{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:48:29Z","timestamp":1775674109260,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T00:00:00Z","timestamp":1610323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques.<\/jats:p>","DOI":"10.3390\/s21020471","type":"journal-article","created":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T11:36:11Z","timestamp":1610364971000},"page":"471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-5217","authenticated-orcid":false,"given":"Sarah","family":"Kentsch","sequence":"first","affiliation":[{"name":"Faculty of Agriculture, Yamagata University, Tsuruoka 997-8555, Japan"},{"name":"Faculty of Natural Sciences, Leibniz Universit\u00e4t, 30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4417-1704","authenticated-orcid":false,"given":"Mariano","family":"Cabezas","sequence":"additional","affiliation":[{"name":"Brain and Mind Centre, University of Sydney, Sydney 2015, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Tomhave","sequence":"additional","affiliation":[{"name":"Faculty of Natural Sciences, Leibniz Universit\u00e4t, 30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jens","family":"Gro\u00df","sequence":"additional","affiliation":[{"name":"Faculty of Natural Sciences, Leibniz Universit\u00e4t, 30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8636-9009","authenticated-orcid":false,"given":"Benjamin","family":"Burkhard","sequence":"additional","affiliation":[{"name":"Faculty of Natural Sciences, Leibniz Universit\u00e4t, 30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9748-7120","authenticated-orcid":false,"given":"Maximo Larry","family":"Lopez Caceres","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Yamagata University, Tsuruoka 997-8555, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katsushi","family":"Waki","sequence":"additional","affiliation":[{"name":"Faculty of Science, Yamagata University, Yamagata 990-8560, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4521-9113","authenticated-orcid":false,"given":"Yago","family":"Diez","sequence":"additional","affiliation":[{"name":"Faculty of Science, Yamagata University, Yamagata 990-8560, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.tplants.2008.03.004","article-title":"Adaptive evolution in invasive species","volume":"13","author":"Prentis","year":"2008","journal-title":"Trends Plant Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev-environ-033009-095548","article-title":"Invasive Species, Environmental Change and Management, and Health","volume":"35","author":"Richardson","year":"2010","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ecolecon.2004.10.002","article-title":"Update on the environmental and economic costs associated with alien-invasive species in the United States","volume":"52","author":"Pimentel","year":"2005","journal-title":"Ecol. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3391\/mbi.2014.5.1.01","article-title":"Tackling Invasive Alien Species in Europe: The Top 20 Issues","volume":"5","author":"Caffrey","year":"2014","journal-title":"Manag. Biol. Invasions"},{"key":"ref_5","unstructured":"Rabitsch, W., and Genovesi, P. (2021, January 08). Invasive Alien Species Indicators in Europe; EEA Technical Report; 2012. Available online: https:\/\/op.europa.eu\/en\/publication-detail\/-\/publication\/0e70dca6-0213-4420-b04a-8951cb9a0df7\/language-en."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.tree.2005.07.006","article-title":"Are invasive species the drivers of ecological change?","volume":"20","author":"Didham","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"273","DOI":"10.3391\/mbi.2017.8.3.02","article-title":"Tackling invasive alien species in Europe II: Threats and opportunities until 2020","volume":"8","author":"Piria","year":"2017","journal-title":"Manag. Biol. Invasions"},{"key":"ref_8","unstructured":"Hollenbach, M. (2020, November 11). Verst\u00e4rktes Vorgehen der Naturschutzbeh\u00f6rde Gegen Die Nordamerikanische Kulturheidelbeere. Available online: https:\/\/www.nlwkn.niedersachsen.de\/naturschutz\/fach_und_forderprogramme\/life\/hannoversche_moorgeest\/aktuelles_termine\/verstaerktes-vorgehen-der-naturschutzbehoerde-gegen-die-nordamerikanische-kulturheidelbeere-126107.html."},{"key":"ref_9","unstructured":"Schepker, H., and Kowarik, I. (1998). Invasive North American Blueberry Hybrids (Vaccinium corymbosum x angustifolium) in Northern Germany. Plant Invasions: Ecological Mechanisms and Human Responses, Backhuys Publishers."},{"key":"ref_10","unstructured":"Stieper, L.C. (2018). Distribution of Wild Growing Cultivated Blueberries in Kr\u00e4henmoor and Their Impact on Bog Vegetation and Bog Development. [Bachelor\u2019s Thesis, Leibniz University of Hannover]."},{"key":"ref_11","first-page":"1","article-title":"Naturschutzfachliche Invasivit\u00e4tsbewertungen f\u00fcr in Deutschland wild lebende gebietsfremde Gef\u00e4\u00dfpflanzen","volume":"352","author":"Nehring","year":"2013","journal-title":"BfN-Skripten"},{"key":"ref_12","unstructured":"Deilmann, H.C., Eichhorn, G., Falkenberg, H., G\u00fcnther, J., Hayen, H., Kuntze, H., Pollak, E., Schmatzler, E., and Steffens, P.J.T. (1990). Moor und Torf in Niedersachsen. Nieders\u00e4chsische Akad. Geowiss., 5, Available online: https:\/\/www.schweizerbart.de\/publications\/detail\/artno\/183010500\/Moor-und-Torf-in-Niedersachsen."},{"key":"ref_13","unstructured":"Kowarik, U.S.I. (2020, November 11). Vaccinium angustifolium x corymbosum. Available online: https:\/\/neobiota.bfn.de\/handbuch\/gefaesspflanzen\/vaccinium-angustifolim-x-corymbosum.html."},{"key":"ref_14","first-page":"346","article-title":"Verwilderung nordamerikanischer Kultur-Heidelbeeren (Vaccinium subgen. Cyanococcus) in Niedersachsen und deren Einsch\u00e4tzung aus Naturschutzsicht","volume":"72","author":"Schepker","year":"1997","journal-title":"Nat. Landsch."},{"key":"ref_15","unstructured":"Essl, F. (2004). Erstfund eines verwilderten Vorkommens der Kultur-Heidelbeere (Vaccinium angustifolium x corymbosum) in \u00d6stereich. Linzer Biologische Beitr\u00e4ge, Available online: https:\/\/www.zobodat.at\/pdf\/LBB_0036_2_0785-0796.pdf."},{"key":"ref_16","first-page":"1207","article-title":"The photogrammetric potential of low-cost UAVs in forestry and agriculture","volume":"31","author":"Grenzdorffer","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9070","DOI":"10.1080\/01431161.2019.1569793","article-title":"A bibliometric analysis on the use of unmanned aerial vehicles in agricultural and forestry studies","volume":"40","author":"Raparelli","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"475","DOI":"10.5194\/isprs-archives-XLII-2-W13-475-2019","article-title":"Resnet-based tree species classification using UAV images","volume":"XLII-2\/W13","author":"Natesan","year":"2019","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.12988\/ces.2016.68130","article-title":"Forest and UAV: A bibliometric review","volume":"9","author":"Gambella","year":"2016","journal-title":"Contemp. Eng. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kentsch, S., Lopez Caceres, M.L., Serrano, D., Roure, F., and Diez, Y. (2020). Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study. Remote Sens., 12.","DOI":"10.3390\/rs12081287"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1111\/0031-868X.00160","article-title":"Image Analysis for GIS Data Acquisition","volume":"16","author":"Heipke","year":"2000","journal-title":"Photogramm. Rec."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Carlsson, G. (2020). Persistent Homology and Applied Homotopy Theory. arXiv.","DOI":"10.1201\/9781351251624-8"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/15481603.2017.1419602","article-title":"Remote sensing for wetland classification: A comprehensive review","volume":"55","author":"Mahdavi","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_24","unstructured":"Huang, B. (2018). 2.07-GIS and Remote Sensing Applications in Wetland Mapping and Monitoring. Comprehensive Geographic Information Systems, Elsevier."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"98","DOI":"10.3390\/ijerph2006030011","article-title":"GIS and Remote Sensing Applications in the Assessment of Change within a Coastal Environment in the Niger Delta Region of Nigeria","volume":"3","author":"Twumasi","year":"2006","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lang, S. (2008). Object-based image analysis for remote sensing applications: Modeling reality\u2014Dealing with complexity. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9_1"},{"key":"ref_27","unstructured":"Joshi, C., de Leeuw, J., and van Duren, I. (2004, January 12\u201323). Remote sensing and GIS applications for mapping and spatial modelling of invasive species. Proceedings of the XXth ISPRS Congress: Geo-Imagery Bridging Continents, Istanbul, Turkey."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1016\/j.jenvman.2007.06.027","article-title":"Remote sensing and GIS for wetland inventory, mapping and change analysis","volume":"90","author":"Rebelo","year":"2009","journal-title":"J. Environ. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.procs.2015.07.415","article-title":"Ndvi: Vegetation Change Detection Using Remote Sensing and Gis\u2014A Case Study of Vellore District","volume":"57","author":"Gandhi","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"638296","DOI":"10.1155\/2014\/638296","article-title":"Monitoring the Invasion of Spartina alterniflora Using Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China)","volume":"2014","author":"Wan","year":"2014","journal-title":"Sci. World J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"781","DOI":"10.5194\/isprs-archives-XLI-B1-781-2016","article-title":"Wetland assessment using unmanned aerial vehicle (UAV) photogrammetry","volume":"XLI-B1","author":"Boon","year":"2016","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"186","DOI":"10.4314\/sajg.v5i2.7","article-title":"Unmanned Aerial Vehicle (UAV) photogrammetry produces accurate high-resolution orthophotos, point clouds and surface models for mapping wetlands","volume":"5","author":"Boon","year":"2016","journal-title":"South Afr. J. Geomat."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dvo\u0159\u00e1k, P., M\u00fcllerov\u00e1, J., Bartalo\u0161, T., and Br\u016fna, J. (2015). Unmanned aerial vehicles for alien plant species detection and monitoring. Remote Sens. Spat. Inf. Sci., XL-1\/W42015.","DOI":"10.5194\/isprsarchives-XL-1-W4-83-2015"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.04.009","article-title":"Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study","volume":"129","author":"Mafanya","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1109\/JSTARS.2018.2846178","article-title":"Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery","volume":"11","author":"Rezaee","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isprsjprs.2018.03.006","article-title":"Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification","volume":"139","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","first-page":"28","article-title":"Identifying Images of Invasive Hydrangea Using Pre-Trained Deep Convolutional Neural Networks","volume":"3","author":"Ashqar","year":"2019","journal-title":"Int. J. Acad. Dev."},{"key":"ref_39","unstructured":"Schneekloth, H., and Tuexen, J. (2021, January 08). Die Moore in Niedersachsen. GOTTINGEN Kommissionsverl. Goettinger Tageblatt, 1975, P. 1 A 198. Available online: http:\/\/pascal-francis.inist.fr\/vibad\/index.php?action=getRecordDetail&idt=PASCALGEODEBRGM7620141242."},{"key":"ref_40","unstructured":"Agisoft (2019, August 19). Agisoft Metashape 1.5.5, Professional Edition. Available online: http:\/\/www.agisoft.com\/downloads\/installer\/."},{"key":"ref_41","unstructured":"Team, T.G. (2019, August 19). GNU Image Manipulation Program. Available online: http:\/\/gimp.org."},{"key":"ref_42","unstructured":"Asaad, A. (2020). Persistent Homology for Image Analysis. [Ph.D. Thesis, University of Buckingham]."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cabezas, M., Kentsch, S., Tomhave, L., Gross, J., Caceres, M.L.L., and Diez, Y. (2020). Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data. Remote Sens., 12.","DOI":"10.3390\/rs12203431"},{"key":"ref_44","unstructured":"Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., and Kraft, A. (2020, July 01). Imgaug. Available online: https:\/\/github.com\/aleju\/imgaug."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Patrick, A., and Li, C. (2017). High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems. Remote Sens., 9.","DOI":"10.3390\/rs9121250"},{"key":"ref_47","first-page":"46","article-title":"UAV\u2014A useful tool for monitoring woodlands","volume":"18","author":"Zmarz","year":"2013","journal-title":"Misc. Geogr.\u2013Reg. Stud. Dev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/isprsarchives-XL-1-W4-1-2015","article-title":"Assesment of the influence of uav image quality on the orthophoto production","volume":"XL-1\/W4","author":"Wierzbicki","year":"2015","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.3390\/rs71013895","article-title":"Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure","volume":"7","author":"Dandois","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Frey, J., Kovach, K., Stemmler, S., and Koch, B. (2018). UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline. Remote Sens., 10.","DOI":"10.3390\/rs10060912"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/471\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:09:47Z","timestamp":1760159387000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,11]]},"references-count":50,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020471"],"URL":"https:\/\/doi.org\/10.3390\/s21020471","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,11]]}}}