Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Nov 2024
Data description paper |
| 14 Nov 2024
| 14 Nov 2024
Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data
Yuling Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Haitao Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Zekun Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Qiuli Yang
College of Geography and Remote Sensing Science, Xinjiang University, Ürümqi 800017, China
Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Ürümqi 830017, China
Weiyan Liu
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Guoran Huang
College of Forestry, Southwest Forestry University, Kunming 650224, China
Yu Ren
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Kai Cheng
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Tianyu Xiang
College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Mengxi Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Danyang Lin
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Zhiyong Qi
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Jiachen Xu
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Yixuan Zhang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Guangcai Xu
Beijing GreenValley Technology Co., Ltd., Haidian, Beijing 100091, China
Qinghua Guo
CORRESPONDING AUTHOR
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Data sets
Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data Yuling Chen et al. https://doi.org/10.5281/zenodo.12697784
Short summary
The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
The national-scale continuous maps of arithmetic mean height and weighted mean height across...
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