Spatio-Temporal Analysis of Vegetation Dynamics and Thermal Conditions in Adamawa State, Nigeria Using NDVI and Land Surface Temperature (2000–2025)

Abstract

This study investigates the spatio-temporal relationship between vegetation dynamics and land surface temperature (LST) in Adamawa State, Nigeria over the period 2000–2025 using Landsat-derived Normalized Difference Vegetation Index (NDVI) and LST data processed through Google Earth Engine. Annual composites were generated to assess long-term trends, spatial patterns, and the interaction between vegetation greenness and surface temperature. The results show that NDVI values remained relatively stable across the study period, with vegetation classes ranging from very low to high greenness, indicating a heterogeneous landscape typical of savanna ecosystems. NDVI anomaly analysis revealed considerable variability in vegetation conditions, with wider ranges observed in 2000 (−0.56 to 0.43) and 2025 (−0.58 to 0.57), while 2015 exhibited a narrower range (−0.27 to 0.14), suggesting comparatively more stable vegetation conditions during that period. Land surface temperature remained consistently high, ranging approximately from 24°C to over 40°C in earlier years, with a reduction in maximum values to about 37°C by 2025. Spatial analysis further showed that areas with lower vegetation cover corresponded to higher surface temperatures. Pearson correlation analysis revealed a statistically significant inverse relationship between NDVI and LST, with correlation coefficients of r = −0.321 (2000), r = −0.703 (2015), and r = −0.686 (2025), all significant at p < 0.001, indicating strengthening vegetation–temperature coupling over time. This interaction highlights the influence of both climatic variability and land use changes on environmental conditions in the region. Based on these results, the study underscores the need for sustainable land management practices, including afforestation and reforestation, to enhance vegetation cover and help regulate surface temperatures. It also emphasizes the importance of continuous environmental monitoring using remote sensing tools, as well as the integration of vegetation–temperature indicators into policy and planning to support climate adaptation and environmental conservation. The findings demonstrate the effectiveness of integrating NDVI and LST in monitoring ecosystem dynamics and understanding long-term environmental change.