Forest canopy height retrieval method with GEDI bias calibration

doi:10.13474/j.cnki.11-2246.2026.1008

Abstract

Abstract: [Purposes] High-density point cloud data provided by global ecosystem dynamics investigation(GEDI)LiDAR have been applied to the retrieval of forest canopy height over large areas.However,in complex terrain,GEDI data suffer from insufficient systematic bias calibration,which affects the accuracy of forest canopy height retrieval. [Methods] This study innovatively proposes a GEDI data bias calibration method by utilizing UAV LiDAR sample plot observation data to construct a random forest-based bias calibration model,thereby addressing the challenge of GEDI data bias calibration. [Findings] Validation results demonstrate that the proposed bias calibration model is highly effective in reducing systematic bias caused by terrain effects.After bias calibration,the R² increased from 0.29 to 0.80,and the RMSE decreased substantially from 11.80 m to 1.70 m. [Conclusions] The forest canopy height product retrieved from bias-calibrated GEDI data exhibits good temporal transferability and stability．

Key words: bias calibration, forest canopy height, GEDI LiDAR, UAV LiDAR, multi-modal remote sensing

CLC Number:

P237

Yi Ling, Yao Xiaojing, Chen Yaoliang, Jiao Yue, Wang Dacheng, Yang Aixia, Liu Shufu, Li Jiaxuan, Liu Yalan. Forest canopy height retrieval method with GEDI bias calibration[J]. Bulletin of Surveying and Mapping, 2026, 0(10): 55-60.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2026.1008

http://tb.chinasmp.com/EN/Y2026/V0/I10/55

References

[1] Besnard S,Heinrich V H A,Carvalhais N,et al.Global covariation of forest age transitions with the net carbon balance[J].Nature Ecology &Evolution,2025,9(10):1848-1860.
[2] 刘国华,傅伯杰,方精云.中国森林碳动态及其对全球碳平衡的贡献[J].生态学报,2000,20(5):733-740．
[3] Brandt M,Chave J,Li Sizhuo,et al.High-resolution sensors and deep learning models for tree resource monitoring[J].Nature Reviews Electrical Engineering,2025,2(1):13-26.
[4] De Conto T,Armston J,Dubayah R.Characterizing the structural complexity of the Earth's forests with spaceborne LiDAR[J].Nature Communications,2024,15:8116.
[5] Avitabile V,Pilli R,Migliavacca M,et al.Harmonised statistics and maps of forest biomass and increment in Europe[J].Scientific Data,2024,11:274.
[6] Allred B W,McCord S E,Morford S L.Canopy height model and NAIP imagery pairs across CONUS[J].Scientific Data,2025,12:322.
[7] Lang N,Jetz W,Schindler K,et al.A high-resolution canopy height model of the Earth[PP/OL].arXiv (2022-04-13)[2024-08-19].https://doi.org/10.48550/arXiv.2204.08322.
[8] Su Y,Schwartz M,Fayad I,et al.Canopy height and biomass distribution across the forests of Iberian Peninsula[J].Scientific Data,2025,12:678.
[9] Ling Qingping,Chen Yingtan,Feng Zhongke,et al.Monitoring canopy height in the Hainan tropical rainforest using machine learning and multi-modal data fusion[J].Remote Sensing,2025,17(6):966.
[10] Liu Aobo,Chen Yating,Cheng Xiao.Improving tropical forest canopy height mapping by fusion of Sentinel-1/2 and bias-corrected ICESat-2-GEDI data[J].Remote Sensing,2025,17(12):1968.
[11] Liu Aobo,Cheng Xiao,Chen Zhuoqi.Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals[J].Remote Sensing of Environment,2021,264:112571.
[12] Liu Aobo,Chen Yating,Cheng Xiao.Evaluating ICESat-2 and GEDI with integrated Landsat-8 and PALSAR-2 for mapping tropical forest canopy height[J].Remote Sensing,2024,16(20):3798.
[13] Chen Runbo,Wang Xinchuang,Liu Xuejie,et al.Optimizing GEDI canopy height estimation and analyzing error impact factors under highly complex terrain and high-density vegetation conditions[J].Forests,2024,15(11):2024.
[14] Li Yi,Gao Shijuan,Fu Haiqiang,et al.Error analysis and accuracy improvement in forest canopy height estimation based on GEDI L2A product:a case study in the United States[J].Forests,2024,15(9):1536.
[15] Cho M S,Roy D P,Kashongwe H B,et al.Assessment and improvement of GEDI canopy height estimation in tropical and temperate forests[J].Science of Remote Sensing,2025,11:100221.
[16] Rahman M F,Onoda Y,Kitajima K.Forest canopy height variation in relation to topography and forest types in central Japan with LiDAR[J].Forest Ecology and Management,2022,503:119792.
[17] Tang Hailong,Huang Huabing,Zheng Yang,et al.Improved GEDI canopy height extraction based on a simulated ground echo in topographically undulating areas[J].IEEE Transactions on Geoscience and Remote Sensing,2023,61:5705915.
[18] Duncanson L,Kellner J R,Armston J,et al.Aboveground biomass density models for NASA's global ecosystem dynamics investigation (GEDI)LiDAR mission[J].Remote Sensing of Environment,2022,270:112845.
[19] Novo-Fernández A,Barrio-Anta M,Recondo C,et al.Integration of national forest inventory and nationwide airborne laser scanning data to improve forest yield predictions in north-western Spain[J].Remote Sensing,2019,11(14):1693.
[20] Hunter M O,Parente L,Ho Y F,et al.Global 30-m annual median vegetation height maps (2000-2022)based on ICESat-2 data and Machine Learning[J].Scientific Data,2025,12:1470.
[21] Pourshamsi M,Xia J S,Yokoya N,et al.Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning[J].ISPRS Journal of Photogrammetry and Remote Sensing,2021,172:79-94.
[22] Ali Shah S A,Manzoor M A,Bais A.Canopy height estimation at landsat resolution using convolutional neural networks[J].Machine Learning and Knowledge Extraction,2020,2(1):23-36.
[23] Potapov P,Li X Y,Hernandez-Serna A,et al.Mapping global forest canopy height through integration of GEDI and Landsat data[J].Remote Sensing of Environment,2021,253:112165.
[24] Liu Xiaoqiang,Su Yanjun,Hu Tianyu,et al.Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data[J].Remote Sensing of Environment,2022,269:112844.