Ecological Stress Assessment on Vegetation in the Al-Baha Highlands, Saudi Arabia (1991–2023)

Climate change significantly stresses cold-adapted and stenothermic plant species in high-altitude mountain ecosystems. The diverse plant species at elevations ranging from 1324 to 2527 m above mean sea level (AMSL) provide an ideal setting for investigating these impacts in the Al-Baha Highlands, Saudi Arabia. Therefore, this study has three aims: first, to estimate vegetation cover in 2023 and its relationship with environmental factors; second, to analyze long-term trends (1991–2023) in key spectral indices, including the normalized difference vegetation index (NDVI), normalized difference vegetation water index (NDWI), normalized difference open water index (NDWIw), and land surface temperature (LST), using the Kendall tau-b method; and third, to model ecological stress via a generalized additive model (GAM) and assess its impact on vegetation. We utilized Landsat 5/7/8 (C2 SR T1) for spectral indices and the Copernicus DEM for topographic and hydrological analysis. The results indicate significant roles of LST, elevation, and distance from seasonal streams in shaping vegetation patterns (p < 2 × 10−16). There were negative trends in the NDVI (91.66 km2), NDWI (138 km2), and NDWIw (804 km2) (p < 0.05), whereas the LST exhibited positive trends (116.15 km2) (p < 0.05). The GAM achieved high predictive accuracy (R2 = 0.979), capturing nonlinear relationships between the predictors and the stress score. Severe ecological stress occurred in high-altitude zones (>1700 m AMSL) on south-facing slopes due to increased LST and declining NDWI, impacting species such as Juniperus procera. Hypothesis testing was used to assess variations in the NDVI, its long-term trends, and ecological stress between highland and lower-elevation areas, revealing highly significant differences (p < 2.2 × 10−16). This study provides novel insights into ecological stress dynamics in relation to altitude and slope aspects, offering actionable recommendations for sustainable ecosystem management, including targeted reforestation and water resource optimization to mitigate stress and preserve biodiversity.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2025-03-24
DOI
https://doi.org/10.3390/su17072854
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ecological Stress Assessment on Vegetation in the Al-Baha Highlands, Saudi Arabia (1991–2023)

Zubairul Islam, Asma A. Al‐Huqail
1 citations
Sustainability
Remote Sensing in Agriculture
1.72
article

Ecological Stress Assessment on Vegetation in the Al-Baha Highlands, Saudi Arabia (1991–2023)

Zubairul Islam, Asma A. Al‐Huqail
article en
1 citations

Abstract

Climate change significantly stresses cold-adapted and stenothermic plant species in high-altitude mountain ecosystems. The diverse plant species at elevations ranging from 1324 to 2527 m above mean sea level (AMSL) provide an ideal setting for investigating these impacts in the Al-Baha Highlands, Saudi Arabia. Therefore, this study has three aims: first, to estimate vegetation cover in 2023 and its relationship with environmental factors; second, to analyze long-term trends (1991–2023) in key spectral indices, including the normalized difference vegetation index (NDVI), normalized difference vegetation water index (NDWI), normalized difference open water index (NDWIw), and land surface temperature (LST), using the Kendall tau-b method; and third, to model ecological stress via a generalized additive model (GAM) and assess its impact on vegetation. We utilized Landsat 5/7/8 (C2 SR T1) for spectral indices and the Copernicus DEM for topographic and hydrological analysis. The results indicate significant roles of LST, elevation, and distance from seasonal streams in shaping vegetation patterns (p < 2 × 10−16). There were negative trends in the NDVI (91.66 km2), NDWI (138 km2), and NDWIw (804 km2) (p < 0.05), whereas the LST exhibited positive trends (116.15 km2) (p < 0.05). The GAM achieved high predictive accuracy (R2 = 0.979), capturing nonlinear relationships between the predictors and the stress score. Severe ecological stress occurred in high-altitude zones (>1700 m AMSL) on south-facing slopes due to increased LST and declining NDWI, impacting species such as Juniperus procera. Hypothesis testing was used to assess variations in the NDVI, its long-term trends, and ecological stress between highland and lower-elevation areas, revealing highly significant differences (p < 2.2 × 10−16). This study provides novel insights into ecological stress dynamics in relation to altitude and slope aspects, offering actionable recommendations for sustainable ecosystem management, including targeted reforestation and water resource optimization to mitigate stress and preserve biodiversity.

SustainabilityVol. 17(7)
King Saud University (SA), University of Abuja (NG)
Deanship of Scientific Research, King Saud University
Openalex Percentile: Top 19%
Remote Sensing in Agriculture
1.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.