Evaluating and Forecasting Vegetation Cover in Mecca City: A Remote Sensing and Machine Learning Approach

The changes in vegetation cover in Mecca City, Saudi Arabia, from 2014 to 2024 are analyzed in this research, utilizing the Normalized Difference Vegetation Index (NDVI) from Landsat imagery. The NDVI provides valuable information on vegetation density and health, offering insights into environmental changes over time. The findings suggest that the city witnessed a positive shift in vegetation health over the span of ten years, which was mainly attributed to an increase in precipitation (66.9 mm) and human-driven water conservation initiatives. The vegetation in the research area was classified into four groups: Healthy Vegetation, Moderately Healthy Vegetation, Unhealthy or No Vegetation, and No Vegetation. The research revealed a 2.07% uptick in Healthy Vegetation between 2014 and 2024, accompanied by a decrease in bare land. Alongside looking back at past data, this study employs machine learning models to estimate NDVI values for 2030, using historical data from 2015 to 2023. The models utilized encompass Artificial Neural Networks (ANNs), Decision Tree Regression, and Random Forest Regression, among other models. The ANN model anticipates an upward NDVI trend, projecting a 2030 NDVI of 0.0313, indicating potential enhancements in vegetation health if current conditions persist. Conversely, the Random Forest model anticipates a reduction in vegetation coverage, projecting an NDVI of 0.01462 for the same year, suggesting potential degradation under specific circumstances. The Decision Tree model aligns more closely with the ANN, projecting an NDVI of 0.01654. These varied projections underscore both the potential for vegetation recovery and the possibility of decline, contingent on environmental management practices and climate variability. The results underscore the significance of adaptable land and resource management strategies, particularly in dry regions like Mecca City, to guarantee sustainable vegetation growth and biodiversity conservation amid ongoing climate changes.