Analyzing spatiotemporal variations and dynamics of vegetation over Amathole district municipality in South Africa

Abstract

Abstract Global biodiversity loss and ecosystem services are significantly impacted by vegetation stress and environmental degradation. The study evaluates vegetation dynamics and long-term meteorological variables in Amathole District Municipality, South Africa, using satellite-derived vegetation indices from 2001 to 2022. The study utilised Terra MODIS and Landsat time series data to evaluate vegetation cover variation using the seasonal Vegetation Condition Index (VCI) and Landsat Normalized Difference Vegetation Index (NDVI). A MODIS-specific compositing algorithm was used to acquire and extract data from the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) using the study area extent. Pearson correlation and non-parametric trend analysis were used to assess the long-term vegetation cover characteristics and the influence of temperature, precipitation, and potential evapotranspiration (ET o ) on vegetation cover. The results reveal a significant decline (<10%) in the quality of green vegetation condition index from moderate to low due to prolonged dryness in autumn and winter, affecting seasonal VCI trends. In certain years, the western region has experienced browning trends and increased vegetation. Spring and summer months experienced high vegetation greenness (50-100%), with inter-annual and decadal NDVI trends shifting, causing high drought sensitivity in the east. The study found a significant positive correlation (P < 0.001) between precipitation and simulated evapotranspiration anomalies with vegetation dynamics, contrasting with inter-annual and decadal changes in NDVI trends. The changes were primarily observed in small areas, with a greening trend resulting from an increased rate of ET o , which in turn affects temperature change. The approach is suitable for semi-arid environments and can be adopted by stakeholders due to its geospatial implications for vegetation monitoring and evaluation.