{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:13:21Z","timestamp":1773317601639,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014795","name":"North Carolina Department of Transportation","doi-asserted-by":"publisher","award":["RP2020-04"],"award-info":[{"award-number":["RP2020-04"]}],"id":[{"id":"10.13039\/100014795","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wetlands provide critical ecosystem services across a range of environmental gradients and are at heightened risk of degradation from anthropogenic pressures and continued development, especially in coastal regions. There is a growing need for high-resolution (spatially and temporally) habitat identification and precise delineation of wetlands across a variety of stakeholder groups, including wetlands loss mitigation programs. Traditional wetland delineations are costly, time-intensive and can physically degrade the systems that are being surveyed, while aerial surveys are relatively fast and relatively unobtrusive. To assess the efficacy and feasibility of using two variable-cost LiDAR sensors mounted on a commercial hexacopter unmanned aerial system (UAS) in deriving high resolution topography, we conducted nearly concomitant flights over a site located in the Atlantic Coastal plain that contains a mix of palustrine forested wetlands, upland coniferous forest, upland grass and bare ground\/dirt roads. We compared point clouds and derived topographic metrics acquired using the Quanergy M8 and the Velodyne HDL-32E LiDAR sensors with airborne LiDAR and results showed that the less expensive and lighter payload sensor outperforms the more expensive one in deriving high resolution, high accuracy ground elevation measurements under a range of canopy cover densities and for metrics of point cloud density and digital terrain computed both globally and locally using variable size tessellations. The mean point cloud density was not significantly different between wetland and non-wetland areas, but the two sensors were significantly different by wetland\/non-wetland type. Ultra-high-resolution LiDAR-derived topography models can fill evolving wetlands mapping needs and increase accuracy and efficiency of detection and prediction of sensitive wetland ecosystems, especially for heavily forested coastal wetland systems.<\/jats:p>","DOI":"10.3390\/s20164453","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T07:25:03Z","timestamp":1597044303000},"page":"4453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Quantitative Comparison of UAS-Borne LiDAR Systems for High-Resolution Forested Wetland Mapping"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6591-7237","authenticated-orcid":false,"given":"Narcisa Gabriela","family":"Pricope","sequence":"first","affiliation":[{"name":"Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S. College Rd., Wilmington, NC 28403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8427-9181","authenticated-orcid":false,"given":"Joanne Nancie","family":"Halls","sequence":"additional","affiliation":[{"name":"Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S. College Rd., Wilmington, NC 28403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8886-5508","authenticated-orcid":false,"given":"Kerry Lynn","family":"Mapes","sequence":"additional","affiliation":[{"name":"Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S. College Rd., Wilmington, NC 28403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph Britton","family":"Baxley","sequence":"additional","affiliation":[{"name":"Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S. College Rd., Wilmington, NC 28403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James JyunYueh","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S. College Rd., Wilmington, NC 28403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"key":"ref_1","unstructured":"Army, U.S. (1987). Corps of Engineers. Wetlands Delineation Manual, U.S. Army Engineer Waterways Experiment Station."},{"key":"ref_2","unstructured":"Dahl, T.E., Dick, J., Swords, J., and Wilen, B.O. (2015). Data Collection Requirements and Procedures for Mapping Wetland, Deepwater and Related Habitats of the United States, National Standards and Support Team. Division of Habitat and Resource Conservation (version 2)."},{"key":"ref_3","unstructured":"United States Fish and Wildlife Service (1979). Classification of Wetlands and Deepwater Habitats of the United States, Biological Services Program."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1017\/S1466046615000046","article-title":"Environmental Reviews and Case Studies: The North Carolina Wetland Assessment Method (NC WAM): Development of a Rapid Wetland Assessment Method and Use for Compensatory Mitigation","volume":"17","author":"Dorney","year":"2015","journal-title":"Environ. Pract."},{"key":"ref_5","unstructured":"N.C. Wetland Functional Assessment Team. N.C. (2016). Wetland Assessment Method (NC WAM). User Manual, NC Department of Environmental Quality."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2018.07.005","article-title":"Spectral analysis of wetlands using multi-source optical satellite imagery","volume":"144","author":"Amani","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3220","DOI":"10.1016\/j.rse.2011.07.006","article-title":"Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China","volume":"115","author":"Dronova","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.rse.2012.09.018","article-title":"Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods","volume":"127","author":"Dronova","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6380","DOI":"10.3390\/rs70506380","article-title":"Object-Based Image Analysis in Wetland Research: A Review","volume":"7","author":"Dronova","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hubert-Moy, L., Michel, K., Corpetti, T., and Clement, B. (August, January 31). Object-Oriented Mapping and Analysis of Wetlands Using SPOT 5 Data. Proceedings of the 2006 IEEE International Geoscience and Remote Sensing Symposium, Denver, CO, USA.","DOI":"10.1109\/IGARSS.2006.884"},{"key":"ref_11","first-page":"29","article-title":"UAS Lidar for Ecological Restoration of Wetlands","volume":"33","author":"Selve","year":"2019","journal-title":"Gim Int. Worldw. Mag. Geomat."},{"key":"ref_12","first-page":"781","article-title":"Wetland assessment using unmanned aerial vehicle (UAV) photogrammetry","volume":"41","author":"Boon","year":"2016","journal-title":"ISPRS Congr. Comm. I"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Villanueva, J., Martinez, L., and Montiel, J. (2019). DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19.","DOI":"10.3390\/s19143205"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"87","DOI":"10.5194\/isprs-archives-XLII-2-W2-87-2016","article-title":"The potential of light laser scanners developed for unmanned aerial vehicles\u2014The review and accuracy","volume":"Volume XLII-2\/W2","author":"Pilarska","year":"2016","journal-title":"The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Jaakkola, A., Hyyppa, J., Yu, X., Kukko, A., Kaartinen, H., Liang, X., Hyyppa, H., and Wang, Y. (2017). Autonomous Collection of Forest Field Reference-The Outlook and a First Step with UAV Laser Scanning. Remote Sens., 9.","DOI":"10.3390\/rs9080785"},{"key":"ref_17","first-page":"527","article-title":"Experimental Assessment of the Quanergy M8 Lidar Sensor","volume":"41","author":"Mittet","year":"2016","journal-title":"ISPRS Congr. Comm. V"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, W.M., Qi, J.B., Wan, P., Wang, H.T., Xie, D.H., Wang, X.Y., and Yan, G.J. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Weil, J. (1986, January 18\u201322). The Synthesis of Cloth Objects. Proceedings of the ACM SIGGRAPH Computer Graphics, Dallas, TX, USA.","DOI":"10.1145\/15922.15891"},{"key":"ref_20","unstructured":"NC Department of Emergency Management (2018, May 01). QL2 LiDAR, Available online: https:\/\/sdd.nc.gov\/sdd\/."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Anfuso, G., Bowman, D., Danese, C., and Pranzini, E. (2016). Transect based analysis versus area based analysis to quantify shoreline displacement: Spatial resolution issues. Environ. Monit. Assess., 188.","DOI":"10.1007\/s10661-016-5571-1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.spasta.2012.08.001","article-title":"A review of spatial sampling","volume":"2","author":"Wang","year":"2012","journal-title":"Spat. Stat."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, J., and Kwan, M.P. (2018). Hexagon-Based Adaptive Crystal Growth Voronoi Diagrams Based on Weighted Planes for Service Area Delimitation. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7070257"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alsadik, B., and Remondino, F. (2020). Flight Planning for LiDAR-Based UAS Mapping Applications. ISPRS Int. J. Geo Inf., 9.","DOI":"10.3390\/ijgi9060378"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4453\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:58:36Z","timestamp":1760176716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,10]]},"references-count":24,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164453"],"URL":"https:\/\/doi.org\/10.3390\/s20164453","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,10]]}}}