{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:26:25Z","timestamp":1773714385445,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,3,24]],"date-time":"2017-03-24T00:00:00Z","timestamp":1490313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41101403"],"award-info":[{"award-number":["41101403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Surveys of windthrown trees, resulting from hurricanes and other types of natural disasters, are an important component of agricultural insurance, forestry statistics, and ecological monitoring. Aerial images are commonly used to determine the total area or number of downed trees, but conventional methods suffer from two primary issues: misclassification of windthrown trees due to the interference from other objects or artifacts, and poor extraction resolution when trunk diameters are small. The objective of this study is to develop a coarse-to-fine extraction technique for individual windthrown trees that reduces the effects of these common flaws. The developed method was tested using UAV imagery collected over rubber plantations on Hainan Island after the Nesat typhoon in China on 19 October 2011. First, a coarse extraction of the affected area was performed by analyzing the image spectrum and textural characteristics. A thinning algorithm was then used to simplify downed trees into skeletal structures. Finally, fine extraction of individual trees was achieved using a line detection algorithm. The completeness of windthrown trees in the study area was 75.7% and the correctness was 92.5%. While similar values have been reported in other studies, they often include constraints, such as tree height. This technique is proposed to be a more feasible extraction algorithm as it is capable of achieving low commission errors across a broad range of tree heights and sizes. As such, it is a viable option for extraction of windthrown trees with a small trunk diameter.<\/jats:p>","DOI":"10.3390\/rs9040306","type":"journal-article","created":{"date-parts":[[2017,3,24]],"date-time":"2017-03-24T11:38:27Z","timestamp":1490355507000},"page":"306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images"],"prefix":"10.3390","volume":"9","author":[{"given":"Fuzhou","family":"Duan","sequence":"first","affiliation":[{"name":"Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China"}]},{"given":"Yangchun","family":"Wan","sequence":"additional","affiliation":[{"name":"Laboratory of 3D-Information Acquisition and Application, Capital Normal University, Beijing 100048, China"}]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"Laboratory of 3D-Information Acquisition and Application, Capital Normal University, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.biocon.2003.11.014","article-title":"Dead wood threshold values for the three-toed woodpecker presence in boreal and sub-Alpine forest","volume":"119","author":"Butler","year":"2004","journal-title":"Biol. Conserv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.biocon.2003.09.009","article-title":"The effects of windthrow on forest insect communities: A literature review","volume":"118","author":"Bouget","year":"2004","journal-title":"Biol. Conserv."},{"key":"ref_3","unstructured":"Ballard, D.H., and Brown, C.M. (1982). Computer Vision, Prentice Hall."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fransson, J.E.S., Magnusson, M., Folkesson, K., and Hallberg, B. (2007, January 23\u201328). Mapping of wind-thrown forests using VHF\/UHF SAR images. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423313"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fransson, J.E.S., Pantze, A., Eriksson, L.E.B., Soja, M.J., and Santoro, M. (2010, January 25\u201330). Mapping of wind-thrown forests using satellite SAR images. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5654183"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s10661-009-0798-8","article-title":"Comparison of remote sensing change detection techniques for assessing hurricane damage to forests","volume":"162","author":"Wang","year":"2009","journal-title":"Environ. Monit. Assess."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.agrformet.2009.09.009","article-title":"Post-hurricane forest damage assessment using satellite remote sensing","volume":"150","author":"Wang","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","first-page":"548","article-title":"A tool for rapid post-hurricane urban tree debris estimates using high resolution aerial imagery","volume":"18","author":"Szantoi","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_9","unstructured":"Selvarajan, S. (2011). Fusion of LiDAR and aerial imagery for the estimation of downed tree volume using support vector machines classification and region based object fitting. [Ph.D. Thesis, University of Florida]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.3390\/rs5031405","article-title":"Automatic storm damage detection in forests using high-altitude photogrammetric imagery","volume":"5","author":"Honkavaara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_11","first-page":"76","article-title":"Detecting pruning of individual stems using airborne laser scanning data captured from an unmanned aerial vehicle","volume":"30","author":"Wallace","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.3390\/f6051613","article-title":"Assessment of wooded area reduction by airborne laser scanning","volume":"6","author":"Tran","year":"2015","journal-title":"Forests"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Niemi, M.T., and Vauhkonen, J. (2016). Extracting canopy surface texture from airborne laser scanning data for the supervised and unsupervised prediction of area-based forest characteristics. Remote Sens., 8.","DOI":"10.3390\/rs8070582"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2420","DOI":"10.3390\/rs3112420","article-title":"Object-based image analysis of downed logs in disturbed forested landscapes using LiDAR","volume":"3","author":"Blanchard","year":"2011","journal-title":"Remote Sens."},{"key":"ref_15","first-page":"11","article-title":"Comparison of discrete and full-waveform ALS features for dead wood detection","volume":"II-5\/W2","author":"Hollaus","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inform. Sci."},{"key":"ref_16","unstructured":"M\u00fccke, W., Hollaus, M., and Pfeifer, N. (2012, January 16\u201319). Identification of dead trees using small footprint full-waveform airborne laser scanning data. Proceedings of SilviLaser, Vancouver, BC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"S32","DOI":"10.5589\/m13-013","article-title":"Detection of fallen trees in forested areas using small footprint airborne laser scanning data","volume":"39","author":"Deak","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"169","DOI":"10.5194\/isprsannals-II-5-W2-169-2013","article-title":"Detection of lying tree stems from airborne laser scanning data using a line template matching algorithm","volume":"II-5\/W2","author":"Lindberg","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inform. Sci."},{"key":"ref_19","first-page":"21","article-title":"Detection of windthrown trees using airborne laser scanning","volume":"30","author":"Holmgren","year":"2014","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.isprsjprs.2015.01.010","article-title":"Detection of fallen trees in ALS point clouds using a normalized cut approach trained by simulation","volume":"105","author":"Polewski","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Inoue, T., Nagai, S., Yamashita, S., Fadaei, H., and Ishii, R. (2014). Unmanned aerial survey of fallen trees in a deciduous broadleaved forest in eastern Japan. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0109881"},{"key":"ref_22","unstructured":"(2015, October 01). Pix4D. Available online: http:\/\/pix4d.com."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.envsoft.2012.01.014","article-title":"Image RF-A user-oriented implementation for remote sensing image analysis with Random Forests","volume":"35","author":"Waske","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.10.012","article-title":"A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery","volume":"141","author":"Xu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2013.10.026","article-title":"Improving the accuracy of rainfall rates from optical satellite sensors with machine learning\u2014A random forests-based approach applied to MSG SEVIRI","volume":"141","author":"Appelhans","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.rse.2015.07.015","article-title":"Uncertainty analysis of gross primary production upscaling using random forests, remote sensing and eddy covariance data","volume":"168","author":"Tramontana","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for landcover classification","volume":"67","author":"Galiano","year":"2012","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV remote sensing for urban vegetation mapping using random forest and texture analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.13031\/2013.31479","article-title":"Application of morphological image processing in agriculture","volume":"33","author":"Mcdonald","year":"1990","journal-title":"Trans. ASAE"},{"key":"ref_32","unstructured":"Zhang, D. (2009, January 15\u201317). Notice of retraction one-eighth rule in the N-queens problem based on group theory and morphological gene combinations. Proceedings of the International Forum on Information Technology and Applications, Chengdu, China."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ware, J.M., Jones, C.B., and Bundy, G.L. (1995, January 21\u201323). A triangulated spatial model for cartographic generalization of areal objects. Proceedings of the International Conference on Spatial Information Theory, Semmering, Austria.","DOI":"10.1007\/3-540-60392-1_12"},{"key":"ref_34","first-page":"537","article-title":"Edge detection techniques\u2014An overview","volume":"8","author":"Ziou","year":"1998","journal-title":"Int. J. Pattern Recognit. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5201\/ipol.2012.gjmr-lsd","article-title":"LSD: A line segment detector","volume":"2","author":"Jakubowicz","year":"2012","journal-title":"Image Process. On Line"},{"key":"ref_36","unstructured":"Hough, P.C.V. (1962). Methods and Means for Recognizing Complex Patterns. (3,069,654), US Patent."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11468","DOI":"10.3390\/rs61111468","article-title":"Teclines: A Matlab-based toolbox for tectonic lineament analysis from satellite images and DEMs, part 2: Line segments linking and merging","volume":"6","author":"Rahnama","year":"2014","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1080\/01431161.2010.548410","article-title":"Complex building description and extraction based on Hough transformation and cycle detection","volume":"3","author":"Cui","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s00138-009-0206-y","article-title":"Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform","volume":"21","author":"Li","year":"2009","journal-title":"Mach. Vis. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.compag.2009.02.001","article-title":"Automatic greenhouse delineation from QuickBird and Ikonos satellite images","volume":"66","author":"Liu","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","unstructured":"Chanussot, J., Bas, P., and Bombrun, L. (2005, January 29\u201329). Airborne remote sensing of vineyards for the detection of dead vine trees. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea."},{"key":"ref_42","first-page":"58","article-title":"Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping","volume":"34","author":"Turkera","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_43","first-page":"151","article-title":"Evaluation of automatic road extraction","volume":"32","author":"Heipke","year":"1997","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_44","first-page":"1155","article-title":"The effect of training strategies on supervised classification at different spatial resolutions","volume":"68","author":"Chen","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1080\/01431161.2014.885152","article-title":"Assessing the impact of training sample selection on accuracy of an urban classification: A case study in Denver, Colorado","volume":"35","author":"Huiran","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/34.31448","article-title":"Effects of sample size in classifier design","volume":"11","author":"Fukunaga","year":"1989","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/4\/306\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:31:11Z","timestamp":1760207471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/4\/306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,24]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["rs9040306"],"URL":"https:\/\/doi.org\/10.3390\/rs9040306","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,24]]}}}