Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China
With the unprecedented urbanization processes around the world, cities have become the main areas of political, cultural, and economic creation, but these regions have also caused environmental degradation and even affected public health. Ecological landscape is considered as an important way to mit...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/14/3360 |
| _version_ | 1797416144779345920 |
|---|---|
| author | Dongxin Wen Maochou Liu Zhaowu Yu |
| author_facet | Dongxin Wen Maochou Liu Zhaowu Yu |
| author_sort | Dongxin Wen |
| collection | DOAJ |
| description | With the unprecedented urbanization processes around the world, cities have become the main areas of political, cultural, and economic creation, but these regions have also caused environmental degradation and even affected public health. Ecological landscape is considered as an important way to mitigate the impact of environmental exposure on urban residents. Therefore, quantifying the quality of urban road landscape and exploring its spatial heterogeneity to obtain basic data on the urban environment and provide ideas for urban residents to improve the environment will be a meaningful preparation for further urban planning. In this study, we proposed a framework to achieve automatic quantifying urban street quality by integrating a mass of street view images based on deep learning and landscape ecology. We conducted a case study in Xiamen Island and mapped a series of spatial distribution for ecological indicators including PLAND, LPI, AI, DIVISION, FRAC_MN, LSI and SHDI. Additionally, we quantified street quality by the entropy weight method. Our results showed the streetscape quality of the roundabout in Xiamen was relatively lower, while the central urban area presented a belt-shaped area with excellent landscape quality. We suggested that managers could build vertical greening on some streets around the Xiamen Island to improve the street quality in order to provide greater well-being for urban residents. In this study, it was found that there were still large uncertainties in the mechanism of environmental impact on human beings. We proposed to strengthen the in-depth understanding of the mechanism of environmental impact on human beings in the process of interaction between environment and human beings, and continue to form general models to enhance the ability of insight into the urban ecosystem. |
| first_indexed | 2024-03-09T05:59:07Z |
| format | Article |
| id | doaj.art-68ea57e3bbbb4804acde80e939d8168c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:59:07Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-68ea57e3bbbb4804acde80e939d8168c2023-12-03T12:10:50ZengMDPI AGRemote Sensing2072-42922022-07-011414336010.3390/rs14143360Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, ChinaDongxin Wen0Maochou Liu1Zhaowu Yu2College of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry and Ecology in South China, Central South University of Forestry and Technology, Changsha 410004, ChinaDepartment of Environmental Science and Engineering, Fudan University, Songhu Road 2005, Shanghai 200438, ChinaWith the unprecedented urbanization processes around the world, cities have become the main areas of political, cultural, and economic creation, but these regions have also caused environmental degradation and even affected public health. Ecological landscape is considered as an important way to mitigate the impact of environmental exposure on urban residents. Therefore, quantifying the quality of urban road landscape and exploring its spatial heterogeneity to obtain basic data on the urban environment and provide ideas for urban residents to improve the environment will be a meaningful preparation for further urban planning. In this study, we proposed a framework to achieve automatic quantifying urban street quality by integrating a mass of street view images based on deep learning and landscape ecology. We conducted a case study in Xiamen Island and mapped a series of spatial distribution for ecological indicators including PLAND, LPI, AI, DIVISION, FRAC_MN, LSI and SHDI. Additionally, we quantified street quality by the entropy weight method. Our results showed the streetscape quality of the roundabout in Xiamen was relatively lower, while the central urban area presented a belt-shaped area with excellent landscape quality. We suggested that managers could build vertical greening on some streets around the Xiamen Island to improve the street quality in order to provide greater well-being for urban residents. In this study, it was found that there were still large uncertainties in the mechanism of environmental impact on human beings. We proposed to strengthen the in-depth understanding of the mechanism of environmental impact on human beings in the process of interaction between environment and human beings, and continue to form general models to enhance the ability of insight into the urban ecosystem.https://www.mdpi.com/2072-4292/14/14/3360landscape qualityecological exposomeurban renewaldeep learningXiamen Island |
| spellingShingle | Dongxin Wen Maochou Liu Zhaowu Yu Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China Remote Sensing landscape quality ecological exposome urban renewal deep learning Xiamen Island |
| title | Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China |
| title_full | Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China |
| title_fullStr | Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China |
| title_full_unstemmed | Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China |
| title_short | Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China |
| title_sort | quantifying ecological landscape quality of urban street by open street view images a case study of xiamen island china |
| topic | landscape quality ecological exposome urban renewal deep learning Xiamen Island |
| url | https://www.mdpi.com/2072-4292/14/14/3360 |
| work_keys_str_mv | AT dongxinwen quantifyingecologicallandscapequalityofurbanstreetbyopenstreetviewimagesacasestudyofxiamenislandchina AT maochouliu quantifyingecologicallandscapequalityofurbanstreetbyopenstreetviewimagesacasestudyofxiamenislandchina AT zhaowuyu quantifyingecologicallandscapequalityofurbanstreetbyopenstreetviewimagesacasestudyofxiamenislandchina |