{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T18:52:13Z","timestamp":1783709533285,"version":"3.55.0"},"reference-count":129,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Green space is increasingly recognized as an important component of the urban environment. Adequate management and planning of urban green space is crucial to maximize its benefits for urban inhabitants and for the urban ecosystem in general. Inventorying urban vegetation is a costly and time-consuming process. The development of new remote sensing techniques to map and monitor vegetation has therefore become an important topic of interest to many scholars. Based on a comprehensive survey of the literature, this review article provides an overview of the main approaches proposed to map urban vegetation from high-resolution remotely sensed data. Studies are reviewed from three perspectives: (a) the vegetation typology, (b) the remote sensing data used and (c) the mapping approach applied. With regard to vegetation typology, a distinction is made between studies focusing on the mapping of functional vegetation types and studies performing mapping of lower-level taxonomic ranks, with the latter mainly focusing on urban trees. A wide variety of high-resolution imagery has been used by researchers for both types of mapping. The fusion of various types of remote sensing data, as well as the inclusion of phenological information through the use of multi-temporal imagery, prove to be the most promising avenues to improve mapping accuracy. With regard to mapping approaches, the use of deep learning is becoming more established, mostly for the mapping of tree species. Through this survey, several research gaps could be identified. Interest in the mapping of non-tree species in urban environments is still limited. The same holds for the mapping of understory species. Most studies focus on the mapping of public green spaces, while interest in the mapping of private green space is less common. The use of imagery with a high spatial and temporal resolution, enabling the retrieval of phenological information for mapping and monitoring vegetation at the species level, still proves to be limited in urban contexts. Hence, mapping approaches specifically tailored towards time-series analysis and the use of new data sources seem to hold great promise for advancing the field. Finally, unsupervised learning techniques and active learning, so far rarely applied in urban vegetation mapping, are also areas where significant progress can be expected.<\/jats:p>","DOI":"10.3390\/rs14041031","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:48:41Z","timestamp":1645476521000},"page":"1031","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":163,"title":["Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6435-9034","authenticated-orcid":false,"given":"Robbe","family":"Neyns","sequence":"first","affiliation":[{"name":"Cartography and GIS Research Group, Department of Geography, Vrije Universiteit Brussel, 1050 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5850-9577","authenticated-orcid":false,"given":"Frank","family":"Canters","sequence":"additional","affiliation":[{"name":"Cartography and GIS Research Group, Department of Geography, Vrije Universiteit Brussel, 1050 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.puhe.2013.08.016","article-title":"An ecological study investigating the association between access to urban green space and mental health","volume":"127","author":"Nutsford","year":"2013","journal-title":"Public Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.ecolind.2011.03.014","article-title":"Ecosystem properties, potentials and services\u2013The EPPS conceptual framework and an urban application example","volume":"21","author":"Bastian","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0378-7788(02)00081-6","article-title":"Vegetation in the urban environment: Microclimatic analysis and benefits","volume":"35","author":"Dimoudi","year":"2003","journal-title":"Energy Build."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/S0269-7491(01)00214-7","article-title":"Carbon storage and sequestration by urban trees in the USA","volume":"116","author":"Nowak","year":"2002","journal-title":"Environ. Pollut."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.envpol.2018.10.114","article-title":"Urban vegetation loss and ecosystem services: The influence on climate regulation and noise and air pollution","volume":"245","author":"Szlafsztein","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1016\/j.envpol.2011.03.007","article-title":"Positive effects of vegetation: Urban heat island and green roofs","volume":"159","author":"Susca","year":"2011","journal-title":"Environ. Pollut."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1051\/nss:2006002","article-title":"Integrative approaches to investigating human-natural systems: The Baltimore ecosystem study","volume":"14","author":"Cadenasso","year":"2006","journal-title":"Nat. Sci. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jenvman.2010.08.022","article-title":"Urban ecological systems: Scientific foundations and a decade of progress","volume":"92","author":"Pickett","year":"2011","journal-title":"J. Environ. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1016\/j.envpol.2011.01.010","article-title":"Urban forests and pollution mitigation: Analyzing ecosystem services and disservices","volume":"159","author":"Escobedo","year":"2011","journal-title":"Environ. Pollut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.landurbplan.2015.06.005","article-title":"Role of street trees in mitigating effects of heat and drought at highly sealed urban sites","volume":"143","author":"Gillner","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Drillet, Z., Fung, T.K., Leong, R.A.T., Sachidhanandam, U., Edwards, P., and Richards, D. (2020). Urban vegetation types are not perceived equally in providing ecosystem services and disservices. Sustainability, 12.","DOI":"10.3390\/su12052076"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ufug.2018.11.008","article-title":"Mapping and classifying green infrastructure typologies for climate-related studies based on remote sensing data","volume":"37","author":"Osmond","year":"2019","journal-title":"Urban For. Urban Green."},{"key":"ref_13","first-page":"347","article-title":"Plant species differences in particulate matter accumulation on leaf surfaces","volume":"427","author":"Popek","year":"2012","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.ufug.2012.06.006","article-title":"A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones","volume":"11","author":"Roy","year":"2012","journal-title":"Urban For. Urban Green."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.landurbplan.2019.02.010","article-title":"Urban trees, air quality, and asthma: An interdisciplinary review","volume":"187","author":"Eisenman","year":"2019","journal-title":"Landsc. Urban Plan."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"297","DOI":"10.48044\/jauf.2006.038","article-title":"Street Tree Diversity in Eastern North America and Its Potential for Tree Loss to Exotic Borers","volume":"32","author":"Raupp","year":"2006","journal-title":"Arboric. Urban For."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Baker, F., Smith, C.L., and Cavan, G. (2018). A combined approach to classifying land surface cover of urban domestic gardens using citizen science data and high resolution image analysis. Remote Sens., 10.","DOI":"10.3390\/rs10040537"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.018","article-title":"Urban tree species mapping using hyperspectral and lidar data fusion","volume":"148","author":"Alonzo","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2003.09.007","article-title":"Object-based classification of remote sensing data for change detection","volume":"58","author":"Walter","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JSTARS.2008.2007514","article-title":"Detection, characterization, and modeling vegetation in urban areas from high-resolution aerial imagery","volume":"1","author":"Iovan","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2013.02.003","article-title":"Shadow detection in very high spatial resolution aerial images: A comparative study","volume":"80","author":"Adeline","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1016\/j.rse.2009.06.004","article-title":"The influence of urban structures on impervious surface maps from airborne hyperspectral data","volume":"113","author":"Hostert","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_23","unstructured":"Li, D., Ke, Y., Gong, H., Chen, B., and Zhu, L. (2014, January 11\u201314). Tree species classification based on WorldView-2 imagery in complex urban environment. Proceedings of the 2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Changsha, China."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126946","DOI":"10.1016\/j.ufug.2020.126946","article-title":"Remote sensing of urban green spaces: A review","volume":"57","author":"Shahtahmassebi","year":"2020","journal-title":"Urban For. Urban Green."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, T., and Liu, X. (2019). A review: Individual tree species classification using integrated airborne LiDAR and optical imagery with a focus on the urban environment. Forests, 10.","DOI":"10.3390\/f10010001"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Smith, T., Shugart, H., Woodward, F., and Burton, P. (1993). Plant functional types. Vegetation Dynamics & Global Change, Springer.","DOI":"10.1007\/978-1-4615-2816-6_14"},{"key":"ref_28","unstructured":"(2021, April 14). Ecosystem Services and Green Infrastructure. Available online: https:\/\/ec.europa.eu\/environment\/nature\/ecosystems\/index_en.htm."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.landurbplan.2016.09.024","article-title":"Defining greenspace: Multiple uses across multiple disciplines","volume":"158","author":"Taylor","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Degerickx, J., Hermy, M., and Somers, B. (2020). Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data. Sustainability, 12.","DOI":"10.3390\/su12052144"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1007\/BF02868932","article-title":"The classification of life-forms of plants","volume":"5","author":"Adamson","year":"1939","journal-title":"Bot. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.ecocom.2010.04.008","article-title":"Linking vegetation type and condition to ecosystem goods and services","volume":"7","author":"Yapp","year":"2010","journal-title":"Ecol. Complex."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1080\/01431160512331316856","article-title":"Urban development in the Athens metropolitan area using remote sensing data with supervised analysis and GIS","volume":"26","author":"Weber","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.compenvurbsys.2013.12.002","article-title":"Using street based metrics to characterize urban typologies","volume":"44","author":"Hermosilla","year":"2014","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1080\/13658816.2013.865189","article-title":"From land cover-graphs to urban structure types","volume":"28","author":"Walde","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.3390\/s7112860","article-title":"Object-based classification of Ikonos imagery for mapping large-scale vegetation communities in urban areas","volume":"7","author":"Mathieu","year":"2007","journal-title":"Sensors"},{"key":"ref_37","unstructured":"Millennium ecosystem assessment, M. (2005). Ecosystems and Human Well-Being, Island Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S0169-2046(02)00188-3","article-title":"Development of an ecological mapping methodology for urban areas in New Zealand","volume":"63","author":"Freeman","year":"2003","journal-title":"Landsc. Urban Plan."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Anderson, J.R. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data.","DOI":"10.3133\/pp964"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1175\/BAMS-D-11-00019.1","article-title":"Local climate zones for urban temperature studies","volume":"93","author":"Stewart","year":"2012","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cadenasso, M., Pickett, S., McGrath, B., and Marshall, V. (2013). Ecological heterogeneity in urban ecosystems: Reconceptualized land cover models as a bridge to urban design. Resilience in Ecology and Urban Design, Springer.","DOI":"10.1007\/978-94-007-5341-9_6"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ecolind.2014.02.036","article-title":"Urban vegetation structure types as a methodological approach for identifying ecosystem services\u2013Application to the analysis of micro-climatic effects","volume":"42","author":"Lehmann","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"115","DOI":"10.2148\/benv.33.1.115","article-title":"Adapting cities for climate change: The role of the green infrastructure","volume":"33","author":"Gill","year":"2007","journal-title":"Built Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kopeck\u00e1, M., Szatm\u00e1ri, D., and Rosina, K. (2017). Analysis of urban green spaces based on Sentinel-2A: Case studies from Slovakia. Land, 6.","DOI":"10.3390\/land6020025"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ecolind.2009.06.004","article-title":"Classification and quantification of green in the expanding urban and semi-urban complex: Application of detailed field data and IKONOS-imagery","volume":"11","author":"Gulinck","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/01431160902882603","article-title":"Object-oriented method for urban vegetation mapping using IKONOS imagery","volume":"31","author":"Zhang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.rse.2013.02.020","article-title":"Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis","volume":"133","author":"Liu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4153","DOI":"10.1109\/JSTARS.2014.2312717","article-title":"A two-phase classification of urban vegetation using airborne LiDAR data and aerial photography","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kranj\u010di\u0107, N., Medak, D., \u017dupan, R., and Rezo, M. (2019). Machine learning methods for classification of the green infrastructure in city areas. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8100463"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1080\/22797254.2018.1431057","article-title":"Urban vegetation extraction from VHR (tri-) stereo imagery\u2014A comparative study in two central European cities","volume":"51","author":"Kothencz","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1080\/01431161.2012.714508","article-title":"Object-based urban vegetation mapping with high-resolution aerial photography as a single data source","volume":"34","author":"Li","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wania, A., and Weber, C. (2007, January 11\u201313). Hyperspectral imagery and urban green observation. Proceedings of the 2007 Urban Remote Sensing Joint Event, Paris, France.","DOI":"10.1109\/URS.2007.371829"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.06.011","article-title":"A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species","volume":"124","author":"Pu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1080\/10106049.2011.638989","article-title":"Classification of urban tree species using hyperspectral imagery","volume":"27","author":"Jensen","year":"2012","journal-title":"Geocarto Int."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hartling, S., Sagan, V., Sidike, P., Maimaitijiang, M., and Carron, J. (2019). Urban tree species classification using a WorldView-2\/3 and LiDAR data fusion approach and deep learning. Sensors, 19.","DOI":"10.3390\/s19061284"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Katz, D.S., Batterman, S.A., and Brines, S.J. (2020). Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset. Remote Sens., 12.","DOI":"10.3390\/rs12152475"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2013.05.001","article-title":"Urban vegetation classification: Benefits of multitemporal RapidEye satellite data","volume":"136","author":"Tigges","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1109\/TGRS.2003.815384","article-title":"The use of high-resolution imagery for identification of urban climax forest species using traditional and rule-based classification approach","volume":"41","author":"Sugumaran","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2015.06.010","article-title":"Detection of gradients of forest composition in an urban area using imaging spectroscopy","volume":"167","author":"Gu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.3390\/rs4061741","article-title":"Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles","volume":"4","author":"Zhang","year":"2012","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5637","DOI":"10.1080\/01431160412331291224","article-title":"Using AVIRIS data and multiple-masking techniques to map urban forest tree species","volume":"25","author":"Xiao","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1016\/j.rse.2011.07.016","article-title":"Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data","volume":"115","author":"Kim","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s40663-018-0146-y","article-title":"Mapping tree canopies in urban environments using airborne laser scanning (ALS): A Vancouver case study","volume":"5","author":"Matasci","year":"2018","journal-title":"For. Ecosyst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2017.08.010","article-title":"Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data","volume":"200","author":"Liu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1080\/2150704X.2013.764027","article-title":"Identifying Santa Barbara\u2019s urban tree species from AVIRIS imagery using canonical discriminant analysis","volume":"4","author":"Alonzo","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"111811","DOI":"10.1016\/j.rse.2020.111811","article-title":"Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington DC, USA","volume":"246","author":"Fang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_67","first-page":"525","article-title":"Identification of understory invasive exotic plants with remote sensing in urban forests","volume":"21","author":"Shouse","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.3389\/fpls.2016.01528","article-title":"Invasive shrub mapping in an urban environment from hyperspectral and LiDAR-derived attributes","volume":"7","author":"Chance","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Mozgeris, G., Juodkien\u0117, V., Jonikavi\u010dius, D., Straigyt\u0117, L., Gadal, S., and Ouerghemmi, W. (2018). Ultra-light aircraft-based hyperspectral and colour-infrared imaging to identify deciduous tree species in an urban environment. Remote Sens., 10.","DOI":"10.3390\/rs10101668"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"16917","DOI":"10.3390\/rs71215861","article-title":"Object-based urban tree species classification using bi-temporal WorldView-2 and WorldView-3 images","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3020","DOI":"10.3390\/s8053020","article-title":"Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach","volume":"8","author":"Voss","year":"2008","journal-title":"Sensors"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/rs2010019","article-title":"Individual tree species classification by illuminated\u2014Shaded area separation","volume":"2","author":"Puttonen","year":"2010","journal-title":"Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhou, Y., Wang, S., Wang, L., Li, F., Wang, S., and Wang, Z. (2020). A novel intelligent classification method for urban green space based on high-resolution remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12223845"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3285","DOI":"10.1080\/01431161003745657","article-title":"Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery","volume":"32","author":"Pu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.rse.2008.10.005","article-title":"Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications","volume":"113","author":"Tooke","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Le Louarn, M., Clergeau, P., Briche, E., and Deschamps-Cottin, M. (2017). \u201cKill Two birds with one stone\u201d: Urban tree species classification using bi-temporal pl\u00e9iades images to study nesting preferences of an invasive bird. Remote Sens., 9.","DOI":"10.3390\/rs9090916"},{"key":"ref_77","first-page":"144","article-title":"Assessing the potential of multi-seasonal high resolution Pl\u00e9iades satellite imagery for mapping urban tree species","volume":"71","author":"Pu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1080\/01431161.2018.1479798","article-title":"Urban green space classification and water consumption analysis with remote-sensing technology: A case study in Beijing, China","volume":"40","author":"Di","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.ufug.2018.01.021","article-title":"Mapping vegetation functional types in urban areas with WorldView-2 imagery: Integrating object-based classification with phenology","volume":"31","author":"Yan","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Kazakova, A., Moskal, L.M., and Styers, D.M. (2016). Object-based tree species classification in urban ecosystems using LiDAR and hyperspectral data. Forests, 7.","DOI":"10.3390\/f7060122"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.14358\/PERS.78.10.1079","article-title":"Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery","volume":"78","author":"Zhang","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., and Pradhan, B. (2021). Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI). Sensors, 21.","DOI":"10.3390\/s21144738"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"127241","DOI":"10.1016\/j.ufug.2021.127241","article-title":"Deep learning-based tree species mapping in a highly diverse tropical urban setting","volume":"64","author":"Martins","year":"2021","journal-title":"Urban For. Urban Green."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Lobo Torres, D., Queiroz Feitosa, R., Nigri Happ, P., Elena Cue La Rosa, L., Marcato Junior, J., Martins, J., Ola Bressan, P., Gon\u00e7alves, W.N., and Liesenberg, V. (2020). Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery. Sensors, 20.","DOI":"10.3390\/s20020563"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s11676-020-01245-0","article-title":"Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle","volume":"32","author":"Zhang","year":"2020","journal-title":"J. For. Res."},{"key":"ref_86","unstructured":"Wang, J., and Banzhaf, E. (2017, January 6\u20138). Derive an understanding of Green Infrastructure for the quality of life in cities by means of integrated RS mapping tools. Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates."},{"key":"ref_87","unstructured":"Hermosilla, T., Ruiz, L.A., Recio, J.A., and Balsa-Barreiro, J. (30\u20134, January 30). Land-use mapping of Valencia city area from aerial images and LiDAR data. Proceedings of the GEOProcessing 2012: The Fourth International Conference in Advanced Geographic Information Systems, Applications and Services, Valencia, Spain."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1080\/01431161.2016.1178867","article-title":"SVM-based soft classification of urban tree species using very high-spatial resolution remote-sensing imagery","volume":"37","author":"Zhou","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Mozgeris, G., Gadal, S., Jonikavi\u010dius, D., Straigyt\u0117, L., Ouerghemmi, W., and Juodkien\u0117, V. (2016, January 21\u201324). Hyperspectral and color-infrared imaging from ultralight aircraft: Potential to recognize tree species in urban environments. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071756"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.inffus.2020.04.006","article-title":"Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion","volume":"62","author":"Ma","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_91","first-page":"204","article-title":"Tree species classification of individual trees in Sweden by combining high resolution laser data with high resolution near-infrared digital images","volume":"36","author":"Persson","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.agrformet.2012.11.012","article-title":"Classification of tree species based on structural features derived from high density LiDAR data","volume":"171","author":"Li","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.apgeog.2018.01.010","article-title":"Assessing residential front yards using Google Street View and geospatial video: A virtual survey approach for urban pollinator conservation","volume":"92","author":"Burr","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ecolind.2017.01.028","article-title":"Quantifying street tree regulating ecosystem services using Google Street View","volume":"77","author":"Richards","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.landurbplan.2017.05.010","article-title":"Green streets- Quantifying and mapping urban trees with street-level imagery and computer vision","volume":"165","author":"Seiferling","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ufug.2016.11.006","article-title":"Google Street View shows promise for virtual street tree surveys","volume":"21","author":"Berland","year":"2017","journal-title":"Urban For. Urban Green."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Berland, A., Roman, L.A., and Vogt, J. (2019). Can field crews telecommute? Varied data quality from citizen science tree inventories conducted using street-level imagery. Forests, 10.","DOI":"10.3390\/f10040349"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.11.008","article-title":"From Google Maps to a fine-grained catalog of street trees","volume":"135","author":"Branson","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"5158","DOI":"10.3390\/s110505158","article-title":"Tree classification with fused mobile laser scanning and hyperspectral data","volume":"11","author":"Puttonen","year":"2011","journal-title":"Sensors"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1109\/JSTARS.2019.2929546","article-title":"Rapid urban roadside tree inventory using a mobile laser scanning system","volume":"12","author":"Chen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Wu, J., Yao, W., and Polewski, P. (2018). Mapping individual tree species and vitality along urban road corridors with LiDAR and imaging sensors: Point density versus view perspective. Remote Sens., 10.","DOI":"10.3390\/rs10091403"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Mokro\u0161, M., Liang, X., Surov\u1ef3, P., Valent, P., \u010cer\u0148ava, J., Chud\u1ef3, F., Tun\u00e1k, D., Salo\u0148, \u0160., and Mergani\u010d, J. (2018). Evaluation of close-range photogrammetry image collection methods for estimating tree diameters. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030093"},{"key":"ref_103","first-page":"57","article-title":"Generation of visually aesthetic and detailed 3D models of historical cities by using laser scanning and digital photogrammetry","volume":"8","author":"Fritsch","year":"2018","journal-title":"Digit. Appl. Archaeol. Cult. Herit."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"5228","DOI":"10.1080\/01431161.2020.1731002","article-title":"Tree height mapping and crown delineation using LiDAR, large format aerial photographs, and unmanned aerial vehicle photogrammetry in subtropical urban forest","volume":"41","author":"Kwong","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Ghanbari Parmehr, E., and Amati, M. (2021). Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park. Remote Sens., 13.","DOI":"10.3390\/rs13112062"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1654-109X.2009.01053.x","article-title":"Mapping tree species in temperate deciduous woodland using time-series multi-spectral data","volume":"13","author":"Hill","year":"2010","journal-title":"Appl. Veg. Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1111\/j.1469-8137.2011.03803.x","article-title":"Leaf-out phenology of temperate woody plants: From trees to ecosystems","volume":"191","author":"Polgar","year":"2011","journal-title":"New Phytol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.isprsjprs.2021.05.003","article-title":"Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong","volume":"177","author":"Abbas","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_109","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_110","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1109\/JSTARS.2016.2645798","article-title":"Semantic classification of urban trees using very high resolution satellite imagery","volume":"10","author":"Wen","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_112","first-page":"1025","article-title":"The use of WorldView-2 satellite data in urban tree species mapping by object-based image analysis technique","volume":"45","author":"Shojanoori","year":"2016","journal-title":"Sains Malays."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2005.03.009","article-title":"Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales","volume":"96","author":"Clark","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Youjing, Z., and Hengtong, F. (2007, January 23\u201327). Identification scales for urban vegetation classification using high spatial resolution satellite data. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423086"},{"key":"ref_115","unstructured":"Li, C., Yin, J., and Zhao, J. (2010, January 25\u201327). Extraction of urban vegetation from high resolution remote sensing image. Proceedings of the 2010 International Conference On Computer Design and Applications, Qinhuangdao, China."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Wegner, J.D., Branson, S., Hall, D., Schindler, K., and Perona, P. (2016, January 27\u201330). Cataloging public objects using aerial and street-level images-urban trees. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.647"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"4700","DOI":"10.1080\/01431161.2017.1331059","article-title":"Mapping vegetation and land cover in a large urban area using a multiple classifier system","volume":"38","author":"Shi","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Degerickx, J., Hermy, M., and Somers, B. (2017, January 6\u20138). Mapping functional urban green types using hyperspectral remote sensing. Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates.","DOI":"10.1109\/JURSE.2017.7924553"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.rse.2017.07.027","article-title":"High spatial resolution spectral unmixing for mapping ash species across a complex urban environment","volume":"199","author":"Pontius","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1080\/2150704X.2015.1088668","article-title":"Deep learning-based tree classification using mobile LiDAR data","volume":"6","author":"Guan","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"126391","DOI":"10.1016\/j.ufug.2019.126391","article-title":"Urban hedges: A review of plant species and cultivars for ecosystem service delivery in north-west Europe","volume":"44","author":"Blanusa","year":"2019","journal-title":"Urban For. Urban Green."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1111\/rec.12051","article-title":"Urban grassland restoration: A neglected opportunity for biodiversity conservation","volume":"21","author":"Klaus","year":"2013","journal-title":"Restor. Ecol."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.landurbplan.2018.10.010","article-title":"Front and back yard green analysis with subpixel vegetation fractions from earth observation data in a city","volume":"182","author":"Haase","year":"2019","journal-title":"Landsc. Urban Plan."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ufug.2012.01.002","article-title":"The domestic garden\u2013Its contribution to urban green infrastructure","volume":"11","author":"Cameron","year":"2012","journal-title":"Urban For. Urban Green."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/JPROC.2012.2231951","article-title":"Active learning: Any value for classification of remotely sensed data?","volume":"101","author":"Crawford","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/LGRS.2020.2993095","article-title":"Unsupervised land cover classification of hybrid and dual-polarized images using deep convolutional neural network","volume":"18","author":"Chatterjee","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.isprsjprs.2020.01.015","article-title":"Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples","volume":"161","author":"Fang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Sainte Fare Garnot, V., Landrieu, L., Giordano, S., and Chehata, N. (2020, January 14\u201319). Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01234"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/1031\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:23:44Z","timestamp":1760135024000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/1031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,21]]},"references-count":129,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14041031"],"URL":"https:\/\/doi.org\/10.3390\/rs14041031","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,21]]}}}