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Coastal wetland vegetation classification with a Landsat Thematic Mapper image

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Coastal wetland vegetation classification with remotely sensed data has attracted increased attention but remains a challenge. This paper explored a hybrid approach on a Landsat Thematic Mapper (TM) image for classifying coastal wetland vegetation classes. Linear spectral mixture analysis was used to unmix the TM image into four fraction images, which were used for classifying major land covers with a thresholding technique. The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method. Expert rules were finally used to modify the classified image. This research indicates that the hybrid approach employing sub-pixel information, an analyst's knowledge and characteristics of coastal wetland vegetation distribution shows promise in successfully distinguishing coastal vegetation classes, which are difficult to separate with a maximum likelihood classifier (MLC). The hybrid method provides significantly better classification results than MLC.

Document Type: Research Article


Affiliations: 1: College of Forest Resources and Environment, Nanjing Forestry University, Nanjing, China 2: Anthropological Center for Training and Research on Global Environmental Change (ACT), Indiana University, Bloomington, Indiana, USA 3: Yancheng National Nature Reserve, Yancheng, Jiangsu Province, China

Publication date: 2011-01-01

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