How To Understand The Geology Of An Australian Soil

Transcription

1 Modelación y Cartografía Digital de Suelos en Australia Elisabeth Bui CSIRO Land and Water

2 Australia tiene una larga historia en los inventarios de los recursos de la tierra y en los métodos de evaluación de los recursos Newsletter

3 Bui A review of digital soil mapping in Australia. Pp In: Lagacherie, P., McBratney, A.B., Voltz, M. (Eds) Digital Soil Mapping. Developments in Soil Science, vol. 31. Elsevier, Amsterdam.

4 Mi experiencia Murray-Darling Basin Soil Information Strategy (MDBSIS): prediciones de tipos de suelos Australian Soil Resources Information System (ASRIS): prediciones de propriedades de suelos a partido de medidas en puntos

5 Artículos en revistas Bui, EN, Loughhead, A. and R. Corner Extracting soil-landscape rules from previous soil surveys. Aust. J. Soil Res. 37: Bui, EN and Moran, CJ Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Geoderma 103: Moran, CJ and Bui, EN Spatial data mining for enhanced soil map modelling. International Journal Geographical Information Sciences 16: Bui, EN and Moran, CJ A strategy for filling gaps in soil survey over large spatial extents: an example from the Murray-Darling basin of Australia. Geoderma 111: Bui, EN Soil survey as a knowledge system. Geoderma 120: Bui, E., Simon, D., Schoknecht, N., and A. Payne Adequate prior sampling is everything: lessons from the Ord. Pp In: Lagacherie, P., McBratney, A., Voltz, M. (Eds) Digital Soil Mapping. Developments in Soil Science, vol. 31. Elsevier, Amsterdam.

6 Artículos en revistas Johnston, R, Bleys, E, Barry, S, Bui, EN et al ASRIS: The database. Aust. J. Soil Res. 41: Henderson, BL, Bui, EN, Moran, CJ and Simon, DAP Australia-wide predictions of soil properties using decision trees. Geoderma 124: Bui, EN, Henderson, BL and Viergever, K Knowledge discovery from models of soil properties developed through data mining. Ecol. Modelling 191: Bui, E., B. Henderson, and K. Viergever (2009), Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia, Global Biogeochem. Cycles, 23, GB4033, doi: /2009gb

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12 MDBSIS

13 MDBSIS

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15 For details html

16 MDBSIS

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18 Some new terrain attributes

19 Accounting for spatial variability of predictors Moran and Bui (2002) IJGIS 16(6):

20 Map predictions

21 Mas attributos de terreno SEDUM (Moran et al. 2001) details.aspx?id=13626

22 Otros variables ambientales Climaticos ANUCLIM Cobertura satelital (Landsat MSS o TM, MODIS ) γ-radiometricos (geochimica) Edad de la superficie geologica?

23 A B MAT deg C H: 29 MAP mm H: 476 L: 3 L: 11

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25 MDBSIS

26 Classification tree models Dominant soil types from existing soil map units as training data: categorical Environmental predictors: Landsat MSS (4 bands): continuous Lithology: categorical 9 DEM & terrain attributes (e.g., slope, distances to ridges/rivers, relief ): continuous, mix of raw and smoothed

27 A binary classification tree Bui et al. 1999

28 Sampling kappa Moran and Bui

29 Pruning

30 Map predictions

31 Kurrawa-Toowoomba Class Original Predicted Total no. of pixels of that class in the original map % of total area occupied by the class in the original map Class area proportions correctly predicted

32 MDBSIS

33 Classification tree models Existing soil maps as training data: categorical Environmental predictors: Landsat MSS (4 bands): continuous Lithology: categorical 9 DEM & terrain attributes (e.g., slope, distances to ridges/rivers, relief ): continuous, mix of raw and smoothed

34 Training data and domain of extrapolation Bui and Moran (2003) Geoderma 111:21-44

35 For all training areas Bui and Moran Geoderma 111:21-44.

36 Repeated random sampling for generation of multiple trees can be used to estimate certainty of predictions

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38 Land systems

39 Block diagram

40 Land Systems of Western New South Wales intersected with relief classes from a 5x 5 pixel window (1.56 km^2) Map unit <9 m (%area) PPF m (%area) PPF m (%area) PPF Cz 58 Gn Gn Um1.43 Ne 92 Gc Uc Mn 84 Gc Gc Bm 84 Gc Uc Ov 85 Gc Uc Hf 94 Gc Uc Ir 68 Gn Gn Nl 97 Ug Ug La 78 Gn Um Bz 0 40 Uc Uc Principal Profile Form (Northcote, 1979). This is an Australian soil classification scheme that describes soils on the basis of field morphological criteria of the solum.

41 Soil-landscapes of Manilla: examples of soil associations that can be disaggregated Soil-landscape Dominant Great Soil Group Dominant Principal Profile Form Range of Principal Profile Forms No. of clusters Alluvial Black Earth Black Earth Ug5.15 Ug5.15, Ug Black Earth- Euchrozem Association Black Earth Association Siliceous sands- Yellow solodic soil association Red brown earth association Euchrozem Black Earth Black Earth, red brown clays, euchrozems Yellow solodic Siliceous sands Gn3.12 Ug5.1 Ug5.15 Gn3.12, Gn3.13 Ug5.12, Ug5.13, Ug5.14, Ug5.15 Ug5.11, Ug5.12, Uf6.31, Uf6.32, Gn4.12 Dy2.22, Dy3.42 Uc1.22, Uc1.41, Uc2.21, Uc4.1 Red brown earth Dr2.23 Gn4.13, Dr2.13, Dr2.22, Dr

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43 Un otro ejemplo de desgregacion

44 Dos areas de formación

45 Fluvial facies

46 Northcote soil types

47 Evaluation Bui and Moran

48 Comparison with Atlas of Australian Soils

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50 Conclusiones Con el MDBSIS hemos desarrollado varias técnicas para maximizar el valor de mapas de suelos existentes utilizarlos para (re-)mapear (e.g. como areas de formacion para producir reglas a traves de variables ambientes, como mapa base para desagregacion de poligonos) evaluar los resultados