Nighttime Light Images Research Articles

The estimation of building carbon emission using nighttime light images: A comparative study at various spatial scales

As one of the fundamental sectors to measure the carbon emission levels in a certain region, building carbon emission plays an important role in determining low-carbon development plans. Most of the carbon emission estimation research mainly focuses on the establishment of bottom-up GHG inventory and the implication of policy-driven approaches, there are still many theoretical gaps in the usage of remote sensing data to predict building carbon emission. This paper presents a comprehensive study to discuss the performance of different regression models using various open nighttime light (NTL) data sources. The Noord Brabant province was employed as a case study to verify the feasibility of using different estimation models at various spatial scales (city-level, district-level, and neighborhood-level). Among all regression models, the geographically weighted regression (GWR) has been proven to better reflect the relationship between building carbon emissions and the NTL index. For practical applications, the carbon intensity (CI) and annual nighttime light index (ANLI) are a pair of optimal sets to establish a reliable estimation model. It exhibits higher utility value at the city-level due to the fewer interferences caused by non-building lighting sources. The results of this comparative study provide a new reference to support the establishment of carbon inventory. By illustrating the differences among various estimation models, the applicable scope of using open remote sensing data to estimate building carbon emissions can be further defined. The conclusion may provide more detailed instructions during the process of developing low-carbon cities.

Vessel Detection with SDGSAT-1 Nighttime Light Images

The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) Glimmer Imager for Urbanization (GIU) data is very sensitive to low radiation and capable of detecting weak light sources from vessels at night while significantly improving the spatial resolution compared to similar products. Most existing methods fail to use the relevant characteristics of vessels effectively, and it is difficult to deal with the complex shape of vessels in high-resolution Nighttime Light (NTL) data, resulting in unsatisfactory detection results. Considering the overall sparse distribution of vessels and the light source diffusion phenomenon, a novel vessel detection method is proposed in this paper, utilizing the high spatial resolution of the SDGSAT-1. More specifically, noise separation is completed based on a local contrast-weighted RPCA. Then, artificial light sources are detected based on a density clustering algorithm, and an inter-cluster merging method is utilized to realize vessel detection further. We selected three research areas, namely, the Bohai Sea, the East China Sea, and the Gulf of Mexico, to establish a vessel dataset and applied the algorithm to the dataset. The results show that the total detection accuracy and the recall rate of the detection algorithm in our dataset are 96.84% and 96.67%, which is significantly better performance than other methods used for comparison in the experiment. The algorithm overcomes the dataset’s complex target shapes and noise conditions and achieves good results, which proves the applicability of the algorithm.

Using Histogram Extracted from Satelite Imagery and Convolutional Network to Predict GRDP in Java Region

Inequality is one of the problems faced by all countries in the world, including Indonesia. The data used to measure development inequality between regions mostly use GRDP data. However, the GRDP data issued by BPS has a deficiency, it was released after the current year, and this figure is provisional. Therefore, a new data source is needed that can be used to estimate the value of economic activity so that it can be used to measure the level of inequality in development in a region. Nighttime Light (NTL) satellite imagery data can be an alternative to see socioeconomic activity in an area and have been shown to have a strong correlation with socioeconomic activity. In this study, we used VIIRS NTL satellite imagery data and Dynamic World land cover data to estimate GRDP. Rather than using statistical features for each area of interest, we use features in the form of histograms extracted from NTL images and land cover images for each area of interest. Using a histogram, we do not lose spatial information from satellite imagery. Then we proposed a deep learning method in the form of a one-dimensional convolutional neural network using the Huber loss function. This model obtained good precision with an R-square value of 0.8549, beating the baseline method with two-dimensional convolutional networks. The use of the Huber loss function can improve the performance of the model, which has a smaller total loss and has a smoother gradient.

Delineating Peri-Urban Areas Using Multi-Source Geo-Data: A Neural Network Approach and SHAP Explanation

Delineating urban and peri-urban areas has often used information from multiple sources including remote sensing images, nighttime light images, and points-of-interest (POIs). Human mobility from big geo-spatial data could also be relevant for delineating peri-urban areas but its use is not fully explored. Moreover, it is necessary to assess how individual data sources are associated with identification results. Aiming at these gaps, we apply a neural network model to integrate indicators from multi-sources including land cover maps, nighttime light imagery as well as incorporating information about human movement from taxi trips to identify peri-urban areas. SHapley Additive exPlanations (SHAP) values are used as an explanation tool to assess how different data sources and indicators may be associated with delineation results. Wuhan, China is selected as a case study. Our findings highlight that socio-economic indicators, such as nighttime light intensity, have significant impacts on the identification of peri-urban areas. Spatial/physical attributes derived from land cover images and road density have relative low associations. Moreover, taxi intensity as a typical human movement dataset may complement nighttime light and POIs datasets, especially in refining boundaries between peri-urban and urban areas. Our study could inform the selection of data sources for identifying peri-urban areas, especially when facing data availability issues.

Identifying the operational status of container terminals from high-resolution nighttime-light satellite image for global supply chain network optimization

Container terminals are cargo gateways in the global maritime supply chain network. Major container terminals generally operate throughout the year, but do not operate at night, when container vessels are not calling at ports, or when there is no need to handle containers. Terminal congestion can delay containers’ shipping schedules, which impacts the supply chain network. To optimize global logistics, it is therefore important to understand fully the daily operational status of container terminals. A vessels’ automatic identification system data are not sufficient to determine whether containers are being handled in container terminals at night. Remote sensing, especially nighttime-light (NTL) imagery, might solve this problem. Recently, high-resolution images for the CE-SAT-IIB satellite with a pixel resolution of 5.1 m became available to observe NTL. This study assessed the operational status of container terminals based on satellite image taken at night. Eight terminals in the Port of Tokyo, Japan, were selected for the study. A Sentinel-2A image recorded during the day on 7 April 2021, and a CE-SAT-IIB image recorded during the night on 6 April 2021, were obtained. The digital numbers (DNs) of each red-, green-, and blue-(RGB) band image were analyzed, revealing that the red, green, and blue bands, in that order, had higher DNs in the Sentinel-2A daytime image and the CE-SAT-IIB NTL image at all terminals. One of the eight terminals had a low DN in the CE-SAT-IIB RGB image because its lights were off at the time the image was taken. The operational status of the terminals could be verified from the CE-SAT-IIB image by setting the DN threshold to the green or red bands. We also found that the CE-SAT-IIB image could distinguish white-light-emitting diode (LED) lamps from high-pressure sodium lamps based on color differences in the DNs of the RGB bands. If high-resolution NTL sensors were placed onboard microsatellites, a high-frequency observation constellation network could be constructed using a combination of NTL data and daytime images. This study showed the benefits and usefulness of NTL images of ports; the results will contribute to the overall optimization of the global maritime supply chain network.

Downscaling satellite night-time lights imagery to support within-city applications using a spatially non-stationary model

For mapping and monitoring socioeconomic activities in cities, night-time lights (NTL) satellite sensor images are used widely, measuring the light intensity during the night. However, the main challenge to mapping human activities in cities using such NTL satellite sensor images is their coarse spatial resolution. To address this drawback, spatial downscaling of satellite nocturnal images is a plausible solution. However, common approaches for spatial downscaling employ spatially stationary models that may not be optimal where the data are spatially heterogeneous. In this research, a geostatistical model termed Random Forest area-to-point regression Kriging (RFATPK) was employed to disaggregate coarse spatial scale VIIRS NTL images (450 m) to a fine spatial scale (100 m). The RF predicts at a coarse resolution from fine spatial resolution variables, such as a Population raster. ATPK then downscales the coarse residuals from the RF prediction. In numerical experiments, RFATPK was compared with three benchmark techniques, including the simple Allocation of pixel values from the coarse resolution NTL data, Machine Learning with Splines and Geographically Weighted Regression. The downscaled results were validated using fine resolution LuoJia 1-01 satellite sensor imagery. RFATPK produced more accurate disaggregated images than the three benchmark approaches, with mean root mean square errors (RMSE) for the year 2018 of 13.89 and 6.74 nWcm−2 sr−1, for Mumbai and New Delhi, respectively. Also, the property of perfect coherence, measured by the Correlation Coefficient, was preserved consistently when applying RFATPK and was almost 1 for all years. The applicability of the disaggregated NTL data to monitor socioeconomic activities at the within-city scale against the reference NTL was illustrated by utilizing them as a proxy for the Gross National Income (GNI) per capita and the Night Light Development Index. The GNI estimation from the downscaled NTL outperformed the coarse resolution NTL when examining their coefficients of determination, with R2 of 0.67 and 0.47 for the GNI estimation using the fine and coarse resolution NTL data, respectively. For the Night Light Development Index (NLDI), the results of the index were compared by measuring their correlation with the Human Development Index (HDI). The NLDI from the downscaled NTL outperformed the coarse resolution NTL when measuring the correlation with the HDI, with Pearson’s correlation coefficients of −0.48 and −0.35 for the NLDI using the fine and coarse resolution NTL data, respectively, for New Delhi. The outcomes indicate that RFATPK provides more accurate predictions than the three benchmark techniques and the downscaled NTL data are more suitable for fine scale socioeconomic applications, as demonstrated by the NLDI and GNI. This research, thus, shows that the RFATPK solution for NTL disaggregation can facilitate data enhancement for fine-scale sub-national applications in social sciences and can be generalized worldwide by including other cities as well as other applications.

Coordination Analysis between the Development of Urban Built-Up Areas and Urban Environmental Factors through Remote Sensing of Nighttime Lights: A Case Study in Nanjing, China

The fast development of urban built-up areas in China is causing many problems, such as pollution, congestion, etc. How to effectively evaluate the coordination between urban areas and environmental problems has been attracting many scholars worldwide. This paper intends to discover this “secretary” through investigating the built-up areas and their accompanied economic and environmental factors over almost 30 years (1992 to 2020) in Nanjing, China. DMSP/OLS nighttime lights images from 1992 to 2013 and the NPP/VIIRS nighttime lights images from 2012 to 2022 are used for extraction of built-up areas. A spatiotemporal evolution model is established to evaluate whether the built-up areas have developed in coordination and the relationship between urban built-up areas and various factors, including compactness, the fractal dimension, boundary and shape changes, exhaust emissions, and the production of general industrial solid waste, which was further investigated to ascertain whether there was coordination or not. The investigated results discovered that Nanjing’s built-up areas had maintained continuous growth from 1992 to 2020, with the compactness of built-up areas gradually decreasing from 0.42 to 0.23 and the built-up differentiation dimension changing from 1.31 to 1.39, demonstrating that built-up areas had gradually moved from a loose pattern to a compact pattern and from irregular development to balanced development in all directions. The macro model of the coordination index change trend is 0.847 from 1995 to 2020, which indicates that the coordination between urban built-up areas of development and their environments has been improving; however, the reduction in urban green space, the increase in waste emissions, and the increased production of general industrial solid waste has raised questions regarding sustainable development.

Temporal expansion of the nighttime light images of SDGSAT-1 satellite in illuminating ground object extraction by joint observation of NPP-VIIRS and sentinel-2A images

Poverty is the leading cause of social instability around the world. Reducing poverty has become a crucial objective for global sustainable development. Timely and consistently monitoring poverty status plays a vital role in evaluating the efficiency of poverty reduction policies. Nighttime light images from satellites allow for frequent monitoring of poverty status by recording lighting intensity. The coarse spatial resolution of the recent, widely used, publicly available nighttime light images hinders research on smaller unit scales, such as county-level. The SDGSAT-1 satellite, with a glimmer sensor and a spatial resolution of 10-m, was launched in 2021 to meet the sustainable development goals. This study proposes a model to sharpen the existing nighttime light images with a coarse spatial resolution to expand the available period of nighttime light images with a 10-m spatial resolution in discriminating illuminating ground objects. The expanded nighttime light images with discriminated illuminating ground objects are then utilized to calculate the time-series county-level poverty index for three typical Chinese urban agglomerations with a statistically significant correlation coefficient of 0.675 (p < 0.01). Compared to the poverty statistics derived from the Chinese county statistical yearbook, the estimated poverty index can explore county-level poverty status change more effectively by completing the missing census. The time-series county-level poverty analysis demonstrates the effectiveness of Chinese endeavor in poverty reduction. >90% of the counties experienced a reduction in poverty in Beijing–Tianjin–Hebei region due to time-series economic development. Statistical analysis of the poverty index of impoverished counties demonstrates a remarkable reduction of poverty in the study areas and the significance of the persistent poverty reduction policy implementation. This study's proposed pipeline compensates for the lack of nighttime light images with a higher spatial resolution prior to 2021 and provides a reliable poverty analysis foundation for sustainable development and policymaking.

Spatiotemporal evolution and influencing factors of land-use emissions in the Guangdong-Hong Kong-Macao Greater Bay Area using integrated nighttime light datasets

Carbon emissions from land-use and land-cover change (together referred to as ‘land-use emissions’) are an important way to influence the regional carbon balance. However, due to the limitations and complexity of obtaining carbon emissions data at spatial scales, previous studies rarely reveal the long-term evolution characteristics of regional land-use emissions. Therefore, we propose a method to integrate DMSP/OLS and NPP/VIIRS nighttime light images to calculate land-use emissions over a long time series. The accuracy validation results show that the integrated nighttime light images and land-use emissions have a good fit and can accurately assess the long-term evolution of regional carbon emissions. In addition, by combining the Exploratory Spatial Analysis (ESTDA) model and the Vector Autoregressive Regression (VAR) model, we found significant spatial variation in carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), with the two regional emission centres spreading outwards between 1995 and 2020, with an increase in construction land area of 3445 km2, resulting in 257 million tons (Mt) of carbon emissions over the same period. The rapid increase in emissions from carbon sources is not offset by a correspondingly large amount of carbon sinks, resulting in a serious imbalance. Controlling the intensity of land use, optimizing the structure of land use and promoting the transformation of the industrial structure are now the keys to achieving carbon reduction in the GBA. Our study demonstrates the enormous potential of long-time-series nighttime light data in regional carbon emission research.

CRYSTAL: A novel and effective method to remove clouds in daily nighttime light images by synergizing spatiotemporal information

Satellite nighttime light (NTL) images offer a valuable depiction of the rapidly changing world by revealing the presence of artificial illumination. Thus, daily NTL images are increasingly applied to monitor human dynamics and environmental events. However, data gaps caused by cloud contamination and low-quality observations inevitably impair the effectiveness of such applications. Although a temporal gap-filling method is employed in recent Black Marble NTL products to produce seamless images, the filled images are unsuitable for quantitative analysis. Therefore, we developed an effective method, named as Cloud Removing bY Synergizing spatio-TemporAL information (CRYSTAL), to generate cloud-free NTL images with satisfactorily accurate pixel brightness and spatial continuity. Simulation experiments show that CRYSTAL can produce more accurate results than the temporal gap-filling method in fifteen cities worldwide, with an average RMSE reduction of 33.69%. Images generated by CRYSTAL restore temporal variances in NTL and are thus suitable for multi-temporal quantitative analysis. CRYSTAL can reconstruct daily NTL time series by filling gaps using available partially clear images. Experiments in two cities demonstrated that the reconstructed time series had 31.85% more valid values than the original time series and effectively revealed urban dynamics during the early stages of the coronavirus disease 2019 pandemic. In summary, CRYSTAL is a novel and effective gap-filling method for the restoration of invalid NTL observations and has the potential to generate high-quality NTL data for use in future applications.

Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.

Automatic built-up area extraction by feature-level fusion of Luojia 1–01 nighttime light and Sentinel satellite imageries in Google Earth Engine

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales.

Estimating GDP growth using VIIRS night-time light data

ABSTRACT The accuracy of GDP per capita growth rate in countries with low statistical capacity has been questioned, which hinders understanding progress of Sustainable Development Goals (SDGs) accurately. This study aims to estimate GDP per capita growth rate in countries with poor statistical quality. To achieve this goal, the study builds an econometric model with processed Visible Infrared Imaging Radiometer (VIIRS) night-time light data and other economic variables and the study further assesses the accuracy of the model by comparing the estimated GDP growth to high-quality economic data from Organization for Economic Co-operation and Development (OECD) countries between 2014 and 2018. First, due to the abnormal fluctuations in the time series generated from yearly composite night-time light images, we propose a trend analysis method for the monthly night-time light data to synthesize a more stable yearly time series. Then, we construct artificially low-quality GDP simulations and combine VIIRS data and official statistics data to construct regression models to correct the simulated GDP. Finally, to assess the model accuracy, we compare its estimates of GDP per capita growth rates to official data from high-quality OECD countries, looking for any deviations. We have two major findings as follows: first, the VIIRS data, which comprise information on human activity, are a valuable resource for estimating GDP growth; second, the GDP estimation model can reduce the artificial bias of the simulated GDP per capita growth rate by using VIIRS night-time light data and the average accuracy of the model with simulated data can reach 89%.

Material stock analysis of urban road from nighttime light data based on a bottom-up approach

In recent years, there has been an increasing focus on the dynamics of material stock, that is, the basis of material flow in the entire ecosystem. With the gradual improvement of the global road network encryption project, the uncontrolled extraction, processing, and transportation of raw materials impose serious resource concerns and environmental pressure. Quantifying material stocks enable governments to formulate scientific policies because socio-economic metabolism, including resource allocation, use, and waste recovery, can be systematically assessed. In this study, OpenStreetMap road network data were used to extract the urban road skeleton, and nighttime light images were divided by watershed to construct regression equations based on geographical location attributes. Resultantly, a generic road material stock estimation model was developed and applied to Kunming. We concluded that (1) the top three stocks are stone chips, macadam, and grit (total weight is 380 million tons), (2) the proportion of asphalt, mineral powder, lime, and fly ash is correspondingly similar, and (3) the unit area stock decreases as the road grade declines; therefore, the branch road has the lowest unit stock.

Spatiotemporal expansion modes of urban areas on the Loess Plateau from 1992 to 2021 based on nighttime light images

Long-term nighttime light data can effectively reflect the spatiotemporal evolution pattern of urban areas. Based on nighttime light data obtained from 1992 to 2021, the spatiotemporal expansion modes of urban areas on the Loess Plateau were investigated by remote sensing image band fusion processing, geospatial analysis, statistics and classification methods. Results show that the characteristics of the spatiotemporal expansion of urban areas were retained in the extraction boundaries. From the perspective of time scales, the urban areas have grown tripled to 11,481 km2. Four significant instances of urban areas increase occurred, with one large increase occurring almost every ten years. From the perspective of spatial scales, urban area expansion has two important features. First, the expansion speed of urban areas, which is based on three indicators, has large spatial dissimilarities. Five modes can be divided based on the expansion velocity of 44 cities/counties. Second, urban development increases uniquely, and can be described based on three expansion modes: Ring Extension, which mainly occurs in wide and flat basins of the main rivers; Linear Extension primarily arises on both banks of rivers in hilly and gully areas; and Point Extension mainly occurs in areas away from the main rivers. Our results highlight that the spatiotemporal expansion modes of urban areas on the Loess Plateau retain the significant influence of the geological environment, which can provide scientific guidance for the sustainable development of human-land relationships.

Stability and Changes in the Spatial Distribution of China’s Population in the Past 30 Years Based on Census Data Spatialization

As the world’s most populous country, China has experienced massive population growth and dramatic regional migration over the past 30 years. From 1990 to 2020, the national population increased by 24.4%, the urban population tripled, and the rural population declined by 41.0%. Combined with complex topographic features, unique characteristics of the population distribution have emerged. Many studies have examined changes in the spatial distribution of the population. However, few studies have examined the stability of certain aspects of this distribution over the last 30 years, particularly at the raster scale, which may provide important information for future research and development plans. Based on land use maps and nighttime light images, China’s census data from 1990 to 2020 was scaled down to a resolution of 1 km using a method called multiple linear regression based on spatial covariates. The results show that there were some striking features of both stability and change in the spatial distribution of China’s population over the past three decades. The population shares divided by the Hu line, the Qinling-Huaihe line, and the three-step staircase have remained almost unchanged. In contrast, the population share of the coastal region has risen from 23.7% to 29.0% during the study period. The urban areas have expanded by 1.35 times and their population has doubled. In addition, for every 1 km2 increase in the urban areas, an area of 29.4 km2 has been depopulated on average. This suggests that urbanization can alleviate population pressure in larger areas. However, the coastal regions and urban and peri-urban areas were the main areas of population density growth, so they required a great deal of attention for ecological protection.

Geometric Correction of Luojia 1-01 Nighttime Light Image based on Road Network

Geometric Correction of Luojia 1-01 Nighttime Light Image based on Road Network

Extraction of Impermeable Surfaces Based on Multi-Source Nighttime Light Images of Different Geomorphological Partitions

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.

Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities.

Dynamic Information Grid Optimization of Trade Logistics Industry Sharing Based on Remote Sensing Image Analysis of Guangdong, Hong Kong and Macao

Using the nighttime light image of "Luojia No. 1", the urban built-up area of the Guangdong-Hong Kong-Macao Greater Bay Area is extracted by the simple threshold method and the urban night light index method adjusted by vegetation. It can weaken the oversaturation phenomenon of the "Luojia No. 1" image and reduce Mistaken extraction due to the "overflow" of the image. Calculate and compare the landscape index of urban built-up areas in the Greater Bay Area. Then use the gravity model to measure the highway logistics gravity in Guangdong, Hong Kong and Macau, determine the road freight volume between cities, and use the logistics network The road logistics network in Guangdong, Hong Kong and Macao is optimized with the goal of minimizing the total cost, and the robustness of the optimization results under different carbon policies is further tested. The economic and social development of mainland China has had an important impact on the evolution of logistics layout in the Guangdong-Hong Kong-Macao Greater Bay Area. In regional logistics planning, attention should be paid to the impact of industrial agglomeration such as spatial correlation, and attention should be paid to the impact of the environment and economic cycles on the optimization of regional logistics layout and the deep integration of industries. influences.