Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans |
| |
| Authors: | Tanumi Kumar Abhishek Mandal Dibyendu Dutta R Nagaraja Vinay Kumar Dadhwal |
| |
| Institution: | 1. Regional Remote Sensing Centre-East, National Remote Sensing Centre, Indian Space Research Organisation, Kolkata, India;2. Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, India;3. National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad, India;4. Indian Institute of Space Science and Technology, Department of Space, Government of India, Thiruvananthapuram, India |
| |
| Abstract: | In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels. |
| |
| Keywords: | Avicennia spp hyperspectral satellite image vegetation indices decision tree full-pixel classifiers |
|
|
