Spatial variability in classification accuracy of agricultural crops in the Dutch national land-cover database

Abstract

Variability in per cell classification accuracy is predominantly modelled with land-cover class as the explanatory variable, i.e. with users' accuracies from the error matrix. Logistic regression models were developed to include other explanatory variables: heterogeneity in the 3×3 window around a cell, the size of the patch and the complexity of the landscape in which a cell is located. It was found that per cell, the probability of correct classification was significantly (α=0.05) higher for cells with a less heterogeneous neighbourhood, for cells part of larger patches and for cells in regions with a less heterogeneous landscape. To validate the models, a leave-one-out procedure was applied in which the absolute difference between the actual and the model-estimated number of cells correctly classified was summarized over 55 regions in the Netherlands. The sum of differences reduced from 60.9 to 48.1 after adding the variables ‘patch size’ and ‘landscape dominance’ to the land-cover class model. Spatial variability thus modelled therefore led to a substantial improvement in the estimation of the per cell classification accuracy.

Acknowledgements

The authors thank the Dutch Ministry of Agriculture, Fisheries and Nature Management for making the data available for the validation of the crop classification. The work presented in this study is part of a PhD project funded by the Netherlands Geodetic Commission (NCG), the Survey Department of the Ministry of Transport, Public Works and Water Management, Geo-Information and ICT Department (RWS-AGI), the Netherlands Topographical Service (TDN), the Netherlands Council for Geographical Information (Ravi), the Netherlands Institute of Applied Geoscience—National Geological Survey (TNO-NITG), and the Netherlands Green World Research Institute (Alterra).