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Article

Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery

1
School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
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Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station, Ministry of Science and Technology, Shanghai 200240, China
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Division of Forest, Nature and Landscape, KU Leuven, 200E Celestijnenlaan, 3001 Leuven, Belgium
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Earth Observation Data Science, Digitaal Vlaanderen, 9000 Ghent, Belgium
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Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, 6708PB Wageningen, The Netherlands
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Department of Environment, Flemish Government, 1000 Brussel, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Bas van Wesemael, Trissevgeni Stavrakou, Jean-Christophe Schyns, Joost Vandenabeele, Hans Lievens, Dimitry van der Zande and Kim Calders
Remote Sens. 2022, 14(5), 1169; https://doi.org/10.3390/rs14051169
Received: 17 January 2022 / Revised: 23 February 2022 / Accepted: 23 February 2022 / Published: 26 February 2022
(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)
Domestic gardens provide residents with immediate access to landscape amenities and numerous ecological provisions. These ecological provisions have been proven to be largely determined by greenspace composition and landscape, but the fragmentation and heterogeneity of garden environments present challenges to greenspace mapping. Here, we first developed a recognition method to create a garden parcel data set in the medieval Leuven city of Belgium, based on the land use layers and agricultural land parcels. Then, we applied multi-sourced satellite imagery to evaluate the added value of spatial resolution, plant phenology and 3D structure in identifying four vegetation types. Finally, we characterized the greenspace landscapes in garden parcels. Compared with single ALOS-2 imagery, SPOT-7 imagery and Pleiades-1A imagery increased the overall accuracy by 4% and 8%, respectively. The accuracy improvement (21%) produced from multi-temporal stereo Pleiades-1A imagery strongly verified the significance of plant phenology and 3D structure in garden mapping. The average greenspace cover in garden parcels was 71% but varied from 56% in urban gardens to 82% in rural gardens. The garden greenspace landscape is fragmented by the artificial structures in urban areas but has a more aggregated size and less complex shapes in rural areas. This study calls for greater attention to be paid to gardens, and for multi-disciplinary studies conducted in collaboration with urban ecologists and landscape designers to maximize the benefits to residents of both immediate landscape amenities and ecological provisions, in the face of global environmental changes and public health risks. View Full-Text
Keywords: domestic gardens; greenspace mapping; garden landscapes; multi-temporal stereo imagery; vegetation types domestic gardens; greenspace mapping; garden landscapes; multi-temporal stereo imagery; vegetation types
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MDPI and ACS Style

Yan, J.; Van der Linden, S.; Tian, Y.; Van Valckenborgh, J.; Strosse, V.; Somers, B. Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery. Remote Sens. 2022, 14, 1169. https://doi.org/10.3390/rs14051169

AMA Style

Yan J, Van der Linden S, Tian Y, Van Valckenborgh J, Strosse V, Somers B. Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery. Remote Sensing. 2022; 14(5):1169. https://doi.org/10.3390/rs14051169

Chicago/Turabian Style

Yan, Jingli, Stijn Van der Linden, Yunyu Tian, Jo Van Valckenborgh, Veerle Strosse, and Ben Somers. 2022. "Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery" Remote Sensing 14, no. 5: 1169. https://doi.org/10.3390/rs14051169

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