Comparison of different sensors and analysis techniques for tropical mangrove forest mapping

The objective of this study is to compare different remote sensing sensors and analysis techniques for the purpose of mangrove mapping. A study area in Southern Thailand of approximately 40/spl times/30 km size was selected. A systematic assessment of strengths and limitations of data taken from different sensors, namely Landsat TM, SPOT HRV, MOS MESSR, JERS-1 SAR and ERS-1 SAR, was carried out. The results of the investigation show that optical remote sensing data is highly suitable for mapping mangrove forests and can discriminate reasonably well four mangrove forest classes, namely homogeneous rhizophora, homogeneous nypa, mixed dense and mixed open mangrove forest. The classification accuracy is approximately 87%. The use of radar data alone resulted in a significantly lower classification accuracy, but on the other hand provided additional information related to the age distribution of rhizophora stands.