Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data
It is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based on consistent night...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/18/4632 |
| _version_ | 1797577191524925440 |
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| author | Neel Chaminda Withanage Kaifang Shi Jingwei Shen |
| author_facet | Neel Chaminda Withanage Kaifang Shi Jingwei Shen |
| author_sort | Neel Chaminda Withanage |
| collection | DOAJ |
| description | It is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based on consistent night light images. After the urban entities were extracted, a rationality assessment was carried out to compare the derived urban entities with the LandScan population product, Landsat, and road network results. Additionally, the results were compared with other physical extent products, such as the Moderate Resolution Imaging Spectrometer (MODIS) and urban built-up area products (HE) products. According to the findings, the urban entities were basically consistent with the LandScan, road network, and HE and MODIS products. However, the urban entities more accurately reflected the concentration of human activities than did the impervious extents of the MODIS and HE products. At the prefecture levels, the area of urban entities increased from 8082 km<sup>2</sup> to 74,417 km<sup>2</sup> between 2000 and 2020, showing an average growth rate of 10.8% over those twenty years. As a reliable supplementary resource and guide for urban mapping, this research will inform new research on the K-means algorithm and on variations in NTL data brightness threshold dynamics at regional and global scales. |
| first_indexed | 2024-03-10T22:04:35Z |
| format | Article |
| id | doaj.art-a0a8b4e01f664adca8a94af58a316f68 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:04:35Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a0a8b4e01f664adca8a94af58a316f682023-11-19T12:50:29ZengMDPI AGRemote Sensing2072-42922023-09-011518463210.3390/rs15184632Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like DataNeel Chaminda Withanage0Kaifang Shi1Jingwei Shen2School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaKey Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Normal University, Wuhu 241002, ChinaSchool of Geographical Sciences, Southwest University, Chongqing 400715, ChinaIt is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based on consistent night light images. After the urban entities were extracted, a rationality assessment was carried out to compare the derived urban entities with the LandScan population product, Landsat, and road network results. Additionally, the results were compared with other physical extent products, such as the Moderate Resolution Imaging Spectrometer (MODIS) and urban built-up area products (HE) products. According to the findings, the urban entities were basically consistent with the LandScan, road network, and HE and MODIS products. However, the urban entities more accurately reflected the concentration of human activities than did the impervious extents of the MODIS and HE products. At the prefecture levels, the area of urban entities increased from 8082 km<sup>2</sup> to 74,417 km<sup>2</sup> between 2000 and 2020, showing an average growth rate of 10.8% over those twenty years. As a reliable supplementary resource and guide for urban mapping, this research will inform new research on the K-means algorithm and on variations in NTL data brightness threshold dynamics at regional and global scales.https://www.mdpi.com/2072-4292/15/18/4632impervious extentsnighttime light dataprefecture citiesSNPP-VIIRS-like dataurban entities |
| spellingShingle | Neel Chaminda Withanage Kaifang Shi Jingwei Shen Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data Remote Sensing impervious extents nighttime light data prefecture cities SNPP-VIIRS-like data urban entities |
| title | Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data |
| title_full | Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data |
| title_fullStr | Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data |
| title_full_unstemmed | Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data |
| title_short | Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data |
| title_sort | extracting and evaluating urban entities in china from 2000 to 2020 based on snpp viirs like data |
| topic | impervious extents nighttime light data prefecture cities SNPP-VIIRS-like data urban entities |
| url | https://www.mdpi.com/2072-4292/15/18/4632 |
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