{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:47:59Z","timestamp":1771699679610,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T00:00:00Z","timestamp":1623369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha\u22121, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (\u226435 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.<\/jats:p>","DOI":"10.3390\/rs13122297","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T22:25:46Z","timestamp":1623709546000},"page":"2297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?"],"prefix":"10.3390","volume":"13","author":[{"given":"Jonathon J.","family":"Donager","sequence":"first","affiliation":[{"name":"Ecological Restoration Institute, Northern Arizona University, Flagstaff, AZ 86011, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4238-8587","authenticated-orcid":false,"given":"Andrew J.","family":"S\u00e1nchez Meador","sequence":"additional","affiliation":[{"name":"Ecological Restoration Institute and School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA"}]},{"given":"Ryan C.","family":"Blackburn","sequence":"additional","affiliation":[{"name":"School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jensen, M.E., and Bourgeron, P.S. (2001). A Guidebook for Integrated Ecological Assessments, Springer Science and Business Media LLC.","DOI":"10.1007\/978-1-4419-8620-7"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"807","DOI":"10.5558\/tfc84807-6","article-title":"The role of LiDAR in sustainable forest management","volume":"84","author":"Wulder","year":"2008","journal-title":"For. Chron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.foreco.2014.07.029","article-title":"Contemporary forest restoration: A review emphasizing function","volume":"331","author":"Stanturf","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1111\/1365-2664.12261","article-title":"Satellite remote sensing for applied ecologists: Opportunities and challenges","volume":"51","author":"Pettorelli","year":"2014","journal-title":"J. Appl. Ecol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1191\/0309133305pp432ra","article-title":"Satellite remote sensing of forest resources: Three decades of research development","volume":"29","author":"Boyd","year":"2005","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_6","first-page":"253","article-title":"Imputing Forest Structure Attributes from Stand Inventory and Remotely Sensed Data in Western Oregon, USA","volume":"60","author":"Hudak","year":"2014","journal-title":"For. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"788","DOI":"10.3390\/rs70100788","article-title":"Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data","volume":"7","author":"Hansen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.biocon.2016.03.027","article-title":"Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring","volume":"198","author":"Zhang","year":"2016","journal-title":"Biol. Conserv."},{"key":"ref_9","first-page":"102116","article-title":"Predicting biomass dynamics at the national extent from digital aerial photogrammetry","volume":"90","author":"Price","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"137409","DOI":"10.1016\/j.scitotenv.2020.137409","article-title":"Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model","volume":"720","author":"Tang","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vauhkonen, J., Maltamo, M., McRoberts, R.E., and N\u00e6sset, E. (2014). Introduction to Forestry Applications of Airborne Laser Scanning, Springer Science and Business Media LLC.","DOI":"10.1007\/978-94-017-8663-8"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bauwens, S., Bartholomeus, H., Calders, K., and Lejeune, P. (2016). Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning. Forests, 7.","DOI":"10.3390\/f7060127"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, H., Feng, Z., Shen, C., and Chen, P. (2019). Applicability of personal laser scanning in forestry inventory. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211392"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2016.01.006","article-title":"Terrestrial laser scanning in forest inventories","volume":"115","author":"Liang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hyypp\u00e4, E., Yu, X., Kaartinen, H., Hakala, T., Kukko, A., Vastaranta, M., and Hyypp\u00e4, J. (2020). Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests. Remote Sens., 12.","DOI":"10.3390\/rs12203327"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.envsoft.2016.04.025","article-title":"Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification","volume":"82","author":"Marselis","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.06.021","article-title":"International benchmarking of terrestrial laser scanning approaches for forest inventories","volume":"144","author":"Liang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1029\/2018EA000417","article-title":"Examining Forest Structure with Terrestrial Lidar: Suggestions and Novel Techniques Based on Comparisons Between Scanners and Forest Treatments","volume":"5","author":"Donager","year":"2018","journal-title":"Earth Space Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Del Perugia, B., Giannetti, F., Chirici, G., and Travaglini, D. (2019). Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning. Forests, 10.","DOI":"10.3390\/f10030277"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1111\/nph.15517","article-title":"Terrestrial Li DAR: A three-dimensional revolution in how we look at trees","volume":"222","author":"Disney","year":"2018","journal-title":"New Phytol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112102","DOI":"10.1016\/j.rse.2020.112102","article-title":"Terrestrial laser scanning in forest ecology: Expanding the horizon","volume":"251","author":"Calders","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cabo, C., Del Pozo, S., Rodr\u00edguez-Gonz\u00e1lvez, P., Ord\u00f3\u00f1ez, C., and Gonz\u00e1lez-Aguilera, D. (2018). Comparing Terrestrial Laser Scanning (TLS) and Wearable Laser Scanning (WLS) for Individual Tree Modeling at Plot Level. Remote Sens., 10.","DOI":"10.3390\/rs10040540"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s13595-011-0102-2","article-title":"The use of terrestrial LiDAR technology in forest science: Application fields, benefits and challenges","volume":"68","author":"Dassot","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s40725-015-0025-5","article-title":"Terrestrial Laser Scanning for Plot-Scale Forest Measurement","volume":"1","author":"Newnham","year":"2015","journal-title":"Curr. For. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1080\/07038992.2017.1252907","article-title":"Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds","volume":"43","author":"Ayrey","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2015.01.018","article-title":"A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data","volume":"104","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2015.10.007","article-title":"Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories","volume":"110","author":"Tao","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1109\/TGRS.2019.2940146","article-title":"3D Segmentation of Trees Through a Flexible Multiclass Graph Cut Algorithm","volume":"58","author":"Williams","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1890\/07-1747.1","article-title":"The National Fire and Fire Surrogate Study: Effects of fuel re-duction methods on forest vegetation structure and fuels","volume":"19","author":"Schwilk","year":"2009","journal-title":"Ecol. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1071\/WF06018","article-title":"Fluctuations in fuel moisture across restoration treatments in semi-arid ponderosa pine forests of northern Arizona, USA","volume":"16","author":"Faiella","year":"2007","journal-title":"Int. J. Wildland Fire"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hereford, R. (2021, March 20). Climate variation at Flagstaff, Arizona-1950 to 2007, Available online: https:\/\/pubs.usgs.gov\/of\/2007\/1410\/of2007-1410.pdf.","DOI":"10.3133\/ofr20071410"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1890\/1051-0761(1999)009[0228:ROPASO]2.0.CO;2","article-title":"Restoration of presettlement age structure of an Ar-izona ponderosa pine forest","volume":"9","author":"Mast","year":"1999","journal-title":"Ecol. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112061","DOI":"10.1016\/j.rse.2020.112061","article-title":"lidR: An R package for analysis of Airborne Laser Scanning (ALS) data","volume":"251","author":"Roussel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"75","DOI":"10.14358\/PERS.78.1.75","article-title":"A New Method for Segmenting Individual Trees from the Lidar Point Cloud","volume":"78","author":"Li","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.foreco.2017.09.012","article-title":"Reference conditions are influenced by the physical template and vary by forest type: A synthesis of Pinus ponderosa-dominated sites in the southwestern United States","volume":"404","author":"Rodman","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_38","unstructured":"De Conto, T. (2020). TreeLS: Terrestrial Point Cloud Processing of Forest Data, Swedish University of Agricultural Sciences. R package version 2.0.2."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1139\/x93-070","article-title":"Variable-shape stem-profile predictions for western hemlock. Part I. Predictions from DBH and total height","volume":"23","author":"Flewelling","year":"1993","journal-title":"Can. J. For. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1890\/04-0868","article-title":"Initial Carbon, Nitrogen, And Phosphorus Fluxes Following Ponderosa Pine Restoration Treatments","volume":"15","author":"Kaye","year":"2005","journal-title":"Ecol. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1111\/ecog.04617","article-title":"landscapemetrics: An open-source R tool to calculate landscape metrics","volume":"42","author":"Hesselbarth","year":"2019","journal-title":"Ecography"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"111770","DOI":"10.1016\/j.rse.2020.111770","article-title":"Detection of sub-canopy forest structure using airborne LiDAR","volume":"244","author":"Jarron","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bienert, A., Georgi, L., Kunz, M., Maas, H.-G., and Von Oheimb, G. (2018). Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories. Forests, 9.","DOI":"10.3390\/f9070395"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/02827581.2015.1012114","article-title":"A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models","volume":"30","author":"Temesgen","year":"2015","journal-title":"Scand. J. For. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1139\/cjfr-2015-0006","article-title":"Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America","volume":"45","author":"Margolis","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"20170048","DOI":"10.1098\/rsfs.2017.0048","article-title":"Weighing trees with lasers: Advances, challenges and opportunities","volume":"8","author":"Disney","year":"2018","journal-title":"Interface Focus"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1002\/rob.21980","article-title":"Automatic three-dimensional mapping for tree diameter measurements in inventory operations","volume":"37","author":"Tremblay","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_48","first-page":"43","article-title":"Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest","volume":"64","author":"Zhu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.agrformet.2018.04.008","article-title":"An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN","volume":"262","author":"Ferrara","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Abegg, M., K\u00fckenbrink, D., Zell, J., Schaepman, M.E., and Morsdorf, F. (2017). Terrestrial Laser Scanning for Forest Inventories\u2014Tree Diameter Distribution and Scanner Location Impact on Occlusion. Forest, 8.","DOI":"10.3390\/f8060184"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:13:24Z","timestamp":1760163204000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,11]]},"references-count":50,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122297"],"URL":"https:\/\/doi.org\/10.3390\/rs13122297","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,11]]}}}