Deep Learning-Based Prediction of Forest Cover Change in Duhok, Iraq: Past and Future
Hendaf N. Habeeb, Yaseen T. Mustafa
Abstract
This study examines the spatiotemporal dynamics of forest cover in Duhok district, Iraq, where urbanization, industrial activities, and agricultural expansion have significantly impacted the environment. Utilizing an Ensemble Deep Learning approach, this study integrated Land Use and Land Cover data and environmental factors (climate, topography, and geology) to model forest cover changes from 2000 to 2060. Land Use and Land Cover maps were generated using the Random Forest classifier, whereas future climate scenarios were based on Coupled Model Intercomparison Project Phase 6 projections. The findings of this study highlight the critical role of environmental factors in shaping forest cover distribution and demonstrate the efficacy of advanced deep learning techniques in environmental modeling. The analysis reveals a projected decline in forest cover from 364.17 km. in 2000 to 240.50 km. in 2022, with a further decrease to 156.93 km. by 2060 under the most severe climate scenario. These insights highlight the profound impact of environmental factors on forest cover and underscore the transformative potential of advanced deep learning techniques for predicting and understanding environmental changes. These findings offer a crucial roadmap for policymakers, equipping them with the tools needed to devise strategies that can mitigate the looming threats posed by climate change and human activities to these vital ecosystems. Cite this article as Habeeb, H. N., & Mustafa, Y. T. (2025). Deep learning-based prediction of forest cover change in Duhok, Iraq: Past and future. Forestist, 75, 0068, doi: 10.5152/forestist.2025.24068.