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.

Current Challenges in Forest Foresight

Forest Foresight models face several challenges, including:

1. Balancing precision and recall in predictions

2. Accounting for seasonal variations in deforestation patterns

3. Accurately pinpointing the location of potential deforestation events

4. Overcoming limitations in existing ground truth data

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. For an overview of our current datasets, have a look here: Predictive features

1. Improving Seasonality Predictions

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

While improving seasonality predictions is valuable, it's essential to balance this with efforts to enhance spatial accuracy, as the latter is often more critical for practical conservation efforts.

2. Enhancing Spatial Accuracy of Deforestation Predictions

Improving the spatial accuracy of predictions is crucial for effective forest conservation efforts. However, this task is challenging due to limitations in ground truth data, such as the Global Forest Watch (GFW) integrated alerts. Some false negatives or false positives in these alerts may be inherently unpredictable. Nevertheless, several types of datasets could potentially enhance spatial accuracy:

2.1 Direct Physical Signals of Human Activity

Datasets that provide direct evidence of human activity in or near forested areas can significantly improve prediction accuracy. These may include:

• 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

Such datasets can provide early warning signs of impending deforestation, allowing for more targeted and timely interventions.

2.2 Direct Digital Signals of Human Activity

In the modern era, digital footprints can offer valuable insights into human activities that may precede deforestation. Potential data sources include:

• Anonymized and aggregated mobile phone data, such as:

  • Location pings

  • Call detail records (CDRs)

  • Mobile data usage patterns

• Vehicle tracking data from commercial fleets or public transportation

• Social media activity and geotagged posts in areas of interest

These digital signals can provide near real-time indications of increased human presence or activity in forested areas, potentially signaling impending deforestation events.

2.3 High Spatial Resolution Data with Strong Predictive Power

While high spatial resolution data can be valuable, its utility is often limited by low temporal frequency. However, certain types of data in this category can still contribute to improved predictions:

• 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

It's important to note that datasets updated monthly or more frequently tend to be significantly more valuable for forest foresight than those with lower update frequencies.

Conclusion

By combining these diverse data sources with advanced machine learning techniques, we can develop more accurate and actionable forest foresight models. This, in turn, will enable more effective conservation efforts and help combat global deforestation.

As organizations consider contributing their datasets, it's crucial to balance data privacy and ethical considerations with the potential environmental benefits. Collaborative efforts between technology companies, conservation organizations, and local communities will be key to realizing the full potential of enhanced forest foresight.