Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data (2024)

Status: this preprint is currently under review for the journal ESSD.

Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory estimation, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot be effectively achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range Light Detection and Ranging (LiDAR) data, which enables the measurement of individual tree height in forest plots with high precision. Besides, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and Synthetic Aperture Radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining basal area of individual tree within plots using UAV LiDAR data, this study calculated weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at large spatial scale, we also developed a machine learning-based mixed-effects model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 m and 13.3 m with standard deviations of 2.9 m and 3.3 m, respectively. The accuracy of mapped products was validated utilizing LiDAR and field measurement data. The correlation coefficient (𝑟) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while RMSE ranged from 2.6 to 4.1 m and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

How to cite. Chen, Y., Yang, H., Yang, Z., Yang, Q., Liu, W., Huang, G., Ren, Y., Cheng, K., Xiang, T., Chen, M., Lin, D., Qi, Z., Xu, J., Zhang, Y., Xu, G., and Guo, Q.: Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-274, in review, 2024.

Received: 03 Jul 2024

Discussion started: 30 Jul 2024

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

Download & links

  • Preprint (PDF, 2864 KB)
  • Supplement (265 KB)

Download & links

  • Preprint (2864 KB)
  • Metadata XML
  • Supplement (265 KB)
  • BibTeX
  • EndNote
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo

Viewed

Total article views: 183 (including HTML, PDF, and XML)

HTML PDF XML Total Supplement BibTeX EndNote
150 30 3 183 12 2 3
  • HTML: 150
  • PDF: 30
  • XML: 3
  • Total: 183
  • Supplement: 12
  • BibTeX: 2
  • EndNote: 3

Views and downloads (calculated since 30 Jul 2024)

Cumulative views and downloads (calculated since 30 Jul 2024)

Viewed (geographical distribution)

Total article views: 162 (including HTML, PDF, and XML) Thereof 162 with geography defined and 0 with unknown origin.

Country # Views %
  • 1

1

Latest update: 07 Aug 2024

Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data (2024)
Top Articles
Latest Posts
Recommended Articles
Article information

Author: Kimberely Baumbach CPA

Last Updated:

Views: 5812

Rating: 4 / 5 (61 voted)

Reviews: 92% of readers found this page helpful

Author information

Name: Kimberely Baumbach CPA

Birthday: 1996-01-14

Address: 8381 Boyce Course, Imeldachester, ND 74681

Phone: +3571286597580

Job: Product Banking Analyst

Hobby: Cosplaying, Inline skating, Amateur radio, Baton twirling, Mountaineering, Flying, Archery

Introduction: My name is Kimberely Baumbach CPA, I am a gorgeous, bright, charming, encouraging, zealous, lively, good person who loves writing and wants to share my knowledge and understanding with you.