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Metodi Panev [03-2015]:

Approaches to topographic normalization for forest type mapping in Shitai County, Anhui Province, China

Diese Arbeit ist online verfügbar: Download


The aim of this master thesis is to research different approaches for topographic normalization of Rapid Eye imagery with the objective of creating accurate forest type maps of regions dominated by rugged terrain. The study area is situated in the Shitai County, Anhui Province, China and covers the extent of one tile from a Rapid Eye image. It is predominantly hilly and mountainous area, where steep mountains are intersected by valleys. Forest are dominating the mountain slopes whereas agriculture dominates in the valleys. Most common forest types are Chinese fir (Cunninghamia lanceolata), Cyclobalanopsis and Castanopsis broadleaved forests and bamboo. Whereas tea and especially rice are dominant agricultural crops. Three common methods for topographic normalization were used in order to compare which one compensates the topographic effect most efficient. Additionally the images were separated in vegetation classes, using several criteria (NDVI, slope and illumination) to see if the efficiency of the normalization process would improve. Besides the RapidEye images, a Landsat image of the study area was also processed with the three methods of topographic normalization, in order to see the effect of different resolution imagery on the topographic normalization. The normalized images were classified using a maximum likelihood classifier. In order to provide reference data for the classification, a Land Use Inventory (LUI) was carried out. The LUI was performed on a systematic 3x3 grid, with plots of 200x200m, in which the current land use types were delineated. Ancillary data (DEM and NDVI) was added additionally to the classification, to see if the addition would improve the forest type maps and compensate for the left over topographic effect. The results showed that the c-correction and rotation method provide better normalization compared to the minnaert method. The separation in classes is not improving significantly the normalization, and the topographic normalization of the Landsat images does not differ significantly from the RapidEye images. The results also showed the the topographically normalized images provide better classification results. These results are improved further with the addition of the ancillary data. Furthermore the addition of the ancillary data is compensating to some extent the topographic effect which is left over after the normalization.


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