International Journal of Applied Earth Observation and Geoinformation
Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data
Introduction
Impervious surfaces are generally defined as artificial structures that impede the infiltration of water into the underlying soil, e.g. roads, rooftops, and parking lots (Weng, 2012; Wu, 2009; Yang et al., 2003). They are of great importance to human beings, not only being a significant indicator for the level of urbanization, but also playing a key role in the change of urban environment (Yang et al., 2012; Deng et al., 2012). With their construction, the impervious surfaces can affect hydrological systems through sealing the soil surface, avoiding rainwater infiltration and natural groundwater recharge (Brabec et al., 2002; Jacobson, 2011; Lu and Weng, 2006). Besides, the transformation of natural surfaces into impervious areas has an impact on the land surface energy balance, inducing an increase in the air temperature (e.g. urban heat islands) (Jr and Gibbons, 1996; Wang et al., 2016; Weng and Lu, 2008; Wilson et al., 2003). Given the close concern with human activity, the detection of impervious surfaces (including the extent and degree) is vital for monitoring urbanization dynamics as well as analyzing impacts on urban environment.
Currently, remote sensing technology is one of the most effective approaches to detecting the impervious surfaces since it can provide accurate information on the surfaces spatially and temporally (Lu et al., 2011; Parece and Campbell, 2013; Zhang et al., 2012). Many studies have been carried out to obtain the impervious surfaces at various spatial and temporal scales based on remote sensing satellite images. For example, Zhou and Wang (2008) used a high-resolution imagery (QuickBird-2) for the extraction of impervious surface areas. Sexton et al. (2013) focused on time series of Landsat images for retrieving long-term records of impervious surface cover based on an empirical method. Deng and Wu (2013) also developed a compositive approach of machine learning techniques and spectral mixture analysis to obtain the impervious surfaces using the single-date Moderate Resolution Imaging Spectroradiometer (MODIS) image. In another study using multispectral optical data and dual polarization synthetic aperture radar (SAR) data, Zhang et al. (2016) presented a comparative study to identify urban impervious surfaces in a study site. Moreover, based on the imagery collected with various satellites, several well-known datasets, e.g., the High Resolution Layer—Imperviousness (HRL-I) system produced for the European Union (Kuntz et al., 2014) and the Global Man-made Impervious Surface (GMIS) Dataset developed by NASA Socioeconomic Data and Applications Center (Brown De Colstoun et al., 2017) have been built and provided available estimates of impervious surfaces at large scale.
In addition to the daytime imagery, satellite-observations of nighttime lights have also been applied to impervious surface estimation (Liu et al., 2015; Zhuo et al., 2018). The nighttime light imagery, mainly derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), can well detect the anthropogenic lights at night (Imhoff et al., 2012; Small and Elvidge, 2013). Since the nighttime light illumination mostly originates from artificial sources that are closely related to human activities, the DMSP-OLS nightlight imagery can be potentially useful for the measurements of impervious surfaces based on the location and relative intensity of light sources (Pok et al., 2017). For example, using DMSP-OLS nightlight imagery, previous studies by Imhoff et al. (1997); Elvidge et al. (1999); Small et al. (2005), and Ma et al. (2012) approximately mapped the spatial extents of urban areas in different ways. Elvidge et al. (2007) and Sutton et al. (2009) further found that there was a positive relationship between imperviousness degree and light intensity, and proved that nighttime light data was appropriate in detecting impervious surfaces. In spite of that, DMSP-OLS nightlight imagery has some well-known shortcomings (Ou et al., 2015), e.g., coarse spatial resolution (about 1 km), blooming effect (spatial overextension of lighted areas), and saturation in urban cores, which always result in the overestimates for the spatial extents of impervious surface areas (Letu et al., 2012).
As a successor to the DMSP-OLS sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) satellite has offered a series of high-quality imagery of Day/Night Band nighttime lights since 2012. The NPP-VIIRS nightlight imagery has significant improvements, including the wider measurement range, higher spatial resolution, and on-board calibration, which partially eliminate the limitations existing in DMSP-OLS data (Shi et al., 2014a; Zhang et al., 2017). For example, based on a study of 12 cities in China, Shi et al. (2014b) found that the urban areas derived from NPP-VIIRS nighttime light data displayed higher accuracies compared with those from DMSP-OLS data. In another study, Yu et al. (2018) proved that NPP-VIIRS data have better capability in urban built-up area mapping by using a logarithmic transformation method. Besides, NPP-VIIRS nightlight imagery was found to be integrated with other data such as MODIS normalized difference vegetation index (NDVI) for mapping the distributions of impervious surface area accurately (Guo et al., 2015). The literature review shows that NPP-VIIRS nightlight imagery is widely applied in impervious surface estimations, but mostly limited to be at a moderate spatial resolution (about 750 m).
Now, Luojia 1-01, a new generation of nighttime light remote sensing satellite developed by Wuhan University in China, was successfully launched on 2 June 2018. The nighttime light imagery generated from Luojia 1-01 satellite supplement the existing nightlight data with the image features in regard to fine spatial resolution (about 130 m) and high radiometric quantization (14 bits). Compared to the NPP-VIIRS data, the spatial resolution of Luojia 1-01 data has greatly improved with on-board calibration, which can show more spatial details of light sources (Zhang et al., 2019). Besides, the Luojia 1-01 data does not suffer the problems of saturation and blooming which exists in DMSP-OLS data (Li et al., 2019). The advantages of this new data can significantly enhance the detection capacity of artificial lightings, thus bringing new insights and possibilities to the researches on urban and environment. Currently, several studies have employed Luojia 1-01 data to estimate the artificial light pollution and urban extent mapping, and demonstrated that Luojia 1-01 nightlight imagery probably provide higher capacity in comparison with NPP-VIIRS nighttime light data. For example, through assessing the sources and patterns of artificial light pollution with nighttime light data, Jiang et al. (2018) confirmed that Luojia 1-01 data can be usefully applied for investigating urban light pollution. In another study using the Luojia 1-01 data, Li et al. (2018) compared several methods for mapping urban areas, and also found that Luojia 1-01 data can result in better extraction results than NPP-VIIRS data. Unfortunately, they only focused on the extraction of spatial extent and ignored to estimate the imperviousness degree in urban areas. To the best of our knowledge, there is still no work that has examined the potential of Luojia 1-01 nightlight data for detecting impervious surfaces, especially the imperviousness degree. To better understand the quality of Luojia 1-01 nighttime light data as well as support further analysis in related studies of urban dynamics and environment, a comprehensive investigation of this new data is essential for impervious surface detection.
Thus, this study aims to assess the capability of Luojia 1-01 data for detecting the degree and extent of impervious surfaces in three cities of China, such as Beijing, Shanghai, and Guangzhou. For comparison, the NPP-VIIRS data is also used to examine the difference between two kinds of nighttime light data. Based on a reference data derived from Landsat 8 Operational Land Imager (OLI), the accuracy assessment is finally conducted to quantitatively measure the reliability of nighttime light data in impervious surface detection. This study is the first time that Luojia 1-01 nighttime light data is applied for an investigation of impervious surfaces, which will not only fill the gaps in the field of nighttime light research, but also provide useful support for government decision-making to plan the urban development and environmental management.
Section snippets
Study area
Three cities, namely Beijing, Shanghai, and Guangzhou, were selected as the study area for comparison purposes. Beijing, located in the northern part of North China Plain, is the political and cultural capital of China. It is made up of 14 districts and 2 rural counties with an administrative area of 16,410 km2. Shanghai, situated at the estuary of the Yangtze River and on the coast of the East Sea, is one of the economically fastest growing cities around the world. It has approximately 24.18
Methods for imperviousness detection
To estimate the spatial extent of the impervious surface, a dynamic threshold segmentation was adopted in this study. In this method, a threshold value is often used to segment impervious surface areas on nighttime light imagery (Shang et al., 2017; Small et al., 2005). Pixels with a value larger than or equal to the threshold are regarded as part of impervious surface areas. The suitable threshold for delineating impervious surface areas was determined through the following Equation 3 and 4:
Detection of extent of impervious surface
First, this study focus on the potentiality of Luojia 1-01 data in detecting the spatial extent of impervious surface areas regardless of the imperviousness degree. According to the above threshold segmentation method, the threshold value is an important factor for accurately extracting impervious surface areas from nighttime light imagery. To assess the effect of threshold selection on the extraction accuracy, a series of impervious surface maps based on Luojia 1-01 and NPP-VIIRS data were
Discussion
In this study, the Luojia 1-01 data, an important source of information on nighttime light intensity, were used to investigate its potentiality in impervious surface detection at urban scale. The first finding from experimental results is that the accuracy of Luojia 1-01 in extracting the spatial extent of impervious surface areas displays a pattern of firstly increasing and then decreasing with the increase of the DN value as threshold (see Fig. 3). This means that selecting an accurate
Conclusions
This study is the first to evaluate the ability of Luojia 1-01 nighttime light data in impervious surface detection. In this study, the nighttime light data obtained from Luojia 1-01 satellite was used as a data source for detecting the extent and degree of impervious surfaces in Beijing, Shanghai, and Guangzhou. The spatial extent of impervious surface areas was first derived from nighttime light imagery by applying a dynamic threshold segmentation method. Meanwhile, the polynomial regression
Acknowledgments
This research was funded by the National Key R&D Program of China (Grant No. 2017YFA0604404), National Natural Science Foundation of China (Grant No. 41671398, 41801304), China Postdoctoral Science Foundation (Grant No. 2018M633209), and Educational Commission of Guangdong Province of China (Grant NO. 2016KTSCX045).
References (51)
- et al.
The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques
ISPRS J. Photogramm.
(2013) - et al.
Radiance calibration of DMSP-OLS low-light imaging data of human settlements
Remote Sens. Environ.
(1999) - et al.
Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the United States
Remote Sens. Environ.
(1997) Identification and quantification of the hydrological impacts of imperviousness in urban catchments: a review
J. Environ. Manage.
(2011)- et al.
Impervious surface detection with nighttime photography from the International Space Station
Remote Sens. Environ.
(2016) - et al.
High-resolution multi-temporal mapping of global urban land using landsat images based on the google earth engine platform
Remote Sens. Environ.
(2018) - et al.
A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects
Landscape Urban Plan.
(2017) - et al.
Use of impervious surface in urban land-use classification
Remote Sens. Environ.
(2006) - et al.
Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: a case study in an urban-rural landscape in the Brazilian Amazon
ISPRS J. Photogramm.
(2011) - et al.
Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: a comparative case study from China’s cities
Remote Sens. Environ.
(2012)
An easily implemented method to estimate impervious surface area on a large scale from MODIS time-series and improved DMSP-OLS nighttime light data
ISPRS J. Photogramm.
Urban growth of the Washington, D.C.–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover
Remote Sens. Environ.
Night on Earth: mapping decadal changes of anthropogenic night light in Asia
Int. J. Appl. Earth Obs.
Spatial analysis of global urban extent from DMSP-OLS night lights
Remote Sens. Environ.
Characterizing the spatial dynamics of land surface temperature-impervious surface fraction relationship
Int. J. Appl. Earth Obs.
Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends
Remote Sens. Environ.
A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States
Int. J. Appl. Earth Obs.
Evaluating environmental influences of zoning in urban ecosystems with remote sensing
Remote Sens. Environ.
Estimating impervious surface distribution by spectral mixture analysis
Remote Sens. Environ.
Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan
ISPRS J. Photogramm.
A comparison study of impervious surfaces estimation using optical and SAR remote sensing images
Int. J. Appl. Earth Obs.
Mapping urban impervious surface with dual-polarimetric SAR data: an improved method
Landscape Urban Plan.
On-orbit geometric calibration and validation of luojia 1-01 night-light satellite
Remote Sens-Basel
An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data
ISPRS J. Photogramm.
Characterizing relationships between population density and nighttime imagery for Denver, Colorado: issues of scale and representation
Int. J. Remote Sens.
Cited by (66)
Enhancing SDGSAT-1 night light images using a panchromatic guidance denoising algorithm
2024, International Journal of Applied Earth Observation and GeoinformationHow does multiscale greenspace exposure affect human health? Evidence from urban parks in the central city of Beijing
2024, Journal of Environmental ManagementEnhancing nighttime light remote Sensing: Introducing the nighttime light background value (NLBV) for urban applications
2024, International Journal of Applied Earth Observation and GeoinformationImpacts of COVID-19 on urban networks: Evidence from a novel approach of flow measurement based on nighttime light data
2024, Computers, Environment and Urban SystemsSpatial heterogeneity of uncertainties in daily satellite nighttime light time series
2023, International Journal of Applied Earth Observation and Geoinformation