Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping
Blue spaces (or water bodies) have a positive impact on the built-up environment and human health. Various open and high-resolution land-use/land-cover (LULC) datasets may be used for mapping blue space, but they have rarely been quantitatively evaluated and compared. Moreover, few studies have inve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5764 |
| _version_ | 1797464097622589440 |
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| author | Qi Zhou Xuanqiao Jing |
| author_facet | Qi Zhou Xuanqiao Jing |
| author_sort | Qi Zhou |
| collection | DOAJ |
| description | Blue spaces (or water bodies) have a positive impact on the built-up environment and human health. Various open and high-resolution land-use/land-cover (LULC) datasets may be used for mapping blue space, but they have rarely been quantitatively evaluated and compared. Moreover, few studies have investigated whether existing 10-m-resolution LULC datasets can identify water bodies with widths as narrow as 10 m. To fill these gaps, this study evaluates and compares four LULC datasets (ESRI, ESA, FROM-GLC10, OSM) for blue space mapping in Great Britain. First, a buffer approach is proposed for the extraction of water bodies of different widths from a reference dataset. This approach is applied to each LULC dataset, and the results are compared in terms of accuracy, precision, recall, and the F1-score. We find that a high median accuracy (i.e., >98%) is achieved with all four LULC datasets. The OSM dataset gives the best recall and F1-score. Both the ESRI and ESA datasets produce better results than the FORM-GLC10 dataset. Additionally, the OSM dataset enables the identification of water bodies with widths of 10 m, whereas only water bodies with widths of 20 m or more can be identified in the other datasets. These findings may be beneficial for urban planners and designers in selecting an appropriate LULC dataset for blue space mapping. |
| first_indexed | 2024-03-09T18:02:05Z |
| format | Article |
| id | doaj.art-7982e67e718a44fdb42d2fdef1d6cce7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:02:05Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7982e67e718a44fdb42d2fdef1d6cce72023-11-24T09:50:05ZengMDPI AGRemote Sensing2072-42922022-11-011422576410.3390/rs14225764Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space MappingQi Zhou0Xuanqiao Jing1School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaBlue spaces (or water bodies) have a positive impact on the built-up environment and human health. Various open and high-resolution land-use/land-cover (LULC) datasets may be used for mapping blue space, but they have rarely been quantitatively evaluated and compared. Moreover, few studies have investigated whether existing 10-m-resolution LULC datasets can identify water bodies with widths as narrow as 10 m. To fill these gaps, this study evaluates and compares four LULC datasets (ESRI, ESA, FROM-GLC10, OSM) for blue space mapping in Great Britain. First, a buffer approach is proposed for the extraction of water bodies of different widths from a reference dataset. This approach is applied to each LULC dataset, and the results are compared in terms of accuracy, precision, recall, and the F1-score. We find that a high median accuracy (i.e., >98%) is achieved with all four LULC datasets. The OSM dataset gives the best recall and F1-score. Both the ESRI and ESA datasets produce better results than the FORM-GLC10 dataset. Additionally, the OSM dataset enables the identification of water bodies with widths of 10 m, whereas only water bodies with widths of 20 m or more can be identified in the other datasets. These findings may be beneficial for urban planners and designers in selecting an appropriate LULC dataset for blue space mapping.https://www.mdpi.com/2072-4292/14/22/5764water bodyland coverland useopen dataOpenStreetMap |
| spellingShingle | Qi Zhou Xuanqiao Jing Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping Remote Sensing water body land cover land use open data OpenStreetMap |
| title | Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping |
| title_full | Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping |
| title_fullStr | Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping |
| title_full_unstemmed | Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping |
| title_short | Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping |
| title_sort | evaluation and comparison of open and high resolution lulc datasets for urban blue space mapping |
| topic | water body land cover land use open data OpenStreetMap |
| url | https://www.mdpi.com/2072-4292/14/22/5764 |
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