Application of Remote Sensing for Vegetation Cover Change Assessing Based on (NDVI) index

Abstract

The main task of this study is application of remote sensing to assess the vegetation cover change in Koskary area, North Darfur state - Sudan.

In this study, Normalized Difference Vegetation Index (NDVI) derived from Landsat data used, and linked with field investigations, to assess the change during the years 2000, 2010 and 2020. In addition to that, the study observed other factors related to vegetation cove changes, like human activities and terrain which calculated from Digital Elevation Models (DEM). The study results showed that, forest cover classes decreased from 14% in 2000 to 9% in 2010 then increased to 31% in 2020. Moreover, looks variant change in rangelands, from 60% in 2000 to 75% in 2010 then to 59% in 2020. The bare lands also decreased from 24% in 2000, to 16% in 2010 then to 10% 2020. The field observations revealed that, human factors had strong effect on vegetation, especially in charcoal production, illegal felling and wildfire But from other side, land topography such as low slops had positive impact on vegetation by providing multi-watershed, streams and good habitats.

This study concluded that, NDVI index was effective remote sensing tool for detecting vegetation cover change. Moreover, forestlands degradations could reduce through proper forest management and early fire-lines.