{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:00:56Z","timestamp":1774292456131,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Finance Science and Technology Project of Hainan Province","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["2023YFF1303600"],"award-info":[{"award-number":["2023YFF1303600"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["KF-2021-06-081"],"award-info":[{"award-number":["KF-2021-06-081"]}]},{"name":"National Key Research and Development Program of China","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFF1303600"],"award-info":[{"award-number":["2023YFF1303600"]}]},{"name":"National Key Research and Development Program of China","award":["KF-2021-06-081"],"award-info":[{"award-number":["KF-2021-06-081"]}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["2023YFF1303600"],"award-info":[{"award-number":["2023YFF1303600"]}]},{"name":"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["KF-2021-06-081"],"award-info":[{"award-number":["KF-2021-06-081"]}]},{"name":"Special Educating Project of the Talent for Carbon Peak and Carbon Neutrality of University of Chinese of Academy of Science","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"Special Educating Project of the Talent for Carbon Peak and Carbon Neutrality of University of Chinese of Academy of Science","award":["2023YFF1303600"],"award-info":[{"award-number":["2023YFF1303600"]}]},{"name":"Special Educating Project of the Talent for Carbon Peak and Carbon Neutrality of University of Chinese of Academy of Science","award":["KF-2021-06-081"],"award-info":[{"award-number":["KF-2021-06-081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1\/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency (R2 = 0.85, RMSE = 2.2m, pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R2, RMSE, and pRMSE of 0.64, 3.38m, and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models.<\/jats:p>","DOI":"10.3390\/rs16010110","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T02:58:12Z","timestamp":1703645892000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Genetic Programming Guided Mapping of Forest Canopy Height by Combining LiDAR Satellites with Sentinel-1\/2, Terrain, and Climate Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhenjiang","family":"Wu","sequence":"first","affiliation":[{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"The Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572000, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Fengmei","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572000, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Enhua","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Yunnan University, Kunming 650500, China"}]},{"given":"Liping","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhaowei","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112760","DOI":"10.1016\/j.rse.2021.112760","article-title":"Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles","volume":"268","author":"Lang","year":"2022","journal-title":"Remote Sens. 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