Biodiversity Estimation from Satellite Images

Maintaining and protecting biodiversity of plants and animals is essential for our life on earth. Due to climate change, human destruction of precious habitats, and industrial agriculture, biodiversity is decreasing in many countries. A major bottleneck for counter-measures is lack of accurate, dense biodiversity data at large scale. Today, the main technique to monitor biodiversity is field surveys that assess biodiversity in situ by counting the amount of different species per area. These studies are very labor-intensive, costly, and deliver only scarce, point-wise data. Moreover, the temporal resolution is low because revisit cycles of the test sites are long.

In this project, we aim at automating biodiversity estimation in Switzerland using (deep) machine learning and overhead imagery. We combine point-wise, high-quality in-situ data of Swiss institutions like BLW, Agroscope, BAFU, and WSL that have been conducting field surveys for decades with dense, large-scale satellite imagery. First experiments have shown that the spectral resolution of ESA satellites Sentinel 2, designed for vegetation monitoring, partially allows for plant species recognition on the ground. This project aims at directly mapping different species distributions and biodiversity in Switzerland to help protect the environment and measuring the impact of agriculture on biodiversity.