For Forest Foresight, the ability to predict and prevent deforestation, is a critical component in global efforts to combat climate change and preserve biodiversity. Organizations can significantly contribute to this endeavor by leveraging their unique datasets. Below we explore various pathways to enhance the predictive power of Forest Foresight models, focusing on how different types of data can improve both the accuracy and practical utility of these predictions.
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To address these challenges and improve the overall effectiveness of forest foresight, we propose several avenues for enhancement. We have marked the datasets that are already incorporated in blue. greenFor an overview of our current datasets, have a look here: Predictive features
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Enhancing the model's ability to account for seasonal variations in deforestation patterns can lead to improved accuracy scores. However, it's crucial to note that while this improvement affects the overall performance metrics, it may not significantly impact the spatial accuracy of predictions.
Potential approaches:
Incorporate long-term climate data to identify seasonal patterns
Analyze historical deforestation data to detect recurring temporal trends
Integrate phenological data to track seasonal changes in vegetation
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High-temporal resolution land use change classifications, especially focusing on:
Agricultural expansion (currently with low temporal resolution)
Road construction and infrastructure development
Urban sprawl and settlement growth
Night-time light emission data (beyond existing fire and nocturnal activity data)
High-resolution satellite imagery for detecting small-scale changes
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Detailed land cover and land use maps, updated at least monthly
High-resolution terrain models to identify areas susceptible to deforestation
Soil quality and agricultural suitability maps
Detailed infrastructure plans and zoning information
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