Integrating spatial statistics and remote sensing.

This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine continuous spatial properties with classified map units. The paper is illustrated with five case studies: one on heavy metals in groundwater at different scales, one on soil variability within seemingly homogeneous units, one on fuzzy classification for a soillandscape model, one on classification with geostatistical procedures and one on thermal images. The integrated approach offers a better understanding and quantification of uncertainties in remote sensing studies.