Yaseen Taha Mustafa

This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq–Turkey border within the Duhok Governorate between 2015 and 2024. Utilizing paired remote sensing (RS) and high-end machine learning (ML) methods, forest dynamics were simulated from Sentinel-2 imagery, climate datasets, and topographic variables. Seven ML models were evaluated, and XGBoost consistently outperformed the others, yielding predictive accuracies (R²) of 0.903 (2015), 0.910 (2019), and 0.950 (2024), and a low RMSE (≤0.035). Model interpretability was further improved through the application of SHapley Additive exPlanations (SHAP) to estimate variable contributions and a Generalized Additive Model (GAM) to elucidate complex nonlinear interactions. The results showed distinct temporal shifts; climatic factors (rainfall and temperature) primarily influenced vegetation cover in 2015, whereas anthropogenic drivers such as forest fires (NBR), road construction (RI), and soil exposure (BSI) intensified by 2024, accounting for up to 12% of the observed forest loss. Forest canopy cover decreased significantly, from approximately 630 km² in 2015 to 577 km² in 2024, mainly due to illegal deforestation, road network expansion, and conflict-induced fires. This study highlights the effectiveness of an ML-driven RS analysis for geoinformation needs in geopolitically complex and data-scarce regions. These findings underscore the urgent need for robust, evidence-based conservation policies and demonstrate the utility of interpretable ML techniques for forest management policy optimization, providing a reproducible methodological blueprint for future ecological assessment.

Understanding the dynamic relationships between geoenvironmental factors and forest vegetation cover is crucial for effective forest management and planning. This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. The results show that in 2013, altitude and rainfall were the primary factors influencing FVC, with areas of higher altitudes and adequate rainfall exhibiting up to 30% denser forest cover. By 2023, soil moisture and groundwater levels had emerged as the dominant factors, with soil moisture levels accounting for 25% of the variation in FVC. This shift underscores the increasing importance of water management strategies to maintain forest health. The Random Forest model demonstrated high predictive accuracy, achieving an R² value of 0.918 (RMSE of 0.016 and MAE of 0.013) for 2013 and 0.916 (RMSE of 0.018 and MAE of 0.014) for 2023, underscoring the model’s robustness in handling nonlinear ecological processes. This study’s insights are crucial for guiding sustainable forest management practices and assisting decision-makers in formulating strategies for resource management, environmental preservation, and future planning. This study underscores the necessity of adaptive management strategies that consider evolving climatic and hydrological conditions, emphasizing continuous monitoring and advanced technologies to ensure the resilience of forest ecosystems.

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.

Global flood events, intensified by an intricate interplay of natural dynamics and human activities, pose severe environmental and safety threats, driving the demand for advanced vulnerability assessments. This research innovates with a holistic geo-informatics methodology, pinpointing flood-prone regions in Erbil City, Kurdistan Region, Iraq. It harnesses the Analytic Hierarchy Process and Geographic Information System synergies, enriched with insights from multispectral satellite observations, to conduct an exhaustive analysis of key flood susceptibility indicators. These encompass geological, hydrological, meteorological, and anthropogenic parameters, integrated into a robust Geographic Information System analytical matrix. Through strategic factor analysis and prioritization, the study distills a detailed flood susceptibility cartogram, classifying regional vulnerabilities into distinct risk echelons: high (encompassing 25%), moderate (38.1%), low (28.4%), and very low (8.5%). The final flood map highlights an acute susceptibility epicenter in the northeast, contrasting with the minimal threat southwest. This demarcation reaffirms the Analytic Hierarchy Process-Geographic Information System fusion's precision in translating multifaceted data into strategic intelligence, proving instrumental for scholars, administrative frameworks, and developmental strategists. The insights garnered are pivotal in sculpting informed urban development and flood counteractive blueprints, instrumental in mitigating flood repercussions and preserving ecological and human integrities.

The accurate quantification of surface soil moisture (SSM) and vegetation cover using remote sensing techniques is essential for effective environmental management. This study investigated the spatial variations in SSM and vegetation cover in the Batifa region of the Kurdistan Region of Iraq. Landsat-8 images of the study area were classified using a support vector machine (SVM), and the soil land type was subsequently extracted. A random forest (RF) algorithm was developed to retrieve SSM using Landsat data in conjunction with in situ measurements. The results demonstrated that the RF algorithm achieved a high coefficient of determination (R ² = 0.80) for the SSM retrieval. The study area exhibited distinct distributions of SSM and normalized difference vegetation index (NDVI) values across different ranges. The low range of SSM (2.21% to 3.34%) and NDVI (-0.020 to 0.172) values occupied approximately 25% of the soil area, whereas the moderate range of SSM (3.34%–4.05%) and NDVI (0.172–0.238) values covered approximately 50% of the soil area. A high range of SSM (4.05%–6.49%) and NDVI (0.238–0.935) values was found in approximately 25% of the region. The southern part of Batifa experienced drought conditions, whereas the northern part exhibited higher SSM levels. Anthropogenic resources caused a decrease in vegetation and SSM in Batifa. These findings have significant implications for sustainable management of water and soil resources in the Batifa area.