Novel GIS and Remote Sensingbased techniques for soils at European scales

Transcription

1 Novel GIS and Remote Sensingbased techniques for soils at European scales F. Carré, T. Hengl, H.I. Reuter, L. Rodriguez-Lado G. Schmuck (LMNH Unit) & L. Montanarella (MOSES Action) 1

2 Framework of the project Soil Thematic Strategy Data support Data needs Communication European Soil Data Center OUR RESEARCH ACTIVITY Methods & Data 2

3 Innovation of the project From a scientific point of view Problem of traditional soil maps - traditional soil maps are not easy to understand (no methodology described, terminology understandable only by soil science community) Need quantitative methods to map easy to interpret attributes - soil attribute information can be missing at appropriate scale Need easy- to-use models (tools) for soil mapping - Usually soil attributes and classes are represented with crisp boundaries coming from expert interpretation and there is no indication of the soil map quality Need to evaluate the accuracy of the soil maps From an economic point of view Traditional soil surveys are very expensive because they need a lot of auger information Need sampling techniques for augering 3

4 Innovation in images Soil type map 4 uncertainty

5 Core of the methodology Core To provide quantitative soil data, producible at low cost and easyto-interpret-and-use (for other scientists and policy makers) How? By elaborating quantitative methods : - for mapping; - for estimating associated accuracy; Using easily accessible indirect soil information (auxiliary data) Name Digital Soil Mapping 5

6 Presentation of Digital Soil Mapping methodology DSM in practice (example of application) Tools and guidelines addressed to soil data users 6

7 Digital Soil Mapping (DSM) Sampled data Soil observations Soil attributes Soil classes Soil inference system (spatial, attribute) Auxiliary data Statistics Geostatistics Spatial accuracy Soil covariates (RS images, DEM ) Accuracy map Soil attribute map Soil functions Soil threats Erosion map Scenario testing/ risk assessment Suitability map Market / society Environment 7 POLICIES / MANAGEMENT

8 Presentation of Digital Soil Mapping methodology DSM in practice (example of application) Tools and guidelines addressed to soil data users 8

9 DSM application example Heavy Metal Content in Zagreb County (Croatia) Author: Hengl (2006) 9

10 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Heavy Metal content Scenario testing/ risk assessment Market / society Environment 10 POLICIES / MANAGEMENT

11 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment 11 POLICIES / MANAGEMENT

12 Zagreb county 1142 samples over 3700 km 2 : contents of Cu, Pb, Ni, Zn 12

13 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment 13 POLICIES / MANAGEMENT

14 Zagreb county 14

15 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment 15 POLICIES / MANAGEMENT

16 Regression-kriging Multiple Linear Regression Y j Spatially continuous Punctual Y j = a 1 X 1 + a 2 X a n X n + ε j Soil variable j Auxiliary data i residuals j Kriging γ εj Semi-variance (interpolation process according to spatial autocorrelations of the variable) a i X i i Summation of the two maps regression auxiliary data kriging residuals regressionkriging soil variables 16 distance (m)

17 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment 17 POLICIES / MANAGEMENT

18 Soil attribute map 18

19 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment 19 POLICIES / MANAGEMENT

20 Continuous maps of Heavy Metal Content Spatial accuracy map 20 East

21 Soil observations Auxiliary data Soil inference system (spatial, attribute) Soil attributes Soil classes Spatial accuracy Soil functions Soil threats Scenario testing/ risk assessment Market / society Environment 21 POLICIES / MANAGEMENT

22 Limitation scores 30 LS = b 0. HMC b1-1 if HMC X1 0 if HMC < X1 Limitation scores LS= HMC X 1 Permissible (baseline) concentration X 2 Serious pollution 5 Pollution standards in Croatia X 1 X 2 ln(b 0 ) b 1 mg. kg -1 mg. kg -1 Heavy metal concentration (mg kg -1 ) Cd Cr Cu Ni Pb Zn Triantifalis et al., 2001 LS = 1 when HMC = X 1 LS = 5 when HMC = X 2 22 From Hengl in Dobos et al. (2006)

23 Pollution map 23

24 Presentation of Digital Soil Mapping methodology DSM in practice (example of application) Tools and guidelines addressed to soil data users - Technical manual / textbook to process DEMs (Hengl & Reuter) 24

25 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 25

26 CONCLUSIONS 26

27 Present / Future of DSM Erosion (wind, water ) Typology of soil pollutions Mapping of the ecosystem continuum Interpretation of soil attributes with RS data Digital Soil Mapping Modelling soil scenarios Continuous soil classification tool Soil sampling Improving EU soil map Actual work For

28 Support to FP7 Health Risk assessment agriculture Inputs for biomass prediction inputs for STS and other directives Environment Digital Soil Mapping Energy Input for soil - forest continuum Information and communication techno. Auxiliary data needs 28

29 Thanks for your attention 29

30 ANNEXES 30

31 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 ~ Total cost: instead of (38000 saved i.e. 33%)

32 Economic gain of DSM For physical soil parameters: DSM allows for saving 2/3 of the sampling 1500 Km samples (3375 ) 150 samples (1125 ) 2250 SAVED For chemical soil parameters: DSM allows for saving 2/3 of the sampling Km samples (45000 ) 300 samples (30000 ) SAVED

33 Mapping of soil, by J.P. Legros (translated by V.A.K. Sharma). Science Publishers, Enfield, pp ISBN

34 34

35 B Principles A C D Set of soil references OSACA Software Result table A B C D REF dmin 35 Set of soil observations B C B B A

36 SOIL MAP OF AISNE (FRANCE) AT 1: SCALE (Carré & Reuter) OSACA Classes OSACA distances 36 SOIL MAPPING UNITS DISTANCES TO SMU To be published in Elsevier (2007)

37 SOIL INFERENCE SYSTEM Principal Component Analysis Soil contamination for Natura 2000 sites in Italy (Rodriguez-Lado) FACTOR(1) FACTOR(2) FACTOR(3) FACTOR(1) PB CD CD PB HG ZN HG ZN CU CU CR NI CR NI FACTOR(2) ZN ZN CR NI HG HG CR NI CD CD CU PB PB CU FACTOR(3) NI CU HG CU HG NI CR PB PB CR CD CD ZN ZN FACTOR(4) NI CU ZN CU NI ZN ZN CU NI CR CD PB CD CR CD PB CR PB HG HG HG FACTOR(1) FACTOR(2) FACTOR(3) HG HG HG Heavy Metal Contents FACTOR(4) PB CD ZN CU CR NI ZN CR NI PB CD CU NI CU PB CR CD ZN FACTOR(4) Soil Types Hierarchical Cluster Analysis FACTOR(1) FACTOR(2) FACTOR(3) FACTOR(4) Basilicata Calcaric CALCARIC FluvisolFLU Chromic CHROMIC Phaeozem PHAE Chromic CHROMIC Luvisol LUVI Dystric DYSTRIC Luvisol LUVI Gleyic GLEYIC Phaeozem PHAEO Eutric EUTRIC Cambisol CAMBI Calcaric CALCARIC PhaeozemPHA Calcaric CALCARIC Regosol REG Calcaric CALCARIC GleysolGLE Luvic LUVIC Phaeozem PHAEOZ Haplic HAPLIC Phaeozem PHAEO Calcaric CALCARIC Cambisol CAM Humic HUMIC Umbrisol UMBRIS VITRIC Vitric Andosol ANDOS Permuted Data Matrix Cr Ni Hg Cd Zn Pb Cu Cr Ni Hg Cd Zn Pb Cu CR NI HG CD ZN PB CU

38 Climate erodibility of agriculture soils (Reuter) Reuter In Reuter et al. (2006) Wind Speed [m/s] 38