GEOSPATIAL INTELLIGENCE AND MACHINE LEARNING FOR FOREST CONSERVATION: AN INTEGRATED APPROACH

When thinking about forest degradation, it is tempting to see it simply as the loss of trees. However, the reality is much more complex. Degradation does not only reduce forest cover, it triggers a chain reaction, disrupting ecosystems, accelerating climate change, and affecting the livelihoods of communities that depend on forest resources. Although a substantial body of research exists, we noticed that many studies tend to treat deforestation, wildfires, and illegal logging as separate issues. From our perspective, this fragmented approach often overlooks how these problems are interconnected and how they amplify each other. Traditional assessment methods also present limitations. They usually rely on qualitative observations and have not yet fully integrated the possibilities offered by Geographic Information Systems (GIS) and machine learning. This gap partly explains why risk prediction and the development of targeted conservation strategies remain so challenging. In light of these observations, we propose an integrated framework that combines environmental and socio economic prioritization through the Analytic Hierarchy Process (AHP), spatial analysis using GIS, and predictive modeling based on machine learning techniques. Our main contribution lies in bridging fragmented approaches by offering a unified decision-support method that leverages spatial analysis and predictive modeling for more accurate and actionable forest risk assessment. Rather than replacing existing approaches, our objective is to complement them and offer decision-makers practical tools to anticipate degradation risks more effectively. We believe that bridging the gap between research and action is crucial for achieving meaningful, long term forest conservation outcomes.