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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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This document described three experiments and their results: the Amounts and Likelihood Adjusted Quantity Analysis, the prediction of deforestation classes, and the analysis of isolated pixels. Unfortunately, none of these methods were effective enough to be suitable for implementation in the FF project.

Academic Consultancy Project - Analysis of Residuals from ForestForesight XGBoost model in Kaliminatan

Preservation of tropical rainforests is of the utmost importance as these forests are one of the most valuable biomes for global flora and fauna diversity. This project aims to study the spatial patterns of the false positives(FPs) from the World Wildlife Funds (WWF-NL) Forest Foresight model to help improve its performance. The primary goal is to understand and mitigate the significant amount of false positive predictions produced by the model, which is critical for maintaining these essential ecosystems. To achieve the aforementioned goal, the project focuses on identifying spatial patterns in false positives and evaluating both internal and external deforestation drivers that might account for these patterns. Methods used within the study revolve around the assessment of spatial distribution, variation and correlation. To determine these features of the residuals default, isolated and patches scenarios were considered. In these scenarios, a hotspot analysis, kernel density and variogram estimation were performed to identify the spatial distribution and variation. During the study, atemporal analysis was performed to generalize results for different dates and explore future research purposes. Lastly, the input and external drivers of FP predictions were correlated against the FP predictions to explain the patterns in the residuals. The study revealed distinct spatial patterns of false positives, especially in the patch scenarios, while their spatial variation was proven to be non-random. Correlations between false positives and input drivers can be seen in areas where deforestation has previously occurred, while palm oil plantations and logging concessions offer additional valuable information for the model as new additions. In conclusion, this report highlights the importance of an integrated approach to properly assess deforestation, taking into accountspatial and temporal variability. The knowledge gained from this study will play a key role in upgrading theFF model to become a more powerful tool for decision-making.

Miscellaneous Findings

During research for our program we have done some miscellaneous findings that are worth sharing:

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