Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR data
Over the past decades, pollen allergy has become one of the most widespread public health issues. The number of individuals having allergies to pollen has dramatically increased, especially in urban and industrial areas. Quantifying the allergenic potential of urban green spaces and developing aller...
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| Language: | English |
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Elsevier
2025-04-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25002845 |
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| author | Jinzhou Wu Robbe Neyns Markus Münzinger Frank Canters |
| author_facet | Jinzhou Wu Robbe Neyns Markus Münzinger Frank Canters |
| author_sort | Jinzhou Wu |
| collection | DOAJ |
| description | Over the past decades, pollen allergy has become one of the most widespread public health issues. The number of individuals having allergies to pollen has dramatically increased, especially in urban and industrial areas. Quantifying the allergenic potential of urban green spaces and developing allergy sensitive strategies for green space management and planning are therefore becoming increasingly important. Mapping the allergenicity of urban parks requires detailed information on tree species and tree crown volume which for many cities is not available or is not updated on a regular basis. This study assesses the potential of very high-resolution remote sensing for mapping allergenic tree genera and proposes a workflow for quantifying the allergenic potential of urban green spaces (UGS). Using a convolutional network approach six allergenic genera are mapped within 52 urban green spaces across the Brussels Capital Region. The classification model achieves an overall accuracy of 0.86, with precision for the six genera ranging from 0.82 to 0.92. By combining the obtained map with tree crown measures derived from airborne LiDAR data an assessment of the allergenicity of the 52 UGS is made, accounting for misclassification bias in the mapping of tree genera. Smaller, often more centrally located neighborhood parks have the lowest index values. Landscape parks and protected habitats in the periphery of the region have higher allergenicity values. |
| format | Article |
| id | doaj-art-9b254d25318f4bfe97c1e159fb9d522d |
| institution | OA Journals |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-9b254d25318f4bfe97c1e159fb9d522d2025-08-20T02:17:29ZengElsevierEcological Indicators1470-160X2025-04-0117311335310.1016/j.ecolind.2025.113353Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR dataJinzhou Wu0Robbe Neyns1Markus Münzinger2Frank Canters3Cartography and GIS Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium; Corresponding author.Cartography and GIS Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, BelgiumResearch Area Spatial Information and Modelling, Leibniz Institute of Ecological Urban and Regional Development (IOER), Weberplatz 1, 01217 Dresden, GermanyCartography and GIS Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, BelgiumOver the past decades, pollen allergy has become one of the most widespread public health issues. The number of individuals having allergies to pollen has dramatically increased, especially in urban and industrial areas. Quantifying the allergenic potential of urban green spaces and developing allergy sensitive strategies for green space management and planning are therefore becoming increasingly important. Mapping the allergenicity of urban parks requires detailed information on tree species and tree crown volume which for many cities is not available or is not updated on a regular basis. This study assesses the potential of very high-resolution remote sensing for mapping allergenic tree genera and proposes a workflow for quantifying the allergenic potential of urban green spaces (UGS). Using a convolutional network approach six allergenic genera are mapped within 52 urban green spaces across the Brussels Capital Region. The classification model achieves an overall accuracy of 0.86, with precision for the six genera ranging from 0.82 to 0.92. By combining the obtained map with tree crown measures derived from airborne LiDAR data an assessment of the allergenicity of the 52 UGS is made, accounting for misclassification bias in the mapping of tree genera. Smaller, often more centrally located neighborhood parks have the lowest index values. Landscape parks and protected habitats in the periphery of the region have higher allergenicity values.http://www.sciencedirect.com/science/article/pii/S1470160X25002845Urban green spacesAllergenic treesAllergenic potentialRemote sensingLiDARConvolutional neural networks |
| spellingShingle | Jinzhou Wu Robbe Neyns Markus Münzinger Frank Canters Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR data Ecological Indicators Urban green spaces Allergenic trees Allergenic potential Remote sensing LiDAR Convolutional neural networks |
| title | Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR data |
| title_full | Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR data |
| title_fullStr | Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR data |
| title_full_unstemmed | Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR data |
| title_short | Assessing the allergenic potential of urban green spaces using orthoimagery and airborne LiDAR data |
| title_sort | assessing the allergenic potential of urban green spaces using orthoimagery and airborne lidar data |
| topic | Urban green spaces Allergenic trees Allergenic potential Remote sensing LiDAR Convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25002845 |
| work_keys_str_mv | AT jinzhouwu assessingtheallergenicpotentialofurbangreenspacesusingorthoimageryandairbornelidardata AT robbeneyns assessingtheallergenicpotentialofurbangreenspacesusingorthoimageryandairbornelidardata AT markusmunzinger assessingtheallergenicpotentialofurbangreenspacesusingorthoimageryandairbornelidardata AT frankcanters assessingtheallergenicpotentialofurbangreenspacesusingorthoimageryandairbornelidardata |