地球信息科学学报

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (2): 409-420.doi: 10.12082/dqxxkx.2023.220483

• 遥感科学与应用技术 • 上一篇    下一篇

结合ICESat-2和GEDI的中国东南丘陵地区ASTER GDEM高程精度评价与修正

焦怀瑾1,2(), 陈崇成1,2, 黄洪宇1,2,*()   

  1. 1.福州大学地理空间信息技术国家地方联合工程研究中心，福州 350108
    2.福州大学空间数据挖掘与信息共享教育部重点实验室，福州 350108
  • 收稿日期:2022-07-06 修回日期:2022-10-16 出版日期:2023-02-25 发布日期:2023-04-19
  • 通讯作者: *黄洪宇（1971— ），男，福建莆田人，助理研究员，主要从事激光雷达和影像三维数据获取和分析研究。 E-mail: hhy1@fzu.edu.cn
  • 作者简介:焦怀瑾（1999— ），男，江西吉安人，硕士研究生，主要从事地学可视化与虚拟地理环境研究。E-mail: huai_jin@foxmail.com
  • 基金资助:
    福建省科技计划项目(2020I0008);福建省高校产学合作项目(2022N5008);福建省科技创新领军人才项目

Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data

JIAO Huaijin1,2(), CHEN Chongcheng1,2, HUANG Hongyu1,2,*()   

  1. 1. National Engineering Research Center of Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China
    2. Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China
  • Received:2022-07-06 Revised:2022-10-16 Online:2023-02-25 Published:2023-04-19
  • Contact: HUANG Hongyu
  • Supported by:
    Fujian Provincial Science and Technology Plan Project(2020I0008);Fujian Provincial Univer- sity-industry Cooperation Project(2022N5008);Fujian Provincial Leading Talents of Scientific and Technological Innovation Project

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摘要：

星载激光雷达ICESat-2和GEDI可以为数字高程模型产品的精度评价与修正提供全球覆盖的、可靠的高精度参考数据源。然而，现有的DEM修正方法主要是针对DEM误差中的植被高信号且多采用线性回归模型。为此，本文分析了ASTER GDEM v3精度与土地覆盖类型、高程、坡度、起伏度及植被覆盖率的关系。在此基础上，提出了一种考虑上述多种精度影响因素并结合XGBoost和空间插值的DEM误差修正方法。结果分析表明：原始ASTER GDEM的误差整体呈正态分布，平均误差为-3.463 m，存在较大负偏差，高程精度随着高程、坡度、起伏度及植被覆盖率VCF的增大呈降低趋势；经过修正后， ASTER GDEM平均误差降低到了-0.233 m，负偏差得到有效改善，整体平均绝对误差降低了26.04%，整体均方差降低了23.56%，耕地、林地、草地、湿地、水域及人造地表的DEM平均绝对误差和均方差都有不同程度的降低；本文提出的方法对多种特征要素与地形误差间的非线性关系进行拟合建模，在研究区取得了较好的修正效果。

关键词: ICESat-2, GEDI, ASTER, 高程精度评价, XGBoost, 空间插值, 土地覆盖类型, DEM修正

Abstract:

Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) products provide reliable global references for the accuracy evaluation and correction of Global Digital Elevation Model (GDEM). However, existing DEM correction methods mainly address the signal of vegetation in DEM errors and mostly use linear regression models. So, we first analyze the relationship between Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM v3 data accuracy and the land cover type, elevation, slope, relief amplitude, and vegetation coverage. Based on this, this paper proposes a Digital Elevation Model (DEM) error correction method that takes into account various influencing factors and combines Extreme Gradient Boosting (XGBoost) machine learning and spatial interpolation to model the errors. The analysis of the results shows that the overall error of the original ASTER GDEM has a normal distribution with a large negative offset (average error of -3.463 m). The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of original ASTER GDEM are 12.930 m and 16.695 m, respectively, and the elevation accuracy decreases with the increase of elevation, slope, relief amplitude, and vegetation coverage. After correction, the Mean Error (ME) of ASTER GDEM is reduced to -0.233 m, which means the negative deviation is effectively removed and the overall MAE and overall RMSE are reduced by 26.04% and 23.56%, respectively. The MAE and RMSE of DEM for cultivated lands, forests, grasslands, wetlands, water bodies, and man-made surfaces are all reduced by different degrees. The DEM accuracy evaluation and correction method proposed in this paper models the non-linear relationships between multiple feature elements and terrain errors and achieves better correction results in the study area.

Key words: ICESat-2, GEDI, ASTER, elevation accuracy evaluation, XGBoost, spatial interpolation, land cover type, DEM correction