Nighttime Light Data Research Articles

Assessing “Invisible Loss” During Medium-Term Earthquake Recovery: The Case of Indonesia's 2016 Aceh Earthquakes

This study presents the results of a medium-term assessment of earthquake recovery, aiming to measure non-economic welfare losses resulting from the indirect impacts of earthquakes. While most earthquake assessments concentrate on the economic loss and damage caused by direct impacts of earthquakes, this study focuses on capturing these less visible non-economic losses. The 2016 Aceh earthquakes in Indonesia serve as the case study for this assessment. By applying the difference-in-differences method, we compared the changes in a range of non-economic welfare indicators in earthquake-affected and unaffected villages two years before (2014) and two years after (2018) the catastrophic earthquakes. In addition to administrative data, we tested the suitability of average monthly and annual night-time light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as an alternative way to capture non-economic welfare losses. The findings from administrative data suggested a significant deterioration in, poverty, access to healthcare and access to socio-economic services in earthquake-affected villages relative to unaffected villages in 2018, compared to 2014. The analysis of nightlights data revealed contrasting results, suggesting that disaster recovery assessments using nightlights may not accurately capture non-economic welfare changes on the ground. Our research indicates that the detrimental effects of earthquakes on non-economic welfare persisted for years following the initial devastation. Utilizing administrative data can help pinpoint the nature and extent of non-economic losses and inform the development of policies to aid in the effective recovery of affected communities.

Study on the change of urban spatial structure in Three Northeast Provinces of China based on the coupling relationship between POI and nighttime light data

Identifying and measuring urban spatial structure is a prerequisite for understanding urban spatial characteristics, formulating urban development strategies and testing urban planning results. As important data sources that can visually reflect the spatial distribution characteristics of urban socio-economic and other physical elements, urban points of interest (POI) and nighttime light data play an important role in the study of urban spatial structure. In this study, the main urban areas of 36 cities of the Three Northeast Provinces (Heilongjiang Province, Jilin Province, Liaoning Province) were selected as the study area, the POI (6,553,294 points of interest) and nighttime light data from 2010, 2016, and 2022 were chosen as the basic research data, and the methods of point kernel density estimation, data griddedness, and multifactor combination mapping were used to analyze the developmental dynamics of the urban spatial structure. The study demonstrated: (1) The spatial coupling consistency of POI and nighttime light data in the main urban areas of the Three Northeast Provinces was high, and both had good applicability in urban spatial structure research; (2) POI and nighttime light values formed the spatial pattern of “axis + core − periphery” with Shenyang, Dalian, Changchun and Harbin as the core, while the coupling relationship between the POI and nighttime light data identified that the main urban areas in the Three Northeast Provinces presented a centralized agglomeration type, a decentralized grouping type, a belt combination type and a radial expansion type urban spatial structure; (3) From the perspective of changes in the coupling relationship between POI and nighttime light, most of the main urban areas of resource-mature cities, resource-regeneration cities and non-resource cities were affected by the regional development agglomeration and the “T” railway network, and the “high/medium–high/medium” area showed an expanding tendency; most of the main urban areas of resource-decline cities were affected by the lower development potential and the deprivation of economic factors by the surrounding core cities, while the “high/medium–high/medium” areas showed a contracting tendency. The results of the study can provide a scientific basis and theoretical reference for the future adjustment of urban spatial structure, planning and construction as well as resource allocation in the main urban areas of the cities in the Three Northeast Provinces.

Human activities are intensifying the spatial variation of landslides in the Yellow River Basin

Human activities are a triggering factor for landslides in the Yellow River Basin (YRB, China). However, the extent to which the spatial distribution of landslides is affected by human activities is unclear. We constructed a human activity intensity index (HAII) based on nighttime light data and land cover data. Regression and dominance analyses were used to compare the effects of the HAII, precipitation, distance to river, distance to fault, topographic relief and slope on the landslides spatial density (LSD). The results showed that in the YRB, the HAII, as a dominance influencing factor, had a significant positive influence on the LSD. Moreover, regional differences in the human disturbance of nature intensify the spatial variation of LSD. To quantify the intensity of human disturbance to nature, a human-nature conflict index (HNCI) is constructed by quantifying the difference between the slope distributions of artificial and natural landscapes. The results show that in the middle section of the YRB, humans are developing more steep mountainous areas, leading to more dense landslides. This study provides a reference for landslide risk management and land use planning in the YRB.

How Does Heavy Precipitation of Varying Durations Respond to Urbanization in China?

AbstractHeavy precipitation, which is changing significantly as Earth's climate warms, can result in flooding that seriously damages societies. However, little is known about how heavy precipitation of varying durations responds to the diverse gradients of urban development in China. Through statistical analyses spanning from 1990 to 2021, we have examined shorter‐duration (≤3 days) and longer‐duration (>3 days) heavy precipitation across a spectrum of urban development, encompassing long‐term built‐up (LTB), recently built‐up (RTB), and rural background catchments within each urban agglomeration catchment (UAC) across China. We find that urbanization primarily influences shorter‐duration heavy precipitation, with a more pronounced effect observed in the LTB catchments. Conversely, the influence of urbanization on longer‐duration heavy precipitation appears to be more weakened in the RTB catchments. The intensification of shorter‐duration heavy precipitation induced by urbanization is more pronounced in humid regions and within larger UACs, while the urban effect on longer‐duration heavy precipitation is weaker in humid regions and within larger UACs. Notably, the attribution analysis results of the geographical detector model confirm our findings. Anthropogenic‐related factors (population density, nighttime light data, impervious surface percent, land surface temperature) significantly influence shorter‐duration heavy precipitation in more UACs than natural factors (distance from the coast, wind and elevation), while natural factors dominate longer‐duration events in larger UACs across China. Our results highlight the necessity of considering the spatial difference between the UAC center and UAC periphery for accurate projections and effective prevention of heavy precipitation and potential flood risks in the future.

Are Disaster Impact Estimates Distorted by Errors in Popular Night-Time Lights Data?

Satellite-detected night lights data are widely used to evaluate economic impacts of disasters. Growing evidence from elsewhere in applied economics suggests that impact estimates are potentially distorted when popular Defense Meteorological Satellite Program (DMSP) night lights data are used. The low resolution DMSP sensor provides blurred and top-coded images compared to those from the newer and more precise Visible Infrared Imaging Radiometer Suite (VIIRS) images. Despite this, some disaster impact studies continue to use DMSP data, which have also been given a new lease of life through the use of harmonized series linking DMSP and VIIRS data to provide a longer sample. We examine whether use of DMSP data affects evaluations, using expected typhoon damages in the Philippines from 2012–19 as our case study. With DMSP data, negative impacts on economic activity from expected damages at the municipality level appear over 50% larger than when the VIIRS data are used. The DMSP data give the appearance of greater spatial autocorrelation in luminosity and through this channel may tend to spread apparent spatial impacts of shocks. Harmonized data that adjust VIIRS images to be like the DMSP data also have this spurious autocorrelation so researchers should be cautious in using these data for disaster assessments.

Achieving urban vibrancy through effective city planning: A spatial and temporal perspective

Achieving urban vibrancy is a critical goal for urban planning and policy-making, and the city's physical plan plays an important role in shaping and supporting urban vibrancy. However, the effectiveness of urban planning strategies in optimizing resource allocation and fostering a vibrant city remains unclear. This study aims to contribute to this understanding by exploring the spatial and temporal effects underlying the association between urban vibrancy and urban forms. Using mobile-phone location-request data along with urban nighttime light data as a proxy for urban vibrancy in Shenzhen, China, we conducted a geographically and temporally weighted regression (GTWR) analysis to examine the relationship between land use, street configuration, and urban vibrancy. The results showed that a diverse land use mix and the proportions of land allotted for sports and cultural usage, parks, and greenspace generally have a positive impact on urban vibrancy. We noted the facilitating role of degree centrality and closeness centrality on urban vibrancy, compared to the negative impact of betweenness centrality. Our analysis of local spatial variations in the influence of road networks suggests a potential connectivity threshold for attracting people to specific areas. Finally, we identified a divergence between weekdays and weekends in terms of temporal variations.

Spatiotemporal dynamics and influencing factors of CO2 emissions under regional collaboration: Evidence from the Beijing-Tianjin-Hebei region in China

Against the backdrop of global climate change and the “dual carbon” target, cities have a significant responsibility to achieve carbon reduction targets. As a crucial urban agglomeration in northern China, effectively balancing economic growth with CO2 emission reduction to achieve high-quality economic development remains a significant challenge that the Beijing-Tianjin-Hebei region should address both presently and in the future. The objective of this study is to utilize nighttime lighting data and energy consumption information to quantify the CO2 emissions of diverse cities within the Beijing-Tianjin-Hebei region spanning from 2006 to 2020. The research aims to analyze the spatial progression patterns of CO2 emissions across these urban centers, identify key determinants and their interrelations, and delve into the underlying mechanisms pivotal for advancing carbon mitigation strategies within urban agglomerations. The results indicate that: with an exception in Beijing where CO2 emissions slightly decreased compared to 2006, CO2 emissions increased across cities in the Beijing-Tianjin-Hebei region by 2020. High-value CO2 emission areas are primarily concentrated in central of the study area, exhibiting negative spatial correlation characteristics. Based on its urban development positioning, it is imperative for the Beijing-Tianjin-Hebei urban agglomeration to formulate and implement carbon reduction strategies on innovative development, industrial upgrading, and ecological protection among other aspects towards coordinated low-carbon development.

Identifying the Spatial Range of the Pearl River Delta Urban Agglomeration by Fusing Nighttime Light Data with Weibo Sign-In Data

Accurately identifying the spatial range of urban agglomerations holds significant practical importance for the precise allocation of various elements and coordinated development within urban agglomerations. However, current research predominantly focuses on the physical spaces of urban agglomerations, overlooking their sphere of influence. This study begins with the spatial interactions of population elements within urban agglomerations and fuses Weibo sign-in data with NTL data to identify the spatial range of urban agglomerations. It further compares and validates the results before and after the fusion of data. The results reveal that the accuracy of identifying the spatial range of urban agglomerations with the fusion of NTL data and Weibo sign-in data has improved by 7%, with a Kappa increase of 0.1766 compared to using NTL data alone, which indicates that fusing social media data can significantly enhance the accuracy of identifying the spatial range of urban agglomerations. This study proposes a novel approach for identifying the spatial range of urban agglomerations through the fusion of NTL data and social media data from a data fusion perspective. On one hand, it supplements the application of data fusion in the study of urban agglomeration spaces; on the other hand, it accurately identifies the spatial range of urban agglomerations, which holds great practical value for the sustainable development of urban agglomerations.

Dynamics of China’s natural cities and their living structures derived from nighttime lights and populated grids

The term “natural cities” refers to human settlements or human activities in general that are naturally or objectively defined. In this study, we utilized the head/tail breaks method to identify natural cities and characterize their living structures in mainland China from 2000 to 2020. This identification was based on nighttime light (NTL) and gridded population distribution (GPD) data, which, respectively, indicate urban areas and residential settlements. Furthermore, we explicitly identified the evolutions of two distinct categories of natural cities and the dynamics of their inherent living structures. Our objective was to verify that the head/tail breaks method is a powerful approach to deriving natural cities that signify various types of human settlements from diverse data sources, while also effectively characterizing their living structures. Also, this article contributes a novel perspective to examine the inequality between the urban-area expansion and the evolution of residential settlements in urban areas (or urban settlements). Our findings reveal a substantial increase in both the number and sizes of urban areas over time. However, there was an intriguing trend observed in urban settlements, where an increasing number corresponded to a gradual decrease in size. Additionally, the inequality exhibited regional disparities and rapidly developing regions showed a higher potential to enter a turning year of urban areas surpassing urban settlements. The dynamics of living structures show that urban settlements always had more hierarchical levels, more substructures, a much higher degree of order, and a much more living (orderly or beautiful) structure than urban areas. The urban-area expansion in mainland China was extremely rapid over the past decades, but the evolution of urban settlements was more “natural” than that (i.e., it consistently generated a living structure characterized by a higher degree of order).

Integrating “quality-risk-demand” framework and circuit theory to identify spatial range and priority area of ecological security pattern in a rapidly urbanizing landscape

Regional ecological security pattern (ESP) can provide the basis for regional territorial spatial planning and urban ecological protection. Taking the Wuhan metropolitan area as the study area, a binary logistic regression model was used to quantify the evolutionary driving forces and transformation risk of ecological land. An identification framework of ecological source based on “quality-risk-demand” was constructed to identify ecological source by calculating the ecological land comprehensive value from three aspects of ecological land quality, degradation risk, and human demand. The ecological resistance surface was calculated according to land-use type and nighttime light data, further using circuit theory to identify the spatial range and its key areas of ecological corridors, and to identify the specific spatial range of ESP and their priority area. The results showed ecological sources were 382.4 km2, accounting for 17.3% of the metropolitan area. The area of ecological corridors in the terrestrial ecosystem (TEC) was 14.38 km2, with an average width of 77.8 m, including 4.99 km2 of pinch points and 3.71 km2 of barriers, indicating poor actual connectivity of the TEC. Ecological corridors in the aquatic ecosystem (AEC) were 107.67 km long, including 57.86 km of pinch points and 14.24 km of barriers, indicating poor substitutability of the AEC. Overall, the ESP of “three horizontals, three verticals, and four clusters” were identified, where sources, pinch points and barriers were the priority areas for protection and restoration in constructing it. Existing protection spaces were within the ecological source range, and the identification results of the ecological corridor were highly consistent with the actual situation, verifying the ecological significance of the identification framework. This study focuses on the needs of urban residents, combining the intrinsic quality and extrinsic risks of urban ecological land, proposes an integrated framework for identifying urban ecological sources, identifies the spatial range of ecological corridors and their key areas based on circuit theory, and attempts to provide a quantitative framework and case reference for ESP construction in other urban areas.

Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

Analyzing the change trend of urban green space (UGS) and exploring related driving forces can provide scientific reference for sustainable development in rapidly urbanizing areas. However, the spatial and temporal driving mechanisms of the drivers on UGS patterns at different scales are still not deeply understood. Based on the GlobeLand30 land cover data, nighttime lighting data and spatial statistics from 2000 to 2020, this study analyzed the size, shape and diversity of UGS in Guangzhou at the urban level, gradient level and township level with multiple landscape indices. Diversity means the richness of UGS patch types. The selected indices include percent of landscape (PLAND), largest path index (LPI), landscape shape index (LSI), aggregation index (AI) and Shannon’s diversity index (SHDI). The spatiotemporal heterogeneity of the drivers was then explored using the spatiotemporal weighted regression (GTWR) method. Results showed the following: (1) During 2000−2020, the total amount of UGS in Guangzhou increased slightly and then decreased gradually. UGS was mainly transferred into artificial surfaces (lands modified by human activities). (2) The UGS landscape showed a non-linear trend along the urban–rural gradient and fluctuated more in the interval of 20–60% urbanization level. PLAND, LPI and AI decreased significantly in areas with higher levels of urbanization. LSI increased and SHDI decreased significantly in areas with lower levels of urbanization. At township level, the landscape indices showed significant spatial autocorrelation. They transformed from discrete changes at the edge and at the junction of the administrative district to large-scale aggregated change, especially in northern areas. (3) The size of UGSs was mainly influenced by natural factors and population density, but their shape and diversity were mainly influenced by socio-economic factors. More regular shapes of green patches were expected in higher urbanization areas. Population agglomeration positively influenced green space patterns in the northeastern and southern regions (Zengcheng, Conghua and Nansha). Meanwhile the negative influence of urban expansion on the green space pattern in the central and southern regions decreased over time. This study contributes to an in-depth understanding of how the key factors affect the different changes of UGS with time and space and provides methodological support for the long-term zoning planning and management of UGS.

Exploring economic activity from outer space: A Python notebook for processing and analyzing satellite nighttime lights

Nighttime lights (NTL) data are widely recognized as a useful proxy for monitoring national, subnational, and supranational economic activity. These data offer advantages over traditional economic indicators such as GDP, including greater spatial granularity, timeliness, lower cost, and comparability between regions regardless of statistical capacity or political interference. However, despite these benefits, the use of NTL data in regional science has been limited. This is in part due to the lack of accessible methods for processing and analyzing satellite images. To address this issue, this paper presents a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images. First, the notebook introduces a cloud-based Python environment for visualizing, analyzing, and transforming raster satellite images into tabular data. Next, it presents interactive tools to explore the space-time patterns of the tabulated data. Finally, it describes methods for evaluating the usefulness of NTL data in terms of their cross-sectional predictions, time-series predictions, and regional inequality dynamics.

Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data

Carbon emission inventories in urban built-up areas are critical for formulating effective emission reduction policies. Nighttime light data, reflecting socioeconomic activities, is routinely used as a proxy for estimating emissions. However, the reliability of this proxy is limited in areas with minimal or absent lighting and demonstrates variability according to the urban development. To address these limitations, a novel approach was developed. Firstly, it enhances the representation of human activities under different light values through integrating light values and population distributions into multidimensional proxy features. Secondly, cities with different socio-economic development were clustered to minimize the development disparities within the same cluster. Four clusters were detected including ‘Agriculture-dependent & Low-density’, ‘Agriculture-dependent & High-density’, ‘Industry-oriented’, and ‘Tertiary-oriented’. Then A multilayer perceptron regressor, refined with a moving average method, was employed to model the nonlinear relationships between emissions and these proxy features. The results show a significant increase in modeling accuracy when regional development disparities are incorporated through this clustering strategy. Additionally, the Shapley Additive Explanatory method was utilized to analyze the influence of regional development heterogeneity on the estimation model. It demonstrated that areas with high light intensity and population significantly influence emission estimates in all clusters, except for the ‘Industry-oriented cluster’. This finding underscores the varying effectiveness of proxy features across different regions, emphasizing the importance of considering regional development disparities. Finally, based on the estimated inventory obtained, spatio-temporal hot spot detection pinpointed eastern and central China as significant emission hot spots. This research not only enhances the data resources for managing carbon emissions but also underscores the need for inter-city cooperation strategies to achieve China's carbon reduction targets.

Assessment of post-pandemic economic recovery in Beijing, China, using night-time light data

ABSTRACT The coronavirus disease 2019 (COVID-19) pandemic posed a significant impact on human society, resulting in substantial changes in night-time light (NTL). This study developed a novel NTL index to estimate the quarterly Gross Domestic Product (GDP) at the county level in Beijing. This study aimed to investigate the economic recovery status in Beijing by establishing regression models of economic development before and after the pandemic. The estimated quarterly GDP at the county level was found to have a strong correlation with actual quarterly GDP statistics, with an average coefficient of determination (R 2) of 0.790. The regression models of the economic development of Beijing county-level districts before and after the pandemic were established after seasonal decomposition of the quarterly GDP. Based on the economic recovery framework for the pandemic in Beijing, most districts in the city had exhibited low economic resilience in terms of resistance and recoverability before and after the pandemic. Although the economy of Beijing is recovering gradually, it has not yet restored to pre-pandemic normal levels. These findings imply that NTL data can help assess the impact of emergency events on the disturbance of economic activity.

Spatialization of electricity consumption by combining high-resolution nighttime light remote sensing and urban functional zoning information

ABSTRACT An accurate and timely spatialization of electricity consumption is fundamental for energy management and sustainable urban development. While previous research has relied heavily on statistical data or moderate-resolution nighttime light data, this study has presented a new spatialization method by combining high-resolution Luojia 1–01 nighttime light and urban functional zoning information. The total electricity consumption volume can be allocated to each land use pixel based on its strong linear relationship with nighttime light. Specifically, urban functional zoning data were connected with the corresponding economic sectors to differentiate the complex relationships between electricity consumption and nighttime light within different zones. The digital number value of every pixel can be multiplied by its corresponding electricity consumption coefficient. A number of comparisons have indicated that the proposed method can more accurately characterize the spatial distribution of electricity consumption. Our results can exhibit much clearer outlines and more detailed internal characteristics. More importantly, urban functional zoning information can be used to distinguish the electricity consumption of various economic sectors at fine scales. The proposed method is expected to capture the spatial characteristics of electricity consumption in a timely and accurate manner. The findings can help local authorities formulate sustainable energy utilization and emission reduction strategies.

Research on the temporal and spatial pattern evolution characteristics and decoupling effect of electricity power consumption in China

Exploring the evolution of spatial and temporal patterns of electricity power consumption in China is of great significance and value for the promotion of Carbon Peaking and Carbon Neutrality Strategy and the scientific allocation of electricity resources. Therefore, this paper takes China as an example and innovatively applies nighttime light data from a combination of two remote sensing satellites. And using spatial analysis methods to analyze the evolution of spatial and temporal patterns of electricity power consumption over a longer study time series. Meanwhile, this paper also uses the Tapio decoupling model to study the decoupling effect of electricity power consumption and economic growth in China. The results of the study show that, during the period 2000–2019, China's electricity power consumption was in an unbalanced state, with the eastern coastal region dominating. It also formed a gradient distribution from the coastal area to the inland area. In terms of spatial connection, it shows an unbalanced distribution pattern of “low in the west and high in the east”. In addition, the spatial linkage of electricity power consumption among provinces is more than 93 % of weak linkage, and gradually increases to 97 %, indicating that the spatial linkage of electricity power consumption among regions is gradually weakening. The decoupling state of electricity power consumption in China is characterized by two types of decoupling: Weak decoupling and Expansive coupling. The decoupling status has experienced a significant change from 84 % of provinces being Expansive coupling to 100 % of provinces being Weak decoupling.

River valley urban network and morphology: A study on the urban morphology evolution of Lanzhou.

The present study investigates the dynamic evolution characteristics of urban spatial morphology by analyzing real road network data from 2000, 2010, and 2020, along with nighttime lighting data employing spatial analysis methods and spatial syntax models. Accordingly, two separate dimensions of urban morphology: internal and external, are covered. First, the integration and synergy of interior morphology features are analyzed using spatial syntactic modeling. Subsequently, the spatial compactness, fractal dimension, and level of center of gravity shift of the city are assessed by combining the nighttime lighting data with the earlier dataset. This analysis facilitated the deep exploration of the spatiotemporal evolution of the city's external morphology. Building upon this foundation, the interaction between the "internal and external" domains was analyzed further. The main findings of the study reveal a synchronous pattern of urban expansion throughout the evolution of urban spatial morphology. Furthermore, the urban form was observed to undergo a progressive transformation, transitioning from a "single core" morphology to a "primary and secondary double core" morphology. Over time, this development progressed and evolved into a "belt-like multi-core" structure. Additionally, the coupling characteristics further validate the relationship between the structure of the road network and the urban morphology in river valley-type cities. In particular, accessibility of dense and horizontally distributed transportation network was found to significantly influence the spatial development of these cities. As observed, the findings provides valuable insights into understanding the characteristics of internal and external associations regarding urban spatial patterns.

The impact of carbon emission trading schemes on China’s economic inequalities between and within urban and rural areas based on the satellite data: a county-level analysis

ABSTRACT Although China’s carbon emission trading schemes (CETS) garnered significant attention in the field of climate mitigation, limited data availability has hindered exploration into the impact of CETS on economic inequality at the county level in China. Given its potential to facilitate industrial upgrading and generate employment opportunities, the adoption of CETS holds promise for reducing economic inequality. To address data limitations, we employed a combination of gridded nighttime light data and land use data to estimate China’s county-level Dagum Gini coefficient and its components. Subsequently, we applied a difference-in-difference-in-difference method to examine how CETS affected economic inequalities between and within urban and rural areas in China. Our findings reveal that: (1) CETS significantly reduced overall economic inequality in counties with a high proportion of secondary industry output; (2) this reduction primarily occurred within urban and rural areas rather than between them. Further heterogeneity analysis demonstrates that while CETS effectively mitigated economic inequality between urban and rural areas in central and western regions, it did not achieve similar outcomes in the eastern region; (3) service industry development and rural GDP per capita served as important channels through which CETS reduced economic inequality within rural areas. Infrastructure development driven by CETS primarily addressed economic inequality within urban areas rather than rural areas.

Monitoring spatiotemporal changes in urban flood vulnerability of Peninsular Malaysia from satellite nighttime light data

Urban flood vulnerability monitoring requires a large amount of socioeconomic and environmental data collected at regular time intervals. However, collecting such a large volume of data poses a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region as floods are the most devastating and recurrent phenomena in the region. The study developed a flood vulnerability index (FVI) based on socioeconomic and environmental data from a single year. This FVI was then linked to NTL data using an Adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000–2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient, and percentage of foreign nationals. The ANFIS technique performed well in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ∼ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.

Precise GDP Spatialization and Analysis in Built-Up Area by Combining the NPP-VIIRS-like Dataset and Sentinel-2 Images.

Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at -52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.