Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

0576527 - BÚ 2024 RIV CH eng J - Článek v odborném periodiku
Korznikov, Kirill - Kislov, D. - Petrenko, T. - Dzizyurova, V. D. - Doležal, Jiří - Krestov, P. - Altman, Jan
Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species.
Remote Sensing. Roč. 15, č. 18 (2023), č. článku 4394. E-ISSN 2072-4292
Institucionální podpora: RVO:67985939
Klíčová slova: tree crown recognition * multiple-object detection * semantic segmentation
Obor OECD: Ecology
Impakt faktor: 5, rok: 2022
Způsob publikování: Open access
https://doi.org/10.3390/rs15184394

For tasks involving tree crown recognition for counting or mapping multiple tree species, dedicated neural networks designed for object detection and counting, such as the YOLOv8 model, are more suitable and reliable. Although more complex image segmentation algorithms can also yield satisfactory results for mapping, their accuracy may be lower, and the learning process may be longer and computationally intensive. Instance segmentation neural networks are primarily recommended for tasks involving the assessment of separate tree crowns, with results requiring careful expert validation.
We stress to carefully consider the specific research task and the complexity of object classification when selecting segmentation methods. More complex tasks, such as differentiating between visually similar tree species, may necessitate additional strategies or modifications to existing segmentation algorithms to enhance accuracy. The continuous development of robust and accurate segmentation methods for such intricate tasks is an ongoing focus of research in the fields of remote sensing and computer vision.
Solving practical problems related to tree recognition requires a multi-step process that involves collaboration among experts with different skills and experiences. It is essential to adopt biology- and landscape-oriented approaches when applying remote sensing methods, which requires proficiency not only in remote sensing and deep learning techniques but also in understanding the biological aspects of forest ecosystems. This approach will not only aid in collecting primary remote data but will also significantly enhance the quality of the final recognition results.
Trvalý link: https://hdl.handle.net/11104/0346084