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MULTI-TEMPORAL CLASSIFICATION OF ASAR IMAGES IN AGRICULTURAL AREAS

Human activities strongly affect the environment and impact natural resources. To reduce the disadvantages, we have to monitor the human activities as well as the environment. Therefore demand on continuous and inexpensive methods for environmental monitoring is strongly increasing. In this research and development project, ENVISAT polarimetric SAR data are examined for their usefulness to environmental monitoring within a drinking water protection area named "Fuhrberger Feld", north east of the city of Hanover in Germany. This is done by using ENVISAT ASAR images together with GIS information like topographic maps, orthophotos, also ground surveys. Because of only 2 polarisations of ASAR, yielding a coherent response of different vegetation types and the high variance of pixel values, the results from classification approaches using monotemporal images are unsatisfactory. Our experiments and the experience of other authors as well as the knowledge about crop phenology led to a multi-temporal classification approach improving the classical methods. In multi-temporal classification, we assume images from different dates, which cover the phenologic period of desired crops, as bands of a multi-temporal image. The feasibility and accuracy of this multitemporal approach is evaluated within a study area and answers some questions about multi-temporal classification in this paper, namely the necessary images (dates) to be used, the pre-processing (filters) to improve the accuracy of classification together with the accuracy of multi-temporal classification for crops with fixed phenological period. The classification of crops with different phenological periods and the combination of results from classifying different sets of images is shown and the limitations of multitemporal classification is demonstrated.