In the face of global change, concepts for sustainable land management are increasingly requested, among others to cope
with the rapidly increasing energy demand. High resolution land use classifications can contribute spatially explicit
information suitable for land use planning. In this study, the coverage of cereal crops was derived for two regions in
Baden-Wuerttemberg and Rhineland-Palatinate - Germany, as well as in the Alsace - France, by classifying multitemporal
and multi-scale remote sensing data. The presented methodology shall be used as basic input for high resolution
bio-energy potential calculations.
Segmentation of pan-merged 15 m Landsat 7 ETM+ data and pre-classification with CORINE data was applied to derive
homogenous objects assumed to approximate the field boundaries of agricultural areas. Seven acquisitions of moderate
resolution IRS-P6 AWiFS data (60 m) recorded during the vegetation period of 2007 were used for the subsequent
classification of the objects. Multiple classification and regression trees (random forest) were selected as classification
algorithm due to their ability to consider non-linear distributions of class values in the feature space. Training and
validation was based on a subset of 1724 samplings of the official European land use survey LUCAS (Land Use/ Cover
Area Frame Statistical Survey).
Altogether, the object based approach resulted in an overall accuracy of 74 %. The use of 15 m Landsat for mapping
field objects were identified to be one major obstacle caused by the characteristically small agricultural units in
Southwest Germany. Improvements were also achieved by correcting the LUCAS samples for location errors.
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