Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example
Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban gre...
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| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/3946 |
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| author | Bin Li Xiaotian Xu Hongyu Wang Yingrui Duan Hongjuan Lei Chenchen Liu Na Zhao Xu Liu Shaoning Li Shaowei Lu |
| author_facet | Bin Li Xiaotian Xu Hongyu Wang Yingrui Duan Hongjuan Lei Chenchen Liu Na Zhao Xu Liu Shaoning Li Shaowei Lu |
| author_sort | Bin Li |
| collection | DOAJ |
| description | Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation. |
| format | Article |
| id | doaj-art-e3884d791982406f9cdccc366675f0bd |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e3884d791982406f9cdccc366675f0bd2024-11-08T14:40:17ZengMDPI AGRemote Sensing2072-42922024-10-011621394610.3390/rs16213946Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an ExampleBin Li0Xiaotian Xu1Hongyu Wang2Yingrui Duan3Hongjuan Lei4Chenchen Liu5Na Zhao6Xu Liu7Shaoning Li8Shaowei Lu9Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaRemote Sensing Application Center, China Academy of Urban Planning & Design, Beijing 100835, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaInstitute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, ChinaUrban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.https://www.mdpi.com/2072-4292/16/21/3946urban green spacehigh-resolution remote sensingmachine learninggreen space information analysisevaluation system |
| spellingShingle | Bin Li Xiaotian Xu Hongyu Wang Yingrui Duan Hongjuan Lei Chenchen Liu Na Zhao Xu Liu Shaoning Li Shaowei Lu Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example Remote Sensing urban green space high-resolution remote sensing machine learning green space information analysis evaluation system |
| title | Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example |
| title_full | Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example |
| title_fullStr | Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example |
| title_full_unstemmed | Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example |
| title_short | Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example |
| title_sort | analysis and comprehensive evaluation of urban green space information based on gaofen 7 considering beijing s fifth ring area as an example |
| topic | urban green space high-resolution remote sensing machine learning green space information analysis evaluation system |
| url | https://www.mdpi.com/2072-4292/16/21/3946 |
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