{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:15:55Z","timestamp":1772752555541,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T00:00:00Z","timestamp":1674950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Innovation Project for Forestry in Guangdong","award":["2022KJCX004"],"award-info":[{"award-number":["2022KJCX004"]}]},{"name":"Scientific and Technological Innovation Project for Forestry in Guangdong","award":["CBAS2022ORP04"],"award-info":[{"award-number":["CBAS2022ORP04"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["2022KJCX004"],"award-info":[{"award-number":["2022KJCX004"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022ORP04"],"award-info":[{"award-number":["CBAS2022ORP04"]}]},{"name":"Major Key Project of PCL","award":["2022KJCX004"],"award-info":[{"award-number":["2022KJCX004"]}]},{"name":"Major Key Project of PCL","award":["CBAS2022ORP04"],"award-info":[{"award-number":["CBAS2022ORP04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The geolocation accuracy of spaceborne LiDAR (Light Detection And Ranging) data is important for quantitative forest inventory. Geolocation errors in Global Ecosystem Dynamics Investigation (GEDI) footprints are almost unavoidable because of the instability of orbital parameter estimation and GNSS (Global Navigation Satellite Systems) positioning accuracy. This study calculates the horizontal geolocation error of multiple temporal GEDI footprints using a waveform matching method, which compares original GEDI waveforms with the corresponding simulated waveforms from airborne LiDAR point clouds. The results show that the GEDI footprint geolocation error varies from 3.04 m to 65.03 m. In particular, the footprints from good orbit data perform better than those from weak orbit data, while the nighttime and daytime footprints perform similarly. After removing the system error, the average waveform similarity coefficient of multi-temporal footprints increases obviously in low-waveform-similarity footprints, especially in weak orbit footprints. When the waveform matching effect is measured using the threshold of the waveform similarity coefficient, the waveform matching method can significantly improve up to 32% of the temporal GEDI footprint datasets from a poor matching effect to a good matching effect. In the improvement of the ratio of individual footprint waveform similarity, the mean value of the training set and test set is about two thirds, but the variance in the test set is large. Our study first quantifies the geolocation error of the newest version of GEDI footprints (Version 2). Future research should focus on the improvement of the detail of the waveform matching method and the combination of the terrain matching method with GEDI waveform LiDAR.<\/jats:p>","DOI":"10.3390\/rs15030776","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Horizontal Geolocation Error Evaluation and Correction on Full-Waveform LiDAR Footprints via Waveform Matching"],"prefix":"10.3390","volume":"15","author":[{"given":"Yifang","family":"Xu","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}]},{"given":"Sheng","family":"Ding","sequence":"additional","affiliation":[{"name":"Forestry Surveying and Designing Institute of Guangdong Province, Guangzhou 510520, China"}]},{"given":"Peimin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9953-2581","authenticated-orcid":false,"given":"Hailong","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}]},{"given":"Hongkai","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}]},{"given":"Huabing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Peng Cheng Laboratory, Shenzhen 518066, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/10095020.2020.1761763","article-title":"Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: A comprehensive review","volume":"24","author":"Salas","year":"2021","journal-title":"Geo-Spat. 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