ASSESSING DROUGHT DYNAMICS IN NORTHEASTERN ALGERIA USING THE VEGETATION HEALTH INDEX DERIVED FROM MODIS DATA

This study focuses on drought monitoring in Tebessa, Algeria, utilizing remote sensing techniques and various drought indices. The research employs data from MODIS (Moderate Resolution Imaging Spectroradiometer) satellites and utilizes the Google Earth Engine (GEE) platform for data processing and analysis. The study area, Tebessa Province in northeastern Algeria, is characterized by an arid to semi-arid climate, making it particularly vulnerable to drought. The research uses three main indices to assess drought conditions; The (VCI) stands for Vegetation Condition Index, the (TCI) for Temperature Condition Index and the (VHI) for Vegetation Health Index. These indices are calculated using NDVI (Normalized Difference Vegetation Index) and Land Surface Temperature (LST) data from MODIS products spanning from 2001 to 2024. The results show varying drought conditions across different periods, 2005-2006 recorded severe dry conditions in the central and southern areas, 2009-2010 recorded extreme drought in the southern region, with moderate to mild conditions in the central zone, for 2022-2023 recorded widespread extreme drought conditions, particularly affecting the central parts. The TCI consistently indicated wet conditions in most years, especially in 2002, 2009, and 2019, highlighting its sensitivity to seasonal temperature variations. The VCI and VHI analyses revealed distinct spatial variations in drought impacts, with central zones being particularly vulnerable. The study observed declining trends in TCI, VCI, and VHI values over time, suggesting worsening drought conditions potentially driven by climate change. This trend could lead to more frequent and severe droughts, further stressing local vegetation. The research confirms the effectiveness of MODIS-derived drought indices, particularly the VHI, in monitoring regional drought dynamics. It emphasizes the importance of these tools for identifying areas at risk of vegetation degradation and predicting potential decreases in crop yields.