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<p>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, mainly attributed to an increase in precipitation (66.9 mm, measured as the difference in mean annual rainfall between 2014 and 2023 derived from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), representing a notable departure from the decade-long baseline) and human-driven water conservation initiatives, though the statistical significance of this trend relative to long-term climatic norms warrants cautious interpretation given the limited record length. The vegetation in the research area was classified into four groups: Dense Vegetation, Moderate Vegetation, Sparse Vegetation/Bare Soil, and No Vegetation (Water/Urban). The research revealed a 2.07% uptick in Dense Vegetation between 2014 and 2024, accompanied by a decrease in bare land. Alongside looking back at past data, the 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 (ANN), Decision Tree Regression, Random Forest Regression, and others. 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.</p>

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Impact of Climate and Water Management on Vegetation Health in Mecca City: A Decadal Remote Sensing and Multi-model Prediction Approach

  • 期刊论文

  • 发表日期：

    2026年7月13日

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  • Fayaz Ullah Shinwari,
  • Mumtaz Ali Khan,
  • Atifullah Shinwari,
  • Shuaib Ullah

摘要

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, mainly attributed to an increase in precipitation (66.9 mm, measured as the difference in mean annual rainfall between 2014 and 2023 derived from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), representing a notable departure from the decade-long baseline) and human-driven water conservation initiatives, though the statistical significance of this trend relative to long-term climatic norms warrants cautious interpretation given the limited record length. The vegetation in the research area was classified into four groups: Dense Vegetation, Moderate Vegetation, Sparse Vegetation/Bare Soil, and No Vegetation (Water/Urban). The research revealed a 2.07% uptick in Dense Vegetation between 2014 and 2024, accompanied by a decrease in bare land. Alongside looking back at past data, the 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 (ANN), Decision Tree Regression, Random Forest Regression, and others. 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.