An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI
1
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
4
Big Data Institute of Digital Natural Disaster Monitoring in Fujian, Xiamen University of Technology, Xiamen 361024, China
5
College of Harbour and Environmental Engineering, Jimei University, Xiamen 361021, China
*
Author to whom correspondence should be addressed.
Academic Editor: Tilottama Ghosh
Remote Sens. 2021, 13(4), 766; https://doi.org/10.3390/rs13040766
Received: 30 December 2020 / Revised: 30 January 2021 / Accepted: 17 February 2021 / Published: 19 February 2021
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.
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Keywords:
DMSP-OLS nighttime light; logarithmic transformation; NDVI; NDWI; urban land
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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
MDPI and ACS Style
Zheng, Y.; Zhou, Q.; He, Y.; Wang, C.; Wang, X.; Wang, H. An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI. Remote Sens. 2021, 13, 766. https://doi.org/10.3390/rs13040766
AMA Style
Zheng Y, Zhou Q, He Y, Wang C, Wang X, Wang H. An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI. Remote Sensing. 2021; 13(4):766. https://doi.org/10.3390/rs13040766
Chicago/Turabian StyleZheng, Yuanmao; Zhou, Qiang; He, Yuanrong; Wang, Cuiping; Wang, Xiaorong; Wang, Haowei. 2021. "An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI" Remote Sens. 13, no. 4: 766. https://doi.org/10.3390/rs13040766
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