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<p>This study investigates changes in land use and vegetation cover in the Oued Louza watershed, Sidi Bel Abbès province, Algeria, from 1987 to 2020, using remote sensing and Geographic Information Systems (GIS) to assess spatio-temporal dynamics. The analysis employed Landsat-derived vegetation and moisture indices, including the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI), along with the Topographic Wetness Index (TWI) derived from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). Results show a dramatic decline in vegetation cover, from 42% in 1987 to 10% in 2020, a 32% decrease, while urban areas expanded by 27%. The reduction in vegetation was linked to a 22% decrease in rainfall and a 6.5% reduction in relative humidity, both of which exacerbated vegetation loss and soil moisture decline. The study also revealed a strong relationship between areas with higher moisture retention and denser vegetation, as indicated by TWI values. Land use and land cover classification was validated with a kappa coefficient of 0.84 in 1987 and 0.91 in 2020, confirming the accuracy of the analysis. A majority-voting technique was used to combine multiple spectral indices to improve classification reliability. Despite the methodology’s effectiveness, limitations exist, particularly the reliance on satellite-derived climatic data from the NASA POWER database, given the limited availability of ground-based meteorological stations in the region. Additionally, the spatial resolution of Landsat images may not capture small-scale land use changes, although it is suitable for large-scale assessments. The findings underscore the impact of both climatic and anthropogenic factors on vegetation dynamics and highlight the potential of remote sensing and GIS for land use and environmental monitoring in semi-arid regions. This study provides essential insights for sustainable land and water resource management, and future research could build on these findings by incorporating higher-resolution imagery, local meteorological data, and advanced machine learning techniques to enable more detailed land-use change predictions.</p>

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Remote sensing assessment of vegetation and moisture dynamics in semi-arid regions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Sarah Kreri,
  • Nezha Farhi,
  • Ahmed Bennia,
  • Abdessamed Derdour,
  • Lahsen Wahib Kébir,
  • Khalid M. Alharbi,
  • Amanuel Kumsa Bojer,
  • Ahmed A. Arafat

摘要

This study investigates changes in land use and vegetation cover in the Oued Louza watershed, Sidi Bel Abbès province, Algeria, from 1987 to 2020, using remote sensing and Geographic Information Systems (GIS) to assess spatio-temporal dynamics. The analysis employed Landsat-derived vegetation and moisture indices, including the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI), along with the Topographic Wetness Index (TWI) derived from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). Results show a dramatic decline in vegetation cover, from 42% in 1987 to 10% in 2020, a 32% decrease, while urban areas expanded by 27%. The reduction in vegetation was linked to a 22% decrease in rainfall and a 6.5% reduction in relative humidity, both of which exacerbated vegetation loss and soil moisture decline. The study also revealed a strong relationship between areas with higher moisture retention and denser vegetation, as indicated by TWI values. Land use and land cover classification was validated with a kappa coefficient of 0.84 in 1987 and 0.91 in 2020, confirming the accuracy of the analysis. A majority-voting technique was used to combine multiple spectral indices to improve classification reliability. Despite the methodology’s effectiveness, limitations exist, particularly the reliance on satellite-derived climatic data from the NASA POWER database, given the limited availability of ground-based meteorological stations in the region. Additionally, the spatial resolution of Landsat images may not capture small-scale land use changes, although it is suitable for large-scale assessments. The findings underscore the impact of both climatic and anthropogenic factors on vegetation dynamics and highlight the potential of remote sensing and GIS for land use and environmental monitoring in semi-arid regions. This study provides essential insights for sustainable land and water resource management, and future research could build on these findings by incorporating higher-resolution imagery, local meteorological data, and advanced machine learning techniques to enable more detailed land-use change predictions.