Main Article Content
Spatial variability in land use changes creates a need for a wide range of applications, including landslide, erosion, land planning, global warming etc. This study presents the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI) in Godavari eastern delta. Four spectral indices were investigated in this study. These indices were NIR (red and near infrared) based NDVI, green and NIR based GVI (Green Vegetation Index), red and NIR based soil adjusted vegetation index (SAVI), and red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for 2011-12 kharif, rabi and 2016-17 kharif, rabi of Godavari eastern delta. Different threshold values of NDVI are used for generating the false colour composite of the classified objects. For this purpose, supervised classification is applied to Landsat images acquired in 2011-12 and 2016-17. Image classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from satellite images of 2011-12 and 2016-17. There was 11% and 30% increase in vegetation during kharif and rabi seasons from 2011-12 to 2016-17. The vegetation analysis can be used to provide humanitarian aid, damage assessment in case of unfortunate natural disasters and furthermore to device new protection strategies.
Mushtaq Ahmad Ganie, Asima Nusrath. Determining the Vegetation Indices (NDVI) from Landsat 8 satellite data. International Journal of Advanced Research. 2016;4(8): 1459-1463.
Earth Observatory, Measuring Vegetation (NDVI& EVI); 2000.
Fintan Corrigan. Multispectral imaging camera drones in farming yield big benefits. Drone Zon-Drone Technology; 2019.
Hien Phu La, Yang Dam Eo, Jong Hwa Kim, Changjae Kim, Mu Wook Pyeon, Hyun Seung Song. Analysis of correlation between canopy cover and vegetation indices. International Journal of Digital Content Technology and Its Applications. 2013;7:10-17.
NDVI Image Processing –Stack Pointers; 2018.