{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:27:05Z","timestamp":1763684825417,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20244","32071758","2572020BA01"],"award-info":[{"award-number":["U21A20244","32071758","2572020BA01"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["U21A20244","32071758","2572020BA01"],"award-info":[{"award-number":["U21A20244","32071758","2572020BA01"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Increased use of laser scanning in forest inventories is leading to the adoption and development of automated stand delineation methods. The most common categories of these methods are region merging and region growing. However, recent literature proposes alternative methods that are based on the ideas of cellular automata, self-organizing maps, and combinatorial optimization. The studies where these methods have been described suggest that the new methods are potential options for the automated segmentation of a forest into homogeneous stands. However, no studies are available that compare the new methods to each other and to the traditional region-merging and region-growing algorithms. This study provided a detailed comparison of four methods using LiDAR metrics calculated for grids of 5 m by 5 m raster cells as the data. The tested segmentation methods were region growing (RG), cellular automaton (CA), self-organizing map (SOM), and simulated annealing (SA), which is a heuristic algorithm developed for combinatorial optimization. The case study area was located in the Heilongjiang province of northeast China. The LiDAR data were collected from an unmanned aerial vehicle for three 1500-ha test areas. The proportion of variation in the LiDAR metrics that was explained by the segmentation was mostly the best for the SA method. The RG method produced more heterogeneous segments than the other methods. The CA method resulted in the smallest number of segments and the largest average segment area. The proportion of small segments (smaller than 0.3 ha) was the highest in the RG method while the SA method always produced the fewest small stands. The shapes of the segments were the best (most circular) for the CA and SA methods, but the shape metrics were good for all methods. The results of the study suggest that CA, SOM, and SA may all outperform RG in automated stand delineation.<\/jats:p>","DOI":"10.3390\/rs14246192","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T05:50:52Z","timestamp":1670392252000},"page":"6192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Comparison of Four Methods for Automatic Delineation of Tree Stands from Grids of LiDAR Metrics"],"prefix":"10.3390","volume":"14","author":[{"given":"Yusen","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingji","family":"Jin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2853-9510","authenticated-orcid":false,"given":"Timo","family":"Pukkala","sequence":"additional","affiliation":[{"name":"Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China"},{"name":"School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4058-769X","authenticated-orcid":false,"given":"Fengri","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"221","DOI":"10.5558\/tfc84221-2","article-title":"Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery","volume":"84","author":"Wulder","year":"2008","journal-title":"For. Chron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1080\/02827580802552446","article-title":"Automatic segmentation of forest stands using a canopy height model and aerial photography","volume":"23","author":"Mustonen","year":"2008","journal-title":"Scand. J. For. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jia, W., Sun, Y., Pukkala, T., and Jin, X. (2020). Improved Cellular Automaton for Stand Delineation. Forests, 11.","DOI":"10.3390\/f11010037"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1007\/s10342-021-01384-x","article-title":"Stand delineation based on laser scanning data and simulated annealing","volume":"140","author":"Sun","year":"2021","journal-title":"Eur. J. For. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Maltamo, M., N\u00e6sset, E., and Vauhkonen, J. (2014). Species-Specific Management Inventory in Finland BT-Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies, Springer.","DOI":"10.1007\/978-94-017-8663-8"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40663-020-00234-3","article-title":"Combining spatial and economic criteria in tree-level harvest planning","volume":"7","author":"Packalen","year":"2020","journal-title":"For. Ecosyst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1007\/s11676-019-00937-6","article-title":"Using ALS raster data in forest planning","volume":"30","author":"Pukkala","year":"2019","journal-title":"J. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s11676-018-0803-6","article-title":"Optimized cellular automaton for stand delineation","volume":"30","author":"Pukkala","year":"2019","journal-title":"J. For. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1080\/02827581.2021.1897668","article-title":"Can Kohonen networks delineate forest stands?","volume":"36","author":"Pukkala","year":"2021","journal-title":"Scand. J. For. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40663-020-00221-8","article-title":"Delineating forest stands from grid data","volume":"7","author":"Pukkala","year":"2020","journal-title":"For. Ecosyst."},{"key":"ref_11","first-page":"1203","article-title":"Integration of high resolution aerial images and airborne LIDAR data for forest delineation","volume":"37","author":"Wang","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/01431160802395284","article-title":"Airborne laser data for stand delineation and information extraction","volume":"30","author":"Koch","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10342-018-1157-5","article-title":"Influence of size and shape of forest inventory units on the layout of harvest blocks in numerical forest planning","volume":"138","author":"Pascual","year":"2019","journal-title":"Eur. J. For. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1080\/2150704X.2014.900203","article-title":"Forest stand delineation from lidar point-clouds using local maxima of the crown height model and region merging of the corresponding Voronoi cells","volume":"5","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_15","unstructured":"Strobl, J., Blaschke, T., and Griesebner, G. (2000). An Optimization Approach for High Quality Multi-Scale Image Segmentation. Angewandte Geographische Informationsverarbeitung XII, Wichmann."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1111\/j.1939-7445.2001.tb00073.x","article-title":"Land use optimization using self-organizing algorithms","volume":"14","author":"Strange","year":"2001","journal-title":"Nat. Resour. Model."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"561","DOI":"10.14214\/sf.545","article-title":"Eight Heuristic Planning Techniques Applied to Three Increasingly Difficult Wildlife Planning Problems","volume":"36","author":"Bettinger","year":"2002","journal-title":"Silva Fenn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1139\/X07-073","article-title":"The use of cellular automaton approach in forest planning","volume":"37","author":"Heinonen","year":"2007","journal-title":"Can. J. For. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00337288","article-title":"Self-organized formation of topologically correct feature maps","volume":"43","author":"Kohonen","year":"1982","journal-title":"Biol. Cybern."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112769","DOI":"10.1016\/j.rse.2021.112769","article-title":"Estimation and calibration of stem diameter distribution using UAV laser scanning data: A case study for larch (Larix olgensis) forests in Northeast China","volume":"268","author":"Hao","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, Y., Jin, X., Pukkala, T., and Li, F. (2022). Predicting Individual Tree Diameter of Larch (Larix olgensis) from UAV-LiDAR Data Using Six Different Algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14051125"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Peng, W., 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_23","doi-asserted-by":"crossref","unstructured":"Hao, Y., Widagdo, F.R.A., Liu, X., Quan, Y., Dong, L., and Li, F. (2021). Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning. Remote Sens., 13.","DOI":"10.3390\/rs13010024"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Balasubramanian, G.P., Saber, E., Misic, V., Peskin, E., and Shaw, M. (2008, January 18). Unsupervised color image segmentation using a dynamic color gradient thresholding algorithm. Proceedings of the Human Vision and Electronic Imaging XIII, San Jose, CA, USA.","DOI":"10.1117\/12.766184"},{"key":"ref_25","first-page":"1","article-title":"A Hierarchical Region-Merging Algorithm for 3-D Segmentation of Individual Trees Using UAV-LiDAR Point Clouds","volume":"60","author":"Hao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"9875886","article-title":"Region-Growing Algorithm on CT Angiography Images for Detection of Gynecological Malignant Tumor","volume":"2021","author":"Wen","year":"2021","journal-title":"Sci. Program."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/78.80971","article-title":"Detecting Boundaries in a Vector Field","volume":"39","author":"Lee","year":"1991","journal-title":"Trans. Sig. Proc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Quan, Y., Li, M., Hao, Y., and Wang, B. (2021). Comparison and Evaluation of Different Pit-Filling Methods for Generating High Resolution Canopy Height Model Using UAV Laser Scanning Data. Remote Sens., 13.","DOI":"10.3390\/rs13122239"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"89","DOI":"10.14214\/sf.474","article-title":"Possibilities to Aggregate Raster Cells through Spatial Optimization in Forest Planning","volume":"41","author":"Heinonen","year":"2007","journal-title":"Silva Fenn."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hu, H., and Guo, Z. (2021, January 5). A U-net and KMeans based method for brain tumor segmentation and measurement. Proceedings of the 2nd International Conference on Computer Vision, Image, and Deep Learning, Liuzhou, China.","DOI":"10.1117\/12.2604691"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"762","DOI":"10.3390\/rs4030762","article-title":"Forest delineation based on airborne LIDAR data","volume":"4","author":"Eysn","year":"2012","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2015.10.004","article-title":"The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data","volume":"171","author":"Roth","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2014.11.011","article-title":"Quantifying forest canopy traits: Imaging spectroscopy versus field survey","volume":"158","author":"Asner","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111323","DOI":"10.1016\/j.rse.2019.111323","article-title":"Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms","volume":"232","author":"Ometto","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:38Z","timestamp":1760146538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,7]]},"references-count":35,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246192"],"URL":"https:\/\/doi.org\/10.3390\/rs14246192","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,7]]}}}