Deep learning approaches for estimating forest vegetation cover and exploring influential ecosystem factors

Accurate estimation of forest vegetation cover and understanding of ecosystem factors are essential for effective forest management and planning. This study enhances forest vegetation cover precision in Duhok and Amedy districts, Kurdistan Region, Iraq, through deep learning (DL) models, including Convolutional Neural Networks, Deep Neural Networks (DNN), and an Ensemble approach. This study analyzed 12 ecosystem factors, utilizing 11 as inputs to predict forest cover, which served as the output variable. The Ensemble model was the most accurate, with coefficient of determination (R-2) values of 0.790 and 0.740, root mean square error (RMSE) values of 0.037 and 0.057, and mean absolute error (MAE) values of 0.028 and 0.044 for the Duhok and Amedy districts, respectively. It outperformed the other models, highlighting the benefits of integrating multiple DL strategies. The results identified significant influences of anthropogenic factors, climate, and rock composition in Duhok and soil, climate, and topography in Amedy. This study highlights the superior capability of the Ensemble model in discerning key ecosystem drivers and underscores the importance of tailored environmental conservation strategies for regional sustainability. This study advocates a refined model-based approach in ecosystem management and planning.