Using remote sensing techniques in studying vegetation dynamics for the period 2023-2025 - A case study: Alhaffa - Latakia - Syria.

Abstract

This study was conducted in the Al-Haffa area, rural Latakia, a region experiencing human pressures and recurrent droughts. It aimed to identify and map vegetation cover and barren land areas and to evaluate the efficiency of certain vegetation indices for the period 2023–2025. The dynamics of vegetation change were determined using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) values. The highest mean NDVI value (0.35) was recorded in 2024, while the lowest (0.20) occurred in November 2025. For EVI, the highest mean value (0.49) was in 2024, and the lowest (0.21) in November 2025. Based on NDVI, barren land increased from 17% of the total study area in 2023 to 30% in November 2025. Based on EVI, this proportion increased from 21% in 2023 to 33% in November 2025. The results demonstrated that EVI was more sensitive than NDVI to changes in vegetation cover. They also revealed the severity of vegetation changes occurring in Al-Haffa over a short period under the combined influence of human activities and drought episodes.