The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas

The accurate and efficient extraction of urban areas is of great significance for better understanding of urban sprawl, built environment, economic activities, and population distribution. Night-Time Light (NTL) data have been widely used to extract urban areas. However, most of the existing NTL indexes are incapable of identifying non-luminous built-up areas. The high-resolution NTL imagery derived from the Luojia 1-01 satellite, with low saturation and the blooming effect, can be used to map urban areas at a finer scale. A new urban spectral index, named the Modified Normalized Urban Areas Composite Index (MNUACI), improved upon the existing Normalized Urban Areas Composite Index (NUACI), was proposed in this study, which integrated the Human Settlement Index (HSI) generated from Luojia 1-01 NTL data, the Normalized Difference Vegetation Index (NDVI) from Landsat 8 imagery, and the Modified Normalized Difference Water Index (MNDWI). Our results indicated that MNUACI improved the spatial variability and differentiation of urban components by eliminating the NTL blooming effect and increasing the variation of the nighttime luminosity. Compared to urban area classification from Landsat 8 data, the MNUACI yielded better accuracy than NTL, NUACI, HSI, and the EVI-Adjusted NTL Index (EANTLI) alone. Furthermore, the quadratic polynomial regression analysis showed the model based on MNUACI had the best R2 and Root-Mean Square Error (RMSE) compared with NTL, NUACI, HSI, and EANTLI in terms of estimation of impervious surface area. It is concluded that MNUACI could improve the identification of urban areas and non-luminous built-up areas with better accuracy.

[1]  Minghong Tan,et al.  An Intensity Gradient/Vegetation Fractional Coverage Approach to Mapping Urban Areas From DMSP/OLS Nighttime Light Data , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Lu Qiao,et al.  Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery , 2020, Applied Energy.

[3]  Guo Zhang,et al.  Analysis and Reduction of Solar Stray Light in the Nighttime Imaging Camera of Luojia-1 Satellite , 2019, Sensors.

[4]  Bunkei Matsushita,et al.  An easily implemented method to estimate impervious surface area on a large scale from MODIS time-series and improved DMSP-OLS nighttime light data , 2017 .

[5]  Peijun Li,et al.  A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis , 2018 .

[6]  P. Sutton,et al.  Creating a Global Grid of Distributed Fossil Fuel CO 2 Emissions from Nighttime Satellite Imagery , 2010 .

[7]  Dengsheng Lu,et al.  Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .

[8]  Fei Li,et al.  A POI and LST Adjusted NTL Urban Index for Urban Built-Up Area Extraction , 2020, Sensors.

[9]  Osamu Higashi,et al.  A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data , 2009 .

[10]  L. Tian,et al.  Detecting spatiotemporal dynamics of PM2.5 emission data in China using DMSP-OLS nighttime stable light data , 2019, Journal of Cleaner Production.

[11]  Noam Levin,et al.  A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas , 2017 .

[12]  Shenglu Zhou,et al.  Determining the contributions of urbanisation and climate change to NPP variations over the last decade in the Yangtze River Delta, China. , 2014, The Science of the total environment.

[13]  T. Yue,et al.  Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China. , 2019, The Science of the total environment.

[14]  Guohua Liu,et al.  Urban Land Extraction Using DMSP/OLS Nighttime Light Data and OpenStreetMap Datasets for Cities in China at Different Development Levels , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Wenjian Ni,et al.  Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data , 2018 .

[16]  Joshua T. Kelly,et al.  Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[18]  Hongwei Xu,et al.  Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data , 2018, Remote. Sens..

[19]  Michael Jendryke,et al.  A preliminary investigation of Luojia-1 night-time light imagery , 2019, Remote Sensing Letters.

[20]  P. Jia,et al.  Urban sprawl and childhood obesity , 2020, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[21]  L. Zhuo,et al.  An EVI-based method to reduce saturation of DMSP/OLS nighttime light data , 2015 .

[22]  Yonghua Jiang,et al.  Star-Based Calibration of the Installation Between the Camera and Star Sensor of the Luojia 1-01 Satellite , 2019, Remote. Sens..

[23]  Zhifeng Liu,et al.  Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods , 2017, Remote. Sens..

[24]  Wan Hou,et al.  Data Fusion and Accuracy Analysis of Multi-Source Land Use/Land Cover Datasets along Coastal Areas of the Maritime Silk Road , 2019, ISPRS Int. J. Geo Inf..

[25]  Yaping Yang,et al.  Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques , 2015, Remote. Sens..

[26]  G. Lin,et al.  Participatory urban redevelopment in Chinese cities amid accelerated urbanization: Symbolic urban governance in globalizing Shanghai , 2018, Journal of Urban Affairs.

[27]  A. Stein,et al.  Earth Observation: Investigating Noncommunicable Diseases from Space. , 2019, Annual review of public health.

[28]  B. Portnov,et al.  Outdoor light and breast cancer incidence: a comparative analysis of DMSP and VIIRS-DNB satellite data , 2017, Remote Sensing of Night-time Light.

[29]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[30]  Lin Zhu,et al.  Evaluation of resources and environmental carrying capacity of 36 large cities in China based on a support-pressure coupling mechanism. , 2019, The Science of the total environment.

[31]  Xiya Zhang,et al.  Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification , 2015, Remote. Sens..

[32]  S. Running,et al.  Assessing the impact of urban land development on net primary productivity in the southeastern United States , 2003 .

[33]  Lei Deng,et al.  Application of UAV-Based Multi-Angle Hyperspectral Remote Sensing in Fine Vegetation Classification , 2019, Remote. Sens..

[34]  Yugang Tian,et al.  A Novel Index for Impervious Surface Area Mapping: Development and Validation , 2018, Remote. Sens..

[35]  Binge Cui,et al.  A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification , 2020, Remote. Sens..

[36]  Henrik Vejre,et al.  Strong contribution of rapid urbanization and urban agglomeration development to regional thermal environment dynamics and evolution , 2019, Forest Ecology and Management.

[37]  Osama A. Omer,et al.  A two-dimensional image segmentation method based on genetic algorithm and entropy , 2017 .

[38]  J. E. Dobson,et al.  LandScan: A Global Population Database for Estimating Populations at Risk , 2000 .

[39]  Xia Li,et al.  A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area , 2015, Remote. Sens..

[40]  Yaping Yang,et al.  A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL , 2017, Remote. Sens..

[41]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[42]  P. Jia,et al.  Walkability indices and childhood obesity: A review of epidemiologic evidence , 2020, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[43]  Michele Dalponte,et al.  Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data , 2019, PeerJ.