The Application of Artificial Intelligence to Develop Predictive models that Improve Harvesting Efficiency while Protecting biodiversity in Sustainable Forest Ecosystems.

Abstract

The paper proposes the design and use of artificial intelligence-powered predictive models in a way that will facilitate the smooth process of harvesting the forest without affecting the major conservation objectives of the biodiversity. Using all three together, machine learning algorithms, remote sensing data, and ecological modeling models, we have developed a multiobjective optimization model which must optimize the requirements of timber yield efficiency and habitat selection. The study used deep learning networks, ensemble, and reinforcement learning algorithms according to the overall datasets including LiDAR forest structure data, satellite data, species distribution, and historical harvesting data of 47 forest management units in the Pacific Northwest region. The results confirm that AI-managed harvesting schemes were more efficient in terms of operational efficacy (or efficiency 23.7 more), and their adverse impact on biodiversity was smaller (reduced by 31.2 percent) compared to the traditional forest management systems. The predictive models could calculate the optimum areas, timing and intensity of harvesting that would optimize the production of the timber without interfering with the valuable wildlife habitats besides ensuring that nothing affects the integrity of the ecosystem. These findings provide grounds on which sustainable forest management procedures can be followed such that it is possible to balance between the economic and ecological interests by making decision based on data.