Applicability of NDVI temporal database for western Himalaya forest mapping using Fuzzy-based PCM classifier. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Bhavsar, Dhruval; Kumar, Anil; Roy, Arijit
• Is Part Of: European journal of remote sensing. Volume 50:Issue 1(2017)
• Page Start: 614
• Page End: 625
• Publication Date: 2017-01-01
• Subjects: Fuzzy classification -- PCM -- NDVI -- species mapping; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2017.1379363
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: Information about the spatial distribution of the different tree species is important for sustainable ecosystem management and planning in the western Himalaya. Remote sensing has proved to be useful to assess the spatial and qualitative distribution of vegetation cover over large areas. Present investigation has been carried out to discriminate three different gregarious forest types, i.e. sal ( Shorea robusta ), chir pine ( Pinus roxburghii ) and oak ( Quercus spp.) in part of western Himalaya. The use of existing classical classifiers has limitation in classification of overlapping classes and in mixed vegetation formations. To overcome this, fuzzy-based possibilistic C -means (PCM) classifier was used to separate the classes. Temporal Landsat 8 imagery (representing the different phenological states) has been used to classify the three forest types. Phenological information and spectral variability were used to select the best suitable dates, i.e. temporal NDVI to use in PCM classifier. It was observed that the satellite data of March, April, May and November were the best suited for discrimination of sal, pine and oak. The overall accuracy of the classified image was found to be 86%. This method can be used for automated extraction of different species in mixed vegetation formations with appreciable accuracy.