Remote Sensing Applications and Decision Support

Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers

[+] Author Affiliations
Laxmi Kant Tiwari, Satish K. Sinha

Rajiv Gandhi Institute of Petroleum Technology, Ratapur Chowk, Rae Bareli, Uttar Pradesh 229316, India

Sameer Saran, Penumetcha L. N. Raju

Indian Space Research Organization, Indian Institute of Remote Sensing, 4-Kalidas Road, P.O. Box 135, Dehradun, Uttarakhand 248001, India

Valentyn A. Tolpekin

University of Twente, Department of Earth Observation Science, P.O. Box 217, 7500 AE Enschede, The Netherlands

J. Appl. Remote Sens. 10(1), 016016 (Feb 23, 2016). doi:10.1117/1.JRS.10.016016
History: Received September 22, 2015; Accepted January 21, 2016
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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.

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Citation

Laxmi Kant Tiwari ; Satish K. Sinha ; Sameer Saran ; Valentyn A. Tolpekin and Penumetcha L. N. Raju
"Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers", J. Appl. Remote Sens. 10(1), 016016 (Feb 23, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.016016


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