{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:48:36Z","timestamp":1781153316301,"version":"3.54.1"},"reference-count":70,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hardwood Tree Improvement and Regeneration Center, Purdue Integrated Digital Forestry Initiative, and USDA Forest Service","award":["19-JV-11242305-102"],"award-info":[{"award-number":["19-JV-11242305-102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies\u2014DBSCAN-based and height\/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height\/density-based approaches perform well in tree detection (F1 score &gt; 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter\/threshold fine-tuning.<\/jats:p>","DOI":"10.3390\/rs14153738","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T02:12:39Z","timestamp":1659665559000},"page":"3738","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection\/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6423-4090","authenticated-orcid":false,"given":"Tian","family":"Zhou","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0263-312X","authenticated-orcid":false,"given":"Renato C\u00e9sar","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Department of Cartography and Graduate Program on Cartographic Sciences (PPGCC), S\u00e3o Paulo State University, Presidente Prudente 19060-900, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jidong","family":"Liu","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3564-372X","authenticated-orcid":false,"given":"Yi-Chun","family":"Lin","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William Changhao","family":"Fei","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2772-0166","authenticated-orcid":false,"given":"Songlin","family":"Fei","sequence":"additional","affiliation":[{"name":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6498-5951","authenticated-orcid":false,"given":"Ayman","family":"Habib","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1890\/080169","article-title":"Forest Carbon Storage: Ecology, Management, and Policy","volume":"8","author":"Fahey","year":"2010","journal-title":"Front. Ecol. Environ."},{"key":"ref_2","unstructured":"Bettinger, P., Boston, K., Siry, J.P., and Grebner, D.L. (2017). Forest Management and Planning, Academic Press. [2nd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1007\/s10342-009-0281-7","article-title":"Value of Forest Information","volume":"129","author":"Kangas","year":"2010","journal-title":"Eur. J. For. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"West, P.W. (2009). Tree and Forest Measurement, Springer.","DOI":"10.1007\/978-3-540-95966-3"},{"key":"ref_5","first-page":"91","article-title":"Measuring Forest Area Loss over Time Using FIA Plots and Satellite Imagerye","volume":"252","author":"Hoppus","year":"2005","journal-title":"Proc. Fourth Annu. For. Inventory Anal. Symp."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1139\/x02-011","article-title":"Predictive Mapping of Forest Composition and Structure with Direct Gradient Analysis and Nearest-Neighbor Imputation in Coastal Oregon, U.S.A","volume":"32","author":"Ohmann","year":"2002","journal-title":"Can. J. For. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0034-4257(03)00039-7","article-title":"Predictive Relations of Tropical Forest Biomass from Landsat TM Data and Their Transferability between Regions","volume":"85","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_8","unstructured":"Hudak, A.T., Robichaud, P.R., Evans, J.B., Clark, J., Lannom, K., Morgan, P., and Stone, C. (2004). Field Validation of Burned Area Reflectance Classification (BARC) Products for Post Fire Assessment. Tenth For. Serv. Remote Sens. Appl. Conf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1093\/forestry\/cpu050","article-title":"Assessing Height Changes in a Highly Structured Forest Using Regularly Acquired Aerial Image Data","volume":"88","author":"Stepper","year":"2014","journal-title":"Forestry"},{"key":"ref_10","first-page":"22","article-title":"Comparison of UAV and WorldView-2 Imagery for Mapping Leaf Area Index of Mangrove Forest","volume":"61","author":"Tian","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2017.04.018","article-title":"Assessing the Performance of Aerial Image Point Cloud and Spectral Metrics in Predicting Boreal Forest Canopy Cover","volume":"129","author":"Melin","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.isprsjprs.2018.06.006","article-title":"Comparison of High-Density LiDAR and Satellite Photogrammetry for Forest Inventory","volume":"142","author":"Pearse","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Maltamo, M., N\u00e6sset, E., and Vauhkonen, J. (2014). Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies, Springer.","DOI":"10.1007\/978-94-017-8663-8"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jaridenv.2007.04.010","article-title":"Using Airborne Lidar to Predict Leaf Area Index in Cottonwood Trees and Refine Riparian Water-Use Estimates","volume":"72","author":"Farid","year":"2008","journal-title":"J. Arid. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tian, L., Qu, Y., and Qi, J. (2021). Estimation of Forest Lai Using Discrete Airborne Lidar: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13122408"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"833","DOI":"10.3390\/rs2030833","article-title":"Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues","volume":"2","author":"Meng","year":"2010","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, Z., Gao, B., and Devereux, B. (2017). State-of-the-Art: DTM Generation Using Airborne LIDAR Data. Sensors, 17.","DOI":"10.3390\/s17010150"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Micha\u0142owska, M., and Rapi\u0144ski, J. (2021). A Review of Tree Species Classification Based on Airborne Lidar Data and Applied Classifiers. Remote Sens., 13.","DOI":"10.3390\/rs13030353"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.rse.2004.01.006","article-title":"Estimation of Timber Volume and Stem Density Based on Scanning Laser Altimetry and Expected Tree Size Distribution Functions","volume":"90","author":"Maltamo","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5154","DOI":"10.1080\/01431161.2013.787501","article-title":"Predicting the Spatial Pattern of Trees by Airborne Laser Scanning","volume":"34","author":"Packalen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.rse.2005.03.005","article-title":"Mapping Forest Structure for Wildlife Habitat Analysis Using Waveform Lidar: Validation of Montane Ecosystems","volume":"96","author":"Hyde","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1016\/j.rse.2010.08.027","article-title":"Mapping Biomass and Stress in the Sierra Nevada Using Lidar and Hyperspectral Data Fusion","volume":"115","author":"Swatantran","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s40725-017-0051-6","article-title":"Individual Tree Crown Methods for 3D Data from Remote Sensing","volume":"3","author":"Lindberg","year":"2017","journal-title":"Curr. For. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.08.013","article-title":"Comparing Features of Single and Multi-Photon Lidar in Boreal Forests","volume":"168","author":"Yu","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Brede, B., Lau, A., Bartholomeus, H.M., and Kooistra, L. (2017). Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors, 17.","DOI":"10.3390\/s17102371"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.isprsjprs.2018.11.001","article-title":"Estimating Forest Structural Attributes Using UAV-LiDAR Data in Ginkgo Plantations","volume":"146","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.compag.2020.105815","article-title":"Forest Inventory with High-Density UAV-Lidar: Machine Learning Approaches for Predicting Individual Tree Attributes","volume":"179","author":"Corte","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, Q., Gao, T., Zhu, J., Wu, F., Li, X., Lu, D., and Yu, F. (2022). Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests. Remote Sens., 14.","DOI":"10.3390\/rs14122787"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Burt, A., Disney, M.I., Raumonen, P., Armston, J., Calders, K., and Lewis, P. (2013, January 26\u201329). Rapid characterisation of forest structure from TLS and 3D modelling. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Houston, TX, USA.","DOI":"10.1109\/IGARSS.2013.6723555"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Heinzel, J., and Huber, M.O. (2017). Detecting Tree Stems from Volumetric TLS Data in Forest Environments with Rich Understory. Remote Sens., 9.","DOI":"10.3390\/rs9010009"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Comesa\u00f1a-Cebral, L., Mart\u00ednez-S\u00e1nchez, J., Lorenzo, H., and Arias, P. (2021). Individual Tree Segmentation Method Based on Mobile Backpack LiDAR Point Clouds. Sensors, 21.","DOI":"10.3390\/s21186007"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ko, C., Lee, S., Yim, J., Kim, D., and Kang, J. (2021). Comparison of Forest Inventory Methods at Plot-Level between a Backpack Personal Laser Scanning (BPLS) and Conventional Equipment in Jeju Island, South Korea. Forests, 12.","DOI":"10.3390\/f12030308"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping global forest canopy height through integration of GEDI and Landsat data","volume":"253","author":"Potapov","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7199","DOI":"10.1080\/01431161.2014.967886","article-title":"Isolating individual trees in a closed coniferous forest using small footprint lidar data","volume":"35","author":"Zhao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","first-page":"97","article-title":"Comparison of Terrestrial and Airborne LiDAR in Describing Stand Structure of a Thinned Lodgepole Pine Forest","volume":"110","author":"Hilker","year":"2012","journal-title":"J. For."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"LaRue, E., Wagner, F., Fei, S., Atkins, J., Fahey, R., Gough, C., and Hardiman, B. (2020). Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity. Remote Sens., 12.","DOI":"10.20944\/preprints202003.0339.v1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Babbel, B.J., Olsen, M.J., Che, E., Leshchinsky, B.A., Simpson, C., and Dafni, J. (2019). Evaluation of Uncrewed Aircraft Systems\u2019 Lidar Data Quality. ISPRS Int. J. Geo-Inform., 8.","DOI":"10.3390\/ijgi8120532"},{"key":"ref_39","first-page":"566","article-title":"UAV-based LiDAR acquisition for the derivation of high-resolution forest and ground information","volume":"36","author":"Morsdorf","year":"2017","journal-title":"Geophysics"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Lin, Y.-C., Shao, J., Shin, S.-Y., Saka, Z., Joseph, M., Manish, R., Fei, S., and Habib, A. (2022). Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory. Remote Sens., 14.","DOI":"10.3390\/rs14030649"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"118268","DOI":"10.1016\/j.foreco.2020.118268","article-title":"A comparative assessment of the vertical distribution of forest components using full-waveform airborne, discrete airborne and discrete terrestrial laser scanning data","volume":"473","author":"Fournier","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/36.921414","article-title":"A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners","volume":"39","author":"Hyyppa","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"564","DOI":"10.5589\/m03-027","article-title":"Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass","volume":"29","author":"Popescu","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"357","DOI":"10.14358\/PERS.72.4.357","article-title":"Detection of Individual Tree Crowns in Airborne Lidar Data","volume":"72","author":"Koch","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"923","DOI":"10.14358\/PERS.72.8.923","article-title":"Isolating Individual Trees in a Savanna Woodland Using Small Footprint Lidar Data","volume":"72","author":"Chen","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_47","first-page":"336","article-title":"Applying LiDAR Individual Tree Detection to Management of Structurally Diverse Forest Landscapes","volume":"116","author":"Jeronimo","year":"2018","journal-title":"J. For."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"112307","DOI":"10.1016\/j.rse.2021.112307","article-title":"Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach","volume":"256","author":"Yun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1080\/01431161.2018.1513664","article-title":"Delineation of individual deciduous trees in plantations with low-density LiDAR data","volume":"40","author":"Shao","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","unstructured":"Lin, Y.-C., Liu, J., Fei, S., and Habib, A. (2021). Leaf-Off and Leaf-On UAV LiDAR Surveys for Single-Tree Inventory in Forest Plantations. Drones, 5.","DOI":"10.3390\/drones5040115"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.03.014","article-title":"A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data","volume":"94","author":"Lu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2020.03.021","article-title":"Under-canopy UAV laser scanning for accurate forest field measurements","volume":"164","author":"Hakala","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fu, H., Li, H., Dong, Y., Xu, F., and Chen, F. (2022). Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN. Forests, 13.","DOI":"10.3390\/f13040566"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, J., Dong, P., Ma, W., Liu, Y., Liu, Q., and Zhang, Z. (2022). Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN. Geocarto Int., 1\u201315.","DOI":"10.1080\/10106049.2022.2034988"},{"key":"ref_57","unstructured":"(2022, June 08). Velodyne VLP-32C User Manual. Available online: https:\/\/icave2.cse.buffalo.edu\/resources\/sensor-modeling\/VLP32CManual.pdf."},{"key":"ref_58","unstructured":"(2020, April 25). Applanix APX-15 UAV Datasheet. Available online: https:\/\/www.applanix.com\/downloads\/products\/specs\/APX15_UAV.pdf."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1109\/JSTARS.2018.2812796","article-title":"Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform","volume":"11","author":"Ravi","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_60","unstructured":"Habib, A., Lay, J., and Wong, C. (2021, October 09). LIDAR Error Propagation Calculator. Available online: https:\/\/engineering.purdue.edu\/CE\/Academics\/Groups\/Geomatics\/DPRG\/files\/LIDARErrorPropagation.zip."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Clifton, W.E., Steele, B., Nelson, G., Truscott, A., Itzler, M., and Entwistle, M. (2015). Medium altitude airborne geiger-mode mapping LIDAR system. Laser Radar Technology and Applications XX; and Atmospheric Propagation XII, SPIE.","DOI":"10.1117\/12.2193827"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ullrich, A., and Pfennigbauer, M. (2016). Linear LIDAR versus Geiger-mode LIDAR: Impact on data properties and data quality. Laser Radar Technology and Applications XXI, SPIE.","DOI":"10.1117\/12.2223586"},{"key":"ref_63","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_64","unstructured":"(2022, June 08). VeriDaaS Geiger-Mode LiDAR. Available online: https:\/\/veridaas.com\/geiger-mode-lidar."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lin, Y.-C., Manish, R., Bullock, D., and Habib, A. (2021). Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization. Remote Sens., 13.","DOI":"10.3390\/rs13132485"},{"key":"ref_66","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_67","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_68","first-page":"19","article-title":"DBSCAN Revisited, Revisited. ACM Trans","volume":"42","author":"Schubert","year":"2017","journal-title":"Database Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"5261","DOI":"10.1109\/TGRS.2018.2812782","article-title":"Bias Impact Analysis and Calibration of Terrestrial Mobile LiDAR System with Several Spinning Multibeam Laser Scanners","volume":"56","author":"Ravi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3738\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:04:11Z","timestamp":1760141051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,4]]},"references-count":70,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153738"],"URL":"https:\/\/doi.org\/10.3390\/rs14153738","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,4]]}}}