Abstract
This study investigates vegetation dynamics in a semiarid mountain ecosystem in southwestern Saudi Arabia from 1990 to 2024 by integrating multisource remote sensing data, bioclimatic variables, and machine learning models. Trends in vegetation greenness (NDVI), water content (NDWI), and land surface temperature (LST) were quantified via Kendall’s τ and Sen’s slope estimators. To capture fine-scale climatic heterogeneity in complex terrains, CHELSA bioclimatic variables were downscaled from ~ 1 km to 30 m resolution via random forest regression. These predictors were used to model NDVI trend patterns through a comparative framework including a baseline artificial neural network (ANN), metaheuristic-optimized ANN variants (ANN–PSO and ANN–GWO), and ensemble models (random forest and XGBoost). Model performance was assessed via independent validation, error metrics (RMSE, MAE, R²), bootstrap uncertainty analysis, and spatial residual diagnostics. All the models exhibited strong predictive ability, with XGBoost achieving the highest accuracy (RMSE ≈ 0.051; R² ≈ 0.92) and the lowest residual spatial autocorrelation. The spatial results indicate dominant greening across ~ 73–74% of the area, whereas ~ 25% of the area exhibits degradation concentrated in thermal and moisture-stressed zones. These findings highlight the value of integrated spectral-climatic modeling for monitoring vegetation changes in semiarid mountainous environments.
Similar content being viewed by others
Effects of climate change and anthropogenic activities on vegetation coverage changes in the Taihang Mountains, China
An effective BiLSTM-CNN model for predicting large-scale temporal-spatial dynamics of normalized difference vegetation index
Decoupling anthropogenic and climate impacts on vegetation dynamics in China’s Huaihe River Basin using geodetector
Abbreviations
- NDVI:
Normalized Difference Vegetation Index
- NDWI:
Normalized Difference Water Index
- EVI:
Enhanced Vegetation Index
- LS:
Land Surface Temperature
- K_NDVI:
Kendall’s τ–based NDVI Trend
- BIO:
Bioclimatic Variable (BIO1–BIO19)
- CHELSA:
Climatologies at High Resolution for the Earth’s Land Surface Areas
- RF:
Random Forest
- ANN:
Artificial Neural Network
- ANN-PSO:
Artificial Neural Network optimized using Particle Swarm Optimization
- ANN-GWO:
Artificial Neural Network optimized using Gray Wolf Optimization
- XGB:
Extreme Gradient Boosting
- RMSE:
Root Mean Square Error
- MAE:
Mean Absolute Error
- LISA:
Local Indicators of Spatial Association
Acknowledgements
The authors are grateful to King Saud University, Riyadh, Saudi Arabia, for supporting this research through Ongoing Research Funding program – Research Chairs, (ORF-RC-2026-28-03).
Funding
This work was supported by King Saud University, Riyadh, Saudi Arabia, through Ongoing Research Funding program – Research Chairs, (ORF-RC-2026-28-03).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Al-Huqail, A.A., Islam, Z. & Utazi, C.E. Comparative evaluation of neural networks and ensemble models for vegetation trend prediction in a semiarid mountain ecosystem, Saudi Arabia.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-57406-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-57406-4
Keywords
- Vegetation trend modeling
- Machine learning
- XGBoost
- Random Forest
- Semiarid mountain ecosystems
- Remote sensing
Source: Ecology - nature.com
