Discovering and measuring giant trees through the integration of multi‐platform lidar data
Abstract Giant trees are pivotal in forest ecosystems, yet our current understanding of their significance is constrained primarily by the limited knowledge of their precise locations and structural characteristics. Amidst escalating human‐induced disturbances globally, there is an urgent need to devise a practical approach to discover and measure giant trees accurately and efficiently. Here, we propose a novel light detection and ranging (lidar)‐based framework designed for the discovery and measurement of giant trees. Our framework integrates cutting‐edge lidar platforms, including spaceborne, Unmanned Aerial Vehicle (UAV), and backpack lidar, to create an end‐to‐end workflow. The algorithm involved in the proposed framework was compiled into a code package and made available as open source. The method successfully identified the tallest trees in China, including the tallest tree in Asia, a Cupressus austrotibetica with a height of 102.3 m, discovered in Yarlung Zangbo Grand Canyon in May 2023. This finding has not only established a new record but also demonstrated the efficacy of our proposed framework. Utilising lidar data, we performed meticulous measurements at both individual and stand levels, revealing the unique characteristics of this giant tree. The new framework for the discovery and measurement of giant trees, encompassing detailed procedures and codes, is expected to facilitate the discovery and measurement of giant trees with high efficiency, thus fostering advancements in giant tree ecology.
Authors
- Shengli Tao (ORCID: https://orcid.org/0000-0002-2145-4736)
- Kai Cheng (ORCID: https://orcid.org/0000-0002-6045-0244)
- Wenkai Li (ORCID: https://orcid.org/0000-0001-6548-7882)
- Zhi Lü (ORCID: https://orcid.org/0000-0002-1746-4448)
- Qinghua Guo (ORCID: https://orcid.org/0000-0002-1065-0838)
- Guoran Huang
- Yu Ren (ORCID: https://orcid.org/0000-0001-6042-396X)
- Hongcan Guan (ORCID: https://orcid.org/0000-0003-0457-7395)
- Yanjun Su (ORCID: https://orcid.org/0000-0001-7931-339X)
- Zekun Yang (ORCID: https://orcid.org/0000-0003-2790-8521)
- Haitao Yang (ORCID: https://orcid.org/0000-0002-6053-9505)
- Cheng Li (ORCID: https://orcid.org/0000-0001-6045-6139)
- Guangcai Xu (ORCID: https://orcid.org/0009-0003-9157-5386)
Institutions
- Sun Yat-sen University (CN)
- Guangdong Medical College (CN)
- Chinese Academy of Sciences (CN)
- Peking University (CN)
- Hainan University (CN)
- Southwest Forestry University (CN)
- Beijing Botanical Garden (CN)
- Green Technology (US)
- Institute of Botany (CN)
- Center for Life Sciences (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Methods in Ecology and Evolution
- Published
- 2024-08-31
- DOI
- https://doi.org/10.1111/2041-210x.14401
- Citations
- 3
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.62
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China