Novel GIS and Remote Sensing- based techniques for soils at … · 2016-06-03 · forest continuum...

JRC Ispra - IES1

Novel GIS and Remote Sensing-based techniques for soils at

European scales

F. Carré, T. Hengl, H.I. Reuter, L. Rodriguez-Lado

G. Schmuck (LMNH Unit) & L. Montanarella (MOSES Action)

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Framework of the project

Soil Thematic Strategy

European Soil Data Center

OUR RESEARCH ACTIVITY

Data support

Data nee

ds

Methods & Data

Communication

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Innovation of the project

Problem of traditional soil maps

From a scientific point of view

- traditional soil maps are not easy to understand (no methodology described, terminology understandable only by soil science community)

- soil attribute information can be missing at appropriate scale

From an economic point of view

- Usually soil attributes and classes are represented with crisp boundaries coming from expert interpretation and there is no indication of the soil map quality

Traditional soil surveys are very expensive because they need a lot of auger information

Need quantitative methods to map easy to interpret attributes

Need easy- to-use models (tools) for soil mapping

Need to evaluate the accuracy of the soil maps

Need sampling techniques for augering

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uncertainty

Innovation in images…

Soil type map

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To provide quantitative soil data, producible at low cost and easy-to-interpret-and-use (for other scientists and policy makers)

Core

How?

- for mapping;

By elaborating quantitative methods :

- for estimating associated accuracy;

Using easily accessible indirect soil information (auxiliary data)

Core of the methodology

Digital Soil MappingName

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DSM in practice (example of application)

Presentation of Digital Soil Mapping methodology

Tools and guidelines addressed to soil data users

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Soil observations Auxiliary data

Soil inference system

(spatial, attribute)

Soil attributes Soil classes

Spatial accuracy

Soil threatsSoil functions

Scenario testing/ risk assessment

Market / society Environment

POLICIES / MANAGEMENT

Sampled data

Soil covariates

(RS images, DEM…)

Statistics

Geostatistics

Accuracy map

Soil attribute map

Suitability map

Erosion map

Digital Soil Mapping (DSM)

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DSM application example

Heavy Metal Content in Zagreb County (Croatia)

Author: Hengl (2006)

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Spatial accuracy





Heavy Metal content

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Spatial accuracy





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1142 samples over 3700 km2: contents of Cu, Pb, Ni, Zn

Zagreb county

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Spatial accuracy





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Zagreb county

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Spatial accuracy





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Regression-kriging

Multiple Linear Regression

Yj = a1 X1 + a2X2 + … + an Xn + εj

Soil variable j residuals j

Kriging

Yj

...

... ..... ...

.

. .. ..

..

∑ aiXii

.

..

...

. .. .. .. ..

γεj

distance (m)

Sem

i-var

ianc

e

(interpolation process according to spatial autocorrelations of the variable)

Auxiliary data i

Spatially continuous Punctual

Summation of the two maps

regression

kriging

regression-kriging

auxiliary data

residuals

soil variables

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Spatial accuracy





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Soil attribute map

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Spatial accuracy





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Continuous maps of Heavy Metal Content

Spatial accuracy map

East

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Spatial accuracy





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Limitation scores

From Hengl in Dobos et al. (2006)

Triantifalis et al., 2001

LS =b0 . HMCb1 -1 if HMC ≥ X1

0 if HMC < X1

00

5

10

15

20

25

50 100 150 200 250

30

Lim

itatio

n sc

ores

Permissible (baseline)

concentration

Serious pollution

Heavy metal concentration (mg kg-1)

LS= 0.000114. HMC2.322 -1

X1

X2

LS = 1 when HMC = X1

LS = 5 when HMC = X2

X1mg. kg-1

X2mg. kg-1

ln(b0) b1

CdCrCuNiPbZn

0.850503050

150

51001001060300

0.392-9.083-9.083-7.897-5.731

-11.634

1.7562.3222.3222.3221.4652.322

Pollution standards in Croatia

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Pollution map

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- Technical manual / textbook to process DEMs (Hengl & Reuter)




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Geomorphometry book (Hengl & Reuter)

DEM is the main source of data for DSM (70%)

Technical manual / textbook to process DEMs and extract surface parameters and objects

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CONCLUSIONS

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Digital Soil Mapping

Soil sampling

Continuous soil

classification

Interpretation of soil

attributes with RS data

Erosion (wind, water…)

tool

Actual work For 2007

Typology of soil pollutions

Improving EU soil map

Mapping of the ecosystem continuum

Modellingsoil

scenarios

Present / Future of DSM

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Digital Soil Mapping

Risk assessment

Support to FP7

Health

Inputs for biomass prediction

agriculture

Auxiliary data needs

Information and communication techno.

Input for soil -forest continuum

Energy

inputs for STS and other directives

Environment

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Thanks for your attention

[email protected]

[email protected]

[email protected]

[email protected]

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ANNEXES

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Economic gain of DSM

For physical soil parameters

We consider that DSM allows for saving 2/3 of the sampling

So for an area of 3700 km² where 1150 samples were measured, only 380 should be observed.

20 profile observations/ day can be done, paid around 150 €

Total cost: 2850 € instead of 8625 € (5775 € i.e. 67% saved)

For chemical soil parameters

We consider that DSM allows for saving 1/3 of the sampling

So for an area of 3700 km² where 1150 samples were measured, 770 should be measured.

1 profile measurement with 10 HMC + pH, OC, P, K, N is estimated to cost ~100 €

Total cost: 77000 € instead of 115000 € (38000 € saved i.e. 33%)

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Economic gain of DSMFor physical soil parameters: DSM allows for saving 2/3 of the sampling

1500 Km2 450 samples(3375 €)

150 samples(1125 €)

2250€SAVED

For chemical soil parameters: DSM allows for saving 2/3 of the sampling

1500 Km2 450 samples(45000 €)

300 samples(30000 €)

15000€SAVED

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Mapping of soil, by J.P. Legros (translated by V.A.K. Sharma). Science Publishers, Enfield, 2006. 409 pp ISBN 1-57808-363

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http://eusoils.jrc.it/ESDB_Archive/eusoils_docs/other/EUR22123.pdf

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Principles

Set of soil observations

1 2

65

3 4

7

8

11109

12 13 14

15

A

B

CD

Set of soil references

OSACA Software

12345

A B C D REF

0.72.5

3.00.2

1.20.1

0.4

1.30.1

0.80.6

0.61.50.10.1

0.0

0.30.11.21.9

A

BC

BB

Result table

dmin

0.1

0.10.10.10.1

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SOIL MAP OF AISNE (FRANCE) AT 1:250.000 SCALE (Carré & Reuter)

To be published in Elsevier (2007)SOIL MAPPING UNITS

OSACA Classes

DISTANCES TO SMU

OSACA distances

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SOIL INFERENCE SYSTEMFACTOR(1)

FAC

TOR

(1)

FACTOR(2)

ZNHG

CR

PB

CU

CD

NI

FACTOR(3)

CU

HG

NI

ZNCDPB

CR

FACTOR(4)

FAC

TOR

(1)

CUZN

NI

HGPBCD

CR

FAC

TOR

(2) PB

CD

HGNICR

CU

ZN

CU

HGNIZN

CD

PB

CR

FAC

TOR

(2)

CU

ZNNI HG

PB

CD

CR

FAC

TOR

(3)

PB

CD

HGNICR

CU

ZN ZN

HGCR PB

CU

CD

NI

FAC

TOR

(3)

CU

ZN

NIHGPB

CD

CR

FACTOR(1)

FAC

TOR

(4)

PBCD

HG

NICR

CUZN

FACTOR(2)

ZN

HG

CRPB

CUCDNI

FACTOR(3)

CU

HG

NI ZNCD

PBCR

FACTOR(4)

FAC

TOR

(4)

FACTOR(1)

FAC

TOR

(1)

FACTOR(2)

ZNHG

CR

PB

CU

CD

NI

FACTOR(3)

CU

HG

NI

ZNCDPB

CR

FACTOR(4)

FAC

TOR

(1)

CUZN

NI

HGPBCD

CR

FAC

TOR

(2) PB

CD

HGNICR

CU

ZN

CU

HGNIZN

CD

PB

CR

FAC

TOR

(2)

CU

ZNNI HG

PB

CD

CR

FAC

TOR

(3)

PB

CD

HGNICR

CU

ZN ZN

HGCR PB

CU

CD

NI

FAC

TOR

(3)

CU

ZN

NIHGPB

CD

CR

FACTOR(1)

FAC

TOR

(4)

PBCD

HG

NICR

CUZN

FACTOR(2)

ZN

HG

CRPB

CUCDNI

FACTOR(3)

CU

HG

NI ZNCD

PBCR

FACTOR(4)

FAC

TOR

(4)

Principal Component

Analysis

Soil contamination for Natura 2000 sites in Italy (Rodriguez-Lado)

Soil TypesHierarchical

Cluster Analysis

Heavy Metal Contents

P e r m u te d D a ta M a t r ix

-1012

C A L C A R IC F L U

C H R O M I C P H A E

C H R O M I C L U V I

D Y S T R I C L U V I

G L E Y I C P H A E O

E U T R IC C A M B I

C A L C A R IC P H A

C A L C A R IC R E G

C A L C A R IC G L E

L U V IC P H A E O Z

H A P L I C P H A E O

C A L C A R I C C A M

H U M IC U M B R IS

V I T R IC A N D O S

CR NI HG CD ZN PB CU

Calcaric FluvisolChromic Phaeozem

Chromic LuvisolDystric Luvisol

Gleyic PhaeozemEutric Cambisol

Calcaric PhaeozemCalcaric RegosolCalcaric GleysolLuvic Phaeozem

Haplic PhaeozemCalcaric Cambisol

Humic UmbrisolVitric Andosol

Cr Ni Hg Cd Zn Pb Cu

Basilicata

Calcaric FluvisolChromic Phaeozem

Chromic LuvisolDystric Luvisol

Gleyic PhaeozemEutric Cambisol

Calcaric PhaeozemCalcaric RegosolCalcaric GleysolLuvic Phaeozem

Haplic PhaeozemCalcaric Cambisol

Humic UmbrisolVitric Andosol

Cr Ni Hg Cd Zn Pb Cu

JRC Ispra - IES38

Reuter In Reuter et al. (2006)

Wind Speed [m/s]

Climate erodibility of agriculture soils (Reuter)

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Novel GIS and Remote Sensing- based techniques for soils at … · 2016-06-03 · forest continuum...

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