Drones, Big Data and Rainforests - how can drone data help regrow rainforests?

The rainforests across West Africa have been severely impacted by deforestation over the years, with many areas losing up to 90% of their forest cover. This has resulted in a loss of biodiversity, disrupted ecosystems, and reduced carbon sequestration, which is essential in mitigating the effects of climate change. To address these challenges, there is a growing need for innovative solutions to help regenerate these forests. One promising technology that could make a significant contribution is the use of multi-spectral drones to capture and analyse big data at scale. Oko has recently added the use of multi-spectral drones to its every-day operations in West Africa, with the first drone flights starting in January 2023. The potential drone technology presents in being able to capture and analyse big data at scale to aid in Oko’s reforestation efforts is incredibly exciting.

Multi-spectral drones are unmanned aerial vehicles (UAVs) equipped with sensors that can capture images in different parts of the electromagnetic spectrum. This allows them to capture data beyond what is visible to the human eye, including infrared and ultraviolet light. By combining this data with advanced analytics, Oko can gain insights into the health and condition of the forests, such as tracking forest growth, identifying problem areas or illegal deforestation activities as well as measuring forest density and health.

One of the primary advantages of using multi-spectral drones for forest regeneration is their ability to cover vast areas quickly and efficiently. Traditional methods of forest monitoring and analysis can be time-consuming, labour-intensive, and expensive, often involving teams of scientists on the ground collecting data manually. In contrast, drones can fly over large areas in a fraction of the time, collecting high-resolution data that can be analysed in real-time.

Another advantage of using multi-spectral drones is their ability to capture data in different weather conditions. Unlike satellite imagery, which can be hampered by cloud cover or atmospheric interference, drones can fly at lower altitudes, capturing data in high resolution, even in cloudy or overcast conditions. This means that Oko can collect data year-round, providing a more comprehensive and accurate picture of the forest's health and condition.

To use multi-spectral drones effectively for forest regeneration, Oko is first establishing a baseline dataset. This involves collecting data on the current state of the environment,such as the location and extent of deforested areas, the density and species composition of vegetation, and the presence of any invasive species. Once this baseline is established, Oko can use the data collected by the drones to monitor changes in the regrown forests over time, measuring the success of reforestation efforts and identifying areas that require additional attention.

One of Oko’s sister companies also working in West Africa is Aya Data. Aya is West Africa’s largest AI services provider, specialising in AI, data labelling and machine learning across industries. Aya has been able to take the drone data Oko is capturing on the ground and design and create programmes that can process and annotate the big data and provide real-time analysis that is helping Oko understand the status of the forests and constantly learn and improve our approach to reforestation.

Overall, the use of multi-spectral drones to capture and analyse big data at scale has the potential to make a significant contribution to forest regeneration efforts across West Africa. By providing Oko with real-time data on the health and condition of the forest, drones can help guide our reforestation efforts, monitor progress, and identify areas that require additional attention. With continued exploration of how the use of drones and the analysis of big data capture can improve reforestation practices, we can hope to see a brighter future for the rainforests across West Africa and the many communities and species that depend on them.

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