Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution
Urban green spaces of cities are crucial elements of city structure that ensure habitat for species and ecological functionality of habitat patches, maintain biodiversity, and provide environmental services. However, detailed maps intended for planning and improving the existing network require a q...
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| Language: | English |
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European Association of Geographers
2022-02-01
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| Series: | European Journal of Geography |
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| Online Access: | https://eurogeojournal.eu/index.php/egj/article/view/144 |
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| author | Vanda Eva MOLNAR Edina SIMON Szilárd SZABÓ |
| author_facet | Vanda Eva MOLNAR Edina SIMON Szilárd SZABÓ |
| author_sort | Vanda Eva MOLNAR |
| collection | DOAJ |
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Urban green spaces of cities are crucial elements of city structure that ensure habitat for species and ecological functionality of habitat patches, maintain biodiversity, and provide environmental services. However, detailed maps intended for planning and improving the existing network require a quick and effective technique for assessing the possibilities. Multispectral imagery is an accessible source for species-level classification of urban trees. Using a multispectral image from the WorldView–2 satellite sensor, we classified six of the most common urban tree species in Debrecen, Hungary. Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers were applied to different numbers of the MNF-transformed bands. The best overall accuracy was achieved with the ML algorithm applied to the first four transformed bands (75.1%), and with the SVM algorithm applied to eight bands (71.0%). In general, ML performed better than SVM. Despite the relatively low number of spectral bands, we achieved moderately good accuracy for basic vegetation mapping, which can be used in spatial planning and decision making. In a future interdisciplinary research study, we could merge the classification results with the dust adsorption capacity of individual species to assess the reduction of dust pollution by urban trees
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| format | Article |
| id | doaj-art-d8e4563540c343f58dcd6afcf7bd0711 |
| institution | Kabale University |
| issn | 1792-1341 2410-7433 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | European Association of Geographers |
| record_format | Article |
| series | European Journal of Geography |
| spelling | doaj-art-d8e4563540c343f58dcd6afcf7bd07112025-01-06T06:59:11ZengEuropean Association of GeographersEuropean Journal of Geography1792-13412410-74332022-02-0111210.48088/ejg.v.mol.11.1.33.46Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollutionVanda Eva MOLNAR Edina SIMONSzilárd SZABÓ Urban green spaces of cities are crucial elements of city structure that ensure habitat for species and ecological functionality of habitat patches, maintain biodiversity, and provide environmental services. However, detailed maps intended for planning and improving the existing network require a quick and effective technique for assessing the possibilities. Multispectral imagery is an accessible source for species-level classification of urban trees. Using a multispectral image from the WorldView–2 satellite sensor, we classified six of the most common urban tree species in Debrecen, Hungary. Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers were applied to different numbers of the MNF-transformed bands. The best overall accuracy was achieved with the ML algorithm applied to the first four transformed bands (75.1%), and with the SVM algorithm applied to eight bands (71.0%). In general, ML performed better than SVM. Despite the relatively low number of spectral bands, we achieved moderately good accuracy for basic vegetation mapping, which can be used in spatial planning and decision making. In a future interdisciplinary research study, we could merge the classification results with the dust adsorption capacity of individual species to assess the reduction of dust pollution by urban trees https://eurogeojournal.eu/index.php/egj/article/view/144hidden geographies, remote sensing, multispectral image, maximum likelihood, support vector machine |
| spellingShingle | Vanda Eva MOLNAR Edina SIMON Szilárd SZABÓ Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution European Journal of Geography hidden geographies, remote sensing, multispectral image, maximum likelihood, support vector machine |
| title | Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution |
| title_full | Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution |
| title_fullStr | Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution |
| title_full_unstemmed | Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution |
| title_short | Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution |
| title_sort | species level classification of urban trees from worldview 2 imagery in debrecen hungary an effective tool for planning a comprehensive green network to reduce dust pollution |
| topic | hidden geographies, remote sensing, multispectral image, maximum likelihood, support vector machine |
| url | https://eurogeojournal.eu/index.php/egj/article/view/144 |
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