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Discovering and measuring giant trees through the integration of multi‐platform lidar data

Authors :
Yu Ren
Hongcan Guan
Haitao Yang
Yanjun Su
Shengli Tao
Kai Cheng
Wenkai Li
Zekun Yang
Guoran Huang
Cheng Li
Guangcai Xu
Zhi Lu
Qinghua Guo
Source :
Methods in Ecology and Evolution, Vol 15, Iss 10, Pp 1889-1905 (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

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.

Details

Language :
English
ISSN :
2041210X
Volume :
15
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Methods in Ecology and Evolution
Publication Type :
Academic Journal
Accession number :
edsdoj.b24abfbdfea4287a896e221381be565
Document Type :
article
Full Text :
https://doi.org/10.1111/2041-210X.14401