A Forest Change Detection Using Auto Regressive Model-based Kernel Fuzzy Clustering: Advanced Study

This chapter focuses on the use of satellite images for the forest change detection, forest cover
management. In this chapter, the vegetation indices play a major role in extracting the useful
information from the satellite images. Also analysis was done on the imagery data from the remote
sensing satellites for detecting the changes in the forest over the year’s 2007-2017 using the pixelbased
Bhattacharya distance. The indices from the satellite images are fed to the automatic
segmentation model using the proposed Kernel Fuzzy Auto regressive (KFAR) model, which is the
modified Kernel Fuzzy C-Means (KFCM) Clustering algorithm with the Conditional Autoregressive
Value at Risk (CAVIAR). The forest change detection using the pixel-based Bhattacharya distance
follows the segmentation and the experimentation reveals that the proposed method acquired the
minimal Mean Square Error (MSE) and maximal accuracy of 0.0581 and 0.9211.