A Scalable Satellite Approach to Deforestation Prediction
Understanding deforestation is critical for environmental conservation. By leveraging satellite data, researchers have created an innovative approach to monitor and predict forest loss.
The Pure Satellite Model
To tackle the challenges of deforestation monitoring, we adopted a “pure satellite” model, relying solely on inputs from satellites like Landsat and Sentinel 2. This method incorporates a unique input called “change history,” which tracks each pixel that has been deforested along with the year of deforestation, ensuring accurate modeling using satellite-derived labels.
Advantages of Satellite Data
The pure satellite approach offers remarkable consistency, enabling us to apply the same methodology globally. This uniformity facilitates meaningful comparisons between different regions and makes the model future-proof, as satellite data streams are expected to continue for years, allowing for ongoing updates and risk assessments.
Custom Model Development
To enhance accuracy and scalability, we developed a custom model using vision transformers. This model processes entire tiles of satellite pixels simultaneously, capturing the necessary spatial context of landscapes and recent deforestation trends. Its ability to output predictions for large areas in one go significantly boosts scalability.
Accuracy and Performance
Our findings indicate that this model not only matches but often exceeds the accuracy of those based on specialized inputs, like roads. It accurately predicts tile-to-tile variations in deforestation and pinpoints which pixels are most susceptible to future deforestation.
Importance of Change History
Interestingly, the change history input emerged as the most crucial factor in our model’s performance. Even when isolated, this input provided predictions that were remarkably accurate, underscoring its dense information capacity related to recent deforestation rates and trends.
Promoting Transparency
To foster transparency and encourage further research, we are releasing our training and evaluation data as a benchmark. This initiative allows the wider machine learning community to verify outcomes, understand the predictive mechanisms, and improve upon existing deforestation risk models.
Global Application
Our benchmark and findings also lay down a roadmap for applying this scalable approach to monitor tropical deforestation across regions in Latin America and Africa, and eventually to temperate and boreal areas, where forest loss is influenced by different factors.
Conclusion
By employing a scalable satellite model, we can enhance the accuracy and efficiency of deforestation monitoring. This innovation not only supports current research but paves the way for future advancements in environmental conservation.
Keywords: satellite data, deforestation prediction, Landsat, Sentinel 2, change history, machine learning, vision transformers.
