Field Deployment of BiodivX Drones in the Amazon Rainforest for Biodiversity Monitoring
Christian Geckeler, Steffen Kirchgeorg, Georg Miguel Strunck, Frederik Bendix Thostrup, Florencia Sangermano, Andrea Desiderato, Martina Lüthi, Meret Jucker, Mailyn Adriana Gonzalez Herrera, Nicolás D. Franco-Sierra, Paola Pulido-Santacruz, Jia Jin Marc Chang, Yin Cheong Aden Ip, Elvira Mächler, Asger Svenning, Guillaume Mougeot, Toke Thomas Høye, Fabian Fopp, Loic Pellissier, David Dao, Kristy Deiner, Claus Melvad, Salua Hamaza, Stefano Mintchev
AI summary
Problem
Tropical rainforests are critically biodiverse yet poorly documented due to difficult access, costly traditional survey methods, and rapid threats like deforestation. There is a need for scalable, remote biodiversity monitoring that can capture data across all forest layers efficiently.
Approach
The team deployed a commercial drone fleet with swappable modular payloads to autonomously collect remote sensing maps, environmental DNA, close-up imagery, and bioacoustic data from the canopy to the forest floor, supported by a long-range mesh communication network.
Key results
- Surveyed 100 hectares of Amazon rainforest within a 24-hour window
- Generated a full RGB and digital surface map in 1.5 hours
- Autonomously collected surface environmental DNA up to 1.4 km from the base station
- Deployed canopy rafts for long-term bioacoustic and insect specimen collection
Why it matters
Provides a field-tested, scalable framework for rapid biodiversity assessment in remote ecosystems, directly supporting conservation efforts and informing policy.
Abstract
Tropical rainforests are among the most biodiverse ecosystems on Earth, and also among the most threatened by anthropogenic pressures such as deforestation and climate change. Understanding human impact and the efficacy of conservation and preservation efforts requires scalable and comprehensive biodiversity monitoring solutions. As a winning finalist of the XPRIZE Rainforest Competition, ETH BiodivX collected biodiversity data from 100 ha of rainforest in the Amazon, in 24 hours. A suite of complementary data types were captured, from remote sensing maps and close-up images to surface and water environmental DNA (eDNA), along with canopy rafts which collect specimens, close-up images, and bioacoustic recordings. A distributed mesh communication network allows for a persistent link to the drone, up to the edges of the competition area. Optimized workflows allow for a full RGB and digital surface map (DSM) after only one-and-a- half hours. The captured DSM was then used to collect surface eDNA fully autonomously, and using the communication network, surface eDNA was collected at distances up to 1.4 km from the base station. Pre-processed multispectral satellite remote sensing provides indicators of water locations, which were then sampled for water eDNA. The canopy rafts can act as communication nodes or data collection stations, providing long-term bioacoustic recordings, insect images, and specimens. By utilizing a commercial drone platform with modular payloads for diverse tasks, the solutions are robust and easy to use. These field-proven systems mark a major step towards scalable biodiversity monitoring, including in some of the world’s most remote and biodiverse regions: tropical rainforests.