ABSTRACT Remote sensing has emerged as the main tool for mapping and monitoring of forest resources globally. In India, this technological tool is in use for biennial monitoring of forest cover of the country for the last 25 years. Among the numerous applications of remote sensing in forest management, change detection is the one which is most frequently used. In this paper, a new paradigm of change detection has been presented in which change of vegetation in a grid (a square shaped unit area) is the basis of change analysis instead of change at the pixel level. The new method is a simpler approach and offers several advantages over the conventional approaches of remote sensing based change detection. The study introduces an index termed as ‘Grid Vegetation Change Index (GVCI)’, its numerical value gives quantified assessment of the degree of change. The minus value of GVCI indicates loss or negative change and similarly positive value vice versa. By applying the GVCI on a pair of remotely sensed images of two dates of an area, one can know degree of vegetation change in every unit area (grid) of the large landscape. Based on the GVCI values, one can select those grids which show significant changes. Such ‘candidate grids with significant changes’ may be shortlisted for ground verification and studying the causes of change. Since the change identification is based on the index value, it is free from human subjectivity or bias. Though there may be some limitations of the methodology, the GVCI based approach offers an operational application for monitoring forests in India and elsewhere for complete scanning of forest areas to pointedly identify change locations, identifying the grids with significant changes for objective and discrete field inspections with the help of GPS. It also offers a method to monitor progress of afforestation and conservation schemes, monitor habitats of wildlife areas and potential application in carbon assessment methodologies of CDM and REDD+.
Cite this paper
S. Ashutosh, "Monitoring Forests: A New Paradigm of Remote Sensing & GIS Based Change Detection," Journal of Geographic Information System, Vol. 4 No. 5, 2012, pp. 470-478. doi: 10.4236/jgis.2012.45051.
 Andrew J. Elmore, John F. Mustard, Sara J. Manning and David B. Lobell (2000), Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index Remote Sens. Environ. 73:87-102
 Lyon, J. G., Yuan, D., Lunetta, R. S., and Elvidge, C. D. (1998), A change detection experiment using vegetation indices. Photogramm. Eng. Remote Sens. 64:143-150.