 30-Jun-1982 5,560

Woodland Domain Corp. 11-Nov-1982 30-Jun-2007 57,489

Prudent Logging Dev't. Corp. 9-Jul-1985 30-Jun-2000 37,160

El Salvador Lumber Co. 16-Sep-1985 3-Sep-1995 49,115

43

Figure 7. Map showing the location of TLAs and IFMAs issued in the Agusan provinces.

On the other hand, issuance of CBFMA in Agusan del Norte and Agusan del Sur

started in 1997. Total area of CBFMA in Agusan del Norte is 37,806.61 hectares or

14.59% of its total area is covered with CBFMA while in Agusan del Sur the total area

44

covered by CBFMA is 68654.37 hectares or 7.66% of its total land area. Figure 8 shows

the location of issued CBFMAs and CBRMs issued in the Agusan provinces.

Figure 8. Map showing the location of CBFMAs and CBRMs issued in Agusan

Provinces.

45

Chapter 4

Methodology

4.1 Overview

The overall flow of methodology of the study is presented in Figure 9. The

methodology is subdivided into three phases: (i.) RS image analysis to derive multi-

temporal land-cover and change maps, (ii.) GIS analysis of detected forest cover change

or deforestation, and (iii.) statistical analysis to determine the degree of association of

bio-physical and socio-economical factors with deforestation.

In Phase 1, RS images of the study area acquired by the Landsat Multi-spectral

Scanner (MSS) and Enhanced Thematic Mapper Plus (ETM+) sensors for 1976 and

2001, respectively, were analyzed in order to derive land-cover maps and to determine

the changes in land-cover during this time period. The detected changes, in the form of a

land-cover change map, are directed to Phase 2 wherein it is visualized and analyzed in a

GIS. Much of the GIS analysis focused on the characterization and visualization of the

detected deforestation vis-à-vis sets of georeferenced bio-physical and socio-economical

factors which are hypothesized to be associated with deforestation [12] such as elevation,

slope, distance to road, distance to water resources, distance to forest resource industries

(TLA, IFMA, CBFMA, and CBRM) and population density, among others. The analysis

is further expanded in Phase 3 where exploratory statistical data analysis and logistics

46

regression techniques are used to determine the significance and magnitude of the

relationship between the detected deforestation and the georeferenced factors.

Figure 9. The three phases of the study’s methodology.

Phase 1

Remotely-sensed image analysis

Processing of 1976 Landsat MSS and 2001 Landsat ETM+

images of Agusan del Norte and Agusan del Sur to derive land-

cover maps

Detection of land-cover change and derivation of a land-cover

change map

Phase 2

GIS Spatial Data Analysis

Visualization of land-cover change

Characterization of deforestation vis-à-vis sets of georeferenced

environmental factors such as elevation, slope, distance to road,

distance to water resources, distance to forest licenses and

population density, etc.

Phase 3

Statistical Data Analysis

Exploratory statistical analysis to determine the magnitude and

relationship of deforestation vis-à-vis georeferenced bio-physical

and socio-economical factors

Logistic regression to model the significance and magnitude of

influence of the factors to deforestation

47

4.2 Remote sensing image analysis

Figure 10 shows the process flow for the analysis of remotely-sensed images of

the study area to derive the land-cover maps for the years 1976 and 2001, as well as the

land-cover change map between these years. Each step is discussed in the following

subsections.

Figure 10. Process flow diagram of remotely-sensed image analysis

4.2.1 Landsat images

Landsat 2 MSS image acquired on April 17, 1976 (path 120/row 54 of World

Reference System 1 – WRS1 tiling) covering the study area was downloaded free-of-

charge from the University of Maryland - Global Land Cover Facility (GLCF) website

Geometric

accuracy

assessment,

image

processing,

cloud and

shadow masking

DENR 2003 Land-

cover map, ground

truth data,

2005Quickbird image

Post-classification

change detection

Classification

accuracy

assessments

1976 – 2001

Land-cover Change

Map

1976 Land-cover

Map

2001 Land-cover

Map

Supervised image

classifications

1976

Landsat

MSS

2001

Landsat

ETM+

SRTM

DEM

48

(http://glcf.umiacs.umd.edu), while Landsat 7 ETM+ image acquired on May 22, 2001

(path 112/row 54 WRS2 tiling) was obtained free-of-charge from the U.S. Geological

Survey through the web-based application of Earth Explorer

(http://edcsns17.cr.usgs.gov/EarthExplorer/). These 8-bit images (Figure 11) were

already orthorectified upon download and are georeferenced in Universal Transverse

Mercator Zone 51 (UTM51) projection on the World Geodetic System (WGS) 1984

datum. Characteristics of these images are listed in Table 3.

Table 3. Characteristics of the Landsat images used in the study.

a. April 17, 1976 Landsat MSS image.

Band

No.*

Spectral Range (μm) Band Name Spatial Resolution (m)

4 0.5 – 0.6 Green 57

5 0.6 – 0.7 Red 57

6 0.7 – 0.8 Near infra red 1 57

7 0.8 – 1.1 Near infra red 2 57

*Bands 1 to 3 were assigned to three Return Beam Vidicon (RBV) cameras on board the

Landsat 2 satellite. The MSS bands were numbered to follow on in this sequence [43].

b. May 22, 2001 Landsat ETM+ image.

Band

No. Spectral Range (μm) Band Name

Spatial Resolution

(m)

1 0.45 – 0.52 Blue 30

2 0.52 – 0.60 Green 30

3 0.63 – 0.69 Red 30

4 0.76 – 0.90 Near infra red 30

5 1.55 – 1.75 Middle infra red 1 30

6 10.4 – 12.5 Thermal 60

7 2.08 – 2.35 Middle infra red 2 30

49

126°30'0"E

126°30'0"E

126°0'0"E

126°0'0"E

125°30'0"E

125°30'0"E

125°0'0"E

125°0'0"E

9°3

0'0

"N

9°3

0'0

"N

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®

20KM

April 17, 1976

Landsat 2 MSS

RGB = 6-5-4

Agusan

del Norte

Agusan

del Sur

126°30'0"E

126°30'0"E

126°0'0"E

126°0'0"E

125°30'0"E

125°30'0"E

125°0'0"E

125°0'0"E

9°3

0'0

"N

9°3

0'0

"N

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®

20KM

May 22, 2001

Landsat 7 ETM+

RGB = 4-2-1

Agusan

del Norte

Agusan

del Sur

Figure 11. The two Landsat images of the study area that were subjected to image analysis to derive land-covers maps for the

years 1976 and 2001.

50

4.2.2 Image geometric accuracy assessment

Prior to any image pre-processing, the geometric accuracy of the Landsat images

were first assessed, i.e. to determine the degree of accuracy of the UTM 51 WGS 1984

coordinates (Easting, Northing) of the pixels in the Landsat images as well as their co-

registration. This task is important as it could minimize the error in change detection.

The assessment was conducted by first checking the geometric accuracy of the

2001 Landsat image by comparing it to 1:50,000 NAMRIA topographic maps of the

image coverage (in paper form). The rule-of-thumb of a Global Root Mean Square Error

(GRMSE) of less than or equal to half a pixel (i.e., 15-m) was adapted to test whether the

geometric accuracy of the 2001 Landsat image is acceptable or not. In case the GRMSE

is more than 15-m, re-georeferencing the image must be done. However, if the GRMSE

is ≤ 15-m, the 2001 image’s geometric accuracy is deemed acceptable and re-

georeferencing is not needed. If this is the case, the next step is to test the co-registration

of the 1976 Landsat image to the 2001 Landsat image, with the 2001 Landsat image as

the reference or base image.

In testing the 2001 Landsat image’s geometric accuracy, a total of thirty eight (38)

points identifiable on both the image and the NAMRIA maps were used. These points

(Figure 12) are mostly road intersections, bridges, and river bends and their intersections.

The UTM 51 WGS 1984 coordinates of each point were determined both on the image

and on the maps. Linear interpolation by scaling was used to determine the coordinates of

the points in the NAMRIA maps while a direct coordinate readout using ITT Visual

Information Solutions’ Environment for Visualizing Images (ENVI) 4.4 software [83]

was used to determine the coordinates of the same points in the 2001 Landsat image.

51

Prior to comparison, a datum transformation of the UTM 51 coordinates of the points

determined from the NAMRIA maps was done because NAMRIA maps have the Clarke

1866 spheroid as the datum. Datum transformation from Clarke 1866 to WGS 1984 was

done using the Environmental Systems Research Institute’s Arcview GIS 3.2 Projection

Utility software [84].

Comparison of UTM51 WGS 1984 coordinates of 38 points in the 2001 Landsat

image with those in the NAMRIA maps showed that the geometric accuracy of the image

is acceptable because the GRMSE is 10.25 meters, and is less than 15-m (Figure 12).

Also, the local RMSE of the points are all less than 15-m with a mean of 10.05 m.,

further indicating the good accuracy of the UTM 51 WGS 1984 coordinates of the pixels.

The co-registration of the 1976 Landsat image to the 2001 Landsat image was

next performed. In this case, the 2001 Landsat image is the reference image where the

UTM coordinates of points on the 1976 Landsat image will be compared. Considering

that the pixel size of the 1976 image is 57 m., the target value of the GRMSE must be ≤

28.5 m in order for the 1976 image to be geometrically acceptable. Based on 22 points

common on both images (Figure 13), the GRMSE was computed as 16.62 m, with

average local RMSE equal to 15.20 m. This indicates that the geometric accuracy of the

1976 Landsat image is acceptable and its co-registration with the 2001 Landsat image is

good. Furthermore, as the GRMSE and average local RMSE is less than 28.5 m., the 30-

m. resolution land-cover map derived from the 2001 Landsat image after undergoing

resampling to 57-m resolution, will perfectly align with the land-cover map derived from

the 1976 Landsat image. This minimizes the error due to image mis-registration in the

change detection and analysis.

52

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9.97

9.76

8.95

6.68 8.43

5.61

12.24

11.41

11.69

12.52

11.25

11.41

10.12

10.03

11.05

10.46

11.36

11.98

13.23

13.66

12.34

13.54

10.57

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°3

0'0

"N9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®

25

Kilometers

Agusan del

Norte

Agusan del

Sur

Local RMS Error

Magnitude (m.)

and Direction

! Test Point Location

Geometric Accuracy of the

2001 Landsat ETM+ Image

Average Local

RMS Error: 10.05 m.

Global RMS Error: 10.25 m.


Landsat image and the resulting RMSE vectors of the comparisons with NAMRIA maps.

The numerical values and the lines indicate the magnitude and direction of the

differences in coordinates (local RMSE), with the arrows pointing to the “actual” (i.e.,

NAMRIA map) coordinates.

53

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

11.4

3.27

9.93

5.66

5.56

8.51

14.9

20.81

26.84

14.66

27.73

17.03

22.83

24.32

22.31

12.97

13.86

19.83

10.63

12.22

14.66

14.51

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°3

0'0

"N

9°3

0'0

"N

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®

25

Kilometers

Agusan del

Norte

Agusan del

Sur

Geometric Accuracy

of the1976 Landsat MSS

Image

Average Local

RMS Error: 16.62 m.

Global RMS Error: 15.20 m.

Local RMS Error

Magnitude (m.)

and Direction

! Test Point Location


Landsat image and its co-registration with the 2001 Landsat image. Also shown are the

resulting RMSE vectors of the comparisons. The numerical values and the lines indicate

the magnitude and direction of the differences in coordinates, with the arrows pointing to

the “actual” (i.e., 2001 Landsat) coordinates.

54

4.2.3 Image pre-processing

After testing for the geometric accuracy, the Landsat MSS and ETM+ images

were subjected to radiometric calibration and atmospheric correction using the steps

provided by Schowengerdt [85]. All image processing was done using ENVI 4.4.

First, the pixel values of each band of the image which are in digital numbers

(DN), with grey-scale level from 0-255 were converted to at-sensor or “top-of-

atmosphere” radiance. For the Landsat ETM+ image, the spatial resolution of Band 6

(which is 60 meters) was resampled using nearest neighbor method to 30 meters first

prior to conversion. This is to make it compatible with the other 6 bands.

The conversion of DN values to top-of-atmosphere radiance was done using the

standard formula available from the Landsat 7 Science Data Users Handbook [86] and

from Markham & Barker [87]:

( )( )

( )

cal calmin

calmax calmin

LMAX LMIN Q QL LMIN

Q Q (1)

where Lλ = spectral radiance at the sensor's aperture in W/(m2

· sr · μm);

Qcal = quantized calibrated pixel value in DN's;

Qcal min = minimum quantized calibrated pixel value corresponding to LMINλ;

Qcal max = maximum quantized calibrated pixel value corresponding to LMAXλ;

LMINλ = spectral radiance that is scaled to Qcal min in W/(m2

· sr · μm);

LMAXλ = spectral radiance that is scaled to Qcal max in W/(m2

· sr · μm).

All the variables needed by Eqn. 1 are available from the metadata file of the

Landsat images, and are presented in Table 4 (for Landsat MSS) and in Table 5 (for

Landsat ETM+).

55

Table 4. Values used for the calibration of the Landsat MSS image to radiance.

MSS Band No. LMAXλ LMINλ Qcal max Qcal min

4 26.300 0.800 255.0 1.0

5 17.600 0.600 255.0 1.0

6 15.200 0.600 255.0 1.0

7 13.00 0.400 255.0 1.0

Table 5. Values used for the calibration of the Landsat ETM+ image to radiance.

ETM+ Band

No. LMAXλ LMINλ Qcal max Qcal min

1 191.600 -6.200 255.0 1.0

2 196.500 -6.400 255.0 1.0

3 152.900 -5.000 255.0 1.0

4 241.100 -5.100 255.0 1.0

5 31.060 -1.000 255.0 1.0

6 17.040 0.000 255.0 1.0

7 10.800 -0.350 255.0 1.0

Second, a fast atmospheric correction by means of the dark-object subtraction

method using band minimum [85] was applied to the at-sensor radiance image. A further

nominal calibration using a standard atmospheric model was not done because the

necessary input data and software facilities for such model were not available during the

conduct of image calibrations. With the dark-object subtraction method, the atmospheric

absorption was disregarded and atmospheric scattering was assumed to be an additive

component that has the effect of adding a constant value to each pixel in a spectral band

of the images. The pixel in each band of the images with the minimum value was

considered as the “dark object”. The method further assumes that the dark object has

uniformly zero radiance for all bands, and that any non-zero measured radiance must be

due to atmospheric scattering into the object’s pixels [85]. This correction was applied

uniformly to each band of the Landsat MSS and ETM+ images, thus assuming a constant

56

atmosphere across the images. The results were surface radiance images for the years

1976 and 2001, respectively.

After conversion to surface radiance, the 1976 and 2001 radiance images (except

for Band 6 of the 2001 radiance image) was further calibrated to surface reflectance using

the formula [86],[87]:

2

p

s

L d

ESUN cos

(2)

where ρP is the surface reflectance, Lλ is the surface radiance, d is the earth-sun distance in

astronomical units, ESUNλ is the mean solar exoatmospheric irradiances, and θs is the

solar zenith angle in degrees. ESUNλ values are listed in Table 6 (for Landsat MSS) and

Table 7 (for Landsat ETM+).

The earth-sun distance was approximated as [88]:

2 ( 93.5)

1 0.0167sin365

Dd

(3)

where D is the Julian day number of the day of acquisition. Other needed values used for

the computation of Eqn. 2 and 3 are shown in Table 8.

Table 6. Landsat MSS mean solar exoatmospheric spectral irradiances [87].

Band ESUNλ (Units: W/m2∙µm)

4 185.600

5 155.900

6 126.900

7 90.600

57

Table 7. Landsat ETM+ mean solar exoatmospheric spectral irradiances [86].

Band ESUNλ (Units: W/m2∙µm)

1 1969.000

2 1840.000

3 1551.000

4 1044.000

5 225.700

7 82.070

Table 8. Values used for the computation of the surface reflectance.

Variable Landsat MSS Landsat ETM+

Sun Elevation Angle 54.58 61.07

Solar Zenith Angle, θs 34.42 28.93

D 107 142

d

1.000067 1.00024

In the case of the Landsat ETM+ image, the band 6 radiance image was converted

to surface temperature under an assumption of unity emissivity using the formula [86]:

2

1ln 1

KT

K

L

(4)

where T is the surface temperature, Lλ is the band 6 radiance image, and K1 and K2 are

Landsat ETM+ pre-calibration coefficients where K1 = 666.09 W/(m2

· sr · μm) and K2 =

1282.71 Kelvin. After conversion to surface temperature, a linear normalization was done

to re-scale the temperature values from 0-1 so that it will be compatible with the 6

reflectance bands. This normalization is necessary prior to image classification.

From the reflectance images, Normalized Difference Vegetation Index (NDVI)

and synthetic reflectance bands were also created to supplement the limited number of

bands of the Landsat 2 MSS. This was found necessary in order to increase the number of

R-G-B band combinations that could be made such that the image could be interpreted

properly during the course of image classification. Using the Landsat 2 MSS reflectance

58

bands as inputs, three additional bands were created that when combined in an R-G-B

mode, the band combination simulates a “true color” image. The following equations

were implemented in ENVI 4.4 in order to create the additional bands:

RED = MSS Band 5

2 1GREEN = (MSS Band 4) MSS Band 6

3 3

2 1BLUE = (MSS Band 4) MSS Band 6

3 3

Simulated

Simulated

Simulated

(5) (a to c)

The resulting RGB image using the simulated Red, Green and Blue bands were

subjected to photographic stretching to produce another RGB image that correspond well

to the response of the human eye [83], an image that is “more realistic” and very useful

for identifying various land-cover classes present in it.

In preparation for the succeeding image analysis procedures, the radiometrically

calibrated and atmospherically corrected Landsat images (as well as other by-products)

were sub-setted to the portions bounded by the study area.

4.2.4 Cloud and shadow masking

The presence of clouds and shadows in the images especially in highly elevated

areas and mountain ranges was a great obstacle in the extraction of accurate land-cover

information. To minimize the error and confusion that cloud cover and shadows may

introduce to the extraction of land-cover information during the image classification

process, a simple cloud and shadow detection and masking technique was developed and

used to mask them in the images. The technique (Figure 14) is generally composed of

manual segmentation of cloud and shadow contaminated regions of the image, and

application of Maximum Likelihood supervised classification to label pixels

59

contaminated and not contaminated with clouds and shadows. The technique was applied

individually to the 1976 and 2001 subset Landsat images. In the manual segmentation of

cloud and shadow contaminated regions, portions of the images with clouds and shadows

were delineated through on-screen digitizing in ENVI 4.4. The delineated regions may

contain areas not contaminated by clouds and shadows. The purpose of this step is to

limit the classification to regions were cloud and shadow contaminations area present.

This is more appropriate than doing a cloud and shadow detection by subjecting the

whole image to a supervised or unsupervised classification, which by experience, is more

prone to misclassification especially of urban areas which are usually mislabeled as

“clouds”. In Step 2, the segmented regions were subjected to Maximum Likelihood

classification. Groups of pixels representing clouds, shadows and others (non-cloud and

no-shadow) were collected from the segmented regions and used to train the classifier.

Then, the classifier was run to label each pixel in the segmented regions, thereby

separating clouds and shadows pixels from others. For both the two Landsat images, all

bands were used as input for classification. Pixels labeled as clouds and shadows were

then merged to create a cloud-shadow mask that was applied to their corresponding

Landsat image.

4.2.5 Image classification and accuracy assessment

The cloud free, radiometrically calibrated and atmospherically corrected Landsat

images (as well as other by-products such as NDVI and synthetic bands) which were sub-

setted to the portions bounded by the study area were subjected to supervised

classification to derive the 1976 and 2001 land-cover maps.

60

Figure 14. Flowchart of the simple cloud and shadow detection and masking technique

developed and applied in this study.

Eight (8) land-cover classes namely, Forest, Rangeland, Built-up, Palm Trees,

Cropland, Bare Soil, Exposed Rocks and Water, were identified from the images through

visual interpretation using existing land-cover maps of the DENR, topographic maps and

Clouds

Shadow

Non-cloud and non-shadow

a. Input Image b. Segmentation

d. Segmented Image c. Maximum Likelihood

Classification

e. Classified Image f. Cloud and Shadow-free

Image

61

Google Earth images as references. Ground truth dataset collected from fieldwork

conducted in October-December 2006 and April-May 2007 were also used. Definitions of

these land-cover types are presented in Table 9. Table 10 and Table 11 show various

image keys used in the visual interpretations.

Table 9. Definitions of land-cover types used in this study.

Land-cover Type

Bare Soil Areas with exposed soil and in which less than one half of an area

unit has vegetation or other cover.

Built-up Areas

Comprised of areas of intensive use with much of the land covered

by structures. Includes settlement areas, buildings, farmsteads, and

surrounding lots.

Cropland Comprised of areas planted with crops (e.g. rice)

Exposed Rocks

Exposed rocks, sands, stones, cobbles, and boulders along rivers,

streams, and shorelines that are not covered by water during the

time of image acquisition.

Forest

Parcels of lands having a tree-crown areal density (crown closure

percentage) of 10 percent or more and are stocked with trees

capable of producing timber or other wood products. Includes

deciduous and evergreen forestlands.

Palm Trees Areas covered with palm oil, nipa and coconut plantation.

Rangeland

Areas where the potential natural vegetation is predominantly

grasses, grasslike plants, or shrubs and less permanently used for

that purpose.

Water Area covered with water. Includes sea water, rivers, and streams.

Representative samples of each class (training set) were collected from the images

for supervised image classification; another independent set of samples (validation set)

were likewise collected for accuracy assessment (Table 12). A minimum number of 30

pixels were chosen randomly for each class, following the guidelines of Van Genderen et

al. [89] to obtain a reliable estimate of classification accuracy of at least 90%. The

classification algorithms included traditional classifiers such as Minimum Distance,

Mahalanobis Distance and Maximum Likelihood and the recently developed SVM

classifier. SVM was implemented as a non-linear classifier using the Radial Basis

Functions (RBF) kernel available in the ENVI 4.4 image analysis software.

62

Table 10. Image keys used in visual interpretations of the 1976 Landsat MSS image.

Land Cover

Type

Band Combinations (R-G-B)

SimR-

SimG-

SimB

SimR-NDVI-

Band7

NDVI-Band

7-Band 6

BDVI-Band

7-Band 6

Band 6-

Band 5-

Band 4

NDVI-

SimG-SimB

NDVI-

SimR-

Band 7

Band 5-

Band 4-

SimB

Bare Soil

Built-up Areas

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

63

Table 11. Image keys used in visual interpretations of the 2001 Landsat ETM+ image.

Land Cover

Type

Band Combinations (R-G-B)

3-2-1 4-2-1 4-5-1 5-3-1 5-4-7 7-4-2 7-5-3 7-5-4

Bare Soil

Built-up Areas

Cropland

Exposed

Rocks

Forest

Palm Trees

Rangeland

Water

64

Table 12. Number of pixels collected for image classifications and accuracy assessments.

Land-cover Class

1976 Land-cover Classification 2001 Land-cover

Classification

Training Accuracy

Assessment Training

Accuracy

Assessment

Bare Soil 592 122 1028 298

Built-up 553 50 2356 640

Cropland 1465 354 3928 1287

Exposed Rocks 265 36 714 198

Forest 2059 602 2680 1148

Palm Trees 539 160 929 422

Rangeland 1073 565 928 374

Water 4874 387 7670 2214

Total 11420 2276 20233 6581

For both the 1976 and 2001 image dataset, each classifier was implemented using

various combinations of input bands (Table 13). The use of 4 classifiers and various

combinations of image bands and by-products was done to generate several classified

images and selecting from these outputs the best classified image. A 90-m spatial

resolution digital elevation model (DEM) from the Shuttle Radar Topography Mission

(SRTM) was also included as additional band during image classification as it has been

found that DEMs could significantly increase the classification accuracy [90-93]. This

DEM was first re-sampled (using bilinear interpolation) to 57-m to be compatible with

the Landsat MSS dataset, and to 30-m to be compatible with the Landsat ETM+ datasets.

Then the elevation values were normalized from 0 to 1 to be compatible with the data

range of the multispectral bands and by-products.

65

Table 13. Various combinations of input bands used in image classification

1976 image

classification

4 Reflectance Bands (Bands 4, 5, 6 and 7)

4 Reflectance Bands with NDVI

4 Reflectance Bands with DEM

4 Reflectance Bands with NDVI and DEM

4 Reflectance Bands with Simulated Red and Green Bands


and NDVI


and DEM

4 Reflectance Bands with Simulated Red and Green Bands,

NDVI and DEM

2001 image

classification

Reflectance Bands, Temperature (normalized, 0 -1)

Reflectance Bands, Temperature (normalized, 0 -1) and

DEM (normalized 0 – 1)

The computation of accuracy is done by comparing the validation set pixels with

the classification results. From these checks the percentage of pixels from each class in

the image labeled correctly by the classifier is estimated, along with the proportions of

pixels from each class erroneously labeled into every other class. The results are

presented in a confusion or error matrix that lists the number of validation set pixels, in

each case, correctly and incorrectly labeled by the classifier. Two measures of accuracy

were employed to test the classified images, namely the Overall Classification Accuracy

(in percent), Producer’s Accuracy and User’s Accuracy. The overall classification

accuracy is the percentage of correct classifications of the ground truth pixels. It is

computed by dividing the sum of the diagonals of the error matrix (which pertains to the

number of correctly classified pixels for each class) with the total number of validation

set pixels. The Producer’s Accuracy, which is computed for each land-cover class using

the column values of the error matrix (no. of correctly classified x 100% divided by

column total), is the probability that the classifier has labeled the image pixel exactly as

66

its actual ground truth land-cover type [94]. The User’s Accuracy, on the other hand, is

the probability that the pixels belonging to actual land-cover class in the classified image

have been labeled correctly [94] (i.e., do all the pixels labeled as “forest” in the land-

cover map are actually “forest” on the ground?). This measure of accuracy is computed

using the row values of the error matrix ((no. of correctly classified over row total x

100%).

For 1976 and 2001 image classifications, the selection of the “best” classified

image for each year which will be the source of the land-cover maps is based on the

criteria that the classified image must have the highest overall classification accuracy (at

least 90%) among all the classifications and that the Producer’s and User’s Accuracy of

land-cover types relevant to this study which include forest, built-up, rangeland, palm

trees, cropland and bare soil are at least 85% each [95] and must also be highest among

all classification results.

4.2.6 Post-classification change detection

The two land-cover maps derived were then subjected to post-classification

comparison change detection analysis [14] to examine the location, extent and

distribution of land-cover change in the study area. The 2001 land-cover map was first re-

sampled to 57-m resolution using nearest neighbor method prior to change detection.

Because of cloud and shadows present in the images used (“No Data” in the LC maps),

only portions of the LC maps that both have data in 1976 and 2001 were subjected to

change detection analysis. Land-cover change statistics were also computed. Overall

accuracy of the land-cover change detection was computed by multiplying the 1976 LC

67

Map Overall Classification Accuracy and the 2001 LC Map Overall Classification

Accuracy times 100 [42].

4.3 GIS spatial change analysis

The detected changes in forest cover, in the form of a change-no change in forest

cover map, were visualized and analyzed in a GIS. The GIS analysis involved

characterization and visualization of the detected changes vis-à-vis sets of georeferenced

bio-physical and socio-economic factors hypothesized to be associated with deforestation

[12] such as presence of forest licenses, population density change, road infrastructures,

increase in built-up areas, elevation, slope, soil quality and proximity to water resources.

These factors are described in Table 14.

Table 14. Definitions of georeferenced bio-physical and socio-economic factors. Factor Description

Bio-physical

ELEV Elevation of the Agusan provinces

SLOPE Slope of the Agusan provinces

DISTRIV Distance to major river networks in the Agusan

provinces

SOILQUAL Soil quality of Agusan provinces, coded as 0 for non-

suitable for agriculture and 1 for suitable.

Socio-

economical

DISTNEWRD Distance to the new roads since 1976 to 2001

DISTNEWBUILT Distance to the new built-up since 1976 to 2001

POPDENCHANGE Change in population density of the Agusan

provinces from 1976-2001.

DIST_TLA-IFMA Distance to the combined land parcels subjected to

Timber License Agreements and Integrated

Forest Management Agreement DIST_CBFMA-CBRM Distance to the combined land parcels subjected to

Community Based Forest Management

Agreement and Community-Based Resource

Management

68

Point shapefiles of “changed” and “no changed” in forest cover (hereafter referred

to as “FCOVER”) were made from the forest-cover change map. Overlay analysis was

then performed to populate the attribute of the FCOVER pixels with their corresponding

socio-economic and bio-physical factor values. The resulting tabular data was exported to

a spreadsheet file and further analyzed. For each factor, the mean values of all ‘change’

and ‘no change’ samples were computed and were displayed graphically for both

qualitative and quantitative analyses. The analyses of the mean factor values was made in

order to gain insights on the possible similarities or differences in trends between the two

provinces’ forest cover in relation to the identified bio-physical factors and socio-

economical.

The geo-referenced socio-economic and bio-physical factors were prepared as

follows. The presence of logging and timber industries were represented as maps of forest

license agreements issued by the Philippine government between 1976 and 2001. This

spatial data, in Arcview polygon shapefile, was obtained from the DENR-Caraga

Regional Office. There were two kinds of license agreements: (1) those issued to private

corporations that include TLAs and IFMAs; and (2) those issued to non-government

organizations that CBFMAs and CBRMAs. Proximity grids were then computed from

these two factors to determine the Euclidean distance (in meters) of pixels within the

study area from the polygons of these license agreements. A value of 0 indicates that the

pixel is within a particular type of license agreement. These two factors were aptly

labeled as “DIST_TLA-IFMA” and “DIST_CBFMA-CRBMA”, respectively. These two

separate proximity grids were created so as to determine how the pattern of deforestation

and forest retention would vary as the type of licensee differs.

69

Population density change, instead of population count change, was used in this

study as an indicator of forest cover change based on the acceptable assumption that it is

the change and increase in number of persons per unit area that the retention or the

change in forest cover could be expected. Municipal-level population data for the years

1976 (estimated from the 1975 census) and 2001 (estimated from the 2000 census) were

obtained from the National Statistics Offices in Agusan del Norte and Agusan del Sur.

Population density change was computed by subtracting the 1976 population to 2001

population of each municipality, and dividing this by the GIS-computed area of the

particular municipality. This resulting factor map was labeled as “POPDENCHANGE”.

Road infrastructure and increase in built-up areas as determinants of forest cover

change were examined in this study by taking only those new roads and new built-up

areas since 1976. This “new” road data was obtained by overlaying road network

(digitized from the 1954 NAMRIA topographic map and from the 1976 Landsat MSS

image) to the 2001 Landsat ETM+ image. “New” roads, which are those not intersecting

the 1976 roads were then digitized. A proximity grid was then computed and labeled as

“DISTNEWRD”. The rationale behind the use of “new roads” as indicator of forest cover

change is based on the hypothesis that it is the construction of new roads (that may have

resulted from economic development or due to the proliferation of logging industries)

that forest cover in the two provinces became more accessible to change. Similarly, the

relative contribution of the increase in built-up areas since 1976 (e.g., difference in 2001

and 1976 built-up areas) was taken account in this study by calculating a distance to new

built areas grid (DISTNEWBUILT).

70

The four biophysical factors namely, elevation, slope, soil quality and proximity

to water resources were also prepared in the same manner as those of the socio-economic

factors. Elevation (ELEV) and percentage slope (SLOPE) grids were computed using the

90-m SRTM DEM. This DEM was first calibrated with spot heights derived from

NAMRIA topographic maps to linearly transform the elevation values to mean sea level.

Soil quality (SOILQUAL) was obtained from the digital 1:250,000 soil taxonomy map

published by the Bureau of Soils and Water Management. Soil quality was coded as “1”

if the soil has low fertility, 2 if moderately fertile and 3 if highly fertile. The proximity to

water resources was considered in this study by calculating a distance to river grid

(DISTRIV). The river network data was digitized from NAMRIA topographic maps and

from the Landsat images. ArcView GIS 3.2 software was used in the analysis.

4.4 Statistical analysis of land-cover change

Logistic regression analysis was employed to ascertain the degree of association

of bio-physical and socio-economical variables with FCOVER. The multivariate logistic

regression equation used in the analysis is of the form [96]:

1 1 2 2

1 1 2 2( )

1

i i

i i

x x x

x x x

ex

e

(6)

where π(x) is the probability that the dependent variable y equals 1, is the equation

constant, and βi is the coefficient of predictor variable xi (i.e. the socio-economic and bio-

physical factors). Each of the regression coefficients describes the size of the contribution

of that factor. A positive regression coefficient implies that as the value of the factor

increases, the probability of deforestation increases. A negative regression coefficient

71

means that as factor values increases, the probability of deforestation decreases. A near-

zero regression coefficient means that that factor has little influence on the probability of

deforestation.

Logistic regression analysis was employed because of its advantage of analyzing

variables that maybe either continuous or discrete or any combination of both types and

they do not necessarily have normal distributions [97]. Three separate logistic regression

analysis were conducted for each province. These include testing for the (i) bio-physical

factors only, (ii) socio-economic factors only, and (iii) combined bio-physical and socio-

economic factors. It should be noted that logistic regression was mainly used as a way to

explain forest cover change in ADN and ADS vis-à-vis bio-physical and socio-economic

factors using the regression coefficients as indicators, and not as a predictor of FCOVER

change.

Because of the large number of FCOVER pixels in each province, representative

samples (about 5% each of ‘changed’ and ‘no-change’ collected in a stratified random

manner) were subjected to logistic regression analysis. Table 15 shows the 5% samples

subjected to logistic regression analysis.

Table 15. The 5% samples used in logistic regression analysis.

Agusan del Norte Agusan del Sur

No Change (0) 6,581 23,051

Change (1) 5,719 8,196

Total 12,300 31,247

For each province, ‘no change’ pixels were coded as “0” and ‘change’ pixels as

“1”. The 5% sample size was chosen as it could be representative of the existing forest-

cover change in the study area as long as the number of samples is large [12] (e.g.,

72

thousands). To allow less-biased comparison of the forest cover change characteristics of

the two provinces, it was necessary to normalize the factor values (transform the values

so that the minimum is 0 and the maximum is 1) to the global minimum and maximum of

each factor; i.e. maximum and minimum of combined ADN and ADS factor values.

Logistic regression analysis requires absence of collinearity (or multicollinearity)

among the independent variables. Collinearity (or multicollinearity) is the undesirable

situation when one independent variable is a linear function of other independent

variables [1]. Tests for collinearity of variables using Tolerance Statistics and Variance

Index Factor (VIF) were made in order to determine the variables that are correlated with

each other before conducting logistic regression analysis. All linear combinations of

biophysical and socioeconomic variables were tried through linear regression to

determine collinearity of the variables (e.g., is slope a linear combination of all the other

variables?). Computed values for each linear combination of the variables indicate

absence of multi-collinearity if the Tolerance Statistic >0.5 and close to 1; and VIF <2.

Aside from the Tolerance Statistic and Variance Index Factor, computation of a Pearson

Pairwise Correlation Matrix (Pearson’s R) was also done to easily determine which

variables are correlated. If the Pearson’s R > 0.5, collinearity exists and one of the

variables can be dropped off from the logistic regression model (Millington et al., 2007).

The correlation matrix was computed using merged ADN and ADS FCOVER datasets.

All statistical analyses were done in SPSS Version 16.

73

Chapter 5

Results and Discussion

5.1 Land-cover maps

5.1.1 The 1976 land-cover map

Figure 15 shows the results of the classification done on the April 17, 1976

Landsat MSS image of the study area using the Support Vector Machine (SVM)

algorithm implemented with the Radial Basis Function (RBF) as the mathematical

surface for land-cover class separation. This land-cover map, already masked out with

clouds and cloud shadows, has an overall classification accuracy of 94.99%, the highest

among 32 classifications that utilized 6 various combinations of inputs bands subjected to

four classifications algorithms (Table 16). The input bands used in the classification to

derive this final land-cover map were the Landsat MSS surface reflectance bands (Bands

4, 5, 6 and 7), NDVI and DEM (normalized from 0 to 1). The total number of ground

truth pixels used for accuracy assessment is 2,276.

The results of SVM-classification of 4 reflectance bands with NDVI and DEM as

the source of the 1976 land-cover map is based on the criteria that the classified image

must have the highest overall classification accuracy in all the classifications and that the

Producer’s and User’s Accuracy of land-cover types relevant to this study which include

74

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

April 17, 1976

Land-Cover Map

®

25

Kilometers

Agusan del

Norte

Agusan del

Sur

Land-cover Types:

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

No Data

forest, built-up, rangeland, palm trees, cropland and bare soil are at least 85% each and

must also be highest in all classification results.


from the classification of the April 17, 1976 Landsat MSS image using SVM. All white

areas within the provincial boundaries classified as “No Data” are clouds and shadow

pixels in the image.

75

Table 16. Matrix of percent overall classification accuracies of 32 classified images (from

various band combinations of the1976 Landsat MSS image and image by-products

(Ground truth pixels = 2, 276)

Input Band

Combinations

Classification Algorithm

Minimum

Distance

Mahalanobis

Distance

Maximum

Likelihood

Support

Vector

Machine

4 Reflectance Bands

(Bands 4, 5, 6 and 7) 68.96 66.40 85.43 85.63

4 Reflectance Bands

with NDVI 74.04 70.07 83.49 85.41

4 Reflectance Bands

with DEM 65.17 65.47 89.80 94.07

4 Reflectance Bands

with NDVI and DEM 73.51 68.43 89.45 94.99

4 Reflectance Bands

with Simulated Red and

Green Bands

69.14 73.42 82.91 85.90

4 Reflectance Bands


Green Bands and NDVI

74.08 78.68 83.80 85.59

4 Reflectance Bands


Green Bands and DEM

65.34 70.86 90.55 93.76

4 Reflectance Bands


Green Bands, NDVI

and DEM

73.60 75.23 90.60 93.63

Table 17 shows the confusion (or error) matrix of the SVM-classified Landsat

MSS reflectance bands with NDVI and DEM. The error matrices of the classification

results with the 2nd

and 3rd

highest overall classification accuracies: SVM-classified 4

reflectance bands with DEM and SVM-classified 4 reflectance bands with simulated Red

and Green bands and DEM, respectively, are shown in Table 18 and Table 19. These

matrices were used in computing the overall classification accuracy, Producer’s Accuracy

76

and User’s Accuracy, that were then used in evaluating the criteria for the selection of

the 1976 land-cover map of the study area.

Table 17. Error matrix of the SVM-classified Landsat MSS reflectance bands with NDVI

and DEM (the source of the 1976 land-cover map of the study area).

Cla

ssif

ied P

ixel

s

Land-cover

Type

Validation Pixels

Forest Range-

land

Built-

up

Palm

Trees Cropland

Bare

Soil

Exposed

Rocks Water Total

Forest 590 4 0 6 3 0 0 0 603

Rangeland 0 557 0 0 0 0 0 0 557

Built-up 0 0 45 0 7 0 9 2 63

Palm Trees 4 4 0 154 1 0 0 0 163

Cropland 2 0 3 0 311 1 0 9 326

Bare Soil 0 0 0 0 1 120 0 2 123

Exposed

Rocks 0 0 1 0 0 0 27 3 31

Water 6 0 1 0 31 1 0 371 410

Total 602 565 50 160 354 122 36 387 2276

In Figure 16, bar charts of Producer’s and User’s Accuracy of each land-cover

type in the three classified images are shown. It is very clear from these charts that the

SVM-classified reflectance bands with NDVI and DEM gained the highest values of the

three measures of accuracies (see Table 20 for values) and modestly satisfied the final

land-cover map selection criteria with >85% Producer’s and User’s accuracies for forest,

rangeland, palm trees, cropland and bare soil. In the case of pixels classified as “built-

up”, the producer’s accuracy in the classified image is 90% and passed the selection

criteria. However, the User’s Accuracy is quite low at 71.43%. Nevertheless, this value is

still acceptable as it is still higher compared to the User’s Accuracy of the two other

classifications results (68.85% and 68.33%, respectively).

77

Table 18. Error matrix of the SVM-classified Landsat MSS reflectance bands with DEM. C

lass

ifie

d P

ixel

s

Land-cover

Type

Validation Pixels

Forest Range-

land

Built-

up

Palm

Trees Cropland

Bare

Soil

Exposed

Rocks Water Total

Forest 582 4 0 7 1 1 0 0 595

Rangeland 0 557 0 0 0 0 0 0 557

Built-up 0 0 42 0 7 0 8 4 61

Palm Trees 4 4 0 153 2 0 0 0 163

Cropland 5 0 3 0 307 3 0 9 327

Bare Soil 0 0 0 0 1 115 0 2 118

Exposed

Rocks 0 0 2 0 0 0 28 4 34

Water 11 0 3 0 36 3 0 368 421

Total 602 565 50 160 354 122 36 387 2276

Table 19. Error matrix of the SVM-classified Landsat MSS reflectance bands with

simulated Red and Green bands and DEM.

Cla

ssif

ied P

ixel

s

Land-cover

Type

Validation Pixels

Forest Range-

land

Built-

up

Palm

Trees Cropland

Bare

Soil

Exposed

Rocks Water Total

Forest 589 5 0 11 6 1 0 0 612

Rangeland 0 556 0 0 0 0 0 0 556

Built-up 0 0 41 0 6 0 9 4 60

Palm Trees 4 4 0 149 1 0 0 0 158

Cropland 1 0 4 0 308 3 0 10 326

Bare Soil 0 0 0 0 6 117 0 10 133

Exposed

Rocks 0 0 1 0 0 0 26 3 30

Water 8 0 4 0 27 1 1 360 401

Total 602 565 50 160 354 122 36 387 2276

78


types in three SVM-classified land-cover maps for 1976.

50

55

60

65

70

75

80

85

90

95

100

Fores

t

Ran

gela

nd

Bui

lt-up

Palm

Tre

es

Cro

plan

d

Bar

e Soi

lExp

osed

Roc

ks

Wat

er

Land-cover Type

Pro

duce

r's

Acc

ura

cy (

%)

Reflectance Bands with NDVI

and DEM

Reflectance Bands with DEM

Reflectance Bands with

Simulated Red and Green Bands,

and DEM

50

55

60

65

70

75

80

85

90

95

100

Fores

t

Ran

gela

nd

Bui

lt-up

Palm

Tre

es

Cro

plan

d

Bar

e Soi

lExp

osed

Roc

ks

Wat

er

Land-cover Type

Use

r's

Acc

ura

cy (

%) Reflectance Bands with NDVI

and DEM

Reflectance Bands with DEM


Simulated Red and Green Bands,

and DEM

`

(a.)

(b.)

79

Table 20. Summary of Producer’s and User’s Accuracies of 1976 land-cover types in

three SVM-classified land-cover maps.

Land-

cover

Type

Classified Image

4 Reflectance Bands

with NDVI and DEM

4 Reflectance Bands

with DEM

4 Reflectance Bands

with Simulated Red

and Green Bands, and

DEM

Producer’s

Accuracy

User’s

Accuracy

Producer’s

Accuracy

User’s

Accuracy

Producer’s

Accuracy

User’s

Accuracy

Forest 98.01 97.84 96.68 97.82 97.84 96.24

Rangeland 98.58 100.00 98.58 100.00 98.41 100.00

Built-up 90.00 71.43 84.00 68.85 82.00 68.33

Palm

Trees 96.25 94.48 95.63 93.87 93.13 94.30

Cropland 87.85 95.40 86.72 93.88 87.01 94.48

Bare Soil 98.36 97.56 94.26 97.46 95.90 87.97

Exposed

Rocks 75.00 87.10 77.78 82.35 72.22 86.67

Water 95.87 90.49 95.09 87.41 93.02 89.78

5.1.2 Accuracy of the 1976 land-cover map

A short discussion of the Producer’s and User’s Accuracy of the 1976 land-cover

map may be necessary to better understand the accuracy of the land-cover type labeling

that was done by the SVM classifier. In the 1976 land-cover map, it could be explained

that for land-cover type “forest”, only 590 of the 602 ground truth pixels were correctly

labeled by the classifier (Table 17). Hence, the producer’s accuracy for this type is

590/602 = 0.9801 or 98.01% (as listed in Table 20). Considering the representativeness

of all ground truth pixels used in the accuracy assessment, it could be stated that of all the

(actual) forest in the study area, only 98.01% were labeled in the 1976 land-cover map.

The remaining 1.99% of forest were not labeled by the classifier as “forest” but labeled

them as otherwise, thereby omitting them in the forest class of the land-cover map (hence

the term “errors of omission”). Looking back to the error matrix, these 1.99% pixels were

80

actually labeled by the classifier as “palm trees” and “cropland”. The same logic applies

for the other land-cover types. It could be stated that there is above 90% probability that

all the forest, rangeland, built-up, palm trees, bare soil and water present in the study area

on April 17, 1976 were accurately labeled in the land-cover map. Only a few percentage

(<10%) of these classes were omitted and labeled incorrectly. In the case of “cropland”

and “exposed rock” classes, there is below 90% probability that all these land-cover types

were correctly labeled. The errors of omissions of these classes are more than 10%.

On the land-cover map’s User’s Accuracy, it could be explained that the user’s

accuracy for “forest” class is 590/603 = 0.9784 (or 97.84%). This means that only

97.84% of the pixels labeled as “forest” in the land-cover map are correct. The remaining

2.16% were supposed to belong to another land-cover class (in this case, “rangeland”,

“palm trees” and “cropland”) but erroneously labeled as “forest”. These are known as the

“errors of commission”. In the final 1976 land-cover map, it is notable that, in the

exception of the “built-up” and “exposed rocks” classes, all the land-cover types have

above 90% probability that they were actually the same land-cover types when checked

on the ground on April 17, 1976.

5.1.3 The 2001 land-cover map and accuracy

Figure 17 shows the results of the classification done on the May 22, 2001

Landsat ETM+ image of the study area using the SVM algorithm implemented with RBF

as the mathematical surface for land-cover class separation. This land-cover map, already

masked out with clouds and cloud shadows, has an overall classification accuracy of

98.25%, the highest among 8 classifications that utilized 2 combinations of input bands

subjected to four classification algorithms (Table 21). The input bands used in the

81

classification to derive this final land-cover map are the Landsat ETM+ surface

reflectance bands (Bands 1,2,3,4,5 and 7), temperature band 6 (normalized from 0 to 1),

and DEM (normalized from 0 to 1). The total number of ground truth pixels used for

accuracy assessment is 6,581.


from the classification of the May 22, 2001 Landsat ETM+ image using SVM. All white

areas within the provincial boundaries classified as “No Data” are clouds and shadow

pixels in the image.

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

May 22, 2001

Land-Cover Map

®

25

Kilometers

Agusan del

Norte

Agusan del

Sur

Land-cover Types:

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

No Data

82

Table 21. Matrix of percent overall classification accuracies of 8 classified images from

various band combinations of the 2001 Landsat ETM+ image and DEM. (Ground truth

pixels= 6,581).

Input Band

Combinations

Classification Algorithm

Minimum

Distance

Mahalanobis

Distance

Maximum

Likelihood

Support

Vector

Machine

Reflectance Bands,

Temperature

(normalized, 0 -1)

82.48 84.93 94.99 95.87

Reflectance Bands,

Temperature

(normalized, 0 -1) and

DEM (normalized 0 – 1)

83.03 74.78 95.76 98.25

Table 22 shows the confusion (or error) matrix of the SVM-classified Landsat

ETM+ reflectance bands with normalized temperature and DEM. The error matrices of

the classification results with the 2nd and 3rd highest overall classification accuracies:

SVM-classified 6 reflectance bands with temperature band (normalized from 0 to 1) and

SVM-classified 6 reflectance bands with temperature band (normalized form 0 to 1) and

DEM (also normalized from 0 to 1), respectively, are shown in Table 23 and Table 24 .

These matrices were used in computing the overall classification accuracy, Producer’s

Accuracy and User’s Accuracy that were then used in evaluating the criteria for the

selection of the 2001 land-cover map of the study area.

In Figure 18, bar charts of Producer’s and User’s Accuracy of each land-cover

type in the three classified images are shown. The SVM-classified reflectance bands with

normalized temperature and DEM gained the highest values of the three measures of

accuracies (see Table 22 for values) and satisfied the final land-cover map selection

criteria with >85% Producer’s and User’s accuracies for forest, rangeland, palm trees,

cropland, and bare soil.

83


normalized temperature and DEM (the source of the 2001 land-cover map of the study

area). C

lass

ifie

d P

ixel

s

Land-class

Types

Ground Truth Pixels

Forest

Range-

land

Built-

up

Palm

Trees Cropland

Bare

Soil

Exposed

Rocks Water Total

Forest 1131 0 0 2 0 0 0 0 1133

Rangeland 3 374 0 1 1 2 0 0 381

Built-up 0 0 633 1 2 3 28 3 670

Palm Trees 14 0 0 414 0 2 0 0 430

Cropland 0 0 4 0 1276 3 0 8 1291

Bare Soil 0 0 0 0 0 285 0 0 285

Exposed

Rocks

0 0 3 1 1 1 168 18 192

Water 0 0 0 3 7 2 2 2185 2199

Total 1148 374 640 422 1287 298 198 2214 6581


temperature band (normalized from 0 to 1).

Cla

ssif

ied P

ixel

s

Land-class

Types

Ground Truth Pixels

Forest

Range-

land

Built-

up

Palm

Trees Cropland

Bare

Soil

Exposed

Rocks Water Total

Forest 1113 0 0 24 0 0 0 0 1137

Rangeland 3 373 0 10 2 1 0 0 389

Built-up 0 0 625 2 2 3 71 11 714

Palm Trees 32 0 0 382 0 3 0 0 417

Cropland 0 1 3 1 1243 22 0 8 1278

Bare Soil 0 0 0 0 4 266 0 0 270

Exposed

Rocks 0 0 11 0 1 1 122 10 145

Water 0 0 1 3 35 2 5 2185 2231

Total 1148 374 640 422 1287 298 198 2214 6581

84

Table 24. Error matrix of the Maximum likelihood-classified Landsat ETM+ reflectance

bands with temperature band (normalized form 0 to 1) and DEM (also normalized from 0

to 1) C

lass

ifie

d P

ixel

s Land-class

Types

Ground Truth Pixels

Forest

Range-

land

Built-

up

Palm

Trees Cropland

Bare

Soil

Exposed

Rocks Water Total

Forest 1125 0 0 2 0 0 0 0 1127

Rangeland 4 369 0 5 1 1 0 0 380

Built-up 0 5 626 9 110 7 24 2 783

Palm Trees 19 0 0 399 0 0 0 0 418

Cropland 0 0 0 2 1153 2 0 22 1179

Bare Soil 0 0 0 0 4 287 0 0 291

Exposed

Rocks 0 0 14 2 1 0 174 21 212

Water 0 0 0 3 18 1 0 2169 2191

Total 1148 374 640 422 1287 298 198 2214 6581


types in the three land-cover maps.

50

55

60

65

70

75

80

85

90

95

100

Fores

t

Range

land

Built-

upPal

m

Cropl

and

Bare

Soil

Expos

ed R

ocks

Wat

er

Land-cover Type

Use

r's A

ccur

acy

(%)

SVM: Reflectance Bands with

Temperature and DEM


Temperature

MaxLike: Reflectance Bands with

Temperature and DEM

(b.)

50

55

60

65

70

75

80

85

90

95

100

Fores

t

Range

land

Built-

upPal

m

Cropl

and

Bare

Soil

Expos

ed R

ocks

Wat

er

Land-cover Type

Prod

ucer

's A

ccur

acy

(%)


Temperature and DEM


Temperature

MaxLike: Reflectance Bands with

Temperature and DEM

(a.)

(b.)

85

Table 25. Summary of the Producer’s and User’s Accuracies of land-cover types in three

derived land-cover maps.

5.2 Land-cover change in the Agusan Provinces

The 57-m resolution land-cover maps of Agusan del Norte and Agusan del Sur for

1976 and 2001, showing areas with data common to both year (i.e., cloud covered areas

in 1976 and 2001 were excluded), are depicted in Figure 19 and Figure 20.

Land-

cover

Type

Classified Image

SVM-classified 6

Reflectance Bands

with Normalized

Temperature Band and

DEM

SVM-classified 6


Normalized Temperature

Band

Maximum Likelihood-

classified 6 Reflectance

Bands with Normalized

Temperature Band and

DEM

Producer's

Accuracy

User's

Accuracy

Producer's

Accuracy

User's

Accuracy

Producer's

Accuracy

User's

Accuracy

Forest 98.52 99.82 96.95 97.89 98.00 99.82

Rangeland 100.00 98.16 99.73 95.89 98.66 97.11

Built-up 98.91 94.48 97.66 87.54 97.81 79.95

Palm

Trees 98.10 96.28 90.52 91.61 94.55 95.45

Cropland 99.15 98.84 96.58 97.26 89.59 97.79

Bare Soil 95.64 100.00 89.26 98.52 96.31 98.63

Exposed

Rocks 84.85 87.50 61.62 84.14 87.88 82.08

Water 98.69 99.36 98.69 97.94 97.97 99.00

86

Figure 19. The 1976-2001 land-cover maps of Agusan del Norte province. Areas with data comprise 66.98% (or

2044.67sq.km.) of the total land area of Agusan del Norte.

125°45'0"E

125°45'0"E

125°30'0"E

125°30'0"E

125°15'0"E

125°15'0"E

9°1

5'0

"N

9°1

5'0

"N

9°0

'0"N

9°0

'0"N

8°4

5'0

"N

8°4

5'0

"N

2001 Land-Cover Map

AGUSAN DEL NORTE

®20

Kilometers

Land-cover Types:

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

No Data

Butuan City

125°45'0"E

125°45'0"E

125°30'0"E

125°30'0"E

125°15'0"E

125°15'0"E

9°1

5'0

"N

9°1

5'0

"N

9°0

'0"N

9°0

'0"N

8°4

5'0

"N

8°4

5'0

"N

1976 Land-Cover Map

AGUSAN DEL NORTE

®20

Kilometers

Land-cover Types:

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

No Data

Butuan City

87

Figure 20. The 1976-2001 land-cover maps of Agusan del Sur province. Areas with data comprise 51.10% (or 4,133.82 sq.

km.) of the total land area of Agusan del Sur.

126°0'0"E

126°0'0"E

125°30'0"E

125°30'0"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

1976 Land-Cover Map

AGUSAN DEL SUR

®

25

Kilometers

Land-cover Types:

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

No Data

126°0'0"E

126°0'0"E

125°30'0"E

125°30'0"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

2001 Land-Cover Map

AGUSAN DEL SUR

®

25

Kilometers

Land-cover Types:

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

No Data

88

0

100

200

300

400

500

600

700

800

900

Bar

e Soil

Bui

lt-up

Cro

plan

d

Expos

ed R

ocks

Forest

Palm

Tre

es

Ran

gela

nd

Wat

er

Land-cover Type

Are

a, in

sq

. k

m.

1976 Land-cover area


Figure 21. Land-cover change in Agusan del Norte province from 1976-2001 for

cloud free areas only. Upper and lower error bars represent errors of omission and

commission, respectively, of the land-cover classifications.

Changes in land-cover of Agusan del Norte from 1976-2001 is very evident in the

areas surrounding the Butuan City. This is where drastic increases in built-up, cropland

and water (e.g., expansion of fishpond) areas can be found. Yet, the most pronounced

change in land-cover is that of forest and rangeland. Quantitative assessments through

change detection using the land cover change map (93.33% accurate) show significant

decrease in forest cover by 32% (or about 255.30 sq. km.) while rangeland areas

increased by 92% (about 327.86 sq. km.) during the 25-year period. Forest to rangeland is

the major land-cover change in Agusan del Norte from 1976 to 2001 (Figure 22).

Although deforestation due to increase in rangeland is significantly evident, “re-

forestation” of rangeland areas from 1976 to 2001 was also present. It can be observed

that large tract of lands planted with palm trees in 1976 have been converted into

croplands in 2001. Perhaps, this is due to the fact that croplands and palm trees in the

89

study area are usually located near each other than any other land-cover type. Hence,

expansion of one type (in this case the cropland) will result to reduction in another type

(in this case the palm tree lands).

0

50

100

150

200

250

300

Forest

to R

angel

and

Palm

Tre

es to

Cro

pland

Range

land

to F

orest

Palm

Tre

es to

Ran

gela

nd

Bare

soil

to R

angel

and

Forest

to P

alm

Tre

es

Cropl

and to

Pal

m T

rees

Cropl

and to

Ran

gela

nd

Range

land

to P

alm

Tre

es

Bare

soil

to P

alm

Tre

es

Land-cover change type

Are

a o

f ch

ang

e, i

n s

q.

km

.

Figure 22. Top 10 land-cover change types in Agusan del Norte province from 1976-

2001 for cloud-free areas only. Upper and lower error bars represent errors of omission

and commission, respectively, of the land-cover classifications

In the case of Agusan del Sur, increase in cropland and decrease in forest cover is

the most significant land-cover change in terms of change in land area. Quantitatively,

these translate to 156% increase in cropland (or about 198.47 sq. km.) and about 6%

decrease in forest cover (or 113.42 sq. km.). In terms of specificity, the two most

prominent land-cover change types from 1976 to 2001 in this province is the conversion

of rangeland to forest and forest to palm trees. Considering errors in classifications, these

two land-cover change types are almost identical in magnitude. A third major type of

change is that of conversion of forest to rangeland. It is very apparent that the changes in

90

land-cover between the years 1976-2001 are somehow different for each province based

on the top 10 land-cover change types (). In Agusan del Norte, the major land-cover

change type is “forest to rangeland”. A decrease in forest area in this province was found

to be due to the conversion of 269 sq. km. of forest area in 1976 to rangeland areas in

2001. This converted tract of land is about 33% of intact forest cover of Agusan del Norte

in 1976. In the case of Agusan del Sur, an opposite type of major change was found

which is “rangeland to forest”. Here, about 300 sq. km. of rangeland has been converted

to forest lands. This area is about 46% of intact rangeland of Agusan del Sur in 1976.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Bar

e Soil

Bui

lt-up

Cro

plan

d

Expos

ed R

ocks

Forest

Palm

Tre

es

Ran

gela

nd

Wat

er

Land-cover Type

Are

a, in

sq

. k

m.



Figure 23. Land-cover change in Agusan del Sur province from 1976-2001 for cloud-free

areas only. Upper and lower error bars represent errors of omission and commission,

respectively, of the land-cover classifications.

91

0

50

100

150

200

250

300

350

Ran

gela

nd to

For

est

Fores

t to

Palm

Tre

es

Fores

t to

Ran

gela

nd

Palm

Tre

es to

Cro

plan

d

Palm

Tre

es to

Ran

geland

Ran

gela

nd to

Palm

Tre

es

Palm

Tre

es to

For

est

Cro

plan

d to

Palm

Tre

es

Bar

e so

il to

Palm

Tre

es

Bar

e so

il to

For

est

Land-cover change type

Are

a o

f ch

ang

e, in

sq

. km

.

Figure 24. Top 10 land-cover change types in Agusan del Sur province from 1976-2001

for cloud-free areas only. Upper and lower error bars represent errors of omission and

commission, respectively, of the land-cover classifications.

It can be deduced from the computed land-cover change statistics that forest cover

in Agusan del Norte have been reduced drastically by conversion to rangeland. This may

have been due to unsustainable logging activities, where, after the trees have been

harvested, the logged-over areas were left behind without replanting that made it suitable

for grasses to grow. In Agusan del Sur, a much slighter decrease in forest cover was

detected compared to that of Agusan del Norte.

The major types of land-cover change in Agusan del Norte is almost similar to

that of Agusan del Sur. It is only the magnitude of change (i.e., the area converted) that

differs. The most interesting, as discussed earlier, is the “forest-to-rangeland” and

“rangeland-to-forest” types of changes. In Agusan del Norte, “forest-to-rangeland” is the

number one type of change but this is number three in Agusan del Sur. The type “palm

trees to cropland” is next to “forest to rangeland” in Agusan del Sur. The same pattern

92

can be observed in Agusan del Sur. It is only that “palm trees to cropland” is number two

in Agusan del Norte but number 4 in Agusan del Sur. While the ranking maybe different,

the conversion of palm tree lands to cropland is the most pronounced type of agricultural

conversions. There were no other types of land-cover except for “palm trees” that have

been put into agricultural use. This type of change is purely an effect brought by the vast

existence of “palm tree” lands in both provinces due to soil suitability and climatic

conditions. As the demand for cropland products such as rice and corn intensifies,

farmers have to clear tracts of land occupied by palm trees for agricultural use. On the

other hand, while there was a reduction in palm tree lands for cropland purposes, majority

of the type of changes in both provinces are conversions to “palm trees”. This is highly

indicative of the proliferation of coconut and palm oil plantations in these provinces. This

indicates some sort of balance between usage of lands for “palm trees” and for crop

production.

5.3 Deforestation in the Agusan Provinces

It has been stated earlier that forest cover change in Agusan del Norte and Agusan

del Sur was found to be mainly factors of (i.) reduction of forest cover by conversion to

rangeland and (ii.) increase in forest cover by conversion of rangeland, respectively. The

contribution by other types of land-cover change in forest cover retention and reduction is

summarized in Table 26. The relative contribution of each of this change relative to the

original forest cover area in 1976 is shown in Figure 25.

93

Table 26. Forest cover change statistic (1976-2001) in the Agusan Provinces.

a. Agusan del Norte

Conversion: from forest to Area converted

(sq. km.)

% of original (1976) forest

cover area

Retained Forest 427.58 53.26

Rangeland 269.26 33.54

Palm Trees 67.73 8.44

Bare Soil 18.83 2.35

Cropland 15.18 1.89

Water 3.32 0.41

Built-up 0.6 0.07

Exposed Rocks 0.32 0.04

Total area of forest cover in 1976 802.82 100.00

b. Agusan del Sur

Conversion: from forest to Area converted

(sq. km.)

% of original (1976) forest

cover area

Retained Forest 1,497.86 73.55

Palm Trees 286.61 14.07

Rangeland 208.27 10.23

Bare Soil 19.53 0.96

Cropland 18.02 0.88

Water 6.15 0.30

Built-up 0.11 0.01

Exposed Rocks 0.09 0.00

Total area of forest cover in 1976 2,036.64 100.00

0

10

20

30

40

50

60

70

80

90

100

Retained

Forest

Rangeland Palm Trees Bare Soil Cropland Water Built-up Exposed

Rocks

Type of forest cover change (from forest to - )

Per

cen

t o

f co

nve

rted

19

76

fo

rest

co

ver

Agusan del Norte

Agusan del Sur

Figure 25. Comparison of magnitude of forest cover area reduction by types of change.

94

It can be observed that deforestation in Agusan del Norte is significant with only

about 53% of its initial forest cover in 1976 remaining. On the other hand, deforestation

in Agusan del Sur is less significant compared to Agusan del Norte because its forest

cover in 1976 was reduced by about 37%. Yet these estimates of deforestation provide

little comfort as these were computed only based on cloud-free portions of the Landsat

images. Hence, these values may not be true as the total area of the Agusan provinces

were not considered in the computation.

Considering only those portions with land-cover data common to both provinces,

the total areas of forest cover in Agusan del Norte and Agusan del Sur in the year 1976

are 802.82 sq. km. and 2,036.64 sq. km., respectively. Clearly, forest cover in Agusan del

Sur is much larger than that of Agusan del Norte due to the fact that Agusan del Sur is

bigger than Agusan del Norte in terms of land area. The forest cover change statistics

showed that conversion to rangeland, palm trees, bare soil and cropland are among the

four major contributors to forest cover reduction in both provinces. In terms of magnitude

of change, conversion of forest to rangeland is very pronounced in Agusan del Norte than

in Agusan del Sur. Based on Table 26, about 33% of forest cover in 1976 have been

converted to rangeland in 2001 for the province of Agusan del Norte. On the other hand,

only 10% of forest cover of Agusan del Sur was converted to rangeland in 2001. While it

can be stated that the greatest contributor in deforestation of Agusan del Norte is

conversion to rangeland, the same can not be confirmed in the Agusan del Sur. For the

latter, it is a mix of conversion to palm trees and rangeland (14% and 10%, respectively)

that explains forest cover reduction. The contributions of bare soil and cropland are also

greater in magnitude in Agusan del Norte than in Agusan del Sur. Each account for about

95

2% of forest cover reduction in Agusan del Norte. In Agusan del Sur, their contributions

is minimal at approximately 1%. Other changes in forest cover due to conversion to built-

up as well as natural deforestations (e.g., conversion to exposed rocks and water) are

minimal in both provinces.

Although the statistics varies, it can be speculated that deforestation in the Agusan

provinces is largely due to conversion of forest cover to rangeland and palm trees, with

minimal contributions from conversion to cropland and bare soil. The question on why

there were such kinds of conversions that drove deforestations may be answered by

taking into account the interplay between various bio-physical and socio-economical

factors in both provinces.

5.4 Characterizing 25-year deforestation in the Agusan Provinces

Remote sensing image analysis was able to provide data on the location and

magnitude of deforestation and other types of land-cover change in the Agusan

provinces. A further analysis as to the factors associated with deforestation was made

through GIS overlay analysis. In this section, the location of retained forest and the

location of occurrences of all types of deforestation with respect to the mean values of

bio-physical and socio-economical factors (e.g. ELEV, SLOPE, DISTRIV,

DISTNEWBUILT, DISTNEWRD, DIST_TLA-IFMA, DIST_CBFMA-CBRM, and

POPDENCHNGE) of the two Agusan provinces are described.

Figure 26 shows the mean elevation of location of forest cover change

occurrences. In both Agusan del Norte and Agusan del Sur, conversion from forest to

bare soil and rangeland occurred in areas with higher elevation (elevation from 200 m. to

96

230 m.). In areas with elevation less than 55 meters, the evident forest conversion that

occurred is forest to built-up, forest to cropland, and forest to palm trees. Combining all

types of deforestation (forest to bare soil, built-up, cropland, palm trees, and rangeland),

it was observed that it occurred in areas with mean elevation of 230 meters in Agusan del

Norte and 140 meters in Agusan del Sur. In terms of the retained forest, it is mostly

located in areas with an elevation of 380 meters in Agusan del Norte and 250 meters in

Agusan del Sur. It can be stated that retained forest and deforestation in Agusan del Norte

are located in areas with higher elevation compared to Agusan del Sur. This strongly

implies that remaining forest in ADN are located in higher areas because forest in

relatively lower areas have been deforested, since 1976.

Mean ELEV of Location of Forest Cover Change Occurences

382

224 223

2642

78

271

249

141

209

4049

65

247

0

50

100

150

200

250

300

350

400

450

Retained

Forest

Deforested Forest to

Bare Soil

Forest to

Built-up

Forest to

Cropland

Forest to

Palm Trees

Forest to

Rangeland

Forest-Cover Change Type

Mean

Ele

vati

on

, m

.

Agusan del Norte

Agusan del Sur

Figure 26. Mean elevation of location of forest cover occurrences in Agusan del Norte

and Agusan del Sur. Error bars indicate 95% confidence interval of the mean.

97

In terms of SLOPE (Figure 27), retained forest in Agusan del Norte are mostly

located in areas with mean slope of 30%, while in Agusan del Sur, retained forest are

located in areas with mean slope of 20%. However, deforestation in Agusan del Norte is

also located areas with higher mean slope (22%) as compared to Agusan del Sur where

deforestation occurred in areas with 18% mean slope. With respect to the specific forest

cover conversion, the changes forest to built-up and forest to cropland occurred in areas

with mean slope of 5-10%. The conversion from forest to palm trees and forest to

rangeland in Agusan del Norte occurred in areas with mean slope of 16% and 18%,

respectively. In Agusan del Sur, conversion from forest to palm trees and forest to

rangeland occurred in areas with milder slope (9% and 19%, respectively) as compared to

Agusan del Norte.

Mean SLOPE of Location of Forest Cover Change Occurences

30

22 22

5

10

16

24

20

13

16

6

9

19

6

0

5

10

15

20

25

30

35

Retained

Forest


Bare Soil

Forest to

Built-up

Forest to

Cropland

Forest to

Palm Trees

Forest to

Rangeland


Mea

n S

lop

e, %

Agusan del Norte

Agusan del Sur

Figure 27. Mean SLOPE of location of forest cover occurrences in Agusan del Norte and

Agusan del Sur. Error bars indicate 95% confidence interval of the mean.

98

Mean DISTRIV of Location of Forest Cover Change Occurences

1770

2863

5382

4030

3126

1922

2906

691 759 745

440

741 766 752

0

1000

2000

3000

4000

5000

6000

Retained

Forest


Bare Soil

Forest to

Built-up

Forest to

Cropland

Froest to

Palm Trees

Forest to

Rangeland


Mean

Dis

tan

ce t

o R

iver,

m.

Agusan del Norte

Agusan del Sur

Figure 28. Mean DISTRIV of location of forest cover occurrences in Agusan del Norte

and Agusan del Sur. Error bars indicate 95% confidence interval of the mean.

With respect to DISTRIV (Figure 28), all type of forest cover change in Agusan

del Norte occurred in areas 2000 meters up to 5,300 meters away from the river. In

Agusan del Sur, these types occurred in areas 500-800 meters from the rivers. In terms of

retained forest in Agusan del Norte, it is mostly located in areas 1900 meters away from

the river while in Agusan del Sur, it is mostly located in areas nearer the river or about

800 meters from the river. Looking at the general occurrences of the combined types of

deforestation, in Agusan del Norte, deforestation occurred in areas 2,900 meters away

from the river while in Agusan del Sur, deforestation occurred in areas much nearer in the

river, about 900 meters, compared to Agusan del Norte.

99

Mean DISTNEWBUILT of Location of Forest Cover Change Occurences

8381

4302 4169

0

811

1806

5145

10357

5992

9364

0

2535

3451

9474

0

2000

4000

6000

8000

10000

12000

Retained

Forest


Bare Soil

Forest to

Built-up

Forest to

Cropland

Forest to

Palm Trees

Forest to

Rangeland


Mean

Dis

tan

ce t

o N

ew

Bu

ilt-

up

s, m

.

Agusan del Norte

Agusan del Sur

Figure 29. Mean DISTNEWBUILT of location of forest cover occurrences in Agusan del

Norte and Agusan del Sur. Error bars indicate 95% confidence interval of the mean.

With respect to DISTNEWBUILT (Figure 29), the following could be observed:

in both Agusan del Norte and Agusan del Sur, retained forest can be found in areas

farther from the newly constructed built-up. On the other hand, deforestation in Agusan

del Norte occurred in areas 4000 meters away from the new built-up while in Agusan del

Sur, deforestation occurred in areas 6000 meters away from the new built-up. In terms of

the specific forest cover change, change in forest to bare soil in Agusan del Norte

occurred in areas 4000 meters away from the new built-up while in Agusan del Sur it

occurred in areas much farther from the new built-up (i.e. 9000 meters away from the

new built-up). The changes: forest to cropland and forest to palm trees in Agusan del

Norte occurred in areas 2000 meters or less from the new built-up areas. In Agusan del

100

Sur, these changes occurred 2500 meters to 3800 meters away from new built-up areas.

The conversion from forest to rangeland in Agusan del Norte occurred at areas 5000

meters away from the new built-ups while in Agusan del Sur it occurred in areas 9,500

meters away from the new built-ups.

Mean DISTNEWRD of Location of Forest Cover Change Occurences

7745

3821 3621

7511091

1624

4549

11647

7129

9374

47014275

4901

10232

0

2000

4000

6000

8000

10000

12000

14000

Retained

Forest


Bare Soil

Forest to

Built-up

Forest to

Cropland

Forest to

Palm Trees

Forest to

Rangeland


Mean

Dis

tan

ce t

o N

ew

Ro

ad

s, m

.

Agusan del Norte

Agusan del Sur

Figure 30. Mean DISTNEWRD of location of forest cover occurrences in Agusan del


In terms of occurrences of deforestation with respect to the new roads in the

Agusan provinces (Figure 30), it can be observed that in Agusan del Norte deforestation

occurred in areas 3900 meters away from the new roads while in Agusan del Sur,

deforestation occurred in areas 7000 meters away from new roads. This implies that

accessibility does not hinder deforestation in Agusan del Sur. Retained forest, however,

are found in areas much farther from the new roads in Agusan del Sur than in Agusan del

101

Norte. In terms of specific forest cover change, in Agusan del Norte, forest to bare soil

and forest to rangeland occurred in areas 3,900 meters and 4,200 meters away from the

new road, respectively. In Agusan del Sur, forest to bare soil and forest to rangeland

occurred in areas 4,200 meters and 10,100 meters away from the new road, respectively.

The change from forest to built-up in Agusan del Norte occurred in areas near the new

roads, 900 meters away, while in Agusan del Sur, the same forest cover change occurred

in areas much farther from the new road, 5000 meters away. Forest to cropland and forest

to palm trees in Agusan del Norte, respectively, occurred in areas 1000 meters and 1900

meters away from the new road, while in Agusan del Sur forest to cropland occurred

4,100 meters away from new road and forest to palm trees occurred 5,000 meters away

from the new road.

Mean DIST_TLA-IFMA of Location of Forest Cover Change Occurences

49065454

5252

11303

9520

6440

4978

2548

5024 5099

62026706

3342

7186

0

2000

4000

6000

8000

10000

12000

14000

Retained

Forest


Bare Soil

Forest to

Built-up

Forest to

Cropland

Forest to

Palm Trees

Forest to

Rangeland


Mean

Dis

tan

ce t

o T

LA

's a

nd

IF

MA

's, m

.

Agusan del Norte

Agusan del Sur

Figure 31. Mean DIST_TLA-IFMA of location of forest cover occurrences in Agusan del


102

With respect to DIST_TLA-IFMA (Figure 31), the change: forest to bare soil

occurred in almost the same distance from the TLA’s and IFMA’s both in Agusan del

Norte and Agusan del Sur but retained forest in Agusan del Sur are observed to be

located nearer in TLA’s and IFMA’s compared to Agusan del Norte (e.g., retained forest

Agusan del Norte are observed more in areas farther in TLA’s and IFMA’s as compared

to Agusan del Sur). The change forest to built-up in Agusan del Sur occurred in areas

11,000 meters away from TLA’s and IFMA’s while in Agusan del Norte the same forest

cover change occurred in areas about 6,000 meters away from TLA’s and IFMA’s. The

change forest to cropland and forest to palm trees in Agusan del Norte occurred farther

from TLA’s and IFMA’s than in Agusan del Sur but the change forest to rangeland in

Agusan del Norte occurred in areas nearer TLA’s and IFMA’s compared to Agusan del

Sur. In general, deforested areas in both ADN and ADS are located at approximately the

same distance from TLAs/IFMAs. In ADN, unchanged and changed forest areas are

located farther from TLAs/IFMAs; in ADS unchanged forest areas are nearer to

TLAs/IFMAs.

With regards to the occurrences of forest cover change with respect to distance to

CBFMA’s and CBRM’s (Figure 32), all types of forest cover change in Agusan del Sur

occurred in areas farther from CBFMA’s and CBRM’s compared to Agusan del Norte.

Retained forest in Agusan del Norte are located in municipalities with mean

population density change of 50 person per sq. km. while in Agusan del Sur, retained

forest are located in municipalities with population density change of 22 persons per sq.

km (Figure 33) . On the other hand, deforestation in Agusan del Norte are located in areas

with population density change of 75 persons per sq. km. while in Agusan del Sur,

103

deforestation occurred in municipalities with population density change of 35 persons per

sq. km. This implies that more deforestation happens in areas with higher population

density change. All types of forest cover change showed that in Agusan del Norte

deforestation occurred in areas with higher population density change than in Agusan del

Sur.

Mean DIST_CBFMA-CBRM of Location of Forest Cover Change Occurences

3623

29692834

3754

3199

2719

3026

4937

43374500 4570

5092

4500

4033

0

1000

2000

3000

4000

5000

6000

Retained

Forest


Bare Soil

Forest to

Built-up

Forest to

Cropland

Forest to

Palm Trees

Forest to

Rangeland


Mean

Dis

tan

ce t

o C

BF

MA

's a

nd

CB

RM

's, m

. Agusan del Norte

Agusan del Sur

Figure 32. Mean DIST_CBFMA-CBRM of location of forest cover occurrences in

Agusan del Norte and Agusan del Sur. Error bars indicate 95% confidence interval of the

mean.

104

Mean POPDENCHANGE of Location of Forest Cover Change Occurences

51

74

64

119

101

92

69

23

33 34

41 39

26

42

0

20

40

60

80

100

120

140

Retained

Forest


Bare Soil

Forest to

Built-up

Forest to

Cropland

Forest to

Palm Trees

Forest to

Rangeland


Mean

Po

pu

lati

on

Den

sity

Ch

an

ge (

19

76

-20

01

),

no

./sq

. k

m.

Agusan del Norte

Agusan del Sur

Figure 33. Mean POPDENCHANGE of location of forest cover occurrences in Agusan

del Norte and Agusan del Sur. Error bars indicate 95% confidence interval of the mean.

5.5 Logistic regression analysis results

GIS-based characterization of FCOVER change vis-à-vis bio-physical and socio-

economic factors can only provide a generalization on the location of FCOVER with

respect to these factors. In order to determine which variables are strongly or least

associated with changes and no change in FCOVER, logistic regression analysis is

necessary. Prior to the analysis, test for multicollinearity of variables was performed.

Computed values of Tolerance Statistic and Variance Index Factor for all linear

combinations of the variables indicated absence of multi-collinearity (Tolerance Statistic

>0.5 and VIF <2). The computed Pearson Pairwise Correlation Matrix (Pearson’s R) also

indicates absence of collinearity, with all values less than 0.5. Hence, all variables were

105

included in the logistic regression analysis. In order to have an easier understanding of

the logistic regression coefficient, a simple interpretation diagram was prepared (Figure

34). The results of the logistic regression analysis are presented in the next sub-sections.

Figure 34. Diagram for interpreting the logistic regression coefficients.

5.5.1 Logistic regression based on bio-physical factors only

The results of binary logistic regression of FCOVER with bio-physical variables

for the Agusan provinces are presented in Table 27 and Figure 35.

Large Factor Values

Negative β

Small Factor Values

Positive β

106

Table 27. Binary logistic regression of FCOVER versus bio-physical factors for Agusan

del Norte and Agusan del Sur

Biophysical

Factors

β

Agusan del

Norte

Agusan del

Sur

ELEV -6.247 -5.768

SLOPE -3.501 -1.243

DISTRIV 4.2 1.801

SOILQUAL -0.386 -0.988

Magnitude of Association of Bio-physical Factors with Deforestation

-6.247

-3.501

4.2

-0.386

2.067

-5.768

-1.243

1.801

-0.988

1.543

-7

-5

-3

-1

1

3

5

ELEV SLOPE DISTRIV SOILQUAL Constant

Bio-physical Factors

Logis

tic

Reg

ress

ion

Coef

fici

ent

(B)

Agusan del Norte

Agusan del Sur

Figure 35. Graph showing β values indicating the magnitude of association of bio-

physical factors with deforestation. Error bar indicate standard error.

The results for Agusan del Norte indicates that among the 4 bio-physical

variables, the distance to rivers (DISTRIV) has the largest and positive regression

coefficient of 4.2. As the value is positive, this means that as distance to river increases,

the probability of forest cover change increases (similarly, an area nearer to river would

have lesser probability of being deforested). This result is quite contrary to what can be

107

expected in forested areas nearer to rivers. In the study area, rivers provide a means of

transportation for loggers to access forested lands as well as for transporting logs.

Following this logic, the nearer a forestland to rivers, the higher the probability that it

will be deforested. Apparently, the result of the logistic regression deviates from this

assumption. Interestingly, however, this result is consistent with the GIS characterization

of FCOVER versus DISTRIV for Agusan del Norte, where it was shown that changes in

FCOVER are found in areas farther from rivers (Figure 28). A plausible explanation for

this maybe the historical deforestation that have occurred during the 1950s to the 1970s

in the study area, most especially in the upland watersheds [7],[8]. During this time

period, forestlands near rivers have been drastically harvested through logging and very

little were left to regenerate. As a consequence, forestlands from 1970s onwards can no

longer be found near rivers but rather in areas far from it. This is heavily supported by the

results of GIS-based characterization (Figure 28) where deforested areas are very far

from rivers (average of 2,863 m). Another explanation would be the implementation of

National Integrated Protected Areas System (NIPAS) Act of 1992 which aimed to protect

areas comprising of large natural parks, landscapes and small watersheds from forestry

activities [98]. Because a network of interconnecting rivers comprises protected

watersheds in the study area, their protection under the NIPAS Act may have prevented

loggers to use the rivers as access to forest. The loggers may have used alternative routes

to conduct logging activities far from these rivers.

With regards to elevation and slope, the computed regression coefficients are

negative which indicate that as its value increases, the probability of deforestation in

Agusan del Norte decreases. This is acceptable because higher elevation and slope values

108

would naturally be indicative of inaccessibility. Again, these results confirm the initial

findings of GIS characterization using the factors values of ELEV and SLOPE (see

Figure 26 and Figure 27). The computed regression coefficient for soil quality

(SOILQUAL) is also negative (-0.386) which indicate that soils with good quality

decreases the probability of deforestation. This result contradicts the expected, that

supposedly good soil quality would increase the probability of a certain area to be

deforested. However, the coefficient is so small that it may not strongly influence the

probability of deforestation.

The logistic regression results for Agusan del Sur show similar patterns on

contributions of ELEV, SLOPE, DISTRIV and SOILQUAL as those of Agusan del

Norte. However, regression coefficient of ELEV and SLOPE in Agusan del Sur is larger

than those of Agusan del Norte. This implies that the influence of elevation and slope in

decreasing the probability of deforestation is more pronounced in Agusan del Norte than

those in Agusan del Sur. However, the contribution of soil quality in decreasing the

probability of deforestation is larger compared to that of Agusan del Norte.

5.5.2 Logistic regression based on socio-economic factors only

The results of binary logistic regression of FCOVER with socio-economic

variables for the Agusan provinces are presented in Table 28 and Figure 36.

109

Table 28. Binary logistic regression of FCOVER versus socio-economic factors for

Agusan del Norte and Agusan del Sur

Socio-economic

Factors

β

Agusan del

Norte

Agusan del

Sur

CHNGROAD -3.19 -3.775

BUILTCHNGE -3.302 3.117

POPDENCHNG 1.074 0.615

DIST_TLAIFMA -1.594 0.684

DIST_CBFMACBRM 2.379 -3.534 *significant at 95% level (p<0.05).

Magnitude of Association of Socio-economic Factors with Deforestation

-3.19 -3.302

1.074

-1.594

2.379

0.792

-3.775

3.117

0.615 0.684

-3.534

-0.397

-5

-4

-3

-2

-1

0

1

2

3

4

5

DISTNEWRD DISTNEWBUILT POPDENCHNG DIST_TLAIFMA DIST_CBFMACBRM Constant

Socio-economic Factors

Logis

tic

Reg

ress

ion

Coef

fici

ent

(B)

Agusan del Norte

Agusan del Sur

Figure 36. Graph showing the magnitude of association of socio-economic factors with

deforestation. Error bars indicate +/- standard error.

In Agusan del Norte, DIST_CBFMA-CBRM and POPDENCHANGE have strong

influence in increasing the probability of deforestation. The positive regression

coefficient for the DIST_CBFMA-CBRM implies that the farther a forested land is to a

110

CBFMA/CBRM, the higher the probability that it will be deforested. The result of the

logistic regression is meaningful in the sense that it provides a confirmation that CBFMA

and CBRMs are restrictive in nature, perhaps in Agusan del Norte; that it follows

regulations in proper planting, harvesting and re-planting of forest lands. Because of this

restrictive nature, the probability of deforestation decreases as the distance of forestlands

to CBFMA and CBRMs decreases, and vice-versa. On the other hand, the regression

coefficient value of DIST_TLA-IFMA showed a negative value. This value indicates that

as DIST_TLA-IFMA increases, the probability of deforestation in Agusan del Norte

decreases; this actually means that forestlands nearer to TLAs and IFMAs have higher

probabilities of being deforested. These results of the logistic regression analysis for

ADN are not surprising albeit some of it is contrary to expectations. Referring back to the

results of the GIS-based characterization of FCOVER vis-à-vis DIST_TLA-IFMA and

DIST_CBFMA-CBRM (Figure 31 and Figure 32), the averaged distance to TLA and

IFMAs of deforested pixels is lesser compared to the averaged distance of unchanged

(retained) forests. This implies that deforestation occurs in areas nearer to TLAs and

IFMAs while unchanged forest areas are far from them. This confirms the result of the

logistic regression that decreasing distance to TLAs and IFMAs increases deforestation.

The same logic applies in the case of the association of FCOVER with DIST_CBFMA-

CRBMs. Distances to CBFMAs and CBRMs in ADN of deforested pixels are, on the

average, greater that of retained forest. The logistic regression results is confirmed by this

where increasing distance to CBFMAs and CBRMs increases the probability of

deforestation. With these confirmations by the GIS-based characterizations, it can be

111

stated that the logistic regression analysis, more or less, describes the deforestation

process in the ADN as far as the location of occurrences is concerned.

On the other hand, the regression coefficient of POPDENCHANGE is indicative

of strong influence of population density change to deforestation in Agusan del Norte.

This result is true because of highly dynamic economy of Agusan del Norte brought

about by the timber industries as well as urbanization that accommodated in-migration,

some of which maybe work forces in logging and timber industries. In the case of

DISTNEWRD and DISTNEWBUILT, regression coefficients for these factors are

indicative of decreasing the probability of deforestation as these factor values increases.

The influences by these two factors are almost similar, signifying that a forestland,

whether near to a new road or to new built-up areas, will likely to be deforested.

In Agusan del Sur, the influence of POPDENCHANGE in increasing the

probability of deforestation is still evident but it is not as strong as to that of Agusan del

Norte. This maybe due to the fact that population growth in this province is low

compared to Agusan del Norte. While the influences of DIST_CBFMA-CBRM and

DIST_TLA-IFMA is opposite in Agusan del Norte (i.e., probability of deforestation is

higher in areas nearer to TLA/IFMAs than those nearer to CBFMA/CBRMs), the same

can also be said in the case of Agusan del Sur. The only difference is that the probability

of deforestation is higher in areas nearer to CBFMA/CBRMs than those nearer to

TLA/IFMAs. This is quite expected especially that deforestation occurrences in ADS

between 1976-2001 are farther from TLAs and IFMA than those of retained forest

(Figure 31). Similarly, deforested pixels in ADS are mostly found in areas nearer to

CBFMAs and CBRMs (Figure 32) compared to unchanged forest that are found farther.

112

Again, the results of the GIS-based characterization complements the results of the

logistic regression analysis. On the other hand, the influence of DISTNEWRD in

decreasing the probability of deforestation is stronger compared to its influence in

Agusan del Norte. The influence of DISTNEWBUILT is opposite to the effect of

DISTNEWRD. DISTNEWBUILT in Agusan del Sur has positive regression coefficient

indicating that the farther an area from new built up, the higher the probability that gets

deforested. This result is true because of the very large land area of Agusan del Sur,

hence the density of built-up is less compared to that of Agusan del Norte.

5.5.3 Logistic regression using combined socio-economic and bio-

physical factors

The results of binary logistic regression of FCOVER with combined bio-physical

and socio-economic variables for the Agusan provinces are presented in Table 29 and

Figure 37.

Table 29. Binary logistic regression of FCOVER versus the combined bio-physical and

socio-economic factors for Agusan del Norte and Agusan del Sur

Combine Bio-physical

and

Socio-economic Factors

Β

Agusan del

Norte

Agusan del

Sur

ELEV -3.155 -8.124

SLOPE -3.247 -1.013

DISTRIV 4.785 1.61

SOILQUAL -0.105 -1.111

DISTNEWRD -3.752 0.513

DISTNEWBUILT -0.836 3.247


DIST_TLAIFMA -1.412 2.263

DIST_CBFMACBRM 1.252 -2.986

113

Magnitude of Association of Factors with Deforestation

-3.155 -3.248

4.785

-0.105

-3.752

-0.836

1.618

-1.412

1.252

-8.124

-1.013

1.61

-1.111

0.513

3.247

1.38

2.263

-2.986

1.2011.425

-9

-7

-5

-3

-1

1

3

5

ELEV

SLOPE

DIS

TRIV

SOIL

QU

AL

DIS

TNEW

RD

DIS

TNEW

BU

ILT

POPD

ENCHN

G

DIS

T_T

LAIF

MA

DIS

T_C

BFM

ACBRM

Con

stan

t

Combined Bio-physical and Socio-economic Factors

Lo

gis

tic

Reg

ress

ion

Co

effi

cien

t (B

)

Agusan del Norte

Agusan del Sur

Figure 37. Graph showing the magnitude of association of the combined bio-physical and

socio-economic factors with deforestation.

In Agusan del Norte, DISTRIV has the largest positive regression coefficient,

indicating that its influence in increasing the probability of deforestation increases as its

value increases. The result is similar to the regression analysis based on bio-physical

factors alone. The explanations provided earlier with regards to historical deforestation in

the 1950s to 1970s and the implementation of NIPAS Act of 1992 may be helpful in

qualifying this result. Next to DISTRIV, two socio-economic factors were found to have

influenced deforestation in Agusan del Norte. These are POPDENCHANGE and

DIST_CBFMA-CBRM. It can be observed that the values of regression coefficients for

these two factors are very near to those computed earlier using the socio-economic

factors only-based logistic regression analysis. The results are also consistent with

114

regards to factors that decrease the probability of deforestation: ELEV, SLOPE,

SOILQUAL, DISTNEWRD, DISTNEWBUILT, and DIST_TLA-IFMA. This means that

when the values of these factors increase, the probability of deforestation decreases.

In Agusan del Sur, the major factor with the strongest influence in increasing the

probability of deforestation, as its value increases, is a combination of DISTNEWBUILT

and DIST_TLA-IFMA. One thing noticeable is the change of sign of DISTNEWRD from

negative when computed using the socio-economic factors only that became positive

when computed using the combined bio-physical and socio-economic factors. The

change of its value from positive to negative may have been affected by the roles of

socio-economic factors incorporated during the computation. This result exemplified the

fact that land-cover change is interplay between socio-economic and bio-physical factors.

As for the factors that decreases the probability of deforestation, the results of the logistic

regression is consistent to earlier findings (ELEV, SLOPE, SOILQUAL, and

DIST_CBFMA-CBRM). This means that when the value of these factors increases, the

probability of deforestation decreases and vice versa.

5.5.4 Logistic regression analysis using new set of 5% sample

In order to determine the representativeness of the samples used in the logistic

regression analysis, another set of 5% sample was analyzed. The expected result should

have insignificant difference with the original set of sample analyzed using logistic

regression in order to confirm that the samples used are indeed representative of the study

area. A simple t-Test was employed to determine whether the results of the analysis using

the two sets of samples have significant or insignificant difference.

115

Table 30 and Figure 38 show the results of the comparison done between the

logistic regression coefficients computed using the original and the new 5% samples. t-

Test results for Agusan del Norte (Table 31) showed that there was an insignificant

difference between the regression coefficient, β, of the original samples and the new 5%

samples collected (i.e., t(9)=-0.361, p>0.05, both for one-tailed and two-tailed tests). The

Pearson correlation coefficient between the two sets of samples is 0.987.

Table 30. Comparison between the β values for Agusan del Norte

Agusan del Norte Combined Factors

Factors Original 5% New 5%

ELEV -3.155 -3.204

SLOPE -3.248 -2.609

DISTRIV 4.785 4.741

SOILQUAL -0.105 -0.038

DISTNEWRD -3.752 -3.223

DISTNEWBUILT -0.836 -1.797


DIST_TLAIFMA -1.412 -1.199

DIST_CBFMACBRM 1.252 1.354

116

Comparison between original and new 5% samples

Agusan del Norte

-6

-4

-2

0

2

4

6

ELEV

SLOPE

DIS

TRIV

SOIL

QUAL

DIS

TNEW

RD

DIS

TNEW

BUIL

T

POPD

ENCHNG

DIS

T_TLA

IFM

A

DIS

T_CBFM

ACBR

M

Fcators

Re

gre

ss

ion

co

eff

icie

nt

Original

New


in Agusan del Norte. Error bars indicate +/- standard error.

Table 31. t-Test results for Agusan del Norte

t-Test for Agusan del Norte Original Sample New Sample

Mean -0.539 -0.484

Variance 7.718 7.116

Observations 9 9

Pearson Correlation 0.987

Hypothesized Mean

Difference 0

df 8

t Stat -0.361

p value (T<=t) one-tailed 0.363*

t Critical one-tail 1.859

p value (T<=t) two-tailed 0.727**

t Critical two-tail 2.306

*not significant; p>0.05 (one-tailed)

**not significant; p>0.05 (two-tailed)

117

Table 32 and Figure 39 show the comparison of the logistic regression coefficient

values for Agusan del Sur. Although the sign of the regression coefficients of some

factors has changed, the analysis using t-test (Figure 39) revealed that the difference

between the sets of samples is insignificant. The computed Pearson correlation

coefficient computed was 0.826.

Table 32. Comparison between the β values for Agusan del Sur

Agusan del Sur Combined

Factors Original 5% New 5%

ELEV -8.124 -8.674

SLOPE -1.013 -0.705

DISTRIV 1.61 -0.337

SOILQUAL -1.111 -2.338

DISTNEWRD 0.513 0.861

DISTNEWBUILT 3.247 -0.5


DIST_TLAIFMA 2.263 2.199

DIST_CBFMACBRM -2.986 0.562

118

Comparison between original and new 5% samples

Agusan del Sur

-10

-8

-6

-4

-2

0

2

4

6

ELEV

SLOPE

DIS

TRIV

SOIL

QUA

L

DIS

TNEWRD

DIS

TNEWBU

ILT

POPD

ENCH

NG

DIS

T_TLAIF

MA

DIS

T_CB

FMAC

BRM

Factors

Reg

ress

ion

co

effi

cien

tOriginal

New


in Agusan del Sur. Error bars indicate +/- standard error.

Table 33. t-Test results for Agusan del Sur

t-Test Results for Agusan del Sur Original Samples New Samples

Mean -0.469 -0.795

Variance 11.952 10.617

Observations 9 9

Pearson Correlation 0.826

Hypothesized Mean Difference 0

df 8

t Stat 0.491

p value (T<=t) one-tailed 0.318*

t Critical one-tail 1.859

p value (T<=t) two-tailed 0.636**

t Critical two-tail 2.306

*not significant; p>0.05 (one-tailed)

**not significant; p>0.05 (two-tailed)

119

5.6 Characterization of “No Data” pixels

This section is a discussion on the limitation on the analysis brought by the “no

data” pixels due to the presence of clouds and cloud shadow in the image of the study

area.

In order to determine as to what factors where biases were made during the

analysis due to the absence of complete land-cover information, the mean factor values of

the “no data” and with data pixels were compared. The comparison revealed that pixels

with “no data” are found in areas with much higher elevation and farther from new roads

and new built-ups (Figure 40). This result was expected because clouds and cloud

shadows are often found in elevated areas in the study area. Consequently, there are no

roads and built-ups that can be found in highly elevated areas. The implication of this is

that: should complete dataset will be available, the factors that might have a change in its

influence or degree of association to deforestation in the study area is ELEV. With

regards to the other factors, “no data” and “with data” pixels are both found in almost the

same mean factor values.

120

Mean factor values of No data and with Data Pixels

(Agusan del Norte and Agusan del Sur)

-5000

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

Elev*

100

slope

*100

DIS

TRIV

SOIL

QUA

L

DIS

T_NEW

RD

DIS

T_NEW

BUIL

T

POPD

ENCH

AN

GE

DIS

T_TLA-I

FMA

DIS

T_CB

FMA-C

BRM

Factors

Mea

n f

acto

r v

alu

es

No data

With data

Figure 40. Graph showing the mean factor values of no data and with data pixels for

Agusan del Norte and Agusan del Sur. Error bars indicate +/- standard error.

5.7 Summary of findings

This research was able to detect deforestation and other types of land-cover

change in the provinces of Agusan del Norte and Agusan del Sur for the 1976-2001

periods. Using state of the art RS image analysis techniques provided by Support Vector

Machine classification algorithm, highly accurate land-cover maps of 1976 and 2001 with

overall accuracy 94.99% and 98.25% respectively, were obtained and used to detect land-

cover transitions in the study area. The land cover change map desired is 93.33%

accurate. The limitations brought by cloud cover contamination in the images were

addressed by a simple cloud masking algorithm developed in this study that was

comprised of image segmentation and maximum likelihood classification.

The detected changes in land-cover were found to be different in the Agusan

provinces. Forest to rangeland is the major land-cover change in Agusan del Norte from

121

1976 to 2001. Although deforestation due to increase in rangeland is significantly

evident, “re-forestation” of rangeland areas from 1976 to 2001 was also present. It was

also observed that large tract of lands planted with palm trees in 1976 have been

converted into croplands in 2001. In the case of Agusan del Sur, increase in cropland and

decrease in forest cover is the most significant land-cover change in terms of change in

land area. Quantitatively, these translate to 156% increase in cropland (or about 198.47

sq. km.) and about 6% decrease in forest cover (or 113.42 sq. km.). In terms of

specificity, the two most prominent land-cover change types from 1976 to 2001 in this

province is the conversion of rangeland to forest and forest to palm trees. Considering

errors in classifications, these two land-cover change types are almost identical in

magnitude. A third major type of change is that of conversion of forest to rangeland.

The forest cover change statistics showed that conversion to rangeland, palm

trees, bare soil and cropland are among the four major contributors to forest cover

reduction in both provinces.

Using GIS, deforestation in the Agusan provinces were characterized with respect

to bio-physical and socio-economic factors that were hypothesized to be the main drivers

of deforestation. GIS-based characterization provided an overview on the relative

relationship of these factors to forest cover retention and reduction. GIS-based

characterization indicates that deforestation in Agusan del Norte are located in areas with

higher elevation and steeper slope compared to Agusan del Sur. Deforestation occurrence

in Agusan del Norte is located much farther from the river (approx. 3km.) compared to

Agusan del Sur where deforestation can be found in areas less than 1km away from the

river. However, with regards to the occurrence of deforestation with respect to the new

122

built-up and new road, it was observed that deforestation in Agusan del Norte are located

in areas nearer to new built-up and new road compared to Agusan del Sur. Deforestation

in Agusan del Norte also occurred in areas with higher population density compared to

Agusan de Sur. This may be due to the fact that population density, built-up and roads in

Agusan del Norte is more dense than in Agusan del Sur. With regards to the occurrence

of deforestation in Agusan del Norte with respect to the TLA-IFMA and CBFMA-CBRM

parcels, it is located in areas farther from TLA-IFMA but nearer to CBFMA-CBRM

parcels compared to Agusan del Sur. This scenario was attributed to the fact that the

largest and the earliest TLA-IFMA concession in the Agusan province were awarded to

Nasipit Lumber Company which is located in Agusan del Norte (see Table 2).

The logistic regression analysis of bio-physical variables as sole factors

influencing deforestation in the Agusan provinces showed promising results. The results

are highly indicative of the inverse relationship of deforestation with elevation and slope

values. Only the factor DISTRIV was found to have positive regression coefficient that

influence deforestation.

The logistic regression analysis of socio-economic variables as sole factors

influencing deforestation in the Agusan provinces also showed promising results. The

results are highly indicative of the inverse relationship of deforestation with distance to

new roads which confirms the fact that accessibility is a major factor in deforestation.

Population density change was found to be a major indicator of forest cover change in

both provinces, with its influence stronger in Agusan del Norte than in Agusan del Sur.

The results of the influence of new road and population density to deforestation is similar

to the results of the studies conducted by Kummer [30], Vagen [45], and Geist &Lambin

123

[22]. With regards to the influence of TLA/IFMAs and CBFMA/CRBMs to deforestation

in Agusan del Norte, increasing distance from TLAs/IFMAs indicates decrease in the

probability of deforestation; while increasing distance to CBFMA/CBRM indicates

increase in the probability of deforestation. The exact opposites were found in the case of

Agusan del Sur.

The results of logistic regression based on combined bio-physical and socio-

economic factors provided significant results as to what has influenced deforestation in

the Agusan provinces. For Agusan del Norte, the bio-physical factor DISTRIV was found

to be positively related to deforestation, followed by socio-economic factors

POPDENCHANGE and DIST_CBFMA-CBRM. Compared to DISTRIV, the

contributions of these two socio-economic factors are minimal. The bio-physical factors

ELEV and SLOPE, and the socio-economic factors DISTNEWRD and DIST_TLA-

IFMA, were all found to be negatively related to deforestation, thus, the probability of

deforestation decreases as the values of these factors increases. Although the socio-

economic factor DISTNEWBUILT was found to be a contributor to deforestation, its

effect is minimal. For Agusan del Sur, DISTNEWBUILT, DIST_TLA-IFMA,

POPDENCHANGE, and DISTRIV are found to be positively related to deforestation (i.e.

when there value increases, the probability of deforestation increases). Although the

socio-economic factor DISTNEWRD was found to be positively related to deforestation,

its effect is minimal. ELEV, SLOPE, SOILQUAL, and DIST_CBFMA-CBRM were the

factors that decrease the probability of deforestation (i.e. negatively related to

deforestation).

124

The results of logistic regression using the combined bio-physical and socio-

economic factors emphasized the fact that deforestation is the result of the interplay

between socio-economic, institutional and environmental factors [1]. Running the

logistic regression using only the bio-physical or the socio-economic factors may not

provide adequate description of their influence to deforestation in the Agusan Provinces.

The combined approach re-affirmed initial findings based on the logistic regression using

bio-physical or socio-economic factors alone that the most prominent factor that is positively

related to deforestation in Agusan del Norte and Sur are DISTRIV and DISTNEWBUILT,

respectively. On the other hand, the prominent factors that are negatively related to

deforestation in Agusan del Norte are DISTNEWRD, ELEV and SLOPE. In Agusan del Sur,

the factor negatively related to deforestation are ELEV and DIST_CBFMA-CBRM.

The result of the logistic regression analysis using another set of 5% sample revealed

that the original set of 5% sample used in logistic regression analysis indeed represents the

characteristic of the study area. Should complete dataset will be available, the factors that

might have a change in its influence to the deforestation in the study area is ELEV.

125

Chapter 6

Conclusions and Recommendations

6.1 Conclusions

This study has provided a comprehensive analysis of land-cover change in the

Agusan provinces. A series of methodology was developed to understand the history of

forest utilization using RS and GIS. This study also provided a series of techniques to

understand deforestation as well as to relate to bio-physical and socio-economic factors

using an un-ideal dataset. This study was able to establish a technique that has bridged

the gap in RS problems. The results of this study made sense although datasets used to

derive land-cover information were contaminated with clouds. Thus, it is possible to

analyze deforestation using cloud contaminated RS images.

Results of this study exemplified the fact that although the two Agusan provinces

have history of forest resource industry, the presence of these industries is not the most

prominent factor that influenced deforestation. The most prominent factors that

influenced deforestation in Agusan del Norte is the socio-economic factor DISTNEWRD.

Its influence was found to be negatively correlated with deforestation: the nearer an area

to a newly-constructed road, the higher the probability it will be deforested. Conversely,

the influence of the biophysical factor DISTRIV was found to be positively correlated

126

with deforestation in ADN: the farther the area to a river, the higher the probability of

deforestation.

In Agusan del Sur, the biophysical factor ELEV has the greatest influence with

deforestation: low-lying areas are more prone to deforestation. This factor is seconded by

the socio-economic factor DIST_CBFMA-CBRM: forested areas nearer to CBFMAs and

CBRMs are more prone to be deforested. The factors DISTNEWBUILT, DIST_TLA-

IFMA and DISTRIV were found to be positively correlated with deforestation: increasing

distance from these factors increases the probability of deforestation.

The influences of timber industries to deforestation in the Agusan provinces were

found to be different. In ADN, DIST_TLA-IFMA is negatively correlated (increasing

distance decreases deforestation) while DIST_CBFMA-CRBM is positively correlated

(increasing distance increasing deforestation). The exact opposite was found in ADS.

As the factors associated with deforestation vary in ADN and ADS, it is

concluded that the factors influencing deforestation in one area may not be the same

factors that can influence deforestation in another area. Deforestation is indeed a

combination and the interplay between several bio-physical and socio-economic factors.

This study demonstrated the usefulness of RS and GIS not only in obtaining

accurate information on the location, extent and type of land-cover change (including

deforestation) but also in characterizing the relationships of the detected changes with

bio-physical and socio-economic factors. With statistical analysis, the information and

the characterizations can be expounded further leading to a more comprehensive analysis

of the deforestation process.

127

6.2 Recommendations

This study was sorely limited by the incomplete land-cover information from the

Landsat images due to cloud cover contamination. Hence, results of the analysis should

be used carefully.

Although a simple cloud masking algorithm was developed, it did not avoid the

problem of missing land-cover information due to the masking process. Moreover, it is

admitted that this study may not provide a complete analysis of the deforestation process

in the Agusan provinces. In this regard, continuation of this study is hereby

recommended. The use of the developed cloud masking algorithm is also recommended

because of its ease-of-use and accuracy. The problem of cloud cover contamination may

be addressed by producing a cloud-free mosaic from multi-temporal remotely-sensed

images obtained at closer time interval.

The use of Support Vector Machine algorithm is also recommended for land

cover classification as it could provide very accurate land cover maps provided that it is

implemented with greater number of bands (at least 7) and sufficient number of training

pixels.

Results of this study already gave fair description on the interplay of the bio-

physical and socio-economic factors that are associated with deforestation in the Agusan

provinces. Local agencies in Agusan del Norte and Agusan del Sur may use the land-

cover maps and statistics generated in this study to further evaluate the process of

deforestation in these provinces in order to create and evaluate strategies that attempt to

mitigate its negative effects.

128

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135

Appendices

136

Appendix 1. Maps showing the location of retained and deforested areas.

Figure A1. 1 Map showing the retained and

deforested areas in Agusan del Sur

Figure A1. 2 Map showing the retained and

deforested areas in Agusan del Norte

137

Appendix 2. Factor Maps

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

SRTM ELEVATION

Factor Map

Elevation,above mean sea level

1 - 26 meters

26 - 42

42 - 67

67 - 108

108 - 174

174 - 280

280 - 452

452 - 731

731 - 1,180

1,180 - 1,907

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

SLOPE (%)

Factor Map

Slope, in %

0 - 3

3 - 6

6 - 8

8 -18

18 - 32

32 - 50

>50

Figure A1. 4 The SLOPE Factor Map Figure A1. 3 The ELEV Factor Map

138

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

DISTANCE TO

RIVERS

Factor Map

Rivers

Distance to Rivers

0 - 1 Kilometers

2 - 2

3 - 3

4 - 5

6 - 7

8 - 10

11 - 14

15 - 20

21 - 28

29 - 38

39 - 52

53 - 70

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

DISTANCE TO NEW

ROADS SINCE 1976

Factor Map

1976 Roads

New Roads (since 1976)

Distance to Road

0 - 1 Kilometer

1 - 3

3 - 5

5 - 8

8 - 12

12 - 18

18 - 26

26 - 36

36 - 50

50 - 70

Figure A1. 5 The DISTNEWRD Factor Map Figure A1. 6 The DISTRIV Factor Map

139

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

DISTANCE TO NEW

BUILT-UP AREAS

SINCE 1976

Factor Map

1976 Built-up Areas

New Built-up since 1976

Distance to Built-up Areas

0 - 1 Kilometer

1 - 3

3 - 5

5 - 8

8 - 12

12 - 18

18 - 26

26 - 36

36 - 50

50 - 70

LA PAZ

LORETO

SAN LUIS

BAYUGAN

ESPERANZA

BUNAWAN

VERUELA

BUTUAN CITY

TRENTO

ROSARIO

LAS NIEVES

PROSPERIDAD

CABADBARAN

BUENAVISTA

SAN FRANCISCO

JABONGA

SANTIAGO

KITCHARAO

CARMEN

NASIPIT

TUBAY

STA. JOSEFA

TALACOGON

MAGALLANES

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

POPULATION DENSITY

CHANGE

Factor Map*

:.mk .qs rep snosrep fo .oN

21 - 6

71 - 31

32 - 81

23 - 42

54 - 33

36 - 64

78 - 46

221 - 88

171 - 321

042 - 271

* as per May 1975 and May 2000

National Census of the

Philippine Government.

Figure A1. 8 The DISTNEWBUILT Factor Map Figure A1. 7 The POPDENCHANGE Factor Map

140

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

DISTANCE TO

TLAs & IFMAs

Factor Map

TLAs & IFMAs

Distance

0 - 1 Kilometer

2 - 2

3 - 4

5 - 7

8 - 11

12 - 16

17 - 24

25 - 34

35 - 49

50 - 70

126°1'0"E

126°1'0"E

125°30'30"E

125°30'30"E

9°0

'0"N

9°0

'0"N

8°3

0'0

"N

8°3

0'0

"N

8°0

'0"N

8°0

'0"N

®25

Kilometers

Agusan del

Norte

Agusan del

Sur

DISTANCE TO

CBFMAs & CBRMs

Factor Map

CBFMAs & CBRMAs

Distance

0 - 1

2 - 2

3 - 4

5 - 7

8 - 11

12 - 16

17 - 24

25 - 35

36 - 50

51 - 71

Figure A1. 9 The DIST_TLA-IFMA Factor Map Figure A1. 10 The DIST_CBFMA-CBRM Factor Map

141

Appendix 3. Maps showing the location of retained forest and deforested areas with CBFMA, CBRM,

TLA, and IFMA.

Figure A1. 11 TLA and IFMA locations with retained

forest and deforested areas in the Agusan provinces

Figure A1. 12 CBFMA and CBRM locations with retained

forest and deforested areas in the Agusan provinces

142

Appendix 4. Maps showing the distance to new roads of retained forest and deforested areas.

Figure A1. 13 Distance to new roads of 5% retained

forest samples

Figure A1. 14 Distance to new roads of 5% deforested

samples

143

Appendix 5. Maps showing the distance to river of retained forest and deforested areas.

Figure A1. 15 Distance to rivers of 5% deforested areas

samples

Figure A1. 16 Distance to rivers of 5% retained

forest samples

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