GEDI偏差校准后的森林冠层高度反演方法

doi:10.13474/j.cnki.11-2246.2026.1008

测绘通报 ›› 2026, Vol. 0 ›› Issue (10): 55-60.doi: 10.13474/j.cnki.11-2246.2026.1008

• 学术研究 • 上一篇

GEDI偏差校准后的森林冠层高度反演方法

易玲^1,2, 姚晓婧¹, 陈耀亮³, 焦越¹, 王大成^1,2, 杨爱霞¹, 柳树福¹, 李佳轩⁴, 刘亚岚^1,2

1. 中国科学院空天信息创新研究院, 北京 100101;
2. 中国科学院大学, 北京 100049;
3. 福建师范大学地理科学学院, 福建福州 350117;
4. 武汉大学遥感信息工程学院, 湖北武汉 430079

收稿日期:2025-12-31 发布日期:2026-11-06
通讯作者: 姚晓婧。E-mail:yaoxj@aircas.ac.cn
作者简介:易玲(2000—),女,主要研究方向为环境遥感。E-mail:yiling23@mails.ucas.ac.cn
基金资助:
南平科技重大项目(N2023A001)

Forest canopy height retrieval method with GEDI bias calibration

Yi Ling^1,2, Yao Xiaojing¹, Chen Yaoliang³, Jiao Yue¹, Wang Dacheng^1,2, Yang Aixia¹, Liu Shufu¹, Li Jiaxuan⁴, Liu Yalan^1,2

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. School of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China;
4. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Received:2025-12-31 Published:2026-11-06

摘要/Abstract

摘要： [目的] 全球生态系统动态调查(GEDI)激光雷达提供的高密度点云数据已被应用于大范围森林冠层高度的反演中。本文旨在解决复杂地形下 GEDI数据存在的系统偏差校准不足的问题，从而提升森林冠层高度反演的精度。[方法] 本文创新性地提出了一种GEDI数据偏差校准方法，即利用无人机激光雷达样地观测数据，构建了基于随机森林的偏差校准模型，以解决GEDI数据偏差校准难题。[结果] 验证结果表明，本文提出的偏差校准模型在降低地形效应引起的系统偏差方面效果显著，偏差校准后的R²由0.29跃升至0.80，RMSE由11.80 m大幅降至1.70 m。[结论] 基于偏差校准后的GEDI反演的森林冠层高度产品具有良好的时间迁移性和稳定性。

关键词: 偏差校准, 森林冠层高度, GEDI激光雷达, 无人机激光雷达, 多模态遥感

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

中图分类号:

P237

易玲, 姚晓婧, 陈耀亮, 焦越, 王大成, 杨爱霞, 柳树福, 李佳轩, 刘亚岚. GEDI偏差校准后的森林冠层高度反演方法[J]. 测绘通报, 2026, 0(10): 55-60.

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.

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GEDI偏差校准后的森林冠层高度反演方法

Forest canopy height retrieval method with GEDI bias calibration

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