Leveraging AI for Reforestation in Madagascar

Background

Madagascar, the 5th poorest country globally, is facing severe consequences from climate change, including rapid deforestation and loss of biodiversity. This crisis threatens local ecosystems and communities. Bôndy, a Madagascar-based startup focused on innovative reforestation, works to combat these challenges by planting high-value tree species on farmer lands. These efforts help replace degraded forests while providing rural populations with more sustainable sources of income. The Omdena team partnered with Bôndy to apply AI and machine learning to monitor the success of reforestation initiatives and ensure long-term environmental and social benefits.

Objective

The AI for Reforestation project aimed to develop a machine learning model to monitor the progress of reforestation efforts in Madagascar. Specifically, the goal was to use satellite and drone imagery to track tree growth and survival over the first five years of reforestation. By accurately detecting tree patches and estimating the number of trees, the team sought to enhance the monitoring of forest recovery and assess the impact of environmental factors on reforestation outcomes.

Approach

The Omdena team applied a variety of advanced technologies and data sources to tackle the reforestation challenge:

• Data Collection: Data for the project was sourced from multiple platforms, including Planet satellite images (monthly data), Sentinel-2 cloud-free imagery, drone images, and meteorological data (ERA-5 data).
• Vegetation Indices: To assess tree health and growth, three vegetation indices were calculated: NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index). These indices help identify vegetation health, water availability, and soil conditions.
• Drone Image Preprocessing: Drone images were preprocessed to extract key information, such as flight altitude, camera details, and the number of trees in each image. This involved using Python libraries like Rasterio to analyze satellite and drone data.
• Environmental Impact Analysis: Three environmental parameters—temperature, precipitation, and evaporation—were chosen to monitor their impact on tree health. Time series models were built using the FaceBook Prophet library to predict trends in these parameters for three key regions in Madagascar.
• AI and Machine Learning: The team developed a machine learning model capable of detecting tree patches and estimating tree density. The model was trained on the satellite and drone data, incorporating various pre-trained super-resolution models to enhance image quality and accuracy.

Results and Impact

The machine learning model successfully detected tree patches and estimated the number of trees, providing valuable insights into the effectiveness of reforestation efforts. By monitoring tree growth and survival over time, the model enabled the team to evaluate the success of planting efforts and identify areas needing improvement. The project’s outcomes have contributed to better-informed decision-making for reforestation strategies in Madagascar.

The results also highlighted the impact of environmental factors—such as temperature, precipitation, and evaporation—on the health and survival of the trees. This information can be used to fine-tune reforestation techniques and ensure more resilient forest ecosystems.

Key outcomes:

• Development of a robust AI model for monitoring tree growth and survival.
• Enhanced understanding of environmental influences on reforestation.
• Creation of data-driven tools to support reforestation planning and decision-making.
• Empowerment of local communities with insights into the success of reforestation projects.

Future Implications

The AI model developed through the AI for Reforestation in Madagascar project can be applied to other reforestation efforts in regions facing similar challenges. The ability to monitor tree growth and survival with high precision will help stakeholders refine their strategies, prioritize areas for intervention, and improve long-term forest management. This project also paves the way for further research into integrating AI with environmental monitoring, offering a scalable solution for global reforestation efforts.

Future applications may include:

• Policy Development: The model’s findings could inform reforestation policies and land-use strategies, helping governments and NGOs implement more effective programs.
• Scaling AI in Environmental Monitoring: The success of this project demonstrates the potential of AI in large-scale environmental monitoring, which could be applied to other conservation efforts, such as biodiversity preservation and ecosystem restoration.
• Sustainable Development: By combining AI with community-based reforestation, this project offers a model for sustainable development that balances environmental conservation with economic opportunity for local populations.

This AI-driven approach is not only advancing reforestation efforts in Madagascar but also demonstrating the potential of technology to tackle climate change on a global scale.