CanopyWatch – Enhancing Deforestation Monitoring in the Congo Basin using Machine Learning

Background

The Congo Basin, spanning over 2.5 million square kilometers across six countries, is home to the world’s second-largest tropical rainforest. This critical ecosystem is a global carbon sink, supports over 75 million people, and harbors more than 1,000 threatened species. However, deforestation in the Congo Basin is an ongoing challenge driven by factors like slash-and-burn agriculture, logging, mining, and industrial agriculture. These activities lead to biodiversity loss, increased carbon emissions, and threaten the livelihoods of local communities.

Existing deforestation monitoring tools lack the ability to identify the specific causes of deforestation. This gap prevents policymakers and local communities from implementing targeted interventions to combat deforestation in the Congo Basin. Recognizing this complexity, Omdena built the CanopyWatch Project to enhance deforestation monitoring in the Congo Basin using machine learning.

Objective

The primary goal of this project was to create the next iteration of CanopyWatch, a machine learning-powered application designed to:

1. Detect and categorize deforestation by type (e.g., logging, slash-and-burn agriculture, industrial agriculture).
2. Improve the accuracy and frequency of satellite image analysis to provide actionable insights in near real-time.
3. Empower local communities and policymakers to address illegal activities and implement conservation measures effectively.

Approach

The project utilized cutting-edge machine learning algorithms and satellite data to tackle the problem. Key steps included:

• Data Integration: Combining optical band imagery from Sentinel-2 and SAR imagery from Sentinel-1 for enhanced accuracy.
• Algorithm Improvement: Enhancing precision and recall for detecting deforestation types such as logging, slash-and-burn, mining, and industrial agriculture.
• Cloud-Free Imagery: Refining processes to secure and assemble cloud-free satellite images for uninterrupted monitoring.
• Automated Pipelines: Creating workflows for regular image pulls and inference, ensuring consistent and timely updates.
• Mapping and Insights: Utilizing OpenStreetMap metadata to distinguish commercial roads from logging roads and eliminate false positives.

The project also emphasized collaboration with local NGOs to ensure that insights are actionable for on-the-ground interventions.

Results and Impact

The CanopyWatch Project in the Congo Basin led to significant outcomes:

• Improved Detection Algorithms: Achieved over 80% precision and recall in detecting key deforestation drivers, enhancing monitoring capabilities.
• Expanded Deforestation Categories: Integrated industrial agriculture and mining into the detection framework, offering a more comprehensive understanding of deforestation causes.
• Timely Interventions: Enabled near-real-time alerts for local communities and NGOs, improving their ability to address illegal activities.
• Biodiversity Protection: Provided actionable data to help preserve the region’s unique ecosystems, reducing the risk to endangered species.
• Climate Mitigation: Contributed to global efforts by preserving the Congo Basin’s role as a vital carbon sink, reducing carbon emissions, and supporting climate goals like the Paris Agreement.

Future Implications

This initiative lays the groundwork for transformative changes in combating deforestation in the Congo Basin:

1. Policy Development: Accurate data will enable governments to design effective conservation policies and manage resources sustainably.
2. Community Empowerment: Local populations can leverage insights to address illegal activities, increasing their resilience and self-sufficiency.
3. Environmental Research: Improved monitoring tools offer a foundation for studying the long-term effects of conservation efforts and ecosystem health.
4. Scalability: The methodologies developed can be adapted to other tropical rainforests, amplifying their global impact on deforestation monitoring and climate mitigation.

Through advanced machine learning techniques, deforestation monitoring in the Congo Basin has reached a new level of precision, empowering stakeholders to safeguard this irreplaceable ecosystem.