Mapping crop distribution in administrative districts of southwest Germany using multi-sensor remote sensing data

Abstract

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.