علوم غیرزیستی دریا
Yasaman Gandomi; Ahmad Savari; Babak Doustshenas; Saleh Arekhi
Abstract
The increasing application of remote sensing for mangrove mapping and monitoring is practical for sustainable management of the biological resources. The emergence of several vegetation indices (VIs) has certainly given significant impacts on mangrove and other forest mappings. In this study, four different ...
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The increasing application of remote sensing for mangrove mapping and monitoring is practical for sustainable management of the biological resources. The emergence of several vegetation indices (VIs) has certainly given significant impacts on mangrove and other forest mappings. In this study, four different vegetation indices including Normalized Different Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI) and Triangular Vegetation Index (TVI) were compared to discover a suitable vegetation index for identifying mangrove area in Nayband bay, Boushehr, Iran and using landsat imagery with 30-m from 2012. Maximum Likelihood Classifier (MLC) was used to classify Mangrove and NonMangrove area. The results demonstrated that the best accuracy (96.85%) was from combination between 7 landsats spectral bands and some vegetation indices including NDVI and SAVI.