Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data

Int J Appl  Earth Obs Geoinformation 81 (2019) 1–12

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Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data

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Jinpei Ou, Xiaoping Liu , Penghua Liu, Xiaojuan Liu Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, 135 West Xingang Road, Guangzhou, 510275, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: Impervious surface Luojia 1-01 NPP-VIIRS Nighttime light

Impervious surface detection is significant to urban dynamic monitoring and environment management. One of the most effective approaches to evaluating the impervious surface is the use of nighttime light imagery. However, little work on this subject was carried out with the new generation nighttime light data from Luojia 101 satellite, which has a finer spatial resolution than the predecessors such as the nightlight data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite. Therefore, this study conducted the first investigation of the capacity of Luojia 1-01 nighttime light data in detecting the extent and degree of impervious surfaces. Focusing on three cities of Beijing, Shanghai, and Guangzhou, several maps of the spatial extent of impervious surface areas were first extracted from two types of nighttime lights data (Luojia 1-01 and NPP-VIIRS data) by applying a dynamic threshold segmentation method. Meanwhile, a series of polynomial regression models were adopted to estimate the relation between imperiousness degree and light intensity. The results compared with the reference data derived from Landsat 8 Operational Land Imager (OLI) show that Luojia 1-01 data can produce a more precise map of the spatial extent of impervious surfaces than NPP-VIIRS data owing to the finer spatial resolution and the wider measurement range. Nevertheless, Luojia 1-01 data failed to provide reliable estimates of the imperviousness degree in comparison with NPP-VIIRS data as this nighttime light imagery with finer spatial resolution can better discriminate the surfaces that have the same imperviousness degree but are illuminated with different light intensities, consequently resulting in a weak correlation between imperviousness degree and light intensity. The over- and under-estimates of imperviousness degree suggested an increase in spatial resolution of nightlight imagery does not always improve the accuracy and reliability of nighttime light-based estimations. These study results confirmed that Luojia 1-01 nightlight imagery is a potential and promising data source for mapping the spatial extent of impervious surface areas, but difficult to accurately estimate the imperviousness degree. Future research may improve the accuracy of imperviousness degree estimation by integrating the Luojia 1-01 nightlight imagery with other useful data sources.

1. 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

Corresponding author. E-mail address: [email protected] (X. Liu).

https://doi.org/10.1016/j.jag.2019.04.017 Received 17 December 2018; Received in revised form 31 March 2019; Accepted 25 April 2019 Available online 10 May 2019 0303-2434/ © 2019 Elsevier B.V. All rights reserved.

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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 DMSPOLS 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 NPPVIIRS 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.

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

2. Data preparation and processing 2.1. 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 million permanent residents in an administrative area of about 6340 km2 (Ou et al., 2017). With regard to Guangzhou, this city is located in the Pearl River Delta and serves as the economic, cultural, and industrial center of southern China. It consists of 10 administrative districts and 2 county-level cities and covers an area of approximately 7434.40 km2. In recent decades, these three cities have been experiencing fast economic growth and rapid urbanization, which has led to dramatic changes in urban spatial patterns of these megacities meanwhile immensely increased the pressure on the ecosystem(Liu et al., 2017). Therefore, the examination of impervious areas in these three cities is significant for monitoring urbanization dynamics and analyzing their impact on the environment.

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Fig. 1. Nighttime imagery from (a) Luojia 1-01 and (b) NPP-VIIRS.

2.2. Luojia 1-01

Table 1 Specifications of Luojia 1-01, NPP-VIIRS and Landsat 8 OLI data.

Several cloud-free Luojia1-01 data covering the study area were obtained from the High-Resolution Earth Observation System of the Hubei Data and Application Center (http://59.175.109.173:8888/, accessed in September 2018). As the Luojia1-01 images are only processed by system geometry correction and have a low positioning accuracy (ranging from 0.49 km to 0.93 km), these released images need to be geometrically corrected in order to accurately investigate impervious surface. In this study, an efficient process for precise geometric correction was performed on the basis of the study of Jiang et al. (2018). Firstly, 30 evenly distributed ground control points (GCPs) were manually collected from road intersections in each image owing to the clear observation of road network in the Luojia1-01 imagery resulted from its high spatial resolution. According to these GCPs, an ortho-rectification was then conducted with the help of Landsat 8 OLI. Finally, through geometric correction, the spatial distributions of Luojia1-01 nighttime light data in three cities are shown in Fig. 1(a).

Parameters

Luojia 1-01

NPP-VIIRS

Landsat 8 OLI

Spatial Resolution Swath width Spectrum Range Radiometric Resolution Available Period

130 m 250 km 0.46-0.98 μm 14 bits 2018-present

750 m 3060 km 0.5-0.9 μm 14 bits 2012-present

30 m (Band 8 = 15 m) 185 km 0.43-1.38 μm 12 bits 2013-present

2.4. Reference data derived from Landsat 8 OLI imagery Landsat 8 OLI is a push-broom sensor carried on NASA’s experimental EO-1 satellite. It collects data from nine spectral bands ranging from wavelengths of 0.43 μm–1.38 μm (Table 1). Seven of the nine bands are the visible and infrared spectrums, providing a measurement of the surface spectral reflectance at about 30 × 30 m resolution (Guo et al., 2015; Liu et al., 2018). This is in accordance with the sensors of Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) on earlier Landsat satellites. The other two new spectral bands are a deep blue coastal/aerosol band and a shortwave-infrared cirrus band, which serve the purposes of detecting water quality and cirrus clouds, respectively. The OLI imagery can provide significant improvements in the capacity of detecting changes on the surfaces owing to its data quality and radiometric quantization (12-bits) that are higher than previous instruments on Landsat satellites (e.g., 8-bit for TM and ETM +). Therefore, this study utilized Landsat 8 OLI to develop the impervious surface area which further served as a criterion for evaluating the performance of LuoJia 1-01 and NPP-VIIRS nighttime light data. The closest cloud-free images for the study area in 2018 were derived from the Geospatial Data Cloud site, Computer Network Information Center at Chinese Academy of Sciences (http://www.gscloud.cn). Through a common method of spectral mixture analysis (Wu and Murray, 2003), the impervious surface areas were derived from these Landsat 8 OLI data. The distributions of impervious surface in each city are shown in Fig. 2.

2.3. NPP-VIIRS As NPP-VIIRS nighttime light data has significant improvements compared to DMSP-OLS data, this study adopted the NPP-VIIRS data to compare the capability of Luojia1-01 data in impervious surface detection. The NPP-VIIRS data, produced by the Earth Observations Group (EOG) at the National Centers for Environmental Information (NCEI) of National Oceanic and Atmospheric Administration (NOAA), is a suite of average radiance composite images which collect persistent lights from towns, cities and other sites. This product spans the globe from 75 N latitude to 65S with a spatial resolution of 15 arc-second geographic grids. As shown in Fig. 1(b), the images of September 2018 in the three cities were derived from the website of EOG (https:// ngdc.noaa.gov/eog/viirs/download_dnb_composites.html), and further used for comparison with LuoJia1-01 data in impervious surface detection. The comparison of parameters for NPP-VIIRS and Luojia 1-01 data are shown in Table 1.

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Fig. 2. The spatial distributions of (a) Landsat 8 OLI and (b) impervious surface areas in each city.

y = α 0 + α1 x + α2 x 2 + ⋯+αn x n

3. Methods for imperviousness detection

where y is the dependent variable in terms of imperiousness degree; x is the independent variable related to the value of light intensity; α denotes the parameter; n refers to the order of the polynomial. Here a group of polynomial regression models from first-order to ninth-order were developed to evaluate the relationship between imperiousness degree and light intensity of nighttime light imagery.

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:

4. Results

DNmin

ISALight =

∑

DN

4.1. Detection of extent of impervious surface

ADNj

DNj = DNmax

ISALightj − ISAStat ≤ εmin

(3)

(1) 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 produced by using a different DN value as the threshold. The spatial accuracy of these extracted impervious surface areas was evaluated by compared with the reference results derived from Landsat 8 OLI. Since the reference results have different extents of impervious surface areas resulted from different imperviousness degrees, several maps with the degrees of imperviousness larger than a given value (such as > 10%, > 30%, > 50%, and > 80%) were create and served as actual reference data. Through a point-to-point comparison, a Kappa coefficient was used to quantitatively measure the performance of impervious surface detection from two types of nighttime light imageries. Fig. 3 presents the accuracy changes of the impervious surface areas extracted from Luojia 1-01 and NPP-VIIRS data with different DN values in each city. It can be clearly found that the curves of Kappa coefficient in all images display a pattern of single peak for different impervious surface areas. Specifically, the accuracy of impervious surface area extracted from Luojia 1-01 nightlight imagery is firstly increased and then decreased with the increase of the DN value, compared with the reference data of imperviousness > 10% in Beijing city. When the DN value is equal to 7785, the maximum value of Kappa

(2)

where DNmin and DNmax are the minimum and maximum value of nighttime light imagery, respectively; and DNj is one step value from DNmax down to DNmin. ADNj is the area of impervious surface obtained from the pixels with the value DNj in the nighttime light imagery; and DN ISA Lighti is the accumulative impervious surface area based on the nighttime light imagery at the value DNj. ISAStat represents the area size of impervious surface derived from Landsat data. εmin denotes the DNj minimum difference between ISA Light and ISAStat for all step values from DNmax to DNmin. The optimal threshold for delineating impervious surface area can be found at the point where Eq. (2) was achieved. Based on the optimal threshold value, the spatial extents of the impervious surface areas are extracted from nighttime light imagery. In addition to the spatial extent, the degree of imperviousness is the other important characteristic of impervious surfaces that also need to be estimated in this study. As the relation between imperviousness degree and light intensity may be nonlinear (Kotarba and Aleksandrowicz, 2016), a polynomial regression model was used to investigate the degree of imperiousness based on two types of nighttime light imageries. Polynomial regression fits a relationship between the independent variable and the dependent variable modelled as an nth degree polynomial, and has been widely used to describe nonlinear phenomena (Chen et al., 2015). In general, the form of polynomial regression model can be expressed as follows. 4

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Fig. 3. Accuracy changes of spatial extent extraction of impervious surface areas from Luojia 1-01 and NPP-VIIRS nighttime light imageries in three cities: (a) Beijing; (b) Shanghai; and (c) Guangzhou.

Fig. 4. The variation of maximum Kappa coefficients of impervious surface area extracted from Luojia 1-01 and NPP-VIIRS with the increase in minimum detectable imperviousness.

the spatial extent of impervious surface with the imperviousness > 10%, the maximum Kappa coefficients of impervious surface area extracted from Luojia 1-01 nighttime light imagery are 0.6316, 0.549, and 0.6096 in Beijing, Shanghai, and Guangzhou, respectively. When detecting the impervious surface area with the imperviousness > 80%, they separately decrease to 0.384, 0.2638, and 0.3578 in these three cities. Similarly, the downward trend is also consistent with the extracted results based on NPP-VIIRS data, showing that an increase in the minimum detectable imperviousness always results in a decrease in the highest accuracy of impervious surface extraction. Apart from the similar downward trends, the highest accuracy of impervious surface area extractions displays different values in different cities by comparing the results from Luojia 1-01 and NPP-VIIRS data. As presented in Fig. 4, for detecting the spatial extent of

coefficient is observed to be 0.6316. Similar results are also occurred in the impervious surface areas based on NPP-VIIRS nighttime light data. As shown in the curve representing the NPP-VIIRS-based results of 10–100% imperviousness range in Beijing (Fig. 3), the value of Kappa coefficient firstly increases to 0.632 and then decreases to 0 with the increase of DN value. Notably, the curve peaks of impervious surface areas extracted from Luojia 1-01 data likely appear at the DN values ranging from 5000 to 30000, while the peaks of extracted results using NPP-VIIRS data are mainly located in a small range of DN values (1–30). This indicates that Luojia 1-01 data can provide more variability and finer spatial details of nighttime lights than NPP-VIIRS data. Furthermore, it is observed that the highest accuracy of impervious surface extraction will decrease if the minimum degree of imperviousness in the reference data increases. As shown in Fig. 4, for detecting 5

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Fig. 5. Spatial distributions of impervious surface areas extracted from Luojia 1-01 and NPP-VIIRS data at the imperviousness ranges of 10–100% in three cities: (a) Beijing; (b) Shanghai; and (c) Guangzhou.

city. Under the situation of same area sizes, quantitative indices including F-score and Kappa coefficient were then used to estimate the spatial accuracy of impervious surface area extraction by means of point-to-point comparison. The F-score is a measure of a test’s accuracy, which considers both the precision and the recall of the test to compute the score. The F-score is generally calculated as the harmonic average of the precision and recall, where an F-score reaches its best value at 1 (perfect precision and recall) and worst at 0. Figs. 5–8 present the spatial distributions of impervious surface areas extracted from Luojia 1-01 and NPP-VIIRS data at different imperviousness ranges. All the extracted results based on Luojia 1-01 and NPP-VIIRS data have a lower and lower accuracy with the increase in the minimum value of detectable imperviousness. In urban regions of each city, it is also observed that results extracted two types of nighttime light imageries exhibit similar spatial patterns of correct pixels. However, impervious surface areas extracted from Luojia 1-01 data present less omission errors than those from NPP-VIIRS in regions away from urban center. In addition, Table 2 shows that the F-score and Kappa coefficient of impervious surface area extractions from Luojia 101 data are higher than those of the NPP-VIIRS based results in these three cities regardless of which imperviousness range to be detected. For example, in terms of impervious surface area with imperviousness > 10% in Beijing city (Impervious area = 4036.7997 km2), the Fscore and Kappa coefficient of results extracted from Luojia 1-01 data (threshold = 7092) are 0.721 and 0.6297, respectively, both of which are larger than the corresponding values from NPP-VIIRS data (threshold = 7.38). The accuracy assessment indicates Luojia 1-01 data display spatial patterns of impervious surface areas that are more

impervious surface areas with any minimum degree of imperviousness in Shanghai city, all the maximum Kappa coefficients of impervious surface areas extracted from Luojia 1-01 data are less than those of the extracted results from NPP-VIIRS data. Inversely, when the same experiments of impervious surface detection were carried out in Guangzhou city, all the maximum Kappa coefficients of impervious surface areas extracted from Luojia 1-01 data are larger than those of the extracted results based on NPP-VIIRS data. This difference is mainly due to the accuracy assessment of impervious surface extraction only considered the best spatial agreement and ignored the area sizes in accordance with the actual reference data. For example, for detecting the spatial extent of impervious surface area with the imperviousness > 10% in Shanghai city, the area sizes of impervious surfaces extracted from Luojia 1-01 and NPP-VIIRS data are 2842.9 km2 and 4629.25 km2, respectively, when both their Kappa coefficients reach the maximum value. Although the impervious surface area derived from NPP-VIIRS nighttime light data has a higher spatial accuracy than that from Luojia 1-01 data, its area size is much larger than that of the reference data with imperviousness > 10% (2836.21 km2). The difference of area sizes between the extracted results inevitably leads to the difficulty in comparing the performance of two types of nighttime light imageries in detecting the spatial extent of impervious surface areas. For comparison, this study further determined the optimal threshold value to make the area sizes of impervious surface extraction from nighttime light imagery consistent with the areas of the actual reference data. Based on Eq. (2), the optimal threshold value that can produced minimum difference of area sizes between extracted result and reference data was selected for impervious surface area extraction in each 6

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Fig. 6. Spatial distributions of impervious surface areas extracted from Luojia 1-01 and NPP-VIIRS data at the imperviousness ranges of 30–100% in three cities: (a) Beijing; (b) Shanghai; and (c) Guangzhou.

have fluctuant values of R2 and RMSE in three cities. For the models based on Luojia 1-01 data, the best estimate was achieved with a third polynomial fit, of which the R2 reaches a maximum value of 0.4667 in Beijing, 0.2167 in Shanghai, and 0.3405 in Guangzhou. The corresponding values of RMSE are 0.1894, 0.2986, and 0.2264 in Beijing, Shanghai, and Guangzhou, respectively, every one of which is less than that of models with the other polynomial orders in each city. However, compared to the performance of models based on Luojia 1-01 data, the NPP-VIIRS based models appear to perform better estimation accuracy. Although they show higher values of RMSE than those in previous study of Guo et al. (2015) where the values of RMSE were 0.182 in Beijing and 0.22 in Guangzhou, the NPP-VIIRS based models with each polynomial order in this study provide higher R2 values and lower RMSE values than those using Luojia 1-01 data in each city. From this comparison, it is demonstrated that the utilization of Luojia 1-01 data is less reliable in estimating the imperviousness degree compared to NPPVIIRS data. According to the above results, this study attempted to apply the models with the highest accuracy for investigating the spatial variability of imperviousness degree estimated from Luojia 1-01 and NPPVIIRS data. The estimated results based on two types of nightlight data were presented in Fig. 10, showing that a large proportion of high imperviousness are distributed in urban regions of each city. Compared to the NPP-VIIRS based estimation (Fig. 10b), the results estimated from Luojia 1-01 (Fig. 10a) present a finer spatial distribution of imperviousness degree due to the higher spatial resolution of Luojia 1-01 nightlight imagery. Especially, it can be clearly seen that most pixels with high degree of imperviousness are distributed in road networks. In

consistent with the actual reference data because of its wider radiometric detection range and higher spatial resolution. Thus it can be seen that Luojia 1-01 nighttime light data is able to accomplish higher accuracy for detecting the spatial extent of impervious surface areas in comparison with NPP-VIIRS data. 4.2. Estimation of imperviousness degree Apart from detecting the spatial extent of impervious surface area, this study also used a polynomial regression model to examine the potential of Luojia 1-01 data in estimating the imperviousness degree. In the polynomial regression model, the imperviousness degree of impervious surfaces derived from Landsat 8 OLI were served as reference data (dependent variable), while the DN value of nighttime light imagery was served as an independent variable. This model was computed based on ten thousand sample points which were randomly selected from the reference data in each city. Through comparing the estimated results with the actual reference data, the performance of the polynomial regression model was evaluated using two indices including the determination coefficient (R2) and root mean square error (RMSE). Considering the impacts of polynomial orders on the model accuracy, in this study a group of polynomial regression models, from first (referring to the linear model) to ninth order, were developed to examine the relation between imperiousness degree and light intensity of nighttime light imagery. The results of imperviousness degree estimation based on different-order polynomial regression models are shown in Fig. 9. It can be seen that, as the polynomial order increases, both the polynomial regression models based on Luojia 1-01 and NPP-VIIRS data 7

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Fig. 7. Spatial distributions of impervious surface areas extracted from Luojia 1-01 and NPP-VIIRS data at the imperviousness ranges of 50–100% in three cities: (a) Beijing; (b) Shanghai; and (c) Guangzhou.

degree to be detected. As supported by the accuracy assessment in Fig. 4, an increase in the minimum detectable imperviousness degree always results in a decrease in the highest accuracy of impervious surface extraction based on Luojia 1-01 data. Thus, it is noteworthy to determine the minimum detectable imperviousness when mapping the spatial extent of impervious surface areas. Although the extracted results based on these two types of nighttime light imagery present similar patterns of maximum Kappa coefficients in different cities (Fig. 4), Luojia 1-01 data display a higher spatial accuracy in impervious surface area extraction rather than NPP-VIIRS data when the extracted area sizes are the same with the reference data. As presented in Table 2, the F-score and Kappa coefficient of results extracted from Luojia 1-01 data are higher than those from NPP-VIIRS data in the three study cities regardless of which detectable imperviousness range. The higher accuracy of impervious surface area extraction may benefit from the spatial characteristics of Luojia 1-01 nighttime light data, which are different from its predecessors, e.g. NPP-VIIRS and DMSP-OLS data. On the one hand, Luojia 1-01 data provide a higher spatial resolution, thereby capturing finer spatial details of anthropogenic lighting than NPP-VIIRS data. The improvement of spatial resolution make Luojia 1-01 data exhibit spatial patterns of impervious surface areas that are more consistent with the reference data. On the other hand, Luojia 1-01 data does not suffer the problem of the blooming effect that exists in DMSP-OLS data. According to the research by Elvidge et al. (2007), the blooming effect is an important factor leading to the great overestimation of the extent of impervious surfaces in urban areas. Thus it can be seen that Luojia 1-01 nighttime light imagery have a strong capacity in detecting the spatial extent of

addition, compared with the actual reference data, the phenomenon of over- and under-estimates can be found in the estimated results. As shown in Fig. 10, the results based on both Luojia 1-01 (Fig. 10c) and NPP-VIIRS (Fig. 10d) data almost overestimated imperviousness in the pixels with low imperviousness degree, while for the pixels with high imperviousness degree, the estimates were much lower than those of actual reference data. This great difference between the estimated results and actual reference data shows that Luojia 1-01 fails in providing a reliable estimate of imperviousness degree. 5. 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 threshold has important impact on the final extraction accuracy, so the determination of best threshold value should be performed by the comparison of all possible thresholds with actual references. From the comparison results in Fig. 3, most high accuracy of Luojia 1-01 based extraction is also observed to appear at a wide range of DN values. This wide measurement range indicates that Luojia 1-01 data can provide more variability and finer spatial details. Additionally, the highest accuracy of impervious surface areas extracted from nighttime light imagery was dependent on the minimum value of imperviousness 8

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Fig. 8. Spatial distributions of impervious surface areas extracted from Luojia 1-01 and NPP-VIIRS data at the imperviousness ranges of 80–100% in three cities: (a) Beijing; (b) Shanghai; and (c) Guangzhou.

Table 2 Accuracy assessment of impervious surface areas extracted from Luojia 1-01 and NPP-VIIRS data in comparison with the reference data at different imperviousness ranges. Imperviousness

10-100%

30-100%

50-100%

80-100%

City

Beijing Shanghai Guangzhou Beijing Shanghai Guangzhou Beijing Shanghai Guangzhou Beijing Shanghai Guangzhou

Impervious area (km2)

4036.7997 2836.2141 1812.1095 3037.6539 2086.8273 1302.2649 2025.1458 1502.37 906.5349 903.3399 808.9884 472.7556

Luojia 1-01

NPP-VIIRS

Threshold

F-score

Kappa

Threshold

F-score

Kappa

7092 7682 8127 10717 18597 15500 18056 28716 23632 34638 43912 36602

0.7210 0.7324 0.7080 0.6647 0.6190 0.6286 0.5756 0.4966 0.5345 0.3876 0.3251 0.3633

0.6297 0.5401 0.6085 0.5883 0.4596 0.5456 0.5157 0.3733 0.4667 0.3519 0.2537 0.3181

7.38 19.24 13.19 11.27 24.48 17.58 17.37 28.77 22.3 27.26 34.89 29.63

0.6621 0.6672 0.6475 0.5891 0.5674 0.5569 0.5079 0.4669 0.4729 0.3334 0.3124 0.3146

0.5796 0.4838 0.5456 0.5178 0.4142 0.4717 0.4540 0.3433 0.4063 0.3027 0.2349 0.2720

only 0.4667 in Beijing. Moreover, compared to the performance of models based on NPP-VIIRS data, the Luojia 1-01 based models provided lower R2 and higher RMSE for the imperviousness degree estimation in each city. The unreliable estimation of imperviousness degree based on Luojia 1-01 data is mainly attributed to the fact that surfaces with the same imperviousness degree are probably illuminated with different light intensities. Taking roads and roofs within urban area for example, although both of them represent the impervious surfaces with 100% imperviousness, roads are generally illuminated by street lights,

impervious surfaces. However, Luojia 1-01 nighttime light data fail to provide a reliable estimation for the degree of impervious surfaces. This can be found in Fig. 9, which displays the relation between imperviousness degree and light intensity using the polynomial regression models. Although the model with third order polynomial can perform the best estimation of imperviousness degree based on Luojia 1-01 data, the relation is approximated to be very weak with respect to the reference values. For example, the maximum value of R2 for Luojia 1-01 based estimation is

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Fig. 9. Accuracy of imperviousness degree estimation based on Luojia 1-01 and NPP-VIIRS data with different polynomial fits.

Luojia 1-01 data is difficult to meet the requirement for time-series analyses, particularly for understanding historical information on urban dynamics. Second, the new products of Luojia 1-01 still have a low positioning accuracy, which constrains its direct and accurate applications in spatial analyses. A preprocessing of high-precision geometric correction is essential for Luojia1-01 data to reduce the geometric positioning errors. Third, the quality of some Luojia 1-01 images is also affected by the clouds and moonlight. This would result in great uncertainties to remote sensing applications. Future studies should be performed to eliminate the impacts of moonlight and clouds on Luojia 1-01 nighttime light imagery.

while roofs do not always have the light illumination in fact. Nevertheless, Luojia 1-01 data can better discriminate between different light sources and land use classes due to its finer spatial resolution compared to NPP-VIIRS data. As a consequence, the relation between imperviousness degree and light intensity of Luojia 1-01 data is found to be weaker than that of the NPP-VIIRS data with a low resolution (in which a single pixel stands for a mix of land use and light source). This finding is consistent with previous studies of Anderson et al. (2010) and Kotarba and Aleksandrowicz (2016), who respectively used the nighttime photography from the International Space Station to estimate the relationship between population density and light intensity as well as the relationship between imperviousness degree and light intensity. They all stated that a higher spatial resolution does not always result in an increase in the accuracy of nighttime light-based estimations. Similarity, the use of Luojia 1-01 data with a high spatial resolution does not immediately generate more reliable results of the imperviousness degree of impervious surfaces although this kind of data is able to accurately map the distribution characteristics of light sources. The above estimation of imperviousness degree was based solely on nighttime light intensities. In fact, this estimation can be improved to a certain extent if Luojia 1-01 data is integrated with other useful data sources. For example, according to the research of Li et al. (2018) on urban extent extraction, the adding of Landsat 8 OLI to Luojia 1-01 data is able to improve the extraction accuracy of urban areas. In another study for investigating the potentiality of the nighttime photography from the International Space Station in impervious surface detection, Kotarba and Aleksandrowicz (2016) found that the estimation of imperviousness degree can obtain an improvement when LandScan population data were included. With regard to Luojia 1-01 data, it may also be possible to obtain an accurate estimation of imperviousness degree based on the combination of light intensity and population data. As the purpose of this study is to examine the potential of Luojia 1-01 data itself, combining the high-resolution Luojia 1-01 data and other data sources for accurately estimating the imperviousness degree should be achieved in the future. Because of the higher spatial resolution and wider radiometric quantization, Luojia 1-01 data can detect the finer spatial details of artificial lights in comparison with NPP-VIIRS data. However, it is also acknowledged that there are still some issues for the utilization of Luojia 1-01 data. First, the Luojia 1-01, as a new generation of nighttime light remote sensing satellite, was launched in June 2018, which indicates that the images before June 2018 are vacant. The absence of

6. 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 models were adopted to estimate the relation between imperiousness degree and light intensity. Finally, based on the reference data derived from Landsat 8 OLI, the accuracy assessment was performed to evaluate the performance of Luojia 1-01 data in impervious surface detection compared to NPP-VIIRS data. The results reveal that Luojia 1-01 data is suitable for detecting the extent of impervious surface areas, particularly with a low imperviousness degree. Moreover, due to the wider measurement range and higher spatial resolution, Luojia 1-01 data can provide a more accurate mapping of the spatial extent of impervious surface areas than NPP-VIIRS data under the condition of same area sizes. However, the imperviousness degree estimation based on Luojia 1-01 data is problematic because of the low correlation between imperviousness degree and light intensity, particularly in the areas with low or high values of imperviousness degree. The over- and under-estimates of imperviousness degree are mainly attributed to the fact that Luojia 1-01 data can better discriminate the surfaces that have the same imperviousness degree but are illuminated with different light intensities due to its finer spatial resolution compared to NPP-VIIRS data. This confirmed previous studies that an increase in the resolution of nighttime light imagery does not always result in an increase in the reliability of nighttime 10

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Fig. 10. Spatial variability of estimated imperviousness degree: (a) estimated results based on Luojia 1-01 data; (b) estimated results based on NPP-VIIRS data; (c) comparison of the estimated results based on Luojia 1-01 data versus the actual reference data; and (d) comparison of the estimated results based on NPP-VIIRS data versus the actual reference data.

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).

light-based estimations. In conclusion, this study confirmed that Luojia 1-01 data can present a great potential for detecting the extent of impervious surface areas, but fails to estimate the imperviousness degree. The integration of Luojia 1-01 nightlight imagery and other useful data sources may provide a possibility to obtain an accurate estimation of imperviousness degree in the future. Besides, since there are still some challenging issues such as the errors of geometric positioning and the influences of moonlight and clouds in Luojia 1-01 nightlight imagery, further studies should be required to improve the quality of the imagery for the widespread applications.

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