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Internship Report - Identifying key deforestation drivers

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Protecting forests across the globe is crucial, now more than ever. To address this, ForestForesight was developed to prevent illegal deforestation by generating highly accurate predictions using machine learning models and collaborates with a network of local stakeholders to take action. This research focuses on exploring high-detail features that can serve as proxies for deforestation to be implemented in the ForestForesight model. A literature review identified 29 deforestation indicators across 10 themes. An online search revealed 76 open-source datasets and repositories, offering over 100 high-detail datasets with global coverage. From these, 15 promising datasets were selected, preprocessed, implemented, and tested in the models. Results showed no significant change in prediction accuracy with the newly implemented features. However, the new features proved to be important in several models, though they did not come close to the importance of GLAD integrated alerts datasets. Implementing new features can lead to marginal improvements and contribute valuable explanations of why deforestation is occurring. However, it is unlikely that adding features unrelated to integrated alerts will lead to significant improvements in model accuracy, as their predictive power is too low at the high resolution of 0.004 degrees.

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