JRC Ispra - IES 1 Novel GIS and Remote Sensing- based techniques for soils at European scales F. Carré, T. Hengl, H.I. Reuter, L. Rodriguez-Lado G. Schmuck.

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)

JRC Ispra - IES2

Framework of the project

Soil Thematic Strategy

European Soil Data Center

OUR RESEARCH ACTIVITY

Data support

Data n

eeds

Communication

Methods & Data

JRC Ispra - IES3

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

JRC Ispra - IES4

uncertainty

Innovation in images…

Soil type map

JRC Ispra - IES5

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

Core

- for mapping;

To elaborate quantitative methods :How?

- for estimating associated accuracy;

Using easily accessible indirect soil information (auxiliary data)

Core of the methodology

Digital Soil Mapping

Name

JRC Ispra - IES6

DSM in practice (example of application)

Presentation of Digital Soil Mapping methodology

Tools and guidelines addressed to soil data users

JRC Ispra - IES7

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)

JRC Ispra - IES8




JRC Ispra - IES9

DSM application example

Heavy Metal Content in Zagreb County (Croatia)

Author: Hengl (2006)

JRC Ispra - IES10





Spatial accuracy





Heavy Metal content

JRC Ispra - IES11





Spatial accuracy





JRC Ispra - IES12

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

Zagreb county

JRC Ispra - IES13





Spatial accuracy





JRC Ispra - IES14

Zagreb county

JRC Ispra - IES15





Spatial accuracy





JRC Ispra - IES16

Regression-kriging

Multiple Linear Regression

Yj = a1 X1 + a2X2 + … + an Xn + εj

Soil variable j residuals j

Kriging

Yj

...

... ..... ...

.

. .. ..

..

∑ aiXi

i

.

..

...

..

. .. .. ..

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

JRC Ispra - IES17





Spatial accuracy





JRC Ispra - IES18

Soil attribute map

JRC Ispra - IES19





Spatial accuracy





JRC Ispra - IES20

Continuous maps of Heavy Metal Content

Spatial accuracy map

East

JRC Ispra - IES21





Spatial accuracy





JRC Ispra - IES22

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

ore

s

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

X1 mg. kg-1

X2 mg. kg-1

ln(b0) b1

Cd

Cr

Cu

Ni

Pb

Zn

0.8

50

50

30

50

150

5

100100

10

60

300

0.392

-9.083

-9.083

-7.897

-5.731-11.634

1.756

2.3222.322

2.322

1.4652.322

Pollution standards in Croatia

JRC Ispra - IES23

Pollution map

JRC Ispra - IES24

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




JRC Ispra - IES25

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

JRC Ispra - IES26

CONCLUSIONS

JRC Ispra - IES27


Soil sampling

Continuous soil

classification

Interpretation of soil

attributes with RS data

Erosion (wind, water…)

tool

Actual work For 2007

Typology of soil pollutions

Mapping of the ecosystem continuum

Modelling soil

scenarios

Improving EU soil map

Present / Future of DSM

JRC Ispra - IES28


Support to FP7

Risk assessment

Health

Inputs for biomass prediction

agriculture

Auxiliary data needs

Information and communication technology

Input for soil -forest continuum

Energy

inputs for STS and other directives

Environment

JRC Ispra - IES29

Thanks for your attention

[email protected]

[email protected]

[email protected]

[email protected]

JRC Ispra - IES30

ANNEXES

JRC Ispra - IES31

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%)

JRC Ispra - IES32

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

JRC Ispra - IES33

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

JRC Ispra - IES34

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

JRC Ispra - IES35

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

1

2

3

4

5

A B C D REF

0.7

2.5

3.00.2

1.20.1

0.4

1.30.1

0.8

0.6

0.61.5

0.10.1

0.0

0.3

0.1

1.21.9

A

BC

BB

Result table

dmin

0.1

0.1

0.10.1

0.1

JRC Ispra - IES36

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

JRC Ispra - IES37

SOIL INFERENCE SYSTEM

Figure 2.- Factor Loadings P lot

Factor Loadings Plot

FACTOR(1)

FA

CTO

R(1

)

FACTOR(2)

ZNHG

CR

PB

CU

CD

NI

FACTOR(3)

CU

HG

NI

ZNCDPB

CR

FACTOR(4)

FA

CTO

R(1)

CUZN

NI

HG

PBCD

CR

FA

CTO

R(2

) PB

CD

HGNICR

CU

ZN

CU

HGNIZN

CD

PB

CR

FA

CTO

R(2)

CU

ZNNI

HG

PB

CD

CR

FA

CTO

R(3

)

PB

CD

HGNICR

CU

ZN ZN

HGCR PB

CU

CD

NI

FA

CTO

R(3)

CU

ZN

NIHGPB

CD

CR

FACTOR(1)

FA

CTO

R(4

)

PBCD

HG

NICR

CUZN

FACTOR(2)

ZN

HG

CRPB

CU

CDNI

FACTOR(3)

CU

HG

NI ZNCD

PBCR

FACTOR(4)

FA

CTO

R(4)

Figure 2.- Factor Loadings P lot

Factor Loadings Plot

FACTOR(1)

FA

CTO

R(1

)

FACTOR(2)

ZNHG

CR

PB

CU

CD

NI

FACTOR(3)

CU

HG

NI

ZNCDPB

CR

FACTOR(4)

FA

CTO

R(1)

CUZN

NI

HG

PBCD

CR

FA

CTO

R(2

) PB

CD

HGNICR

CU

ZN

CU

HGNIZN

CD

PB

CR

FA

CTO

R(2)

CU

ZNNI

HG

PB

CD

CR

FA

CTO

R(3

)

PB

CD

HGNICR

CU

ZN ZN

HGCR PB

CU

CD

NI

FA

CTO

R(3)

CU

ZN

NIHGPB

CD

CR

FACTOR(1)

FA

CTO

R(4

)

PBCD

HG

NICR

CUZN

FACTOR(2)

ZN

HG

CRPB

CU

CDNI

FACTOR(3)

CU

HG

NI ZNCD

PBCR

FACTOR(4)

FA

CTO

R(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 Fluvisol

Chromic Phaeozem

Chromic Luvisol

Dystric Luvisol

Gleyic Phaeozem

Eutric Cambisol

Calcaric Phaeozem

Calcaric Regosol

Calcaric Gleysol

Luvic Phaeozem

Haplic Phaeozem

Calcaric Cambisol

Humic Umbrisol

Vitric Andosol

Cr Ni Hg Cd Zn Pb Cu

Basilicata

Calcaric Fluvisol

Chromic Phaeozem

Chromic Luvisol

Dystric Luvisol

Gleyic Phaeozem

Eutric Cambisol

Calcaric Phaeozem

Calcaric Regosol

Calcaric Gleysol

Luvic Phaeozem

Haplic Phaeozem

Calcaric Cambisol

Humic Umbrisol

Vitric 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)

JRC Ispra - IES 1 Novel GIS and Remote Sensing- based techniques for soils at European scales F. Carré, T. Hengl, H.I. Reuter, L. Rodriguez-Lado G. Schmuck.

Documents

quantitative soil data

soil attribute map slide

soil mapping need

jrc ispra ies

images soil type map

augering slide

data users

spatial autocorrelations