{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T14:13:25Z","timestamp":1776176005199,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Defense\u2019s Strategic Environmental Research and Development Program (SERDP)","award":["RC19-1064"],"award-info":[{"award-number":["RC19-1064"]}]},{"name":"Department of Defense\u2019s Strategic Environmental Research and Development Program (SERDP)","award":["RC20-1346"],"award-info":[{"award-number":["RC20-1346"]}]},{"name":"Department of Defense\u2019s Strategic Environmental Research and Development Program (SERDP)","award":["RC20-1025"],"award-info":[{"award-number":["RC20-1025"]}]},{"name":"Department of Defense\u2019s Strategic Environmental Research and Development Program (SERDP)","award":["11-2-1-11"],"award-info":[{"award-number":["11-2-1-11"]}]},{"name":"Department of Defense\u2019s Strategic Environmental Research and Development Program (SERDP)","award":["22-2-02-15"],"award-info":[{"award-number":["22-2-02-15"]}]},{"name":"2012 RxCADRE Project","award":["RC19-1064"],"award-info":[{"award-number":["RC19-1064"]}]},{"name":"2012 RxCADRE Project","award":["RC20-1346"],"award-info":[{"award-number":["RC20-1346"]}]},{"name":"2012 RxCADRE Project","award":["RC20-1025"],"award-info":[{"award-number":["RC20-1025"]}]},{"name":"2012 RxCADRE Project","award":["11-2-1-11"],"award-info":[{"award-number":["11-2-1-11"]}]},{"name":"2012 RxCADRE Project","award":["22-2-02-15"],"award-info":[{"award-number":["22-2-02-15"]}]},{"name":"Joint Fire Science Program","award":["RC19-1064"],"award-info":[{"award-number":["RC19-1064"]}]},{"name":"Joint Fire Science Program","award":["RC20-1346"],"award-info":[{"award-number":["RC20-1346"]}]},{"name":"Joint Fire Science Program","award":["RC20-1025"],"award-info":[{"award-number":["RC20-1025"]}]},{"name":"Joint Fire Science Program","award":["11-2-1-11"],"award-info":[{"award-number":["11-2-1-11"]}]},{"name":"Joint Fire Science Program","award":["22-2-02-15"],"award-info":[{"award-number":["22-2-02-15"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from \u22124 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and \u22122.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (&lt;2.0%), except for CB in ALS (\u22122.53%) and ALS + TLS (\u22122.86%), and SB in ALS + TLS data (\u22122.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.<\/jats:p>","DOI":"10.3390\/rs15041002","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T01:48:56Z","timestamp":1676252936000},"page":"1002","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Crown-Level Structure and Fuel Load Characterization from Airborne and Terrestrial Laser Scanning in a Longleaf Pine (Pinus palustris Mill.) Forest Ecosystem"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5950-8066","authenticated-orcid":false,"given":"Kleydson Diego","family":"Rocha","sequence":"first","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3560","authenticated-orcid":false,"given":"Carlos Alberto","family":"Silva","sequence":"additional","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8495-8002","authenticated-orcid":false,"given":"Diogo N.","family":"Cosenza","sequence":"additional","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1824-1841","authenticated-orcid":false,"given":"Midhun","family":"Mohan","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California\u2014Berkeley, Berkeley, CA 94709, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6898-5593","authenticated-orcid":false,"given":"Carine","family":"Klauberg","sequence":"additional","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6912-4771","authenticated-orcid":false,"given":"Monique Bohora","family":"Schlickmann","sequence":"additional","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}]},{"given":"Jinyi","family":"Xia","sequence":"additional","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}]},{"given":"Rodrigo V.","family":"Leite","sequence":"additional","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}]},{"given":"Danilo Roberti Alves de","family":"Almeida","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, \u201cLuiz de Queiroz\u201d College of Agriculture (USP\/ESALQ), University of S\u00e3o Paulo, Piracicaba 13418-900, SP, Brazil"}]},{"given":"Jeff W.","family":"Atkins","sequence":"additional","affiliation":[{"name":"Southern Research Station, USDA Forest Service, Savannah River Site, New Ellenton, SC 29809, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0185-3959","authenticated-orcid":false,"given":"Adrian","family":"Cardil","sequence":"additional","affiliation":[{"name":"Tecnosylva, Parque Tecnol\u00f3gico de Le\u00f3n, 24009 Le\u00f3n, Spain"}]},{"given":"Eric","family":"Rowell","sequence":"additional","affiliation":[{"name":"Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-8993","authenticated-orcid":false,"given":"Russ","family":"Parsons","sequence":"additional","affiliation":[{"name":"Fire Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Service, 5775 W. Highway 10, Missoula, MT 59801, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0455-3951","authenticated-orcid":false,"given":"Nuria","family":"S\u00e1nchez-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Department of Forest, Rangeland and Fire Sciences, College of Natural Resources, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844, USA"}]},{"given":"Susan J.","family":"Prichard","sequence":"additional","affiliation":[{"name":"School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7480-1458","authenticated-orcid":false,"given":"Andrew T.","family":"Hudak","sequence":"additional","affiliation":[{"name":"Forestry Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Service, 1221 South Main Street, Moscow, ID 83843, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"ref_1","first-page":"31","article-title":"Managing the world\u2019s forests","volume":"29","author":"Sharma","year":"1992","journal-title":"Financ. 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