Ndvi: Vegetation Change Detection Using Remote Sensing and Gis – A Case Study of Vellore District

TLDR

Land resources can be interpreted by computing their Normalized Difference Vegetation Index for land cover classification. The article presents an enhanced change‑detection method for satellite image analysis based on NDVI. The method uses NDVI differencing on Landsat TM imagery combined with DEM layers, applying multi‑spectral remote sensing and curvature indices at various NDVI thresholds to classify land cover. Simulation and empirical results demonstrate that NDVI effectively detects surface features, reveals significant land‑cover changes—such as a 6 % decrease in forest/shrub and 23 % decrease in barren land, and increases in agricultural, built‑up, and water areas—and can aid policy makers and disaster response.

Abstract

This article presents an enhanced Change Detection method for the analysis of Satellite image based on Normalized Difference Vegetation Index (NDVI). NDVI employs the Multi-Spectral Remote Sensing data technique to find Vegetation Index, land cover classification, vegetation, water bodies, open area, scrub area, hilly areas, agricultural area, thick forest, thin forest with few band combinations of the remote sensed data. Land Resources are easily interpreted by computing their Normalized Difference Vegetation Index for Land Cover classification. Remote Sensing data from Landsat TM image along with NDVI and DEM data layers have been used to perform multi-source classification. The Change Detection method used was NDVI differencing. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4 and 0.5. The Simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for policy makers in decision making. The Vegetation analysis can be helpful in predicting the unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies. From the empirical study, the forest or shrub land and Barren land cover types have decreased by about 6% and 23% from 2001 to 2006 respectively, while agricultural land, built-up and water areas have increased by about 19%, 4% and 7% respectively. Curvature, Plan curvature, Profile curvature and Wetness Index areas are also estimated.