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Journal of Applied Remote Sensing

Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers
Author(s): Laxmi Kant Tiwari; Satish K. Sinha; Sameer Saran; Valentyn A. Tolpekin; Penumetcha L. N. Raju
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Paper Abstract

Maximum likelihood classifier (MLC) and support vector machines (SVMs) are commonly used supervised classification methods in remote sensing applications. MLC is a parametric method, whereas SVM is a nonparametric method. In an environmental application, a hybrid scheme is designed to identify forest encroachment (FE) pockets by classifying medium-resolution remote sensing images with SVM, incorporating knowledge-base and GPS readings in the geographical information system. The classification scheme has enabled us to identify small scattered noncontiguous FE pockets supported by ground truthing. On Baratang Island, the detected FE area from the classified thematic map for the year 2003 was ∼202  ha, and for the year 2013, the encroachment was ∼206  ha. While some of the older FE pockets were vacated, new FE pockets appeared in the area. Furthermore, comparisons of different classification results in terms of Z-statistics indicate that linear SVM is superior to MLC, whereas linear and nonlinear SVM are not significantly different. Accuracy assessment shows that SVM-based classification results have higher accuracy than MLC-based results. Statistical accuracy in terms of kappa values achieved for the linear SVM-classified thematic maps for the years 2003 and 2013 is 0.98 and 1.0, respectively.

Paper Details

Date Published: 23 February 2016
PDF: 20 pages
J. Appl. Rem. Sens. 10(1) 016016 doi: 10.1117/1.JRS.10.016016
Published in: Journal of Applied Remote Sensing Volume 10, Issue 1
Show Author Affiliations
Laxmi Kant Tiwari, Rajiv Gandhi Institute of Petroleum Technology (India)
Satish K. Sinha, Rajiv Gandhi Institute of Petroleum Technology (India)
Sameer Saran, Indian Institute of Remote Sensing (India)
Valentyn A. Tolpekin, Univ. Twente (The Netherlands)
Penumetcha L. N. Raju, Indian Institute of Remote Sensing (India)

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