High-Precision Canopy Height Dataset Illuminates Forest Structure Dynamics in Nanping City, China
13 Mar 2026
Researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS), in collaboration with Beijing Normal University and Fujian Normal University, have developed a 10-meter resolution dataset that maps the vertical structure of forests in Nanping City, Fujian Province, China. Covering the years 2022 and 2023, the dataset provides unprecedented insights into the forest canopy height, enabling improved biomass and carbon stock estimations.
"The development of this dataset marks a major step forward in forest structure monitoring, offering accurate, high-resolution data that can be used to refine carbon stock assessments and support sustainable forest management strategies," said YAO Xiaojing, lead researcher from AIRCAS.
The dataset was created by using an innovative Bias Calibration Model, combined with a Random Forest regression framework, which addresses key limitations found in existing spaceborne LiDAR-based canopy height products. This includes overcome issues like systematic measurement biases in complex terrain, spatial discontinuity, and insufficient accuracy over mountainous forests. The team's approaches also incorporated SHAP (SHapley Additive exPlanations) analysis, which allows for a better understanding of the factors influencing canopy height predictions, such as topography, climate, and spectral features
The dataset integrates multi-source data including GEDI spaceborne LiDAR, Sentinel-1/2 imagery, UAV LiDAR field observations, ASTER GDEM topographic data, MODIS land surface temperature, and CHIRPS precipitation data. The result is a comprehensive, high-resolution map of forest canopy height that supports forest monitoring and management in the ecologically important region of Nanping City.
Independent validation of the dataset against UAV LiDAR plot data demonstrated its high accuracy, with R² = 0.62 for both 2022 and 2023, and RMSE of 2.88 m and 3.09 m, respectively. The bias calibration model significantly improved the dataset's accuracy, reducing RMSE from 11.80 m to 1.70 m and nearly eliminating bias. This improvement also resulted in an increase in R² from 0.29 to 0.80, demonstrating the model's effectiveness in mitigating systematic errors induced by topographic effects on steep slopes and in complex terrain.
The study, published in Scientific Data highlighting broad application in forest disturbance monitoring, carbon emission assessment, and sustainable forest management planning. The dataset is openly available on Zenodo, and the model preprocessing code has been released on GitHub, promoting widespread use and adaptation.
This research was supported by the Intergovernmental International Science and Technology Innovation Cooperation Program under National Key Research and Development Plan of China and the Science and Technology Service Network Initiative, Chinese Academy of Sciences.
Research News
High-Precision Canopy Height Dataset Illuminates Forest Structure Dynamics in Nanping City, China
Researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS), in collaboration with Beijing Normal University and Fujian Normal University, have developed a 10-meter resolution dataset that maps the vertical structure of forests in Nanping City, Fujian Province, China. Covering the years 2022 and 2023, the dataset provides unprecedented insights into the forest canopy height, enabling improved biomass and carbon stock estimations.
"The development of this dataset marks a major step forward in forest structure monitoring, offering accurate, high-resolution data that can be used to refine carbon stock assessments and support sustainable forest management strategies," said YAO Xiaojing, lead researcher from AIRCAS.
The dataset was created by using an innovative Bias Calibration Model, combined with a Random Forest regression framework, which addresses key limitations found in existing spaceborne LiDAR-based canopy height products. This includes overcome issues like systematic measurement biases in complex terrain, spatial discontinuity, and insufficient accuracy over mountainous forests. The team's approaches also incorporated SHAP (SHapley Additive exPlanations) analysis, which allows for a better understanding of the factors influencing canopy height predictions, such as topography, climate, and spectral features
The dataset integrates multi-source data including GEDI spaceborne LiDAR, Sentinel-1/2 imagery, UAV LiDAR field observations, ASTER GDEM topographic data, MODIS land surface temperature, and CHIRPS precipitation data. The result is a comprehensive, high-resolution map of forest canopy height that supports forest monitoring and management in the ecologically important region of Nanping City.
Independent validation of the dataset against UAV LiDAR plot data demonstrated its high accuracy, with R² = 0.62 for both 2022 and 2023, and RMSE of 2.88 m and 3.09 m, respectively. The bias calibration model significantly improved the dataset's accuracy, reducing RMSE from 11.80 m to 1.70 m and nearly eliminating bias. This improvement also resulted in an increase in R² from 0.29 to 0.80, demonstrating the model's effectiveness in mitigating systematic errors induced by topographic effects on steep slopes and in complex terrain.
The study, published in Scientific Data highlighting broad application in forest disturbance monitoring, carbon emission assessment, and sustainable forest management planning. The dataset is openly available on Zenodo, and the model preprocessing code has been released on GitHub, promoting widespread use and adaptation.
This research was supported by the Intergovernmental International Science and Technology Innovation Cooperation Program under National Key Research and Development Plan of China and the Science and Technology Service Network Initiative, Chinese Academy of Sciences.