From Soil survey to Digital Soil Mapping The LISAH experience First DSM working group meeting University of Miskolc, Hungary, 7-8 April, 2005 P. Lagacherie Laboratoire détude des interactions Sol – Agrosystème – Hydrosystème Montpellier (France)
The roots : soil survey : Soil Information System of the Languedoc-Roussillon at 1:250,000 scale (Bornand, et al, 1994) : 1:10,000 scale reference areas (Favrot et al, 1981, 1989) : Soil surveying at various scale over the French territory (several millions ha)
: A wide range of DSM problems explored Pre-processing of soil covariateModelling soil information inputs Building class Scorpan and class property functions Evaluating and representing the quality of digital soil maps Pre-processing of soil covariateModelling soil information inputs Building class Scorpan and class property functions Evaluating and representing the quality of digital soil maps
CLAPAS: Interactive Classification of Soilscapes (J.M. Robbez-Masson phD 1994, DSM 2004 proc.,2004) Alluvions Colluvions Old alluvions Fallen rocks, glacis Towns, etc. Hard limestones Soft limestones Dolomites Marls, clays Argilites Schists, shales Sandstones Volcanic formations Gneiss and granite Lithological map Low Steep Slope map Images of soil forming factors Reference areas User selected reference areas Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10 Unit 11 Map units Image of classified soilscape (contextual image processing) Reference areas (2 nd pass) Good Medium Bad Mathematical distances Image of landscape distance from reference areas Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10 Unit 11 Unit 12 Unit 13 Unit 14 Unit 15 Unit 16 Map units Good Medium Bad
Pre-processing of soil covariateModelling soil information inputs Building class Scorpan and class property functions Evaluating and representing the quality of digital soil maps
Representing qualitative soil information by means of possibility distributions (Lagacherie, Geod., in press) Clay%silt%sand% Huevaluechroma depth % stone react2acid Soil class Auger hole A: cm Color: 75YR32 text: LSA stone: 20% Rtoacid: None B: cm Color: 75YR32 text: LAS stone: 30% Rtoacid: None C: cm Color: 75YR60 text: ? stone: 90% Rtoacid: None %Clay %Silt%Sand HueChromaValue %Stone %Rtoacid Depth cm %% % Lagacherie et al, 1994
Pre-processing of soil covariateModelling soil information inputs Building class Scorpan and class property functions Evaluating and representing the quality of digital soil maps
Soil Pattern rules Soil landscape rules Scorpan functions using soil surveys of reference area (Lagacherie pHD 1992, Lagacherie et al, Geod. 1995, IJGS 1997, Voltz et al, EJSS 1997, Lagacherie & Voltz, Geod.2001) Conditional probability approaches
Using the reference area scorpan functions Reference area Representative area New mapped area Predicting soils from covariates only (classif. Tree) (Lagacherie et al, 1997) Predicting soils by DEM- driven-interpolation of classified sites (Lagacherie et al, 1995) Predicting soils properties by interpolation of classified sites (Lagacherie et al, 2001)
Pre-processing of soil covariateModelling soil information inputs Building class Scorpan and class property functions Evaluating and representing the quality of digital soil maps
Using fuzzy logic to propagate imprecision in Soil Information Systems DTM Geol map Logical queries Arithmetic expressions Fuzzy pattern matching Degré de possibilité d US Possibility of soil class Cazemier, pHD, 1999, Martin-Clouaire et al, Compag, 2000 loamy clayey deep soil moderate stoniness Pedotransfer functions awc = (w100 i - w1500 i ) * bd i * thickness i * ((100 - stones i )/100)) Possibly > 240 mm Surely > 240 mm Undecided Fuzzy constraint solver Cazemier, pHD, 1999, Cazemier et al, Geod., 2001 Geology = GU1 (1) or GU2 (0.8) or GU3 (0.2 Slope = most likely in [2%,5%], not out of [0.5%, 8%] Soil Class 506
Conclusion A wide range of DSM questions examined Integration of the soil survey experience in numerical procedures Possible contributions to a more generic tool