MONITORING OF REINTRODUCED RARE PLANTS USING UAV DATA

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-1/W2-2023; pp. 1895 - 1900
• Main Authors: Gorodetskaya, L. A., Denisova, A. Y., Kavelenova, L. M., Pomogaybin, A. V., Ruzaeva, I. V., Fedoseev, V. A.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 14.12.2023; Copernicus Publications
• Subjects: Algorithms; Anthropogenic factors; Archives & records; Biodiversity; Botany; Conservation; Conservation status; Data collection; Detection; Ecologists; Ecology; Flowers & plants; Human influences; Machine learning; Methods; Monitoring; Monitoring methods; Natural environment; Neural networks; Object recognition; Plant sciences; Plant species; Plants; Rare species; Reintroduction; Remote sensing; Semantics; Sensors; Unmanned aerial vehicles; Vegetation; Wildlife conservation
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-1-W2-2023-1895-2023

Species reintroduction is one of the most important tasks for biodiversity conservation. In the Samara region, the Department of Ecology, Botany, and Nature Protection of Samara University is constantly working to return rare plants to their natural habitat. Field surveys are carried out to monitor the process of plant development in the natural environment. However, the complex terrain and the special conservation status of the territories, where reintroduction is carried out, require the development of methods for monitoring plants that reduce human contact with the natural environment. As such a method, the article proposes using data from unmanned aerial vehicles (UAVs) in conjunction with the detection of reintroduced plant species using object detection neural networks, namely YOLOv3. The proposed method reduces the anthropogenic impact on the territory during the monitoring process and simplifies plant observations.