Comparison of Landsat image classification methods for detecting mangrove forests in Sundarbans

Abstract

Remote-sensing images taken from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor with a spatial resolution of 30 m were applied for mapping and inventory of mangrove forest areas in Sundarbans, on both sides of the border between Bangladesh and India. Three different classification methods – unsupervised classification with k-means clustering, supervised classification using the maximum likelihood decision rule, and band-ratio supervised classification – were tested and compared in terms of the top of the atmosphere reflectance images. Spectral signature and principal component analyses were applied to select the appropriate band combinations prior to the band ratio–supervised classification. Our results show that the band ratio method is superior to the unsupervised or supervised classification methods considering the visual inspection, producer's and user's accuracy, as well as the overall accuracy of the all the classes in the image. The best discrimination of mangrove/nonmangrove boundary can be achieved when the combinations of B4/B2 (band 4/band 2), B5/B7, and B7/B4 are employed from the ETM+ bands.