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Satellite nighttime lights (NTL) data have been extensively applied in urban studies. However, commonly-used NTL products are not able to provide fine-scale information on the intra-urban changes due to their coarse resolution (500–1000 m). Here, we propose a method for downscaling NTL data by combining the human activity-physical features adjusted NTL index with ordinary kriging approach (HPANI-OK). The proposed method was tested on the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL in 500 m, with Beijing, China. Results indicated that HPANI-OK outperformed the other approach (human activity-water features adjusted NTL index-OK and vegetation adjusted NTL urban index-OK) with a remarkable Pearson correlation coefficient (0.92), root mean square error (6.54 nW∙cm−2∙sr-1) and structural similarity (0.23) in simulating 30 m Downscaled VIIRS (DVIIRS) NTL. The HPANI-OK method significantly effectively addresses the blooming issue of raw VIIRS NTL, improves the texture similarity between the DVIIRS NTL and the reference NTL, enhances the NTL variability from artificial areas to non-artificial areas. Furthermore, the scaling effect is noticeable in simulating DVIIRS NTLs at two target resolution, i.e., 30 m and 100 m. Larger spatial differences between the initial and target resolutions weaken the pixel consistency between DVIIRS NTL and raw VIIRS NTL. However, they enhance the texture similarity between DVIIRS NTL and reference NTL. Given its high accuracy and detailed texture, HPANI-OK may be a straightforward and effective technique for downscaling NTL data in other regions and various remote sensing NTL sensors.

Marine fishery resources are linked to global food security and the livelihoods of millions of people, but their sustainability is seriously affected by the overexpansion of fishing activities. The regulation of fishing activities of fishing vessels has been based on ineffective data sources. Fortunately, rapid developments in nighttime light remote sensing have provided effective data for the formulation of marine fisheries regulations. We conducted a series of experiments to explore the application of Luojia1-01 (LJ-01) nighttime light images for the extraction of information on fishing vessels to provide effective data support for the formulation of marine fisheries regulations. First, a series of preprocessing operations, including radiation correction and masking of the offshore oil and gas platforms were performed on the original image. The preprocessed single-band nighttime light image was combined with two stretching methods to form a triband image. Sample labelling of lit fishing boats was performed. Then, by comparing YOLOv5 metrics, YOLO-V5s was chosen as the base framework of the model, and a small-target detection layer was added to improve the detection of lit fishing vessels. The method developed in this study accurately and effectively detected lit fishing boats in LJ1-01 images with strong robustness and generalizability, and the accuracy of detection rate reached 96.6 %. The precision was improved by 10 % compared to the original YOLO-V5s model that used raw data. The recall rate was improved from 85.4 to 93 %, and the average accuracy(map@0.5) was improved from 85.4 to 93.1 %. The developed model can be successfully used for the extraction of lit fishing boats and for other applications based on medium- and high-resolution nighttime satellite data.

Accurate extraction of impermeable surfaces is important for assessing land use change and improving the urban heat island effect. Nighttime light imagery has the advantage of being efficient and cost effective, providing a new perspective for monitoring and extracting impermeable surfaces and analyzing urban expansion processes. However, for the vast Karst terrain fragmentation area located in southwest China, the extraction of impermeable surface information faces many challenges due to surface landscape fragmentation and nighttime light image resolution. These challenges include light spillover, oversaturation and limited understanding of spatial links with surface types at fine scales. This study uses Luojia1-01, NPP-VIIRS, and Flint as remote sensing data sources to examine the applicability of nighttime light images in extracting impermeable surfaces from geomorphologically complex areas. The results show that Luojia1-01 data can provide finer spatial details and more accurate impermeable surface extraction results than NPP-VIIRS and Flint data. The relative error of extracted area in regions with large topographic relief is higher than that in regions with flat topographic cuttings. The extraction results of the three images are spatially similar; however, the overall accuracy is poor, and a single nighttime light image is not the best solution for obtaining impermeable surface information in large scale terrain fragmentation areas. However, the integrated application of multi-source light images is a trend for future regional research and development, and the best way to extract impermeable surfaces in complex terrain areas should be explored in conjunction with other remote sensing data sources in the future. [ABSTRACT FROM AUTHOR]

The Guangdong-Hong Kong-Macao Greater Bay Area (referred to as "GBA") is an important strategic deployment for China's current economic development. Clarifying the spatial structure characteristics of the GBA urban agglomeration is conducive to optimizing its spatial structure to develop into a multi-center network spatial structure and promoting coordinated regional development. This study uses the advantage of Luojia1-01 nighttime light data, which can distinguish the difference in urban night light intensity. Through multi-scale segmentation of nighttime light intensity, the potential center range is established. The point of interest (POI) data are used for spatial autocorrelation analysis and geographically weighted regression to identify the multi-center distribution of the GBA urban agglomeration, and to analyze its spatial structure characteristics from multiple perspectives such as functional structure identification, spatial correlation measurement, and main center service range. The following list illustrates what the results show. 1) The GBA has five main centers and 14 sub-centers, including the main centers of Guangfo, Shenguan, Hong Kong, Aozhu, and Zhongshan. The functional structures of the five main centers are mainly mixed functional areas, and 14 sub-centers (such as Huadu, Zengcheng, Conghua, Huicheng, Duanzhou, Xinhui, Shiqi) are distributed around the periphery of the main centers. 2) The correlation strength of the five main centers and nine cities plus two special administrative regions in the urban agglomeration, calculated based on the Luojia1-01 nighttime light data, shows characteristics of "strong in the east and weak in the west" and "strong inside and weak outside." 3) The study considered the distribution of the main centers of the urban agglomeration and their spatial correlation strength characteristics, as well as the three groups served by the main centers (Guangfozhao group, Gang-Shenguanhui group and Ao-Zhuzhongjiang group), combined with the planning requirements of the "Outline Development Plan for the Guangdong-Hong Kong-Macao Greater Bay Area" and "Guangdong Province Land and Space Planning (2020-2035)." The findings suggest that the GBA should build a regional spatial structure of "five centers, one area, three groups and four axes" to achieve pole-driven, axis-supported, and group cooperation and promote its coordinated development into a world-class urban agglomeration.

To reduce poverty, it is important to obtain accurate information on poverty conditions in a timely manner. Previous studies indicated that nighttime light products are helpful for poverty estimation. However, there exist no studies that have investigated the potential of LuoJia1-01, a new-generation nighttime light satellite with a much finer resolution (∼130 m), for analyzing poverty. In addition, nighttime light data may not reflect daytime-only socio-economic activities. To address these problems, our study first compared the performance of LuoJia1-01 and NPP-VIIRS products in poverty estimation. Next, we incorporated point of interest (POI) information so that daytime-only socio-economic activities can be considered. Our experiments indicated that LuoJia1-01 performs slightly better than NPP-VIIRS in terms of poverty estimation at county level. More importantly, we revealed that the combination of POIs and nighttime light products can moderately increase the average estimation accuracies. These findings are expected to support poverty monitoring over large regions for long periods, which cannot be fulfilled by traditional household surveys and census. [ABSTRACT FROM AUTHOR]

Mapping socio-economic indicators with a raster format is still a great challenge. The nighttime light (NTL) datasets have been widely utilized to estimate the socio-economic parameters. However, the precision of the published datasets was too coarse to meet related issues such as flood losses assessment, urban planning, and epidemiological studies. The present study calibrated gross domestic product (GDP), population (POP), electric consumption (EC), and urban build-up area (B-A) at 100 m resolution for 45 cities of China in 2018 using Luojia1-01 NTL datasets via random forest (RF) as well as geographically weighted regression (GWR) model. The linear regression (LR), back propagation neural network (BPNN), and support vector machine (SVM) methods were selected for comparison with GWR and RF models. Besides, the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) was chosen for comparison with Luojia1-01. The ten-folded cross-validation (CV) has been used for evaluating accuracy at county and city scales. Finally, the distribution maps of socio-economic parameters were illustrated and some findings were obtained. First, the validation results revealed that the calibration at the city-scale outperformed the county or district scale. Second, the precision of the Luojia1-01 NTL dataset surpassed the NPP-VIIRS NTL dataset on the same administrative scale except for some specific situations. Third, the precision of the simulation for the gross domestic product (GDP) is the highest than the others, followed by electric consumption (EC), build-up area (B-A), and population (POP). Fourth, the optimum model varied according to the socio-economic parameters. Fifth, the distribution of socio-economic parameters exhibited obvious spatial heterogeneity. This paper can supply scientific support for calibrating socio-economic parameters in other regions.

Accurate extraction of impermeable surfaces is important for assessing land use change and improving the urban heat island effect. Nighttime light imagery has the advantage of being efficient and cost effective, providing a new perspective for monitoring and extracting impermeable surfaces and analyzing urban expansion processes. However, for the vast Karst terrain fragmentation area located in southwest China, the extraction of impermeable surface information faces many challenges due to surface landscape fragmentation and nighttime light image resolution. These challenges include light spillover, oversaturation and limited understanding of spatial links with surface types at fine scales. This study uses Luojia1-01, NPP-VIIRS, and Flint as remote sensing data sources to examine the applicability of nighttime light images in extracting impermeable surfaces from geomorphologically complex areas. The results show that Luojia1-01 data can provide finer spatial details and more accurate impermeable surface extraction results than NPP-VIIRS and Flint data. The relative error of extracted area in regions with large topographic relief is higher than that in regions with flat topographic cuttings. The extraction results of the three images are spatially similar; however, the overall accuracy is poor, and a single nighttime light image is not the best solution for obtaining impermeable surface information in large scale terrain fragmentation areas. However, the integrated application of multi-source light images is a trend for future regional research and development, and the best way to extract impermeable surfaces in complex terrain areas should be explored in conjunction with other remote sensing data sources in the future.

The Guangdong-Hong Kong-Macao Greater Bay Area (referred to as "GBA") is an important strategic deployment for China's current economic development. Clarifying the spatial structure characteristics of the GBA urban agglomeration is conducive to optimizing its spatial structure to develop into a multi-center network spatial structure and promoting coordinated regional development. This study uses the advantage of Luojia1-01 nighttime light data, which can distinguish the difference in urban night light intensity. Through multi-scale segmentation of nighttime light intensity, the potential center range is established. The point of interest (POI) data are used for spatial autocorrelation analysis and geographically weighted regression to identify the multi-center distribution of the GBA urban agglomeration, and to analyze its spatial structure characteristics from multiple perspectives such as functional structure identification, spatial correlation measurement, and main center service range. The following list illustrates what the results show. 1) The GBA has five main centers and 14 sub-centers, including the main centers of Guangfo, Shenguan, Hong Kong, Aozhu, and Zhongshan. The functional structures of the five main centers are mainly mixed functional areas, and 14 sub-centers (such as Huadu, Zengcheng, Conghua, Huicheng, Duanzhou, Xinhui, Shiqi) are distributed around the periphery of the main centers. 2) The correlation strength of the five main centers and nine cities plus two special administrative regions in the urban agglomeration, calculated based on the Luojia1-01 nighttime light data, shows characteristics of "strong in the east and weak in the west" and "strong inside and weak outside." 3) The study considered the distribution of the main centers of the urban agglomeration and their spatial correlation strength characteristics, as well as the three groups served by the main centers (Guangfozhao group, Gang-Shenguanhui group and Ao-Zhuzhongjiang group), combined with the planning requirements of the "Outline Development Plan for the Guangdong-Hong Kong-Macao Greater Bay Area" and "Guangdong Province Land and Space Planning (2020-2035)." The findings suggest that the GBA should build a regional spatial structure of "five centers, one area, three groups and four axes" to achieve pole-driven, axis-supported, and group cooperation and promote its coordinated development into a world-class urban agglomeration. [ABSTRACT FROM AUTHOR]

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.

• Spatiotemporal monitoring of lit fishing vessels contributing to fisheries management. • Using high spatial resolution nighttime light images of Luojia1-01 to know the spatial distribution of lit fishing boats. • Deep learning techniques developed based on the YOLO object detection model for improving accuracy in identifying fishing vessels. Marine fishery resources are linked to global food security and the livelihoods of millions of people, but their sustainability is seriously affected by the overexpansion of fishing activities. The regulation of fishing activities of fishing vessels has been based on ineffective data sources. Fortunately, rapid developments in nighttime light remote sensing have provided effective data for the formulation of marine fisheries regulations. We conducted a series of experiments to explore the application of Luojia1-01 (LJ-01) nighttime light images for the extraction of information on fishing vessels to provide effective data support for the formulation of marine fisheries regulations. First, a series of preprocessing operations, including radiation correction and masking of the offshore oil and gas platforms were performed on the original image. The preprocessed single-band nighttime light image was combined with two stretching methods to form a triband image. Sample labelling of lit fishing boats was performed. Then, by comparing YOLOv5 metrics, YOLO-V5s was chosen as the base framework of the model, and a small-target detection layer was added to improve the detection of lit fishing vessels. The method developed in this study accurately and effectively detected lit fishing boats in LJ1-01 images with strong robustness and generalizability, and the accuracy of detection rate reached 96.6 %. The precision was improved by 10 % compared to the original YOLO-V5s model that used raw data. The recall rate was improved from 85.4 to 93 %, and the average accuracy(map@0.5) was improved from 85.4 to 93.1 %. The developed model can be successfully used for the extraction of lit fishing boats and for other applications based on medium- and high-resolution nighttime satellite data. [ABSTRACT FROM AUTHOR]

This paper is aimed at the telemetry data of Luojia1-01 satellite attitude control system, and carries out the research on high-dimensional data feature extraction and fault detection based on supervised local linear embedding (SLLE). Because the general linear feature extraction method can not mine the feature information of nonlinear high-dimensional telemetry data, a data feature extraction and fault detection method based on local linear embedding is designed. Combined with the statistics SPE and $T^{2}$ , the low-dimensional feature information is obtained for data statistics and monitoring faults. Due to the local linear embedded manifold learning method for the traditional batch processing mode is difficult to update and improve the database online, and the supervised local linear embedding method is introduced. The online sample feature extraction and fault detection schemes are designed, and the database is updated by updating the weight matrix through online samples. Finally, the effectiveness of the method is verified by Luojia1-01 satellite telemetry data. The results show that the fault detection method based on SLLE reduces the false alarm rate (FAR) by approximately 3% and the missing alarm rate (MAR) by approximately 10% compared with local linear embedding (LLE). This method effectively improves the detection capability of the anomalous state of Luojia1-01 satellite and has certain engineering application value.

The urban heat island (UHI) is one of the essential phenomena of the modern urban climate. In recent years, urbanization in China has gradually accelerated, and the heat island effect has also intensified as the urban impervious surface area and the number of buildings is increasing. Urban building density (UBD) is one of the main factors affecting UHI, but there is little discussion on the relationship between the two. This paper takes Hefei as the research area, combines UBD data estimated by Luojia1-01 night-time light (NTL) imagery as the research object with land surface temperature (LST) data obtained from Landsat8 images, and carries out spatial correlation analysis on 0.5 × 0.5 km to 2 × 2 km resolution for them, so as to explore the relationship between UBD and UHI. The results show the following: (1) Luojia1-01 data have a good ability to estimate UBD and have fewer errors when compared with the actual UBD data; (2) At the four spatial scales, UBD and LST present a significant positive correlation that increases with the enlargement of the spatial scale; and (3) Moreover, the fitting effect of the Geographically Weighted Regression (GWR) model is better than that of the ordinary least squares (OLS) regression model. [ABSTRACT FROM AUTHOR]

The Luojia1-01 satellite provides high-resolution, high-sensitivity nighttime light data at a resolution of 130 m. To effectively use the Luojia1-01 nighttime light data for global applications, the problems of relative and absolute positioning accuracy should be solved. This paper proposes a high accuracy regional geometric processing method of nighttime light imagery. We utilized a nighttime light image matching algorithm to obtain tie points, which are used in the planar block adjustment with ground control points. Then, orthorectification of all images is implemented. Finally, we obtain the nighttime light map of China by mosaicking all the nighttime light orthoimages. According to the experimental results for 275 Luojia1-01 images, the root mean square error of the tie points is 0.983 pixels and the root mean square error of independent checkpoints is 195.491 m (less than 1.5 pixels) after the planar block adjustment. The experimental results prove the validity and feasibility of the proposed method.

The first professional night-time light remote sensing satellite in China, Luojia1-01, has raised the resolution of night-time light data to 130 m, which provides a possibility for the study of small-scale night-time light. This paper is the first research on spatial analysis and quantitative modeling between night-time light intensity (NTLI) and community housing price (CHP) on a small scale by using the Luojia1-01 night-time light imagery. This paper takes Wuhan as the research area, CHP data obtained by web-crawler technology as the research object, combines Luojia1-01 data, and carries out spatial correlation analysis and quantitative modeling on a small scale for them. The experimental results show that there is a strong linear positive correlation between the NTLI and CHP based on geographically weighted regression (GWR), and the CHP data in Wuhan have obvious spatial non-stationarity. Moreover, the coupling mechanism between the NTLI and CHP is also revealed. We can conclude that there is potential for estimating the CHP by using Luojia1-01 night-time light imagery.

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military’s defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method.

The LuoJia1-01 satellite can acquire high-resolution, high-sensitivity nighttime light data for night remote sensing applications. LuoJia1-01 is equipped with a 4-megapixel CMOS sensor composed of 2048 × 2048 unique detectors that record weak nighttime light on Earth. Owing to minute variations in manufacturing and temporal degradation, each detector’s behavior varies when exposed to uniform radiance, resulting in noticeable detector-level errors in the acquired imagery. Radiometric calibration helps to eliminate these detector-level errors. For the nighttime sensor of LuoJia1-01, it is difficult to directly use the nighttime light data to calibrate the detector-level errors, because at night there is no large-area uniform light source. This paper reports an on-orbit radiometric calibration method that uses daytime data to estimate the relative calibration coefficients for each detector in the LuoJia1-01 nighttime sensor, and uses the calibrated data to correct nighttime data. The image sensor has a high dynamic range (HDR) mode, which is optimized for day/night imaging applications. An HDR image can be constructed using low- and high-gain HDR images captured in HDR mode. Hence, a day-to-night radiometric reference transfer model, which uses daytime uniform calibration to calibrate the detector non-uniformity of the nighttime sensor, is herein built for LuoJia1-01. Owing to the lack of calibration equipment on-board LuoJia1-01, the dark current of the nighttime sensor is calibrated by collecting no-light desert images at new moon. The results show that in HDR mode (1) the root mean square of mean for each detector in low-gain (high-gain) images is better than 0.04 (0.07) in digital number (DN) after dark current correction; (2) the DN relationship between low- and high-gain images conforms to the quadratic polynomial mode; (3) streaking metrics are better than 0.2% after relative calibration; and (4) the nighttime sensor has the same relative correction parameters at different exposure times for the same gain parameters.

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction., (© 2021 The Authors.)

Rapid urbanization has caused radical changes in urban climates. As a result, issues related to urban thermal environments have become more prominent. Finding a balance between urban expansion and thermal environment quality is key to ensuring sustainable urban development. Taking Dalian City (China) as an example, we used multi-source datasets, including Luojia1-01 nighttime light imagery, Landsat-8, Sentinel-2, and building vector data, to analyze the thermal characteristics of different local climate zones (LCZs). Additionally, the LCZ combination mode with the lowest heat island effect intensity in the human settlements was also investigated. The results showed that the human settlements covered an area of 351.976 km2, with 33.476% corresponding to building LCZs (LCZ1-10) and 66.524% to natural LCZs (LCZA-G). The different LCZs had different thermal environment characteristics, and the UHIA values for the building LCZs were significantly higher than those of the natural LCZs. Additionally, for the building LCZs, the UHIA values for compact building LCZs (LCZ 1–3) were also significantly higher than those for open and spare building LCZs. With the current settlement area and population size, the most appropriate LCZ layout model for the study area was LCZ5 + LCZ6 + (LCZ7+LCZ8+LCZA + LCZC + LCZD + LCZE + LCZG), which had areas 28.585, 57.170, 57.170, 28.585, 54.236, 54.236, and 54.236 km2, respectively. This layout model had the smallest UHIA value (11.654 °C), and urban planning according to the above ratio can alleviate the UHI effect in different cities. • Comprehensive analysis of thermal environment characteristics and population. • Human settlements were investigated using high resolution Luojia 1-01 NTL data. • Accumulated amounts of urban heat island intensity (UHIA) were investigated. • Model with lowest UHIA had a limit on population and total area of human settlement. [ABSTRACT FROM AUTHOR]

The Luojia1-01 satellite provides high-resolution, high-sensitivity nighttime light data at a resolution of 130 m. To effectively use the Luojia1-01 nighttime light data for global applications, the problems of relative and absolute positioning accuracy should be solved. This paper proposes a high accuracy regional geometric processing method of nighttime light imagery. We utilized a nighttime light image matching algorithm to obtain tie points, which are used in the planar block adjustment with ground control points. Then, orthorectification of all images is implemented. Finally, we obtain the nighttime light map of China by mosaicking all the nighttime light orthoimages. According to the experimental results for 275 Luojia1-01 images, the root mean square error of the tie points is 0.983 pixels and the root mean square error of independent checkpoints is 195.491 m (less than 1.5 pixels) after the planar block adjustment. The experimental results prove the validity and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Aging, shrinking cities, urban agglomerations and other new key terms continue to emerge when describing the large-scale population changes in various cities in mainland China. It is important to simulate the distribution of residential populations at a coarse scale to manage cities as a whole, and at a fine scale for policy making in infrastructure development. This paper analyzes the relationship between the DN (Digital number, value assigned to a pixel in a digital image) value of NPP-VIIRS (the Suomi National Polar-orbiting Partnership satellite's Visible Infrared Imaging Radiometer Suite) and LuoJia1-01 and the residential populations of urban areas at a district, sub-district, community and court level, to compare the influence of resolution of remote sensing data by taking urban land use to map out auxiliary data in which first-class (R1), second-class (R2) and third-class residential areas (R3) are distinguished by house price. The results show that LuoJia1-01 more accurately analyzes population distributions at a court level for second- and third-class residential areas, which account for over 85% of the total population. The accuracy of the LuoJia1-01 simulation data is higher than that of Landscan and GHS (European Commission Global Human Settlement) population. This can be used as an important tool for refining the simulation of residential population distributions. In the future, higher-resolution night-time light data could be used for research on accurate simulation analysis that scales down large-scale populations. [ABSTRACT FROM AUTHOR]

The first professional night-time light remote sensing satellite in China, Luojia1-01, has raised the resolution of night-time light data to 130 m, which provides a possibility for the study of small-scale night-time light. This paper is the first research on spatial analysis and quantitative modeling between night-time light intensity (NTLI) and community housing price (CHP) on a small scale by using the Luojia1-01 night-time light imagery. This paper takes Wuhan as the research area, CHP data obtained by web-crawler technology as the research object, combines Luojia1-01 data, and carries out spatial correlation analysis and quantitative modeling on a small scale for them. The experimental results show that there is a strong linear positive correlation between the NTLI and CHP based on geographically weighted regression (GWR), and the CHP data in Wuhan have obvious spatial non-stationarity. Moreover, the coupling mechanism between the NTLI and CHP is also revealed. We can conclude that there is potential for estimating the CHP by using Luojia1-01 night-time light imagery. [ABSTRACT FROM AUTHOR]

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military's defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method. [ABSTRACT FROM AUTHOR]

The LuoJia1-01 satellite can acquire high-resolution, high-sensitivity nighttime light data for night remote sensing applications. LuoJia1-01 is equipped with a 4-megapixel CMOS sensor composed of 2048 × 2048 unique detectors that record weak nighttime light on Earth. Owing to minute variations in manufacturing and temporal degradation, each detector's behavior varies when exposed to uniform radiance, resulting in noticeable detector-level errors in the acquired imagery. Radiometric calibration helps to eliminate these detector-level errors. For the nighttime sensor of LuoJia1-01, it is difficult to directly use the nighttime light data to calibrate the detector-level errors, because at night there is no large-area uniform light source. This paper reports an on-orbit radiometric calibration method that uses daytime data to estimate the relative calibration coefficients for each detector in the LuoJia1-01 nighttime sensor, and uses the calibrated data to correct nighttime data. The image sensor has a high dynamic range (HDR) mode, which is optimized for day/night imaging applications. An HDR image can be constructed using low- and high-gain HDR images captured in HDR mode. Hence, a day-to-night radiometric reference transfer model, which uses daytime uniform calibration to calibrate the detector non-uniformity of the nighttime sensor, is herein built for LuoJia1-01. Owing to the lack of calibration equipment on-board LuoJia1-01, the dark current of the nighttime sensor is calibrated by collecting no-light desert images at new moon. The results show that in HDR mode (1) the root mean square of mean for each detector in low-gain (high-gain) images is better than 0.04 (0.07) in digital number (DN) after dark current correction; (2) the DN relationship between low- and high-gain images conforms to the quadratic polynomial mode; (3) streaking metrics are better than 0.2% after relative calibration; and (4) the nighttime sensor has the same relative correction parameters at different exposure times for the same gain parameters. [ABSTRACT FROM AUTHOR]