Title of article :
Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context
Author/Authors :
Clovis Grinand، نويسنده , , Dominique Arrouays، نويسنده , , Bertrand Laroche، نويسنده , , Manuel Pascal Martin، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2008
Pages :
11
From page :
180
To page :
190
Abstract :
This paper aims to investigate the potential of using soil-landscape pattern extracted from a soil map to predict soil distribution at unvisited location. Recent machine learning advances used in previous studies showed that the knowledge embedded within soil units delineated by experts can be retrieved and explicitly formulated from environmental data layers However, the extent to which the models can yield valid prediction has been little studied. Our approach is based on a classification tree analysis which has underwent a recent statistics advance, namely, stochastic gradient boosting. We used an existing soil-landscape map to test our methodology. Explanatory variables included classical terrain factors (elevation, slope, curvature plan and profile, wetness index, etc.), various channels and combinations of channels from LANDSAT ETM imagery, land cover and lithology maps. Overall classification accuracy indexes were calculated under two validation schemes, either taken within the training area or from a separated validation area. We focused our study on the accuracy assessment and testing of two modelling parameters: sampling intensity and spatial context integration. First, we observed strong differences in accuracy between the training area and the extrapolated area. Second, sampling intensity, in proportion to the class extent, did not largely influence the classification accuracy. Spatial context integration by the use of a mean filtering algorithm on explanatory variables increased the Kappa index on the extrapolated area by more than ten points. The best accuracy measurements were obtained for a combination of the raw explanatory dataset with the filtered dataset representing regional trend. However, the predictive capacity of models remained quite low when extrapolated to an independent validation area. Nevertheless, this study offers encouragement for the success of extrapolating soil patterns from existing soil maps to fill the gaps in present soil map coverage and to increase efficiency of ongoing soil survey.
Keywords :
Validation , Digital soil mapping , Boosted classification tree , Extrapolation , Sampling intensity , FRANCE
Journal title :
GEODERMA
Serial Year :
2008
Journal title :
GEODERMA
Record number :
1297284
Link To Document :
بازگشت