Object-based classification of urban plant species from very high-resolution satellite imagery

Adam, 2014, Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers, Int. J. Remote Sens., 35, 3440, 10.1080/01431161.2014.903435 Akbari, 2016, Determining Pleiades satellite data capability for tree diversity modeling, iForest, 10, 348, 10.3832/ifor1884-009 Alonzo, 2016, Mapping urban forest structure and function using hyperspectral imagery and lidar data, Urban For. Urban Green, 17, 135, 10.1016/j.ufug.2016.04.003 Alonzo, 2015, Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry, Remote Sens. Environ., 162, 141, 10.1016/j.rse.2015.02.025 Anenberg, 2022, Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets, Lancet Planet Health, 6, e49, 10.1016/S2542-5196(21)00255-2 Baró, 2015, Mismatches between ecosystem services supply and demand in urban areas: a quantitative assessment in five European cities, Ecol. Indic., 55, 146, 10.1016/j.ecolind.2015.03.013 Beguet B., Chehata N., Boukir S., Guyon D., 2014, “Classification of forest structure using very high-resolution Pleiades image texture”. Conference Paper IGARSS July 2014. Belgiu, 2016, Random Forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24, 10.1016/j.isprsjprs.2016.01.011 Blackburn, 1995, Seasonal variations in the spectral reflectance of deciduous tree canopies, Int. J. Remote Sens., 16, 709, 10.1080/01431169508954435 Blaschke, 2010, Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens., 65, 2, 10.1016/j.isprsjprs.2009.06.004 Bottalico, 2017, A spatially-explicit method to assess the dry deposition of air pollution by urban forests in the city of Florence, Italy, Urban For. Urban Green., 27, 221, 10.1016/j.ufug.2017.08.013 Boukir S., Regniers O., Guo L., Bombrun L., Germain C., 2015, “Texture-based forest cover classification using random forests and ensemble margin”. IEEE International Geoscience and Remote Sensing Symposium 2015, Milan, Italy. Pp. 3072–3075. Bovolo, 2010, Analysis of effect of pan-sharpening in change detection on VHR Images, IEEE Trans. Geosci. Remote Sens. Lett., 7, 53, 10.1109/LGRS.2009.2029248 Braga, 2020, Tree crown delineation algorithm based on a convolutional neural network, Remote Sens, 12, 1288, 10.3390/rs12081288 Breiman, 2001, Random forests, Mach. Learn, 45, 5, 10.1023/A:1010933404324 Cameron, 2012, The domestic garden: Its contribution to urban green infrastructure, Urban For. Urban Green., 11, 129, 10.1016/j.ufug.2012.01.002 Chavez, 1988, An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data, Remote Sens. Environ., 24, 459, 10.1016/0034-4257(88)90019-3 Chen G., Ozelkan E., Singh K.K., Zhou J., Brown M.R., 2017, “Meentemeyer, R.K. Uncertainties in Mapping Forest Carbon in Urban Ecosystems”. J. Environ. Manag. 187: 229–238. Choudhury, 2020, Urban tree species identification and carbon stock mapping for urban green planning and management, Forests, 11, 1226, 10.3390/f11111226 Choudhury M.A.M., Costanzini S., Despini F., Rossi P., Galli A., et al., 2019, “Photogrammetry and Remote Sensing for the identification and characterization of trees in urban areas”. IOP Conf. Series: Journal of Physics: Conf. Series 1249: 012008. Cilliers, 2012, Social aspects of urban ecology in developing countries, with an emphasis on urban domestic gardens, Appl. Urban Ecol.: A Glob. Framew., 123 Deur, 2020, Tree species classification in mixed deciduous forests using very high spatial resolution satellite imagery and machine learning methods, Remote Sens, 12, 3926, 10.3390/rs12233926 Effiom A.E., 2018, “UAV-RGB and Multispectral Pleiades images for tree species identification and forest carbon estimation in Amtsvenn, Germany”. PhD thesis, Faculty of Geo-Information Science and Earth Observation of the University of Twente, February 2018, 63 pp. European Union Biodiversity Strategy for 2030 “Bringing nature back into our lives” (COM(2020) 380 final), Brussels, 20.5.2020. Fusaro, 2017, Mapping and assessment of PM10 and O3 removal by woody vegetation at urban and regional level, Remote Sens, 9, 791, 10.3390/rs9080791 Galle, 2021, Mapping the diversity of street tree inventories across eight cities internationally using open data, Urban For. Urban Green., 61, 10.1016/j.ufug.2021.127099 Genuer, 2010, Variable selection using random forests, Pattern Recognit. Lett., 31, 2225, 10.1016/j.patrec.2010.03.014 Grote, 2016, Functional traits of urban trees in relation to their air pollution mitigation potential: a holistic discussion, Front. Ecol. Environ., 1 Hartling, 2019, Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning, Sensors, 19, 1284, 10.3390/s19061284 Huesca, 2019, Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California, Remote Sens, 11, 1100, 10.3390/rs11091100 Immitzer, 2016, First experience with sentinel-2 data for crop and tree species classifications in Central Europe, Remote Sens, 8, 166, 10.3390/rs8030166 Immitzer, 2012, Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data, Remote Sens., 4, 2661, 10.3390/rs4092661 Jones, 2020, The impact of pan-sharpening and spectral resolution on vineyard segmentation through machine learning, Remote Sens, 12, 934, 10.3390/rs12060934 Ke, 2010, Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification, Remote Sens. Environ., 114, 1141, 10.1016/j.rse.2010.01.002 Key, 2001, A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest, Remote Sens. Environ., 75, 100, 10.1016/S0034-4257(00)00159-0 Key T., Warner T.A., McGraw J.B., Fajvan M.A., 1998, “An evaluation of the relative spectral and phenological information for tree canopy classification of digital images in the eastern deciduous forest,” in Presentation in the Int. Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Victoria, BC, Canada, Feb. 10–12, 1998, pp. 243–254. Kizel F., Bruzzone L., Benediktsson J.A., 2017, “Simultaneous empirical line calibration of multiple spectral images”. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). doi:10.1109/igarss.2017.8127924. Klingberg, 2017, Influence of urban vegetation on air pollution and noise exposure - A case study in Gothenburg, Sweden, Sci. Total Environ., 599–600, 1728, 10.1016/j.scitotenv.2017.05.051 Knauer, 2019, Tree species classification based on hybrid ensembles of a convolutional neural network (CNN) and random forest classifiers, Remote Sens, 11, 2788, 10.3390/rs11232788 Kokubu, 2020, Mapping seasonal tree canopy cover and leaf area using worldview-2/3 satellite imagery: a megacity-scale case study in Tokyo urban area, Remote Sens, 12, 1505, 10.3390/rs12091505 Larondelle, 2014, Mapping the diversity of regulating ecosystem services in European cities, Glob. Environ. Chang., 26, 119, 10.1016/j.gloenvcha.2014.04.008 Lefebvre, 2016, Extraction of urban vegetation with Pléiades multi-angular images”. Remote Sensing Technologies and Applications in Urban, Environ., Proc. SPIE Vol., 10008, 100080H Li, 2015, Object-based urban tree species classification using Bi-temporal WorldView-2 and WorldView-3 images, Remote Sens, 7, 16917, 10.3390/rs71215861 Loram, 2007, Urban domestic gardens (X): the extent and structure of the resource in five major cities, Landsc. Ecol., 22, 601, 10.1007/s10980-006-9051-9 Maack, 2015, Modeling Forest biomass using Very-High-Resolution data - Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images, Eur. J. Remote Sens, 48, 245, 10.5721/EuJRS20154814 Maes, 2016, An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020, Ecosyst. Serv., 17, 14, 10.1016/j.ecoser.2015.10.023 Malashock, 2022, Estimates of Ozone Concentrations and Attributable Mortality 1 in Urban, Peri-Urban and Rural Areas Worldwide in 2019, Environ. Res. Lett., 17, 10.1088/1748-9326/ac66f3 Manes, 2016, Regulating ecosystem services of forests in ten Italian metropolitan 31 cities: Air quality improvement by PM10 and O3 removal, Ecol. Indic., 67, 425, 10.1016/j.ecolind.2016.03.009 Manes, 2012, Urban ecosystem services: Tree diversity and stability of tropospheric ozone removal, Ecol. Appl., 22, 349, 10.1890/11-0561.1 Marando, 2016, Removal of PM10 by forests as a nature-based solution for air quality improvement in the metropolitan city of Rome, Forests, 7, 1, 10.3390/f7070150 McPherson, 2016, “Structure, Funct. Value Str. trees Calif., Usa”. Urban For. Urban Green., 17, 104 Modica, 2016, Using Landsat 8 Imagery in Detecting Cork Oak (Quercus suber L.), 47, 205 Müller N., Werner P., Kelcey J.G., 2010, “Urban biodiversity and design”. Conservation Science and Practice Series No. 7. Blackwell Publishing Ltd., Oxford, England. http://dx.doi.org/10.1002/9781444318654. Niccolai, 2010, Integration of varying spatial, spectral and temporal high-resolution optical images for individual tree crown isolation, Int. J. Remote Sens, 31, 5061, 10.1080/01431160903283850 Nichol, 2007, Remote sensing of urban vegetation life form by spectral mixture analysis of high-resolution IKONOS satellite images, Int. J. Remote Sens., 28, 985, 10.1080/01431160600784176 Nouri, 2014, High spatial resolution WorldView-2 imagery for mapping NDVI and its relationship to temporal urban landscape evapotranspiration factors, Remote Sens., 6, 580, 10.3390/rs6010580 Nowak, 2018, Air pollution removal by urban forests in Canada and its effect on air quality and human health, Urban For. Urban Green., 29, 40, 10.1016/j.ufug.2017.10.019 Nowak D.J., Hirabayashi S., Bodine A., Hoehn R., 2013, “Modeled PM2.5 removal by trees in ten US cities and associated health effects”. Environ. Pollut. 178: 395–402. Nowak, 2008, A ground-based method of assessing urban forest structure and ecosystem services, Arboric. Urban For., 34, 347, 10.48044/jauf.2008.048 Ostberg, 2018, The state and use of municipal tree inventories in Swedish municipalities - Results from a national survey, Urban Ecosyst., 21 Owen J. 2010, “Wildlife of a garden: a thirty-year study”. Royal Horticultural Society. Peterborough, Cambridgeshire, UK, ISBN-10: ‎ 9781907057120. Pace, 2021, A single tree model to consistently simulate cooling, shading, and pollution uptake of urban trees, Int. J. Biometeorol., 65, 277, 10.1007/s00484-020-02030-8 Pace, 2018, Modeling ecosystem services for park trees: sensitivity of i-tree eco simulations to light exposure and tree species classification, Forests, 9, 89, 10.3390/f9020089 Parmehr, 2016, Estimation of urban tree canopy cover using random point sampling and remote sensing methods, Urban For Urban Green., 20, 160, 10.1016/j.ufug.2016.08.011 Pataki, 2011, Coupling biogeochemical cycles in urban environments: ecosystem services, green solutions, and misconceptions, Front. Ecol. Environ., 9, 27, 10.1890/090220 Pauleit, 2005, Urban forest resources in European cities, 49 Persson, 2016, Estimation of boreal forest attributes from very high resolution pléiades data, Remote Sens, 8, 736, 10.3390/rs8090736 Pope, 2013, Leaf area index (LAI) estimation in boreal mixed wood forest of Ontario, Canada using light detection and ranging (LiDAR) and worldview-2 imagery, Remote Sens, 5, 5040, 10.3390/rs5105040 Pretzsch, 2021, Towards sustainable management of the stock and ecosystem services of urban trees. From theory to model and application, Trees Proietti, 2016, A multi-sites analysis on the ozone effects on Gross Primary Production of European forests, Sci. Total Environ., 556, 1, 10.1016/j.scitotenv.2016.02.187 Pu, 2011, Mapping urban forest tree species using IKONOS imagery: preliminary results, Environ. Monit. Assess., 172, 199, 10.1007/s10661-010-1327-5 Pu, 2009, Broadleaf species recognition within situ hyperspectral data, Int. J. Remote Sens., 30, 2759, 10.1080/01431160802555820 Pu, 2019, Evaluating seasonal effect on forest leaf area index mapping using multi-seasonal high resolution satellite Pleiades imagery, Int. J. Appl. Earth Obs. Geoinf., 80, 268 Pu, 2015, Mapping Forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US, Int. J. Appl. Earth Obs. Geoinf., 42, 11 Pu, 2015, Evaluation of atmospheric correction methods in identifying urban tree species with WorldView-2 imagery, J. Sel. Top Appl. Earth Obs. Remote Sens., 8, 1886, 10.1109/JSTARS.2014.2363441 Pu, 2012, A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species, Remote Sens. Environ., 124, 516, 10.1016/j.rse.2012.06.011 Pu, 2011, Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery, Int. J. Remote Sens., 32, 3285, 10.1080/01431161003745657 Pu, 2008, Using CASI hyperspectral imagery to detect mortality and vegetation stress associated with a new hardwood forest disease, Photogramm. Eng. Remote Sens., 74, 65, 10.14358/PERS.74.1.65 Puissant, 2014, Object-oriented mapping of urban trees using random forest classifiers, Intern. J. Appl. Earth Obs. Geoinform., 26, 235 Qian, 2020, A new index to differentiate tree and grass based on high resolution image and object-based methods, Urban For. Urban Green., 53, 10.1016/j.ufug.2020.126661 Quackenbush, 2000, Developing forestry products from high resolution digital aerial imagery, Photogramm. Eng. Remote Sens., 66, 1337 Rahman, 2020, Tree cooling effects and human thermal comfort under contrasting species and sites, Agric. For. Meteor., 287, 10.1016/j.agrformet.2020.107947 Regniers, 2014, Méthodes d′analyse de texture pour la cartographie d′occupations du sol par télédetection très haute résolution: application à la forêt, la vigne et les parcs ostréicoles, Thesis, Univ. Bordx., 164 Ren, 2022, Effects of urban street trees on human thermal comfort and physiological indices: a case study in Changchun city, China, J. For. Res., 33, 911, 10.1007/s11676-021-01361-5 Russo, 2016, Quantifying the local-scale ecosystem services provided by urban treed streetscapes in Bolzano, Italy, AIMS Environ. Sci., 3, 58, 10.3934/environsci.2016.1.58 Salbitano F., Borelli S., Conigliaro M., Yujuan C., 2016, “Guidelines on urban and peri-urban forestry”. FAO Forestry Paper No. 178, Food and Agriculture Organization of the United Nations, Rome, 2016, pp. 158. Samson, 2019, Towards an integrative approach to evaluate the environmental ecosystem services provided by urban forests, J. For. Res., 30, 1981, 10.1007/s11676-019-00916-x Sarp, 2014, Spectral and spatial quality analysis of pan-sharpening algorithms: a case study in Istanbul, Eur. J. Remote Sens., 47, 19, 10.5721/EuJRS20144702 Selmi, 2016, Air pollution removal by trees in public green spaces in Strasbourg city, France, Urban For. Urban Green., 17, 192, 10.1016/j.ufug.2016.04.010 Shojanoori, 2016, Review on the use of remote sensing for urban forest monitoring, Arboric. Urban, 42, 400 Sicard, 2023, Trends in urban air pollution over the last two decades: a global perspective, Sci. Total Environ., 858, 10.1016/j.scitotenv.2022.160064 Sicard, 2018, Should we see urban trees as effective solutions to reduce increasing ozone levels in cities?, Environ. Pollut., 243, 163, 10.1016/j.envpol.2018.08.049 Sicard, 2015, Health and vitality assessment of two common pine species in the context of climate change in Southern Europe, Environ. Res., 137, 235, 10.1016/j.envres.2014.12.025 Silli, 2015, Removal of airborne particulate matter by vegetation in an urban park in the city of Rome (Italy): an ecosystem services perspective, Ann. di Bot., 5, 53 Singh, 2015, Effects of LiDAR point density and landscape context on estimates of urban forest biomass, ISPRS J. Photogramm., 101, 310, 10.1016/j.isprsjprs.2014.12.021 Smith, 2006, Urban domestic gardens: composition and richness of the vascular plant flora, and implications for native biodiversity, Biol. Conserv., 129, 312, 10.1016/j.biocon.2005.10.045 Solano, 2019, A methodology based on GEOBIA and WorldView-3 imagery to derive vegetation indices at tree crown detail in Olive Orchards, Int. J. Appl. Earth Obs. Geoinf., 83 Song, 2016, Estimation of broad-leaved canopy growth in the urban forested area using multi-temporal airborne LiDAR datasets, Urban For. Urban Green, 16, 142, 10.1016/j.ufug.2016.02.007 Southerland V.A., Brauer M., Mohegh A., Hammer M.S., van Donkelaar A., et al., 2022, “Global urban temporal trends in fine particulate matter (PM2.5) and attributable health burdens: estimates from global datasets”. Lancet Planet Health 6: e139–46. Tigges, 2013, Urban vegetation classification: benefits of multitemporal RapidEye satellite data, Remote Sens. Environ., 136, 66, 10.1016/j.rse.2013.05.001 Ugolini, 2020, Effects of the COVID-19 pandemic on the use and perceptions of urban green space: an international exploratory study, Urban For Urban Green, 56, 10.1016/j.ufug.2020.126888 United Nations, 2020, “The World’s Cities in 2016″, Department of Economic and Social Affairs, ISBN 978–92-1–151549-7, available on https://www.un.org. Vila-Ruiz, 2014, Plant species richness and abundance in residential yards across a tropical watershed: implications for urban sustainability, Ecol. Soc., 19, 22, 10.5751/ES-06164-190322 Westfall, 2015, Spatial-scale considerations for a large-area forest inventory regression model, Forestry, 88, 267, 10.1093/forestry/cpv001 Zhang, 2014, Species diversity and performance assessment of trees in domestic gardens, Landsc. Urban Plan., 128, 23, 10.1016/j.landurbplan.2014.04.017 Zhang, 2012, Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imagery, Photogramm. Eng. Remote Sens., 78, 1079, 10.14358/PERS.78.10.1079 Zhou, 2017, The effects of GLCM parameters on LAI estimation using texture values from Quickbird satellite Imagery, Sci. Rep., 7, 7366, 10.1038/s41598-017-07951-w Zhou, 2013, An object-based approach for urban land cover classification: Integrating LiDAR height and intensity data, IEEE Geosci. Remote Sens. Lett., 10, 928, 10.1109/LGRS.2013.2251453