Evaluating Ecosystem Service Trade-Offs and Recovery Dynamics in Response to Urban Expansion: Implications for Sustainable Management Strategies

Land use land cover (LULC) changes due to rapid urbanization pose critical challenges to sustainable development, particularly in arid and semi-arid regions like Saudi Arabia, where cities such as Abha are experiencing unprecedented expansion. Urban sprawl is accelerating environmental degradation, affecting key natural resources such as vegetation, water bodies, and barren land. This study introduces an advanced machine learning (ML) and deep learning (DL)-based framework for high-accuracy LULC classification, urban sprawl quantification, and ecosystem service assessment, providing a more precise and scalable approach compared to traditional remote sensing techniques. A hybrid methodology combining ML models—Random Forest, Artificial Neural Networks, Gradient Boosting Machine, and LightGBM—with a 1D Convolutional Neural Network (CNN) was fine-tuned using grid search optimization to enhance classification accuracy. The integration of deep learning improves feature extraction and classification consistency, achieving an AUC of 0.93 for Dense Vegetation and 0.82 for Cropland, outperforming conventional classification methods. The study also applies the Markov transition model to project land cover changes, offering a probabilistic understanding of urban expansion trends and ecosystem dynamics, providing a significant improvement over static LULC assessments by quantifying transition probabilities and predicting future land cover transformations. The results reveal that urban areas in Abha expanded by 120.74 km 2 between 2014 and 2023, with barren land decreasing by 557.09 km 2 and cropland increasing by 205.14 km 2 . The peak ecosystem service value (ESV) loss was recorded at USD 125,662.7 between 2017 and 2020, but subsequent land management efforts improved ESV to USD 96,769.5 by 2023. The resilience and recovery of natural land cover types, particularly barren land (44,163 km 2 recovered by 2023), indicate the potential for targeted restoration strategies. This study advances urban sustainability research by integrating state-of-the-art deep learning models with Markov-based land change predictions, enhancing the accuracy and predictive capability of LULC assessments. The findings highlight the need for proactive land management policies to mitigate the adverse effects of urban sprawl and promote sustainable ecosystem service recovery. The methodological advancements presented in this study provide a scalable and adaptable framework for future urbanization impact assessments, particularly in rapidly developing regions.