Vegetation Change Detection of Multispectral Satellite Images Using Remote Sensing

Change Detection is depicted to compare the spatial representation of two points in time, with variations in the variables of interest causing changes. Remote sensed data of Landsat 8 satellite imagery can be used to detect changes in multispectral images. Vegetation change detection plays a prominent role in tracking the alteration of vegetation in selective areas. The vegetation change analysis of a specific area for a given time period involves a lot of information that can be used for predicting the impact of change over years. Multispectral images for vegetation change of Landsat 8 satellite for a specific location can be obtained by stacking selective bands together. Over the years, due to urbanization, the vegetative index of cities dropped drastically. To take necessary measures, the impact must be analyzed. To overcome this problem, we are using vegetation change detection analysis. The image stacking is performed using QGIS (Quantum Geographic Information System) software which is facilitated using various image enhancement options. The location Vijayawada is tracked for detecting change over a time period of eight years using the NDTS (Normalized Difference Between Time Series) algorithm followed by comparison with the PCA and K-means algorithm. This paper gives a detailed visualization of the results acquired in this project using metrics like RMSE and PSNR.