The Influence of Surrounding Environmental Factors on NDVI Values Derived from Landsat Satellite Imagery in the Al-Jabal Al-Akhdar Region (Journal of Libya for geographical studies, 2026)
Vegetation Indices (VIs) are widely used to describe vegetation greenness, relative density, and health in satellite imagery. Although vegetation indices were developed to extract vegetation signals exclusively, several external factors—such as soil background, moisture conditions, solar azimuth angle, sensor viewing angle, and atmospheric interactions—can influence the reflectance received by the sensor. Consequently, these factors may alter vegetation index values, leading to results that do not accurately represent vegetation density or its temporal variation. The Normalized Difference Vegetation Index (NDVI), derived from Landsat satellite imagery, is among the most commonly used and simplest vegetation indices for estimating vegetation density and coverage in a given area. However, NDVI values may be affected by surrounding environmental conditions, potentially yielding misleading estimates. Therefore, this study aims to evaluate NDVI values by comparing them with the Soil Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), in order to assess the suitability of NDVI for evaluating vegetation density under varying environmental conditions. The study also aims to monitor vegetation cover changes in the Al-Jabal Al-Akhdar region over the past 50 years and to identify long-term vegetation dynamics. The analysis was based on a high spatial resolution (30 m) Landsat TM image acquired in August 2023, from which NDVI, SAVI, and EVI were calculated for each pixel within the study area to assess vegetation cover and compare index performance. In addition, multi-temporal satellite images spanning the period from 1972 to 2023 were utilized to examine long-term changes in vegetation cover across the region. One of the key findings of the study is that, for accurate vegetation cover assessment, it is preferable to use multiple vegetation indices in conjunction with NDVI to obtain more reliable estimates of actual vegetation conditions in the study area.
Keywords: Vegetation index; Remote sensing; Satellite imagery