{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:42:23Z","timestamp":1781523743889,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,16]],"date-time":"2023-07-16T00:00:00Z","timestamp":1689465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["No. 22KJB170016"],"award-info":[{"award-number":["No. 22KJB170016"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["42171402"],"award-info":[{"award-number":["42171402"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["41930102"],"award-info":[{"award-number":["41930102"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["SJCX23_0418"],"award-info":[{"award-number":["SJCX23_0418"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 22KJB170016"],"award-info":[{"award-number":["No. 22KJB170016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171402"],"award-info":[{"award-number":["42171402"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41930102"],"award-info":[{"award-number":["41930102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["SJCX23_0418"],"award-info":[{"award-number":["SJCX23_0418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate Practice Innovation Program of the Jiangsu Province of China","award":["No. 22KJB170016"],"award-info":[{"award-number":["No. 22KJB170016"]}]},{"name":"Graduate Practice Innovation Program of the Jiangsu Province of China","award":["42171402"],"award-info":[{"award-number":["42171402"]}]},{"name":"Graduate Practice Innovation Program of the Jiangsu Province of China","award":["41930102"],"award-info":[{"award-number":["41930102"]}]},{"name":"Graduate Practice Innovation Program of the Jiangsu Province of China","award":["SJCX23_0418"],"award-info":[{"award-number":["SJCX23_0418"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The removal of low vegetation is still challenging in UAV photogrammetry. According to the different topographic features expressed by point-cloud data at different scales, a vegetation-filtering method based on multiscale elevation-variation coefficients is proposed for terrain modeling. First, virtual grids are constructed at different scales, and the average elevation values of the corresponding point clouds are obtained. Second, the amount of elevation change at any two scales in each virtual grid is calculated to obtain the difference in surface characteristics (degree of elevation change) at the corresponding two scales. Third, the elevation variation coefficient of the virtual grid that corresponds to the largest elevation variation degree is calculated, and threshold segmentation is performed based on the relation that the elevation variation coefficients of vegetated regions are much larger than those of terrain regions. Finally, the optimal calculation neighborhood radius of the elevation variation coefficients is analyzed, and the optimal segmentation threshold is discussed. The experimental results show that the multiscale coefficients of elevation variation method can accurately remove vegetation points and reserve ground points in low- and densely vegetated areas. The type I error, type II error, and total error in the study areas range from 1.93 to 9.20%, 5.83 to 5.84%, and 2.28 to 7.68%, respectively. The total error of the proposed method is 2.43\u20132.54% lower than that of the CSF, TIN, and PMF algorithms in the study areas. This study provides a foundation for the rapid establishment of high-precision DEMs based on UAV photogrammetry.<\/jats:p>","DOI":"10.3390\/rs15143569","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:56:47Z","timestamp":1689555407000},"page":"3569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["UAV-Based Terrain Modeling in Low-Vegetation Areas: A Framework Based on Multiscale Elevation Variation Coefficients"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiaxin","family":"Fan","sequence":"first","affiliation":[{"name":"School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 211800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7424-644X","authenticated-orcid":false,"given":"Wen","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 211800, China"},{"name":"Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 211800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingliang","family":"Li","sequence":"additional","affiliation":[{"name":"Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing 211800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 211800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 211800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103962","DOI":"10.1016\/j.dib.2019.103962","article-title":"Unmanned aerial image dataset: Ready for 3D reconstruction","volume":"25","author":"Shahbazi","year":"2019","journal-title":"Data Brief"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Berrett, B.E., Vernon, C.A., Beckstrand, H., Pollei, M., Markert, K., Franke, K.W., and Hedengren, J.D. (2021). Large-scale reality modeling of a university campus using combined UAV and terrestrial photogrammetry for historical preservation and practical use. Drones, 5.","DOI":"10.3390\/drones5040136"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106244","DOI":"10.1016\/j.catena.2022.106244","article-title":"Monitoring and modeling sediment transport in space in small loess catchments using UAV-SfM photogrammetry","volume":"214","author":"Dai","year":"2022","journal-title":"CATENA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.18306\/dlkxjz.2021.09.011","article-title":"Modelling sediment transport in space in a watershed based on topographic change detection by UAV survey","volume":"40","author":"Dai","year":"2021","journal-title":"Prog. Geogr."},{"key":"ref_5","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_6","first-page":"110","article-title":"DEM generation from laser scanner data using adaptive TIN models","volume":"33","author":"Axelsson","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_7","first-page":"139","article-title":"Adaptive slope filtering for airborne Light Detection and Ranging data in urban areas based on region growing rule","volume":"49","author":"Yang","year":"2016","journal-title":"Emp. Surv. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., 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_9","doi-asserted-by":"crossref","unstructured":"Dong, Y., Cui, X., Zhang, L., and Ai, H. (2018). An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds. Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100409"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.cageo.2012.03.021","article-title":"A sequential iterative dual-filter for Lidar terrain modeling optimized for complex forested environments","volume":"44","author":"Durrieu","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106728","DOI":"10.1016\/j.optlastec.2020.106728","article-title":"A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy","volume":"136","author":"Hui","year":"2021","journal-title":"Opt. Laser Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"175","DOI":"10.14358\/PERS.73.2.175","article-title":"Filtering airborne laser scanning data with morphological methods","volume":"73","author":"Chen","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"016512","DOI":"10.1117\/1.JRS.16.016512","article-title":"SPGCN: Ground filtering method based on superpoint graph convolution neural network for vehicle LiDAR","volume":"16","author":"Huang","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106890","DOI":"10.1016\/j.optlastec.2020.106890","article-title":"Automated ground filtering of LiDAR and UAS point clouds with metaheuristics","volume":"138","author":"Yilmaz","year":"2021","journal-title":"Opt. Laser Technol."},{"key":"ref_15","first-page":"203","article-title":"Filtering of Laser Altimetry Data using a Slope Adaptive Filter","volume":"34","author":"Sithole","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108756","DOI":"10.1016\/j.measurement.2020.108756","article-title":"Filtering airborne LiDAR point clouds based on a scale-irrelevant and terrain-adaptive approach","volume":"171","author":"Chen","year":"2021","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103992","DOI":"10.1016\/j.autcon.2021.103992","article-title":"Automated semantic segmentation of bridge point cloud based on local descriptor and machine learning","volume":"133","author":"Xia","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, C., Guo, J., Wu, H., Li, Y., and Shi, B. (2021). Performance comparison of filtering algorithms for high-density airborne Lidar point clouds over complex landscapes. Remote Sens., 13.","DOI":"10.3390\/rs13142663"},{"key":"ref_19","first-page":"23","article-title":"Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds","volume":"13","author":"Fogl","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, H., Ye, W., Liu, J., Tan, W., Pirasteh, S., Fatholahi, S.N., and Li, J. (2021). High-resolution terrain modeling using airborne lidar data with transfer learning. Remote Sens., 13.","DOI":"10.3390\/rs13173448"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.catena.2019.02.010","article-title":"Effects of DEM resolution on the accuracy of gully maps in loess hilly areas","volume":"177","author":"Dai","year":"2019","journal-title":"CATENA"},{"key":"ref_22","first-page":"338","article-title":"Uncertainty of the morphological feature expression of loess erosional gully affected by DEM resolution","volume":"22","author":"Li","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104191","DOI":"10.1016\/j.earscirev.2022.104191","article-title":"Geomorphometry and terrain analysis: Data, methods, platforms and applications","volume":"233","author":"Xiong","year":"2022","journal-title":"Earth-Sci. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cai, S., Liang, X., and Yu, S. (2023). A Progressive Plane Detection Filtering Method for Airborne LiDAR Data in Forested Landscapes. Forests, 14.","DOI":"10.3390\/f14030498"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6170","DOI":"10.3390\/rs14236170","article-title":"A Filtering Method for LiDAR Point Cloud Based on Multi-Scale CNN with Attention Mechanism","volume":"14","author":"Song","year":"2022","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hui, Z., Hu, Y., Yevenyo, Y.Z., and Yu, X. (2016). An Improved Morphological Algorithm for Filtering Airborne LiDAR Point Cloud Based on Multi-Level Kriging Interpolation. Remote Sens., 8.","DOI":"10.3390\/rs8010035"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bailey, G., Li, Y., McKinney, N., Yoder, D., Wright, W., and Herrero, H. (2022). Comparison of Ground Point Filtering Algorithms for High-Density Point Clouds Collected by Terrestrial LiDAR. Remote Sens., 14.","DOI":"10.3390\/rs14194776"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s12665-022-10726-w","article-title":"Factors influencing the spatial and temporal variations of surface runoff coefficient in the Red River basin of Vietnam","volume":"82","author":"Hiep","year":"2023","journal-title":"Environ. Earth Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, F., Yang, J., Zhang, B., Li, Y., Huang, J., and Chen, N. (2020). Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China. Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090539"},{"key":"ref_30","unstructured":"Sithole, G., and Vosselman, G. (2003). Report: ISPRS Comparison Of Filters, ISPRS Commission III, Working Group."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Koo, K.-Y. (2021). Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net. Appl. Sci., 12.","DOI":"10.3390\/app12010395"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2018.09.009","article-title":"DEM refinement by low vegetation removal based on the combination of full waveform data and progressive TIN densification","volume":"146","author":"Ma","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"110723","DOI":"10.1109\/ACCESS.2021.3097185","article-title":"Overall filtering algorithm for multiscale noise removal from point cloud data","volume":"9","author":"Ren","year":"2021","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6540","DOI":"10.1364\/AO.394341","article-title":"Improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data based on a multiscale cylindrical neighborhood","volume":"59","author":"Wang","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.isprsjprs.2016.07.002","article-title":"Two-step adaptive extraction method for ground points and breaklines from lidar point clouds","volume":"119","author":"Yang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, W., Sun, J., Li, W., Hu, T., and Wang, P. (2019). Deep Learning on Point Clouds and Its Application: A Survey. Sensors, 19.","DOI":"10.3390\/s19194188"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu, X., and Yuan, Y. (2016). Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud. Remote Sens., 8.","DOI":"10.3390\/rs8090730"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TGRS.2003.810682","article-title":"A progressive morphological filter for removing nonground measurements from airborne LIDAR data","volume":"41","author":"Zhang","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3569\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:58Z","timestamp":1760127178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3569"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,16]]},"references-count":38,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143569"],"URL":"https:\/\/doi.org\/10.3390\/rs15143569","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,16]]}}}