Version 1
: Received: 6 July 2023 / Approved: 7 July 2023 / Online: 7 July 2023 (10:32:29 CEST)
How to cite:
Withanage, N.C.; Shi, K.; Shen, J. Extracting and Evaluating China’s Urban Entities From 2000 to 2020 Based on Snpp-Viirs-Like Data. Preprints2023, 2023070488. https://doi.org/10.20944/preprints202307.0488.v1
Withanage, N.C.; Shi, K.; Shen, J. Extracting and Evaluating China’s Urban Entities From 2000 to 2020 Based on Snpp-Viirs-Like Data. Preprints 2023, 2023070488. https://doi.org/10.20944/preprints202307.0488.v1
Withanage, N.C.; Shi, K.; Shen, J. Extracting and Evaluating China’s Urban Entities From 2000 to 2020 Based on Snpp-Viirs-Like Data. Preprints2023, 2023070488. https://doi.org/10.20944/preprints202307.0488.v1
APA Style
Withanage, N.C., Shi, K., & Shen, J. (2023). Extracting and Evaluating China’s Urban Entities From 2000 to 2020 Based on Snpp-Viirs-Like Data. Preprints. https://doi.org/10.20944/preprints202307.0488.v1
Chicago/Turabian Style
Withanage, N.C., Kaifang Shi and Jingwei Shen. 2023 "Extracting and Evaluating China’s Urban Entities From 2000 to 2020 Based on Snpp-Viirs-Like Data" Preprints. https://doi.org/10.20944/preprints202307.0488.v1
Abstract
In the recent past, China has experienced rapid urbanization as a result of diverse growth factors. In such a context, it is crucial to evaluate the expansion of urban entities in order to implement sustainable urban planning strategies in China. Since, urban entities are the spatial reflection of the concentration of human activities, the delineation of urban areas upon the boundaries of built-up surfaces has resulted from inconsistent understanding and identification of urban areas. The study has attempted to extract and evaluate the growth of urban entities in 336 prefecture cities in China mainland (2000-2020) upon a novel approach using consistent night light images. The urban entities were extracted using the light intensities of the SNPP-VIIRS-like data. After extracting urban entities, a rationality assessment was carried out comparing derived urban entities with the LandScan population product, Landsat, and road networks. Also, the results were compared with other physical extents products such as MODIS and the HE. According to the findings, urban entities are basically consistent with the LandScan, road networks, and those with the HE and MODIS. But, urban entities accurately reflect the concentration of human activities than impervious extents of MODIS and the HE. At the prefecture levels, urban entities elevated from 8082 km2 to 74,417 km2 between 2000 and 2020 showing a 10.8% growth rate. By providing a supplementary resource and guide for trustworthy urban mapping, the research will expand new research directions that address the issues of variations of NTL data brightness thresholds dynamics on regional, and global scales.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
