Accuracy Assessment of ICESat-2 Ground Elevation and Canopy Height Estimates in Mangroves

被引:10
|
作者
Yu, Jianan [1 ,2 ]
Nie, Sheng [3 ]
Liu, Wenjie [1 ]
Zhu, Xiaoxiao [3 ]
Lu, Dajin [3 ,4 ]
Wu, Wenyin [1 ]
Sun, Yue [2 ,5 ]
机构
[1] Hainan Univ, Coll Ecol & Environm, Haikou 570208, Hainan, Peoples R China
[2] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
[3] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
[4] Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Yunnan, Peoples R China
[5] Anhui Agr Univ, Sch Forestry & Landscape Architecture, Hefei 230036, Peoples R China
基金
中国国家自然科学基金;
关键词
Laser radar; Photonics; Estimation; Laser beams; Forestry; Ice; Data mining; Accuracy assessment; error analysis; ground and canopy heights; ice; cloud; and land elevation satellite-2 (ICESat-2); mangrove;
D O I
10.1109/LGRS.2021.3107440
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Rapid and accurate ecological surveys of mangroves are of great significance for coastal protection and global carbon balance assessments. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) data provide an opportunity to conduct large-scale surveys of mangroves. The purpose of this study was to assess the expressiveness of ICESat-2 data for ground and canopy height retrievals in mangroves. First, the ICESat-2 data were processed to obtain the ground and canopy heights of mangrove areas. Second, the accuracies of the ground and canopy heights retrieved from the ICESat-2 data were verified by airborne light detection and ranging (LiDAR) data. Finally, we analyzed the influence of various factors on the ground and canopy height estimation accuracies. The results showed that the average errors of ICESat-2 for the ground and canopy heights were 0.28 and -0.21 m and that the root mean squared errors (RMSEs) were 0.96 and 2.50 m. The accuracies of the ICESat-2 ground and canopy height estimates differed significantly when day/night and strong/weak beams were used. The strong beams at night provided the most accurate estimations of canopy height (RMSE% = 24.4%) and are thus the most suitable choice for studying mangrove areas. In addition, the results indicated that slope is the variable that has the greatest influence on the accuracy of the ground elevation estimates of the four factors above, while the accuracy of canopy height estimates is significantly affected by the canopy height itself. Overall, our study found that ICESat-2 data are suitable for ecological investigations of mangroves.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] A Comparison of Machine Learning and Geostatistical Approaches for Mapping Forest Canopy Height over the Southeastern US Using ICESat-2
    Tiwari, Kasip
    Narine, Lana L.
    [J]. REMOTE SENSING, 2022, 14 (22)
  • [42] A denoising approach for detection of canopy and ground from ICESat-2's airborne simulator data in Maryland, USA
    Chen Bowei
    Pang Yong
    [J]. AOPC 2015: ADVANCES IN LASER TECHNOLOGY AND APPLICATIONS, 2015, 9671
  • [43] A Density-Based Adaptive Ground and Canopy Detecting Method for ICESat-2 Photon-Counting Data
    Xie, Huan
    Ye, Dan
    Xu, Qi
    Sun, Yuan
    Huang, Peiqi
    Tong, Xiaohua
    Guo, Yalei
    Liu, Xiaoshuai
    Liu, Shijie
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [44] ICESat-2 Derived Canopy Covers With Radiometric and Reflectance Ratio Corrections
    Zhang, Qianyin
    Zhou, Hui
    Ma, Yue
    Wang, Hong
    Li, Song
    Chen, Yuwei
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 14
  • [45] Evaluating ICESat-2 and GEDI with Integrated Landsat-8 and PALSAR-2 for Mapping Tropical Forest Canopy Height
    College of Geography and Environment, Shandong Normal University, Jinan
    250014, China
    不详
    519082, China
    不详
    519082, China
    [J]. Remote Sens., 2024, 20
  • [46] Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach
    Wu, Zhaocong
    Shi, Fanglin
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61
  • [47] Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data
    Liu, Xiaoqiang
    Su, Yanjun
    Hu, Tianyu
    Yang, Qiuli
    Liu, Bingbing
    Deng, Yufei
    Tang, Hao
    Tang, Zhiyao
    Fang, Jingyun
    Guo, Qinghua
    [J]. REMOTE SENSING OF ENVIRONMENT, 2022, 269
  • [48] Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data
    Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou
    350117, China
    不详
    350117, China
    不详
    310019, China
    [J]. Remote Sens., 19
  • [49] Effect of leaf-on and leaf-off canopy conditions on forest height retrieval and modelling with ICESat-2 data
    Zhou, Jialu
    Deng, Yunyuan
    Nie, Sheng
    Fu, Jing
    Wang, Cheng
    Zheng, Wenwu
    Sun, Yue
    [J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2023, 16 (02) : 4831 - 4847
  • [50] Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach
    Wu, Zhaocong
    Shi, Fanglin
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61