Large-area spatial disaggregation of a mosaic of conventional soil maps: Evaluation over Western Australia

TY - JOUR

T1 - Large-area spatial disaggregation of a mosaic of conventional soil maps: Evaluation over Western Australia

AU - Holmes, Karen

AU - Griffin, E.A.

AU - Odgers, N.P.

PY - 2015

Y1 - 2015

N2 - © CSIRO 2015. Conventional soil maps may be the best available source for spatial soil information in data-limited areas, including individual soil properties. Spatial disaggregation of these maps, or mapping the unmapped soil components, offers potential for transforming them into spatially referenced soil class distributions. We used an automated, iterative classification tree approach to spatially disaggregate a patchwork of soil surveys covering Western Australia (2.5×106km2) to produce raster surfaces of soil class occurrence. The resulting rasters capture the broad spatial patterns of dominant soils and harmonise soil class designations across most survey boundaries. More than 43000 archived profiles were used to evaluate the accuracy of the rasters. In 20% of cases, the predicted soil class with the highest probability matched that recorded for the profile; when any of the three highest probability soil classes predicted were considered correct, the global accuracy was 40%. The accuracy increased to 71% when the rasters were reassembled to represent a higher level in the soil classification system. The predicted surfaces retained features related to the mapping intensity of the original surveys and generally had higher prediction accuracy of profile soil class where the surface geochemistry was more homogeneous. The best indicator of prediction accuracy was how common the profile soil class was in the original mapping (94% variance explained); profile observations collected during soil survey may be biased towards rare soils, making them less suitable for validation or modelling directly from point data.

AB - © CSIRO 2015. Conventional soil maps may be the best available source for spatial soil information in data-limited areas, including individual soil properties. Spatial disaggregation of these maps, or mapping the unmapped soil components, offers potential for transforming them into spatially referenced soil class distributions. We used an automated, iterative classification tree approach to spatially disaggregate a patchwork of soil surveys covering Western Australia (2.5×106km2) to produce raster surfaces of soil class occurrence. The resulting rasters capture the broad spatial patterns of dominant soils and harmonise soil class designations across most survey boundaries. More than 43000 archived profiles were used to evaluate the accuracy of the rasters. In 20% of cases, the predicted soil class with the highest probability matched that recorded for the profile; when any of the three highest probability soil classes predicted were considered correct, the global accuracy was 40%. The accuracy increased to 71% when the rasters were reassembled to represent a higher level in the soil classification system. The predicted surfaces retained features related to the mapping intensity of the original surveys and generally had higher prediction accuracy of profile soil class where the surface geochemistry was more homogeneous. The best indicator of prediction accuracy was how common the profile soil class was in the original mapping (94% variance explained); profile observations collected during soil survey may be biased towards rare soils, making them less suitable for validation or modelling directly from point data.

U2 - 10.1071/SR14270

DO - 10.1071/SR14270

M3 - Article

VL - 53

SP - 865

EP - 880

JO - Australian Journal of Soil Research

JF - Australian Journal of Soil Research

SN - 0004-9573

IS - 8

ER -