{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:34:59Z","timestamp":1780392899634,"version":"3.54.1"},"reference-count":118,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability.<\/jats:p>","DOI":"10.3390\/s23031593","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T05:33:53Z","timestamp":1675229633000},"page":"1593","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Point Density Variations in Airborne Lidar Point Clouds"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5566-9236","authenticated-orcid":false,"given":"Vaclav","family":"Petras","sequence":"first","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5120-5538","authenticated-orcid":false,"given":"Anna","family":"Petrasova","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3256-8314","authenticated-orcid":false,"given":"James B.","family":"McCarter","sequence":"additional","affiliation":[{"name":"Department of Forestry and Environmental Resources, North Carolina State University, 2820 Faucette Dr., Campus Box 8001, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6906-3398","authenticated-orcid":false,"given":"Helena","family":"Mitasova","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA"},{"name":"Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, 2800 Faucette Drive, Campus Box 8208, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1247-6212","authenticated-orcid":false,"given":"Ross K.","family":"Meentemeyer","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA"},{"name":"Department of Forestry and Environmental Resources, North Carolina State University, 2820 Faucette Dr., Campus Box 8001, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1641\/0006-3568(2002)052[0019:LRSFES]2.0.CO;2","article-title":"Lidar Remote Sensing for Ecosystem Studies","volume":"52","author":"Lefsky","year":"2002","journal-title":"BioScience"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kobal, M., Bertoncelj, I., Pirotti, F., Dakskobler, I., and Kutnar, L. (2015). Using Lidar Data to Analyse Sinkhole Characteristics Relevant for Understory Vegetation under Forest Cover\u2014Case Study of a High Karst Area in the Dinaric Mountains. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0122070"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kumar, J., Weiner, J., Hargrove, W.W., Norman, S.P., Hoffman, F.M., and Newcomb, D. (2015, January 14\u201317). Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR. Proceedings of the 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA.","DOI":"10.1109\/ICDMW.2015.178"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1111\/avsc.12048","article-title":"Savanna woody vegetation classification\u2013now in 3-D","volume":"17","author":"Fisher","year":"2014","journal-title":"Appl. Veg. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hardin, E., Mitasova, H., Tateosian, L., and Overton, M. (2014). GIS-Based Analysis of Coastal Lidar Time-Series, Springer.","DOI":"10.1007\/978-1-4939-1835-5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1016\/j.rse.2007.07.020","article-title":"The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets","volume":"112","author":"Hopkinson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/LGRS.2013.2262317","article-title":"Geomorphological Change Detection Using Object-Based Feature Extraction From Multi-Temporal LiDAR Data","volume":"10","author":"Anders","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.rse.2017.09.007","article-title":"Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux","volume":"204","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0924-2716(98)00013-6","article-title":"Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications","volume":"53","author":"Huising","year":"1998","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.isprsjprs.2005.09.002","article-title":"Effects of laser beam alignment tolerance on lidar accuracy","volume":"59","author":"Latypov","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","first-page":"19","article-title":"Point positioning accuracy of airborne LiDAR systems: A rigorous analysis","volume":"36","author":"May","year":"2007","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","unstructured":"Schaer, P., Skaloud, J., Landtwing, S., and Legat, K. (2007, January 29\u201331). Accuracy estimation for laser point cloud including scanning geometry. Proceedings of the Mobile Mapping Symposium, Padova, Italy."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1111\/j.1477-9730.2008.00476.x","article-title":"Accuracy assessment of lidar-derived digital elevation models","volume":"23","author":"Aguilar","year":"2008","journal-title":"Photogramm. Rec."},{"key":"ref_14","unstructured":"Anderson, B.C. (2008). Assessing Accuracy in Varying LIDAR Data Point Densities in Digital Elevation Maps. [Master\u2019s Thesis, Naval Postgraduate School]."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Biasutti, P., Bugeau, A., Aujol, J.F., and Br\u00e9dif, M. (2019, January 25\u201327). Visibility estimation in point clouds with variable density. Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, Czech Republic.","DOI":"10.5220\/0007308600270035"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"735","DOI":"10.5194\/isprs-archives-XLII-2-W15-735-2019","article-title":"Deep learning for semantic segmentation of 3D point cloud","volume":"XLII-2\/W15","author":"Malinverni","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens Spat. Inf. Sci."},{"key":"ref_17","unstructured":"Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T., Guo, J., Ngiam, J., and Vasudevan, V. (, January 16\u201318). End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds. Proceedings of the Conference on Robot Learning, Virtual."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105207","DOI":"10.1088\/1361-6501\/ac750c","article-title":"Real-time rail recognition based on 3D point clouds","volume":"33","author":"Yu","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_19","unstructured":"Roynard, X., Deschaud, J.E., and Goulette, F. (2018). Classification of Point Cloud Scenes with Multiscale Voxel Deep Network. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, W., Liu, X., Wan, Y., Zhu, X., and Tan, Y. (2021). Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13061136"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5194\/isprs-annals-IV-4-W8-139-2019","article-title":"An improved automatic pointwise semantic segmentation of a 3D urban scene from mobile terrestrial and airborne LiDAR point clouds: A machine learning approach","volume":"4","author":"Xing","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, J., Yao, W., Chi, W., and Zhao, X. (2011, January 26\u201329). Comprehensive quality evaluation of airborne lidar data. Proceedings of the International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, International Society for Optics and Photonics, Nanjing, China.","DOI":"10.1117\/12.912588"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2013.04.005","article-title":"High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision","volume":"136","author":"Dandois","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/23754931.2015.1121405","article-title":"Impacts of LiDAR Sampling Methods and Point Spacing Density on DEM Generation","volume":"2","author":"Zhao","year":"2016","journal-title":"Pap. Appl. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pricope, N.G., Halls, J.N., Mapes, K.L., Baxley, J.B., and Wu, J.J. (2020). Quantitative Comparison of UAS-Borne LiDAR Systems for High-Resolution Forested Wetland Mapping. Sensors, 20.","DOI":"10.3390\/s20164453"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"C\u0103\u021beanu, M., and Ciubotaru, A. (2020). Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040224"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108316","DOI":"10.1016\/j.geomorph.2022.108316","article-title":"Point cloud does matter. Selected issues of using airborne LiDAR elevation data in geomorphometric studies of rugged sandstone terrain under forest\u2014Case study from Central Europe","volume":"412","author":"Jancewicz","year":"2022","journal-title":"Geomorphology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive estimates of above-ground biomass using terrestrial laser scanning","volume":"6","author":"Calders","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1016\/j.rse.2010.01.023","article-title":"Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning","volume":"114","author":"Morsdorf","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.10.013","article-title":"Estimating forest canopy fuel parameters using LIDAR data","volume":"94","author":"Andersen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S0034-4257(03)00098-1","article-title":"Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling","volume":"86","author":"Meier","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.rse.2014.01.028","article-title":"Subcanopy Solar Radiation model: Predicting solar radiation across a heavily vegetated landscape using LiDAR and GIS solar radiation models","volume":"154","author":"Bode","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40965-017-0021-8","article-title":"Generalized 3D fragmentation index derived from lidar point clouds","volume":"2","author":"Petras","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2015.05.001","article-title":"Estimation of the leaf area density distribution of individual trees using high-resolution and multi-return airborne LiDAR data","volume":"166","author":"Oshio","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/JSTARS.2009.2037523","article-title":"Analysis on the Use of Multiple Returns LiDAR Data for the Estimation of Tree Stems Volume","volume":"2","author":"Dalponte","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","unstructured":"Carter, B. (2005). Evaluating the Quality of ALSM Observations by Reading Artifacts in the Computed Surface Coordinates, University of Florida, Geosensing Engineering and Mapping Center. Tutorial Rep_2005-01-002."},{"key":"ref_38","unstructured":"Belica, L., Petras, V., Iiames, J.S., Caldwell, P.V., Mitasova, H., and Nelson, S.A. (2016, January 12\u201316). Implementation of a subcanopy solar radiation model on a forested headwater basin in the Southern Appalachians to estimate riparian canopy density and stream insolation for stream temperature models. Proceedings of the AGU Fall Meeting Abstracts, San Francisco, CA, USA."},{"key":"ref_39","unstructured":"Belica, L., Mitasova, H., Caldwell, P., McCarter, J.B., and Nelson, S.A. (2017, January 11\u201315). Combining multiple approaches and optimized data resolution for an improved understanding of stream temperature dynamics of a forested headwater basin in the Southern Appalachians. Proceedings of the AGU Fall Meeting Abstracts, New Orleans, LA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Petras, V., Petrasova, A., Jeziorska, J., and Mitasova, H. (2016, January 12\u201319). Processing UAV and lidar point clouds in GRASS GIS. Proceedings of the ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprsarchives-XLI-B7-945-2016"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Petrasova, A., Harmon, B., Petras, V., Tabrizian, P., and Mitasova, H. (2018). Tangible Modeling with Open Source GIS, 2 ed., Springer.","DOI":"10.1007\/978-3-319-89303-7"},{"key":"ref_42","unstructured":"(2015). NC Floodplain Mapping Program, Statewide. NCFMP Lidar: (Phase 3); North Carolina Spatial Data Download; GUID: gov.noaa.nmfs.inport:49838."},{"key":"ref_43","unstructured":"Wake County (2022, December 08). LiDAR Survey over Wake County, North Carolina. Available online: https:\/\/koordinates.com\/layer\/101719-wake-county-north-carolina-topography-2013\/."},{"key":"ref_44","unstructured":"NCALM (2009). Nantahala NF, NC: Forest Leaf Structure, Terrain and Hydrophysiology, OpenTopography."},{"key":"ref_45","unstructured":"NOAA\/NASA\/USGS (1999). Post Hurricane Floyd NOAA\/USGS\/NASA Airborne LiDAR Assessment of Coastal Erosion (ALACE) Project for the US Coastline."},{"key":"ref_46","unstructured":"NASA\/USGS (2003). Pre-Hurricane Isabel Survey."},{"key":"ref_47","unstructured":"NOAA (2008). NOAA Integrated Ocean and Coastal Mapping (IOCM) LiDAR: North Carolina and Virginia."},{"key":"ref_48","unstructured":"Lemmens, M.J.P.M. (1997, January 3\u20138). Accurate height information from airborne laser-altimetry. Proceedings of the 1997 IEEE International Geoscience and Remote Sensing, 1997, IGARSS \u201997. Remote Sensing\u2014A Scientific Vision for Sustainable Development, Singapore."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/S0924-2716(99)00011-8","article-title":"Airborne laser scanning\u2014An introduction and overview","volume":"54","author":"Wehr","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1080\/01431161.2019.1672218","article-title":"The curvature interpolation method for surface reconstruction for geospatial point cloud data","volume":"41","author":"Kim","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"086801","DOI":"10.1088\/0034-4885\/76\/8\/086801","article-title":"Geodetic imaging with airborne LiDAR: The Earth\u2019s surface revealed","volume":"76","author":"Glennie","year":"2013","journal-title":"Rep. Prog. Phys."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8461","DOI":"10.3390\/s130708461","article-title":"Simulation of a Geiger-Mode Imaging LADAR System for Performance Assessment","volume":"13","author":"Kim","year":"2013","journal-title":"Sensors"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9951","DOI":"10.3390\/rs6109951","article-title":"Now You See It\u2026 Now You Don\u2019t: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica","volume":"6","author":"Carter","year":"2014","journal-title":"Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Stoker, J.M., Abdullah, Q.A., Nayegandhi, A., and Winehouse, J. (2016). Evaluation of Single Photon and Geiger Mode Lidar for the 3D Elevation Program. Remote Sens., 8.","DOI":"10.3390\/rs8090767"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"112772","DOI":"10.1016\/j.rse.2021.112772","article-title":"Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic full-waveform 3D laser scanning","volume":"269","author":"Winiwarter","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_56","unstructured":"Toth, C.K. (2009). Topographic Laser Ranging and Scanning: Principles and Processing, CRC Press."},{"key":"ref_57","unstructured":"Lemmens, D.M. (2011). Geo-Information, Springer. Number 5 in Geotechnologies and the Environment."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Prieur, J.F., St-Onge, B., Fournier, R.A., Woods, M.E., Rana, P., and Kneeshaw, D. (2022). A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees. Sensors, 22.","DOI":"10.3390\/s22010035"},{"key":"ref_59","unstructured":"Behan, A., Maas, H.G., and Vosselman, G. (2000, January 1). Steps towards quality improvement of airborne laser scanner data. Proceedings of the 26th Annual Conference of the Remote Sensing Society, Leicester, UK."},{"key":"ref_60","first-page":"71","article-title":"Validation of airborne lidar intensity values from a forested landscape using hymap data: Preliminary analyses","volume":"36","author":"Boyd","year":"2007","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111135","DOI":"10.1016\/j.measurement.2022.111135","article-title":"A method for data density reduction in overlapped airborne LiDAR strips","volume":"195","author":"Wang","year":"2022","journal-title":"Measurement"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1071\/WF10116","article-title":"Linking complex forest fuel structure and fire behaviour at fine scales","volume":"21","author":"Loudermilk","year":"2012","journal-title":"Int. J. Wildland Fire"},{"key":"ref_63","unstructured":"Shrestha, R., Carter, W., Slatton, C., and Dietrich, W. (2007). \u201cResearch-Quality\u201d Airborne Laser Swath Mapping: The Defining Factors, National Center for Airborne Laser Mapping (NCALM)."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Pu, Y., Xu, D., Wang, H., An, D., and Xu, X. (2021). Extracting Canopy Closure by the CHM-Based and SHP-Based Methods with a Hemispherical FOV from UAV-LiDAR Data in a Poplar Plantation. Remote Sens., 13.","DOI":"10.3390\/rs13193837"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1029\/97WR03347","article-title":"Distributed soil erosion simulation for effective erosion prevention","volume":"34","author":"Mitas","year":"1998","journal-title":"Water Resour. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geomorph.2009.01.002","article-title":"Tectonic geomorphology of the San Andreas Fault zone from high resolution topography: An example from the Cholame segment","volume":"113","author":"Arrowsmith","year":"2009","journal-title":"Geomorphology"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1016\/j.rse.2011.01.016","article-title":"Alternate spatial sampling approaches for ecosystem structure inventory using spaceborne lidar","volume":"115","author":"Lefsky","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5627","DOI":"10.1109\/TGRS.2020.2967880","article-title":"Lattice-Constrained Stratified Sampling for Point Cloud Levels of Detail","volume":"58","author":"Damkjer","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","unstructured":"Arrowsmith, R. (2023, January 30). Analysis of B4 Overlapping Swaths. Available online: http:\/\/lidar.asu.edu\/KnowledgeBase\/B4_overlapping_swaths\/Analysis_of_B4_overlapping_swaths.pdf."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1002\/2017JB014863","article-title":"\u201c3D_Fault_Offsets\u201d, a Matlab Code to Automatically Measure Lateral and Vertical Fault Offsets in Topographic Data: Application to San Andreas, Owens Valley, and Hope Faults","volume":"123","author":"Stewart","year":"2018","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"398","DOI":"10.3390\/rs3020398","article-title":"Sky-View Factor as a Relief Visualization Technique","volume":"3","author":"Kokalj","year":"2011","journal-title":"Remote Sens."},{"key":"ref_72","unstructured":"Tarsha-Kurdi, F., Landes, T., and Grussenmeyer, P. (2007, January 12\u201314). Hough-transform and extended ransac algorithms for automatic detection of 3d building roof planes from lidar data. Proceedings of the ISPRS Workshop on Laser Scanning and SilviLaser 2007, Espoo, Finland."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2988","DOI":"10.1016\/j.rse.2008.02.004","article-title":"Object-based land cover classification using airborne LiDAR","volume":"112","author":"Antonarakis","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_74","unstructured":"Leica Geosystems (2023, January 30). Leica ALS80-HP High-Performance Airborne LIDAR Product Specifications. Available online: https:\/\/leica-geosystems.com\/-\/media\/files\/leicageosystems\/products\/other\/specifications\/leica_als80_hp_productspec_en.ashx."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1016\/j.cageo.2005.11.008","article-title":"Finding the right pixel size","volume":"32","author":"Hengl","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_76","unstructured":"Duldulao, R.L. (2009). Point Density Effects on Digital Elevation Models Generated from LiDAR Data. [Ph.D. Thesis, Naval Postgraduate School]."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Puetz, A.M., Olsen, R.C., and Anderson, B. (2009, January 2). Effects of lidar point density on bare earth extraction and DEM creation. Proceedings of the SPIE 7323, Laser Radar Technology and Applications XIV, Orlando, FL, USA.","DOI":"10.1117\/12.818186"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Jia, Y., Lan, T., Peng, T., Wu, H., Li, C., and Ni, G. (2013, January 21\u201326). Effects of point density on DEM accuracy of airborne LiDAR. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium\u2014IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6721200"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.isprsjprs.2014.12.021","article-title":"Effects of LiDAR point density and landscape context on estimates of urban forest biomass","volume":"101","author":"Singh","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_80","unstructured":"Liu, X., Zhang, Z., Peterson, J., and Chandra, S. (2007, January 10\u201313). The effect of LiDAR data density on DEM accuracy. Proceedings of the International Congress on Modelling and Simulation (MODSIM07), Christchurch, New Zealand."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"032050","DOI":"10.1088\/1757-899X\/780\/3\/032050","article-title":"Application Research of Earth Volume Calculation Based on 3D Laser Point Cloud Data","volume":"780","author":"Wang","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Leal, E., Sanchez-Torres, G., Branch-Bedoya, J.W., Abad, F., and Leal, N. (2021). A Saliency-Based Sparse Representation Method for Point Cloud Simplification. Sensors, 21.","DOI":"10.3390\/s21134279"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.geoderma.2005.06.004","article-title":"Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM","volume":"132","author":"Anderson","year":"2006","journal-title":"Geoderma"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Xu, Z., Liang, Y., Lu, H., Kong, W., and Wu, G. (Eng. Constr. Archit. Manag., 2022). An approach for monitoring prefabricated building construction based on feature extraction and point cloud segmentation, Eng. Constr. Archit. Manag., ahead-of-print.","DOI":"10.1108\/ECAM-11-2021-0985"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1016\/j.agrformet.2009.04.008","article-title":"Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning","volume":"149","author":"Rosell","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"You, H., Li, S., Xu, Y., He, Z., and Wang, D. (2021). Tree Extraction from Airborne Laser Scanning Data in Urban Areas. Remote Sens., 13.","DOI":"10.3390\/rs13173428"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Huang, H., Link, T., Smith, A., and Chen, C. (July, January 29). Accuracy of the LiDAR-derived DEM in dense shrub areas in mountainous NW US. Proceedings of the 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, Fuzhou, China.","DOI":"10.1109\/ICSDM.2011.5969067"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1145\/1138450.1138451","article-title":"Point-based Multiscale Surface Representation","volume":"25","author":"Pauly","year":"2006","journal-title":"ACM Trans. Graph."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Du, X., Yin, B., and Kong, D. (2007, January 2\u20135). Adaptive Out-of-Core Simplification of Large Point Clouds. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, Beijing, China.","DOI":"10.1109\/ICME.2007.4284931"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.cad.2007.10.013","article-title":"A global clustering approach to point cloud simplification with a specified data reduction ratio","volume":"40","author":"Song","year":"2008","journal-title":"Comput.-Aided Des."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Benhabiles, H., Aubreton, O., Barki, H., and Tabia, H. (2013, January 22\u201324). Fast simplification with sharp feature preserving for 3D point clouds. Proceedings of the 2013 11th International Symposium on Programming and Systems (ISPS), Algiers, Algeria.","DOI":"10.1109\/ISPS.2013.6581492"},{"key":"ref_93","first-page":"8","article-title":"A Linear Programming Approach for 3D Point Cloud Simplification","volume":"44","author":"Leal","year":"2017","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_94","first-page":"7","article-title":"Point clouds reduction model based on 3D feature extraction","volume":"11","author":"Sayed","year":"2019","journal-title":"Int. J. Embed. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"065004","DOI":"10.1088\/1361-6501\/abd497","article-title":"Point cloud simplification algorithm based on the feature of adaptive curvature entropy","volume":"32","author":"Wang","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., and Hassner, T. Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach. Proceedings of the Computer Vision\u2014ECCV 2022, Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-19772-7"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1002\/hyp.7582","article-title":"Suitability of LiDAR point density and derived landform curvature maps for channel network extraction","volume":"24","author":"Pirotti","year":"2010","journal-title":"Hydrol. Process."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.measurement.2015.08.008","article-title":"An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area","volume":"75","author":"Polat","year":"2015","journal-title":"Measurement"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/978-3-642-15696-0_20","article-title":"Unstructured Point Cloud Surface Denoising and Decimation Using Distance RBF K-Nearest Neighbor Kernel","volume":"Volume 6298","author":"Hutchison","year":"2010","journal-title":"Advances in Multimedia Information Processing\u2014PCM 2010"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.isprsjprs.2018.03.010","article-title":"Refinement of LiDAR point clouds using a super voxel based approach","volume":"143","author":"Li","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_101","unstructured":"Moenning, C., and Dodgson, N.A. (2004, January 6\u201310). Intrinsic point cloud simplification. Proceedings of the 14th GraphiCon, Moscow, Russia."},{"key":"ref_102","unstructured":"Pauly, M., Gross, M., and Kobbelt, L.P. (November, January 27). Efficient simplification of point-sampled surfaces. Proceedings of the IEEE Visualization 2002 Conference, Boston, MA, USA."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Kulawiak, M., and Lubniewski, Z. (2016, January 2\u20134). Processing of LiDAR and Multibeam Sonar Point Cloud Data for 3D Surface and Object Shape Reconstruction. Proceedings of the 2016 Baltic Geodetic Congress (BGC Geomatics), Gdansk, Poland.","DOI":"10.1109\/BGC.Geomatics.2016.41"},{"key":"ref_104","first-page":"636","article-title":"Terrestrial laser scanning to estimate plot-level forest canopy fuel properties","volume":"13","author":"Danson","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ufug.2016.03.007","article-title":"Fine-scale characterization of bird habitat using airborne LiDAR in an urban park in Japan","volume":"17","author":"Sasaki","year":"2016","journal-title":"Urban For. Urban Green."},{"key":"ref_106","first-page":"W2","article-title":"Reconstruction of laser-scanned trees using filter operations in the 3D raster domain","volume":"36","author":"Gorte","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"135","DOI":"10.2478\/aslh-2013-0011","article-title":"Mapping forest regeneration from terrestrial laser scans","volume":"9","author":"Brolly","year":"2013","journal-title":"Acta Silv. Lignaria Hung."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Okhrimenko, M., and Hopkinson, C. (2019). Investigating the Consistency of Uncalibrated Multispectral Lidar Vegetation Indices at Different Altitudes. Remote Sens., 11.","DOI":"10.3390\/rs11131531"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"dos Santos, R.C., Galo, M., and Habib, A.F. (2020). Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline. Remote Sens., 12.","DOI":"10.3390\/rs12121904"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Liu, D., Li, D., Wang, M., and Wang, Z. (2021). 3D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030127"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Storch, M., Jarmer, T., Adam, M., and de Lange, N. (2022). Systematic Approach for Remote Sensing of Historical Conflict Landscapes with UAV-Based Laserscanning. Sensors, 22.","DOI":"10.3390\/s22010217"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"de Oliveira Junior, E.M., and dos Santos, D.R. (2019). Rigorous Calibration of UAV-Based LiDAR Systems with Refinement of the Boresight Angles Using a Point-to-Plane Approach. Sensors, 19.","DOI":"10.3390\/s19235224"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.isprsjprs.2019.06.003","article-title":"Modelling of buildings from aerial LiDAR point clouds using TINs and label maps","volume":"154","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.isprsjprs.2021.01.007","article-title":"Airborne LiDAR point cloud classification with global-local graph attention convolution neural network","volume":"173","author":"Wen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Huang, J., Stoter, J., Peters, R., and Nan, L. (2022). City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds. Remote Sens., 14.","DOI":"10.3390\/rs14092254"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Bonisteel, J.M., Nayegandhi, A., Wright, C.W., Brock, J.C., and Nagle, D. (2009). Experimental Advanced Airborne Research Lidar (EAARL) Data Processing Manual.","DOI":"10.3133\/ofr20091078"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.envsoft.2011.11.014","article-title":"GRASS GIS: A multi-purpose open source GIS","volume":"31","author":"Neteler","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_118","unstructured":"Landa, M., Neteler, M., Metz, M., Petrasova, A., Clements, G., Bowman, M.H., Petras, V., Gebbert, S., Cho, H., and Delucchi, L. (2022). GRASS GIS. Zenodo."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1593\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:21:03Z","timestamp":1760120463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":118,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031593"],"URL":"https:\/\/doi.org\/10.3390\/s23031593","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,1]]}}}