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Published on: July 3, 2020
A 30 m forest dominant height dataset for China in 2020
Yuling Chen1,2, Guangcai Xu3, Haitao Yang4
1Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, 100871, China.
Scientific Data
|June 4, 2026
Summary
A new nationwide forest dominant height dataset for China (FDH-30C) was created using Unmanned Aerial Vehicle (UAV) LiDAR data and remote sensing predictors. This 30m resolution dataset offers a consistent baseline for forest management and ecological studies.
Area of Science:
- Forestry and Remote Sensing Science
Background:
- Forest dominant height is a key indicator of site conditions and forest growth potential.
- Accurate, high-resolution data on forest structure is crucial for ecological and management applications.
Purpose of the Study:
- To develop a nationwide 30m resolution forest dominant height dataset for China (FDH-30C).
- To provide a spatially continuous and accurate baseline for various forest-related applications.
Main Methods:
- Calibrated using extensive Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) data across China's major vegetation zones.
- Integrated 30 geospatial predictors from multi-source remote sensing (climatic, edaphic, topographic, vegetation, SAR).
- Employed a two-stage hybrid modeling framework to ensure spatial coherence and local accuracy.
Main Results:
- Generated a nationwide 30m resolution forest dominant height map for China (FDH-30C).
- The dataset integrates diverse data sources and advanced modeling for reliable estimates.
- Achieved spatially coherent estimates while minimizing ecozone boundary effects.
Conclusions:
- The FDH-30C dataset provides a valuable national baseline for forest management, biomass estimation, and ecological research.
- This dataset supports applications including site-index mapping, growth-and-yield parameterization, and vertical structure analysis.
- Facilitates the evaluation of spaceborne LiDAR missions and enhances understanding of forest ecosystems.
