Defense Meteorological Satellite Program/Operational Linescan System Research Articles

Which Nighttime Lights Data Better Represent India’s Economic Activities and Regional Inequality?

This paper demonstrates how the estimation of economic activities and inequality for the Indian economy based on nighttime lights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite outperforms those obtained using traditional Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) sources. In the extant literature, nighttime lights data have been frequently used as a proxy for economic activities, especially at granular geographical levels where gross domestic product data are otherwise unavailable. Since the traditional DMSP-OLS-based nighttime lights data suffer from measurement errors due to blurring and top coding, VIIRS-based nighttime lights data, which are devoid of such limitations, are gaining popularity. We show that at the subnational or district level in India, VIIRS satellite-based nighttime lights data are indeed a better proxy for economic activities and inequality. Our robust findings imply that all future scholarly works that employ nighttime lights data for the Indian economy should use VIIRS-based data for greater accuracy compared to other similar data sources.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study

BackgroundUrbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.MethodsCounty-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.ResultsThe mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from − 0.205 to − 0.131.ConclusionsThe influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Urban polycentrism and total-factor energy efficiency: An analysis based on the night light data

Improving energy efficiency is crucial for environmental protection and resource conservation. Considerable urban system distribution is vital to realizing high energy efficiency. However, studies on energy efficiency from this spatial perspective remain limited. Using the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and visible infrared imaging radiometer suite (VIIRS) nighttime light data, we measured urban polycentrism (UPC) and employed improved stochastic frontier analysis (SFA) for total-factor energy efficiency (TEE) measurement. Analyzing panel data from 332 prefecture-level cities across 27 provinces of China, we used the spatial economic and threshold models to calculate UPC's impact on TEE and its threshold conditions. The results revealed UPC's effect on TEE and the control variables (economic development, urbanization rate, and industrial structure). The pattern suggests that only when economic development, urbanization rate, and industrial structure reach a certain (range) level in some zones, does the effect change from one condition (inhibition/promotion) to another (promotion/inhibition). Under certain conditions, a win-win situation between UPC and TEE is achieved. Finally, relevant policy recommendations for energy efficiency improvements based on urbanization are discussed. To the best of our knowledge, this is the first study to systematically investigate the impact of UPC on TEE at the city level.

Spatiotemporal changes of aerosol optical depth and its response to urbanization: a case study of Jinan City, China, 2009-2018.

With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009-2013, the annual average variation of AOD increased by 20.6%, while during 2014-2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements.

Monitoring Urban Expansion (2000–2020) in Yangtze River Delta Using Time-Series Nighttime Light Data and MODIS NDVI

The Yangtze River Delta Urban Agglomeration (YRDUA), which is located in the convergence zone of “The Belt and Road Initiative”, is one of the regions with the best urbanization foundations in China. Referring to the four five-year plans (China’s national economic plan), this study aimed to investigate the spatiotemporal patterns of urban expansion in the YRDUA from 2000 to 2020. To conduct a long-term analysis of urbanization, an extended time series (2000–2020) of a nighttime light (NTL) dataset was built from the multi-temporal Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data (2000–2013), and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data (2014–2020); data from these sources are crucial to understanding the urbanization processes in the region in order for more effective decision making to take place. The support vector machine (SVM) method was used to extract urban clusters from the extended time-series NTL data and MODIS NDVI products. The evolution of the urban expansion intensity was detected at city scales, and the inequality of urban growth was demonstrated using the Lorenz curve and Gini coefficient. Finally, a quantitative relationship between urban NTL intensity and socio-economic data was built to explore the main factors that control urban intensity. The results indicated that the urban extents extracted from time-series NTL data were consistent with those extracted from Landsat data, with an average overall accuracy (OA) of 89%. A relatively fast urbanization pace was observed during the 10th five-year plan (from 2000 to 2005), which then declined slightly in the 11th five-year plan (from 2006 to 2010). By the 12th and 13th five-year plan (from 2011 to 2020), urban clusters in all cities tended to grow steadily. Urban expansion has presented a radial pattern around the main cities, with sprawl inequality across cities. The results further revealed that the primary factors controlling NTL brightness were gross domestic product (GDP), total fixed asset investment, tertiary industry, gross industrial output, urban area, and urban permanent residents in city clusters, but the same driving factors had a different contribution order on the NTL intensity across cities. This study provides significant insight for further urbanization study to be conducted in the YRDUA region, which is crucial for sustainable urban development in the region.

Multi-nighttime-light data comparison analysis based on image quality values and lit fishing vessel identification effect

Fisheries provide high-quality protein for many people, and their sustainable use is of global concern. Light trapping is a widely used fishing method that takes advantage of the phototropism of fish. Remote sensing technology allows for the monitoring of lit fishing vessels at sea from the air at night, which supports the sustainable management of fisheries. To investigate the potential of different nighttime light remote sensing data for lit fishing vessel identification and applications, we used the fuzzy evaluation method to quantitatively assess images in terms of their radiometric and geometric quality, and Otsu’s method to compare the effects of lit fishing vessel identification. Three kinds of nighttime lighting data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), Visible infrared imaging radiometer suite day/night band (VIIRS/DNB), and Luojia1-01(LJ1-01) were analyzed, compared, and application pointers were constructed. The results are as follows. ①In the image radiation quality evaluation, the information entropy, clarity, and noise performance of the LJ1-01 image are higher than those of the DMSP/OLS and VIIRS/DNB images, where the information entropy value of the LJ1-01 image is nearly 10 times that of VIIRS/DNB and 23 times that of DMSP/OLS. The average gradient value is 14 times that of the image from VIIRS/DNB and 1,600 times that of DMSP/OLS, while its noise is only about 2/3 of the VIIRS/DNB image and 1/3 of the DMSP/OLS image. In the geometric quality assessment, the geometric positioning accuracy and ground sampling accuracy of the VIIRS/DNB image is the best among the three images, with a relative difference percentage of 100.1%, and the LJ1-01 and DMSP/OLS images are relatively lower, at 96.9% and 92.3%, respectively. ② The detection of squid fishing vessels in the Northwest Pacific is taken as an example to compare the identification effects of three types of data: DMSP/OLS, VIIRS/DNB, and LJ1-01. Among these data, DMSP/OLS can effectively identify the position of the lit fishing boat, and VIIRS/DNB images can accurately estimate the spatial position and number of lit fishing boats with large distances. However, in the case of fishing boats gathering or clustering, the number of fishing vessels could not be identified. This led to the detected number of lit fishing vessels being less than the real value. For the VIIRS/DNB and LJ1-01 images with a 5′×8′ span in the same spatiotemporal range using the same batch of pelagic squid fishing vessels, LJ1-01 extracted 18 fishing vessels. VIIRS/DNB extracted 15, indicating that LJ1-01 can distinguish multiple fishing vessels in the lighted overlapping area, thus accurately identifying the number of fishing vessels. The application pointing table generated based on the results of the three data analyses can provide a reference for sensor/image selection for nighttime light remote sensing fishery applications and a basis for more refined fishing vessel identification, extraction, and monitoring.

A Review of the Far-Reaching Usage of Low-Light Nighttime Data

To assess the current and future utility of low-light satellite data, this paper reviewed 1630 papers, presentations, theses, and dissertations using day/night band (DNB) data from the visible infrared imaging radiometer suite (VIIRS) imager and its precursor, the Defense Meteorological Satellite Program’s Operational Linescan system (DMSP-OLS) series from the 1970s through to the year 2021. By the way of a categorical system, we take inventory of the myriad applications of these data to a wide variety of disciplines, ranging from social to natural science, oceans to atmosphere, and biology to civil engineering. Papers from social science fields dominate this spectrum, pointing to the unique aspect of low-light observations in their ability to observe aspects of human civilization at night. We also look at the stratification between applications using natural vs. artificial light, the use of moonlight, and the context of the key earth climate system elements. In light of these findings, a discussion is provided for the future of low-light measurements. Since the start of the VIIRS series, there has been a rapid increase in interest in the use of these data for numerous fields, pointing towards a nascent field centered on the nocturnal earth system science that is enabled by these novel and newly quantifiable measurements. This study is of significant importance in evaluating current uses of low-light data and possible architecture solutions for next-generation satellites.

Prediction of Multi-Scale Socioeconomic Parameters from Long-Term Nighttime Lights Satellite Data Using Decision Tree Regression: A Case Study of Chongqing, China

The Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and the Suomi National Polar-Orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light (NTL) data provide an adequate proxy for reflecting human and economic activities. In this paper, we first proposed a novel data processing framework to modify the sensor variation and fit the calibrated DMSP/OLS data and NPP/VIIRS data into one unique long-term, sequential, time-series nighttime-lights data at an accuracy higher than 0.950. Both the supersaturation and digital value range have been optimized through a machine learning based process. The calibrated NTL data were regressed against six socioeconomic factors at multi-scales using decision tree regression (DTR) analysis. For a fast-developing city in China—Chongqing, the DTR provides a reliable regression model over 0.8 (R2), as well explains the variation of factor importance. With the multi-scaled analysis, we matched the long-term time-series NTL indices with appropriate study scale to find out that the city and sub-city region are best studied using NTL mean and stander derivation, while NTL sum and standard deviation could be better applied the scale of suburban districts. The significant factor number and importance value also vary with the scale of analysis. More significant factors are related to NTL at a smaller scale. With such information, we can understand how the city develops at different levels through NTL changes and which factors are the most significant in these development processes at a particular scale. The development of an entire city could be comprehensively explained and insightful information can be produced for urban planners to make more accurate development plans in future.

Spatiotemporal dynamics of CO2 emissions: a case study of the "New Yangtze River Delta" in China.

Due to vast territory and disparate regional energy varieties and efficiency in China, the spatial and temporal distribution of CO2 emissions in regions is quite different. But the formulation of previous carbon reduction policies was mainly based on national or provincial emissions data, lacking of refined scale data. This paper first collected Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) night light datasets from 1992 to 2013 and constructed a CO2 emissions inversion estimation model. Then, the spatiotemporal dynamics were analyzed by global and local spatial autocorrelation methods. Findings are as follows: (1) Total CO2 emissions in the Yangtze River Delta showed an overall growth trend from 396 million tons in 1992 to 1.825 billion tons in 2013, with an average annual growth rate of 17.18%. (2) The relatively slow growth accounted for the highest proportion in five growth types of CO2 emissions and were mainly concentrated in the underdeveloped southwestern regions of the Yangtze River Delta. The rapid-growth were agglomerated in the eastern coast areas. (3) Hot spots and sub-hot spots were concentrated in Shanghai, Suzhou, and Ningbo. Cold spots and sub-cold spots included southwest part of Anhui and Zhejiang. The findings provided a decision-making basis for mitigating CO2 emissions more reasonably.

Improved DMSP nighttime light monthly products over India

ABSTRACT Monthly night-time light (NTL) data can be very useful for studying intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines started providing monthly NTL imagery for free since 2021; based on Defence Meteorological Satellite Program Operational Linescan System (DMSP-OLS) data. In the current study, an attempt has been made to produce an improved monthly DMSP product from 1992 to 2013. The monthly DMSP product suffers from several drawbacks such as spatial inconsistency, random fluctuations in the data for consecutive years, pixel saturation in bright cores, and blooming effect around settlements. As a result, geometric errors, background noise, and radiometric errors persist in the monthly DMSP images. This research tries to address each of these errors by applying various techniques to produce a vigorously tested and improved monthly DMSP dataset consisting of 216 images (1992–2013). Automatic intensity-based registration has been used to register the monthly images to their corresponding annual composite stable light image. Thresholding and land cover data have been used to remove the background noise. Finally, ridgeline regression is implemented considering F14 December 2003 as the reference image. Each technique has been verified by qualitative analysis of the intermediate outputs. The quantitative assessments of the improved monthly DMSP product reveal that it is a good proxy measure when split from 1992 to 2002 and 2003–2013 with an R 2 of 0.84 & 0.82 w.r.t GDP and 0.83 & 0.80 w.r.t population in contrast to the original DMSP data with R 2 of 0.70 & 0.75 w.r.t GDP and 0.73 and 0.80 w.r.t population. Transect analysis across six urban cities verifies that the improved product shows reduced saturation in urban areas and increased contrast in sub-urban regions. Finally, a consistent monthly DMSP product is delivered to the research community which will be useful for studying time-series intra-year changes in urbanization, economic growth, and other socio-economic dynamics.

Spatio-Temporal Characteristics and Influencing Factors of Urban Spatial Quality in Northeast China Based on DMSP-OLS and NPP-VIIRS Nighttime Light Data

The quality of urban spaces is a pivotal part of high-quality spatial development. It is directly connected to the comprehensive, coordinated and sustainable development of a region. In recent years, Northeast China has characterized urban space contraction and development. To study the quality of urban space in Northeast China, this paper fitted the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data with 11 indicators related to high-quality urban development for the period 1992–2018. The feasibility of nighttime light data reflecting urban spatial quality was verified by a linear equation, and the temporal characteristics of urban spatial quality in Northeast China were obtained. The Exploratory Spatial Data Analysis Geographically and Temporally Weighted Regression (ESDA-GTWR) explores the spatial relevance and possible influencing factors of this kind of development. The results suggest that the overall trend of spatial quality in the three northeastern provinces is “initial slow growth and significantly weakened after”. The fast developing cities include Panjin, Liaoyang, Shenyang, and Dalian in the Liaoning Province. On the other hand, cities such as Heihe and Yichun in the Heilongjiang Province have relatively slow development speeds. Furthermore, the spatial quality development in the three northeastern provinces exhibits a trend of continuous concentration. The cities with high spatial qualities are concentrated near the Liaoning Province, with low spatial qualities in the north and high spatial qualities in the southern parts of the three provinces. As there is a notable gap between the northern and the southern regions, the central region represents an area in partial transition. The spatial quality of each city in the three northeastern provinces is the result of a number of intertwined factors, with significant differences in the degree of their influence. The significant degree of influence factors on spatial quality from higher to lower is urbanization, quality of life, rural revitalization, government promotion, and infrastructure.

Spatiotemporal Evolution of West Africa’s Urban Landscape Characteristics Applying Harmonized DMSP-OLS and NPP-VIIRS Nighttime Light (NTL) Data

Investigating urban expansion patterns aids in the management of urbanization and in ameliorating the socioeconomic and environmental issues associated with economic transformation and sustainable development. Applying Harmonized Defense Meteorological Satellite Program-Operational Line-scan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership-Visible Infrared Imagery Radiometer Suite (NPP-VIIRS) Nighttime Light (NTL) data, this paper investigated the characteristics of urban landscape in West Africa. Using the harmonized NTL data, spatial comparison and empirical threshold methods were employed to detect urban changes from 1993 to 2018. We examined the rate of urban change and calculated the direction of the urban expansion of West Africa using the center-of-gravity method for urban areas. In addition, we used the landscape expansion index method to assess the processes and stages of urban growth in West Africa. The accuracy of urban area extraction based on NTL data were R 2 = 0.8314 in 2000, R 2 = 0.8809 in 2006, R 2 = 0.9051 in 2012 for the DMSP-OLS and the simulated NPP-VIIRS was R 2 = 0.8426 in 2018, by using Google Earth images as validation. The results indicated that there was a high rate and acceleration of urban landscapes in West Africa, with rates of 0.016 0, 0.017 3, 0.018 9, and 0.068 6, and accelerations of 0.31, 0.42, 0.54, and 0.90 for the periods of 1998-2003, 2003-2008, 2008-2013, and 2013-2018, respectively. The expansion direction of urban agglomeration in West Africa during 1993-2018 was mainly from the coast to inland. However, cities located in the Sahel Region of Africa and in the middle zone expanded from north to south. Finally, the results showed that the urban landscape of West Africa was mainly in a scattered and disordered 'diffusion' process, whereas only a few cities located in coastal areas experiencing the process of 'coalescence' according to urban growth phase theory. This study provides urban planners with relevant insights for the urban expansion characteristics of West Africa.

Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area

• Random Forest algorithm and a stepwise intercalibration approach were combined. • Multi-filter and bathymetric images were composited to improve intercalibration. • Noise impact on the intercalibration was greatly eliminated. • Consistent time series night-time light data specialized for large-scale oceanic area was build. • Total light index was implied as an effective indicator of ocean fishery activities. Human activities in the ocean have never been chronically and continuously investigated on a large scale. Night-time light (NTL) images collected by the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) have been used as a proxy for monitoring the distribution and intensity of some human activities in the ocean from 1992 to 2013. However, systematic radiometric biases exist among the average visible-light DMSP/OLS NTL images (DMSP avg ) derived from different satellites. Moreover, the high randomness of fishing vessel locations and the large amount of noise impede the intercalibration of DMSP avg . To address these issues, this study has developed a method for generating a series of consistent NTL images from 1992 to 2013 for a large-scale oceanic area. A composite image was first constructed by combining the original DMSP avg , median, and standard deviation filter images derived from the DMSP avg , and a bathymetry image. Thereafter, Random Forest (RF) algorithm was employed to classify the composite image into effective and noisy pixels. Finally, a stepwise intercalibration method was adopted to reduce the systematic radiometric biases in the denoised images. The experimental results showed that RF had an overall accuracy of 96% and a Kappa coefficient of 0.775. Furthermore, the intercalibration was shown to significantly reduce the systematic radiometric biases owing to the noises being effectively discarded by the RF. Specifically, the Sum Normalized Different Index (SNDI) of the images intercalibrated by the proposed method can reach 0.61, which is 68.2% less than that of the original DMSP avg . In addition, the correlation coefficients between the intercalibrated DMSP avg and fishery catches in the exclusive economic zones (EEZs) of Japan and Malaysia can reach 0.949 and 0.901, respectively, which are higher than other values, such as the one intercalibrated using the Pseudo-Invariant Features (PIFs) method. In summary, the proposed method has been proven to be effective and feasible for generating consistent time-series NTL data for a large-scale oceanic area, and the derived Total Light Index (TLI) is an effective indicator of ocean fishery activities for ocean ecosystem research and related applications.

Spatio-Temporal Dynamics and Driving Forces of Multi-Scale CO2 Emissions by Integrating DMSP-OLS and NPP-VIIRS Data: A Case Study in Beijing-Tianjin-Hebei, China

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of “East greater than West”, with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities.

Effects of human activity intensity on habitat quality based on nighttime light remote sensing: A case study of Northern Shaanxi, China

Habitat quality is a crucial expression of the value of ecosystem services. Habitat quality issues caused by human activities are troubling dilemmas worldwide, and there is an urgent need to assess the impact of the large-scale human activity intensity on habitat quality. In this study, Northern Shaanxi, China, is used as an example to demonstrate how the impact on habitat quality can be explored by simulating the intensity of human activities using nighttime light remote sensing data from 2000 to 2020. Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data, Visible Infrared Imaging Radiometer Suite-Day-Night Band (VIIRS-DNB) data, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used in this study. The results showed that from 2000 to 2020, human activity intensity in Northern Shaanxi increased by 361.4 %, and the habitat quality decreased by 2.3 %. Habitat quality was more spatially clustered and spatially dependent than habitat degradation. Human activity intensity and habitat quality were significantly correlated. Habitat quality in Yulin city decreased significantly compared with that in Yan'an city. The differences in human activity types and economic structures of Yulin and Yan'an were an important factor for this phenomenon. The assessment of the impact of human activity intensity on habitat quality using nighttime light data is feasible and can be applied in other larger regions worldwide.

Spatial Expansion and Correlation of Urban Agglomeration in the Yellow River Basin Based on Multi-Source Nighttime Light Data

The Chinese government proposed a major national strategy for ecological protection and high-quality development in the Yellow River Basin. The Framework of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin proposes building a dynamic development pattern characterized by “one axis, two regions and five poles” in the Yellow River Basin with high-quality and high-standard urban agglomerations along the Yellow River. The urban agglomeration is the economic growth pole of the Yellow River Basin and the main carrier of the population and productivity. This study integrates DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night light remote sensing data from 2000 to 2020 and uses methods such as spatial expansion measurement, the center of gravity offset, urban primacy, and the gravity model to study the spatial expansion and correlation characteristics of five urban agglomerations. The results show that: (1) From 2000 to 2020, urban agglomeration in the Yellow River Basin continued to expand, and the area increased by 6.4 times. The total amount of nighttime lights in the city presents a spatial distribution pattern that is high in the east and low in the west. (2) The expansion centers of the five major urban agglomerations all shifted. The centers of gravity of the Shandong Peninsula urban agglomeration, the Jiziwan urban agglomeration of the Yellow River, the Guanzhong Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration all shifted westward, while the center of gravity of the Central Plains urban agglomeration shifted to the southeast. (3) Qingdao, Zhengzhou, Xi’an and Lanzhou are the primate cities of the four urban agglomerations of the Shandong Peninsula, Central Plains, Guanzhong Plain, and Lanzhou–Xining, respectively. The primate city in the Jiziwan urban agglomeration of the Yellow River was changed from Taiyuan to Yinchuan and then to Yulin. (4) The density of the gravitational network of the urban agglomeration in the Yellow River Basin and the distribution of the maximum gravitational line show the spatial differentiation characteristics of being dense in the east and sparse in the west.

An Approach for Retrieving Consistent Time Series “Urban Core–Suburban-Rural” (USR) Structure Using Nighttime Light Data from DMSP/OLS and NPP/VIIRS

The long time series and consistent “urban core-suburban-rural” (USR) structure in a city region is essential to understanding urban–suburban–rural interaction and urbanization pathways. It is always considered to be a single land use type (e.g., impervious area) in remote sensing research. The long-term (1992–present) nighttime light (NTL) data of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide the potential for retrieving time series of USR structure. In this study, we propose an improved approach to mapping the USR structure of the three subcategories based on a heuristic algorithm of Mann–Kendall mutation detection on the NTL quantile curve. First, a minor adjustment of VIIRS NTL is applied for matching the value ranges of DMSP NTL data and keeping the advantage of VIIRS to generate a long-term NTL dataset. Second, the heuristic algorithm of Mann–Kendall mutation detection is processed to find two optimal thresholds in the NTL quantile curve, which is used for USR extraction. Finally, a temporal consistency check is used to post-process the initial USR area for obtaining a more consistent and reliable USR sequence. To evaluate the performance of the proposed method, we retrieved the USR structures of 19 typical cities in China from 1992 to 2020 based on NTL datasets. The evaluations of spatiotemporal consistency compared with the validation data indicate that the USR retrieval results show good agreement with the land use map derived from Landsat images and the time series product from MODIS. The average overall accuracy (OA) of overall urban extent is higher than 0.95 and the average kappa coefficient (KC) reaches 0.6. Moreover, we investigated the urban dynamics and USR interactions of 19 cities from 1992 to 2020. Overall, this study proposes an improved approach for long-term USR mapping from NTL images at a regional scale and it will provide a valuable method for urbanization dynamics analysis.

Analysis on process of temporal and spatial evolution of urban built-up area expansion in the Yellow River Basin.

Urban spatial expansion is known as an important indicator of urbanization. In order to provide a reference for urban spatial expansion in the future high-quality development strategy of the Yellow River Basin (YB) cities in China, it is necessary to identify and calculate urban spatial expansion patterns. For this reason, we provide a "Spatiotemporal pattern-Center of gravity migrationt-Expansion pattern" solution to identify and calculate urban spatial expansion patterns in the YB. More specifically, 78 prefecture-level cities in the YB were selected as the subjects of the study, using the Defense Meteorological Satellite Program/Operational Line Scan System (DMSP/OLS) and the National Polarimetric Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data (NTL), together with the center of gravity shift and common edge detection models, to identify the YB urban expansion patterns from 2000–2018. The results suggest that: (1) on the spatial pattern, there is a obvious difference in the expansion intensity and growth rate of the urban built-up (UB) areas of cities in the upper and middle reaches of YB. In addition, there are also certain differences between the expansion patterns of provincial capital cities and non-capital cities; (2) The UB areas of YB has steadily expand from 3,500 km2 in 2000 to 10,600 km2 in 2018, amongst which the expansion of provincial capital cities is the most obvious 1919 km2; (3) Interestingly it is also discovered that urban expansion in Qinghai Province, the sourceland of the YB, takes place in a diffuse way, with the shifting of the centre of gravity for four types of total area, net increase in area, rate of growth and intensity of expansion followed a "northwest to southeast" tendency of development.

Estimation of fractional vegetation cover dynamics and its drivers based on multi-sensor data in Dera Ismail Khan, Pakistan

Time series study about the changes in Fractional Vegetation Cover (FVC) with its drivers is studied. From 2003 to 2013, FVC was calculated using remotely sensed data from the Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI), Landsat-7, Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), and Compound Night Light Index (CNLI). The mean value of FVC is 0.34 (34 %). The findings indicate that the highest annual mean values are between 34% and 37%, and 31% and 33%, that correspond to levelof 70% and 40%, respectively. The trend analysis by MODIS and Landsat-7 has the low (10%) FVC in the northern parts due to increase in industrial development, urbanization, changes in landuse, landcover and other linked environmental factors. The southwestern-southeastern and eastern areas showed medium (40 %) and high (70 %) FVC respectively. This is because of water availability and less human disturbance.

Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS

Adequate and up-to-date knowledge of the spatiotemporal dynamics of electricity power consumption (EPC) is important for the sustainable use of global electricity power resources. However, global EPC patterns were not clear after Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) in 2013 due to the significant differences between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) and DMSP-OLS. In this paper, global EPC patterns in the recent decade are investigated and assessed for the first time by the proposed locally adaptive method with integrating two nighttime light (NTL) images to global pixel-level EPC from 1992 to 2019. The geospatial dataset of built-up area density (BUAD) is adopted with a higher spatial resolution and more direct relation to human activities. A two-step regression method is designed to simulate DMSP-like images after 2013, based on the inter-annual relationships of provincial-level VIIRS. With this consistent nighttime light dataset, pixel-level EPC over the 28 years are estimated for the first time, and then the spatiotemporal dynamics of EPC are investigated from global, continental, to national scales. The obtained EPC estimates are of satisfactory accuracy in 92.6% of the countries with a MARE (Mean of the Absolute Relative Error) of less than 20%. Over these 28 years, Japan, South Korea, and China experienced high proportion of EPC high-growth. These results provide reliable scientific basis for exploring the spatial pattern and temporal variations of global EPC, especially for the latest years.