Semantic identification of urban green spaces: forest
- Urban Green Spaces (UGSs) are recognized as crucial parts of the human-nature ecosystem in densely populated urban centers. Even though they have been intensively studied, an ultimate list of all types of UGSs in Europe still does not exist. This challenges decision making on whether an area should be considered an UGS or belong to another land-use class. Furthermore, the means of precise identification of UGSs are dependent, among others, on their type and semantics. Therefore, in this paper, we investigate forests as UGSs and automatically identify them using their distinct characteristics from Sentinel-2 images as well as descriptive properties derived from them, i.e., vegetation indices and texture metrics.We enrich these properties with forest relevant features such as minimum vegetation height and homogeneity. To assess the reliability of the proposed workflow, we test our approach in two German cities and compare the results with existing governmental land use data sets. WithUrban Green Spaces (UGSs) are recognized as crucial parts of the human-nature ecosystem in densely populated urban centers. Even though they have been intensively studied, an ultimate list of all types of UGSs in Europe still does not exist. This challenges decision making on whether an area should be considered an UGS or belong to another land-use class. Furthermore, the means of precise identification of UGSs are dependent, among others, on their type and semantics. Therefore, in this paper, we investigate forests as UGSs and automatically identify them using their distinct characteristics from Sentinel-2 images as well as descriptive properties derived from them, i.e., vegetation indices and texture metrics.We enrich these properties with forest relevant features such as minimum vegetation height and homogeneity. To assess the reliability of the proposed workflow, we test our approach in two German cities and compare the results with existing governmental land use data sets. With the implemented approach we precisely identify over 90% of the existing forests in the study areas. The main restriction of the approach is the transferability of the thresholds of predictor variables such as homogeneity and dissimilarity.…
Author: | Irada IsmayilovaORCiDGND, Sabine TimpfORCiDGND |
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URN: | urn:nbn:de:bvb:384-opus4-1086082 |
Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/108608 |
ISSN: | 2700-8150OPAC |
Parent Title (English): | AGILE: GIScience Series |
Publisher: | Copernicus |
Place of publication: | Göttingen |
Type: | Article |
Language: | English |
Year of first Publication: | 2023 |
Publishing Institution: | Universität Augsburg |
Release Date: | 2023/10/23 |
Tag: | General Earth and Planetary Sciences; General Environmental Science |
Volume: | 4 |
First Page: | 28 |
DOI: | https://doi.org/10.5194/agile-giss-4-28-2023 |
Institutes: | Fakultät für Angewandte Informatik |
Fakultät für Angewandte Informatik / Institut für Geographie | |
Fakultät für Angewandte Informatik / Institut für Geographie / Professur für Geoinformatik | |
Dewey Decimal Classification: | 9 Geschichte und Geografie / 91 Geografie, Reisen / 910 Geografie, Reisen |
Licence (German): | CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand) |