Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium |
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| Authors: | Sara Verbeiren Herman Eerens Isabelle Piccard Ides Bauwens Jos Van Orshoven |
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| Institution: | aVlaamse Instelling voor Technologisch Onderzoek (VITO), Expertisecentrum voor teledetectie (TAP), Boeretang 200, B-2400 Mol, Belgium;bKatholieke Universiteit Leuven, Department of Land Management and Economics, GEO-instituut Campus Arenberg, Celestijnenlaan 200E, 3001 Leuven, Belgium |
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| Abstract: | Global time series of low resolution images are available with high repeat frequency and at low cost, but their analysis is hampered by the presence of mixed pixels and the difficulty in locating detailed spatial features. This study examined the potential of sub-pixel classification for regional crop area estimation using time series of monthly NDVI-composites of the 1 km resolution sensor SPOT-VEGETATION. Belgium was selected as test zone, because of the availability of ample reference data in the form of a vectorial GIS with the boundaries and cover type of the large majority of agricultural fields. Two different methods were investigated: the linear mixture model and neural networks. Both result in area fraction images (AFIs), which contain for each 1 km pixel the estimated area proportions occupied by the different cover types (crops or other land use). Both algorithms were trained with part of the reference data and validated with the remainder. Validation was repeated at three different levels: the 1 km pixel, the municipality and the agro-statistical district. In general, the neural network outperformed the linear mixture model. For the major classes (winter wheat, maize, forest) the obtained acreage estimates showed good agreement with the true values, especially when aggregated to the level of the municipality (R2 ≈ 85%) or district (R2 ≈ 95%). The method seems attractive for wide-scale, regional area estimation in data-poor countries. |
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| Keywords: | Linear mixture model Neural network Area fraction images CLC2000 IACS MESTBANK |
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