{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:13:53Z","timestamp":1760148833400,"version":"build-2065373602"},"reference-count":108,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,3]],"date-time":"2023-06-03T00:00:00Z","timestamp":1685750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union (European Social Fund-ESF)"},{"name":"IKY Scholarship Programme for PhD candidates in the Greek Universities"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R2 = 0.89), NB (RMSE% = 17.31% and R2 = 0.93) and BB (RMSE% = 24.09% and R2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method\u2019s applicability in diverse forest structures and biomes is warranted.<\/jats:p>","DOI":"10.3390\/rs15112919","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:18:29Z","timestamp":1685931509000},"page":"2919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4734-253X","authenticated-orcid":false,"given":"Nikos","family":"Georgopoulos","sequence":"first","affiliation":[{"name":"Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0056-5629","authenticated-orcid":false,"given":"Ioannis Z.","family":"Gitas","sequence":"additional","affiliation":[{"name":"Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece"}]},{"given":"Lauri","family":"Korhonen","sequence":"additional","affiliation":[{"name":"School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9899-8662","authenticated-orcid":false,"given":"Konstantinos","family":"Antoniadis","sequence":"additional","affiliation":[{"name":"Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece"}]},{"given":"Alexandra","family":"Stefanidou","sequence":"additional","affiliation":[{"name":"Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/JSTARS.2012.2211863","article-title":"Stem Volume and Above-Ground Biomass Estimation of Individual Pine Trees From LiDAR Data: Contribution of Full-Waveform Signals","volume":"6","author":"Allouis","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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