{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:06:45Z","timestamp":1768878405940,"version":"3.49.0"},"reference-count":161,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202008440522"],"award-info":[{"award-number":["202008440522"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["834709"],"award-info":[{"award-number":["834709"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["202008440522"],"award-info":[{"award-number":["202008440522"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["834709"],"award-info":[{"award-number":["834709"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ITC Research Fund","award":["202008440522"],"award-info":[{"award-number":["202008440522"]}]},{"name":"ITC Research Fund","award":["834709"],"award-info":[{"award-number":["834709"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 \u00b1 0.175 (standard deviation) to 0.861 \u00b1 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data.<\/jats:p>","DOI":"10.3390\/rs15174128","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T08:01:21Z","timestamp":1692777681000},"page":"4128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Yi","family":"Xu","sequence":"first","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1138-8464","authenticated-orcid":false,"given":"Tiejun","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7446-8429","authenticated-orcid":false,"given":"Andrew K.","family":"Skidmore","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"},{"name":"School of Natural Sciences, Macquarie University, 12 Wally\u2019s Walk, Sydney, NSW 2109, Australia"}]},{"given":"Tawanda W.","family":"Gara","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Management, California State Polytechnic University Humboldt, Arcata, CA 95521, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e62922","DOI":"10.7554\/eLife.62922","article-title":"A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network","volume":"10","author":"Weinstein","year":"2021","journal-title":"eLife"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/416389a","article-title":"Ecological responses to recent climate change","volume":"416","author":"Walther","year":"2002","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.isprsjprs.2022.05.002","article-title":"Deep learning-based individual tree crown delineation in mangrove forests using very-high-resolution satellite imagery","volume":"189","author":"Lassalle","year":"2022","journal-title":"ISPRS J. 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