Change detection in deforestation using high resolution satellite image with Haar wavelet transforms

Abstract

In the satellite images the noise is present such as mist, clouds etc., to remove the noise the Haar wavelet transforms are applied. Using the Image segmentation algorithm the major issue Deforestation is evaluated by comparing the image taken from the year 1939 and 2000. Deforestation is a serious issue that most nations face today. Deforestation is primarily due to the urbanization. Most nations that are presently under the scanner for deforestation had immense forest stretch. The application of remote sensing is at present a significant method for forest monitoring, particularly in vast and remote areas. Different methods have been presented by the researchers for finding forest types and change detection in urbanization. In this study, we propose polygon segmentation and 2D haar wavelet for adaptive regional forest change detection. First in order to detect the forest types, 2D haar wavelet is applied to image at different threshold level and identifies the type of forest. The polygon segmentation is applied to low dense forest and segregate forest with non forest region. Finally compare the result with data sets and find decreasing the forest cover. The proposed technique is in real time, given the exigencies of forest urbanization.