Classification of land-cover types in muddy tidal flat wetlands using remote sensing data

Remote sensing classification of tidal flat wetlands is important for obtaining high-precision information on wetland features. In this study, Thematic Mapper (TM) images of the Yancheng National Reserves, Jiangsu Province, China, for the years of 1996, 2002, 2006, and 2010 were considered. First, the optimum combination of bands was chosen. Second, vegetation and nonvegetation regions of interest were established to investigate the spectral reflectance characteristics of the different ground objects. Then we used the knowledge-based decision tree method on different features, such as the normalized difference vegetation index and the spectral reflectance. In particular, the ancillary information is helpful to distinguish the vegetation classes. The results demonstrate that the classification system has advantages in identifying the types of vegetation in ecotones, and it is 4 percentage points higher than the maximum likelihood method in classification accuracy. This study is useful to discriminate vegetation, and it provides an important reference for the effective extraction of tidal flat land-cover information from TM images. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.