Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.

Assessment of Human Impacts on Vegetation in Built-up Areas in China Based on AVHRR,MODIS and DMSP_OLS Nighttime Light Data,1992–2010

Since the reform and opening-up program started in 1978,the level of urbanization has increased rapidly in China.Rapid urban expansion and restructuring have had significant impacts on the ecological environment especially within built-up areas.In this study,ArcGIS 10,ENVI 4.5,and Visual FoxPro 6.0 were used to analyze the human impacts on vegetation in the built-up areas of 656Chinese cities from 1992 to 2010.Firstly,an existing algorithm was refined to extract the boundaries of the built-up areas based on the Defense Meteorological Satellite Program Operational Linescan System(DMSP_OLS)nighttime light data.This improved algorithm has the advantages of high accuracy and speed.Secondly,a mathematical model(Human impacts(HI))was constructed to measure the impacts of human factors on vegetation during rapid urbanization based on Advanced Very High Resolution Radiometer(AVHRR)Normalized Difference Vegetation Index(NDVI)and Moderate Resolution Imaging Spectroradiometer(MODIS)NDVI.HI values greater than zero indicate relatively beneficial effects while values less than zero indicate proportionally adverse effects.The results were analyzed from four aspects:the size of cities(metropolises,large cities,medium-sized cities,and small cities),large regions(the eastern,central,western,and northeastern China),administrative divisions of China(provinces,autonomous regions,and municipalities)and vegetation zones(humid and semi-humid forest zone,semi-arid steppe zone,and arid desert zone).Finally,we discussed how human factors impacted on vegetation changes in the built-up areas.We found that urban planning policies and developmental stages impacted on vegetation changes in the built-up areas.The negative human impacts followed an inverted′U′shape,first rising and then falling with increase of urban scales.China′s national policies,social and economic development affected vegetation changes in the built-up areas.The findings can provide a scientific basis for municipal planning departments,a decision-making reference for government,and scientific guidance for sustainable development in China.

Quantifying urbanization levels on the Tibetan Plateau with high-resolution nighttime light data

The Tibetan Plateau(TP)is undergoing rapid urbanization.To improve urban sustainability and construct eco-logical security barriers,it is essential to quantify the spatial patterns of urbanization level on the TP,but the existing studies on the topic have been limited by the lack of socioeconomic data.This study aims to quantify the urbanization level on the TP in 2018 with Luojia1-01(LJ1-01)high-resolution nighttime light(NTL)data.Specifically,the compounded night light index is used to quantify spatial patterns of urbanization level at mul-tiple scales.The results showed that the TP had a low overall urbanization level with a large internal difference.The urbanization level in the northeast,southeast and south of the TP was relatively high,forming three hotspots centered in Xining City,Lhasa City and Shangri-La City,while the urbanization level in the central and western regions was relatively low.The analysis of influencing factors,based on the random forest model,showed that transportation and topography were the main factors affecting the TP’s spatial patterns of urbanization level.The comparison analysis with socioeconomic statistics and traditional NTL data showed that LJ1-01 NTL data can be used to more effectively quantify the urbanization level since it is more advantageous for reflecting the spatial extent of urban land and describing the spatial structure of socioeconomic activities within urban areas.These advantages are attributed to the high spatial resolution of the data,appropriate imaging time and unaf-fected by saturation phenomena.Thus,the proposed LJ1-01 NTL-based urbanization level measurement method has the potential for wide applications around the world,especially in less-developed regions lacking statistical data.Using this method,we refined the measurement of the TP’s urbanization level in 2018 for multiple scales including the region,basin,prefecture and county levels,which provides basic information for the further urban sustainability research on the TP.

Spatiotemporal Measurement of Regional Expansion in Changsha-Zhuzhou-Xiangtan Metropolitan Area Based on Nighttime Light Data

The research purpose is to accurately reveal the temporal and spatial law of the urban expansion of Changsha-Zhuzhou-Xiangtan, one of the seven major urban agglomeration areas in China, and provide decision-making basis for the future urban construction land layout and regional development policy-making. Based on the night lighting data (DMSP/OLS), this paper extracts the boundary of the urban construction land of Changsha-Zhuzhou-Xiangtan urban agglomeration from 1993 to 2017, and quantitatively studies the spatial and temporal characteristics of the expansion of the metropolitan area in the past 25 years according to the methods of spatial expansion analysis, center of gravity migration measurement, landscape pattern index, spatial autocorrelation, etc. The results show that: 1) it is scientific and feasible to extract urban agglomeration construction land by the method of auxiliary data comparison for the study of urban expansion;2) the expansion of regional space in Changsha-Zhuzhou-Xiangtan metropolitan area shows a trend of “weakening first and strengthening later”. The construction land keeps increasing, and the expansion form gradually changes from extensive type to intensive type;3) the center of gravity of the metropolitan area fluctuated and repeated in part during the past 25 years, but it was always located in the municipal district of Changsha city. The eastern region, mainly Changsha city, was still the core area of urban agglomeration expansion;4) strengthening the territorial space protection and control of ecological green core in the metropolitan area is a key measure for the high-quality development of urban agglomeration.

Spatio-temporal Dynamics of Urbanization in China Using DMSP/OLS Nighttime Light Data from 1992–2013

Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.

Population Spatial Distribution Based on Luojia 1-01 Nighttime Light Image:A Case Study of Beijing

With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.

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 0.0160,0.0173,0.0189,and 0.0686,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.

Differentiated effects of morphological and functional polycentricurban spatial structure on carbon emissions in China: an empiricalanalysis from remotely sensed nighttime light approach

Understanding the relationship between urban development and environmental sustainability to achieve‘double carbon’goals in China can be strengthened by evaluating the environmental effect of urban spatial structure(US).However,there have been few studies that consider the differentiated effects of polycentric US(PUS)on carbon emissions from both functional and morphological perspectives simultaneously.Thus,taking China’s 31 provinces as experimental subjects,our study developed a novel framework with remotely sensed nighttime light(NTL)data to quantify morphological PUS(MPUS)and functional PUS(FPUS)from 2000 to 2019.Then,from these two dimensions,differentiated effects of PUS on carbon emissions were further examined.Results indicated that NTL data presented high potential in quantifying MPUS and FPUS.The effect of FPUS on carbon emission-cutting outperformed that of MPUS.In addition,the spillover effect effectively enhanced the decreasing effect of the FPUS on carbon emissions.Our empiricalfindings can provide guidance for the government in developing strategies for reducing carbon emissions and optimizing USs.

Power outage and environmental justice in Winter Storm Uri: an analytical workflow based on nighttime light remote sensing

The intensity of extreme weather events has been increasing,posing a unique threat to society and highlighting the importance of our electrical power system,a key component in our infrastructure.In severe weather events,quickly identifying power outage impact zones and affected communities is crucial for informed disaster response.However,a lack of household-level power outage data impedes timely and precise assessments.To address these challenges,we introduced an analytical workflow using NASA’s Black Marble daily nighttime light(NTL)images to detect power outages from the 2021 Winter Storm Uri.This workflow includes adjustments to mitigate viewing angle and snow reflection effects.Power outage is detected by comparing storm-time and baseline(normal condition)NTL images using an empirical adjusted equation.The outcomes of the workflow are 500-meter resolution power outage maps,which have the optimal correlation with real outage tracking data when NTL intensity is reduced by 26%.With the resultant power outage maps,we analyzed the relations between power outages and disadvantaged populations in 126 Texas counties and 4182 census tracts to evaluate environmental justice in the storm.The results show that Latino/Hispanic communities tend to suffer more from power outages at both the county and census tract levels.

Local climate zone mapping using remote sensing:a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime lightdata

The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments.In this study,we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations,i.e.daytime Ziyuan-3 stereo imageries(2.1 m)and Luojia-1 nighttime light(NTL)data(130 m).Firstly,we performed land cover classification using multiple machine learning methods(i.e.random forest(RF)and XGBoost algorithms)and various features(i.e.spectral,textural,multi-view features,3D urban structure parameters(USPs),and NTL).In addition,we developed a set of new cumulative elevation indexes to improve building roughness assessments.The indexes can estimate building roughness directly from fused point clouds generated by both along-and across-track modes.Finally,based on the land cover and building roughness results,we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping.The results for Beijing,China,show that our method yielded satisfactory accuracy for LCZ mapping,with an overall accuracy(OA)of 90.46%.The overall accuracy of land cover classification using 3D USPs generated from both along-and across-track modes increased by 4.66%,compared to that of using the single along-track mode.Additionally,the OA value of LCZ mapping using 2D and 3D USPs(88.18%)achieved a better result than using only 2D USPs(83.83%).The use of NTL data increased the classification accuracy of LCZs E(bare rock or paved)and F(bare soil or sand)by 6.54%and 3.94%,respectively.The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livabillity in urban areas.Ultimately,this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method

The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological protection,and other sustainability research.However,the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series,while GHS-POP only utilizes land use data with limited accuracy.In view of the limited remote sensing images of long time series,it is necessary to combine existing multi-source remote sensing data for population spatialization research.In this research,we developed a nighttime light desaturation index(NTLDI).Through the cross-sensor calibration model based on an autoencoder convolutional neural network,the NTLDl was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band(VIRS-DNB)data.Then,the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL.Furthermore,the change characteristics and the driving factors of China's population spatial distribution are analyzed.The large-scale,long-term population spatialization results obtained in this study are of great significance in government planning and decision-making,disaster assessment,resource allocation,and other aspects.

Remote sensing of human beings-a perspective from nighttime light

City lights,fishing boats,and oil fields are the major sources of nighttime lights,therefore the nighttime light images provide a unique source to map human beings and their activities from outer space.While most of the scholars focused on application of nighttime light remote sensing in urbanization and regional development,the actual fields are much wider.This paper summarized the applications of nighttime light remote sensing into fields such as the estimation of socioeconomic parameters,monitoring urbanization,evaluation of important events,analyzing light pollution,fishery,etc.For estimation of socioeconomic parameters,the most promising progress is that Gross Domestic Product and its growth rate have been estimated with statistical data and nighttime light data using econometric models.For monitoring urbanization,urban area and its dynamics can be extracted using different classification methods,and spatial analysis has been employed to map urban agglomeration.As sharp changes of nighttime light are associated with important socioeconomic events,the images have been used to evaluate humanitarian disasters,especially in the current Syrian and Iraqi wars.Light pollution is another hotspot of nighttime light application,as the night light is related to some diseases and abnormal behavior of animals,and the nighttime light images can provide light pollution information on large scales so that it is much easier to analyze the effects of light pollutions.In each field,we listed typical cases of the applications.At last,future studies of nighttime light remote sensing have been predicted.

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study.The Moderate Resolution Imaging Spectroradiometer(MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite(VIIRS)-based nighttime light(NTL)data for robust extraction and mapping of urban built-up areas.The MODIS-based newly proposed Urban Built-up Index(UBI)was combined with NTL data,and the resulting Enhanced UBI(EUBI)was used as a single master image for global extraction of urban built-up areas.Due to higher variation of the EUBI with respect to geographical regions,a region-specific threshold approach was used to extract urban built-up areas.This research provided 500-m-resolution global urban built-up map of year 2014.The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States.The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.

Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data

This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic DMSP”dataset,from 1992 to 2020,to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang,China.Then,this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique.Results reveal that(1)total carbon emissions continued to grow,while the growth rate slowed down in the past five years.(2)Large regional differences exist in total carbon emissions across various regions.Total carbon emissions of these regions in descending order are the northern slope of the Tianshan(Mountains)>the southern slope of the Tianshan>the three prefectures in southern Xinjiang>the northern part of Xinjiang.(3)Economic growth,population size,and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions.The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’spatial differentiation.This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.

Similarities and differences of city-size distributions in three main urban agglomerations of China from 1992 to 2015： A comparative study based on nighttime light data

Comparing the city-size distribution at the urban agglomeration（UA） scale is important for understanding the processes of urban development. However, comparative studies of city-size distribution among China＇s three largest UAs, the Beijing-Tianjin-Hebei agglomeration（BTHA）, the Yangtze River Delta agglomeration（YRDA）, and the Pearl River Delta agglomeration（PRDA）, remain inadequate due to the limitation of data availability. Therefore, using urban data derived from time-series nighttime light data, the common characteristics and distinctive features of city-size distribution among the three UAs from 1992 to 2015 were compared by the Pareto regression and the rank clock method. We identified two common features. First, the city-size distribution became more even. The Pareto exponents increased by 0.17, 0.12, and 0.01 in the YRDA, BTHA, and PRDA, respectively. Second, the average ranks of small cities ascended, being 0.55, 0.08 and 0.04 in the three UAs, respectively. However, the average ranks of large and medium cities in the three UAs experienced different trajectories, which are closely related to the similarities and differences in the driving forces for the development of UAs. Place-based measures are encouraged to promote a coordinated development among cities of differing sizes in the three UAs.

Consistent nighttime light time series in 1992–2020 in Northern Africa by combining DMSP-OLS and NPP-VIIRS data

Human activities modulate the impact of environmental forcing in general and of climate in particular.Information on the spatial and temporal patterns of human activities is in high demand,but scarce in sparsely populated and data-poor regions such as Northern Africa.The intensity and spatial distribution of nighttime lights provide useful information on human activities and can be observed by space-borne imaging radiometers.Our study helps to bridge the gap between the DMSP-OLS data available until 2013 and the NPP-VIIRS data available since 2013.The approach to calibrate the OLS data includes three steps:a)inter-calibrate the OLS DN data acquired by different sensors in 1992-2013;b)cali-brate the OLS DN data using VIIRS data in 2013;c)generate syn-thetic OLS radiance data by degrading the VIIRS data in 2013-2020.We generated a)a time series of calibrated OLS nighttime light radiance data(1992-2013);b)mean annual VIIRS radiance on stable lights at the OLS spatial resolution for 2013-2020;c)synthetic OLS radiance data generated using VIIRS radiance data degraded to match the radiometric specifications of OLS for 2013-2020.The evaluation of these data products in 2013 documented their accu-racy and consistency.

Carbon dioxide(CO_(2))emissions from the service industry,traffic,and secondary industry as revealed by the remotely sensed nighttime light data

Exploring carbon dioxide(CO2)emissions from human activities is essential for urban energy conservation and resource management.Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions.Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions,few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries,such as service industry CO2 emissions(SC),traffic CO2 emissions(TC),and secondary industry CO2 emissions(IC).Here,China was selected as the experimental subject,and we comprehensively explored the relationships between the nighttime lights and SC,TC,and IC,and investigated the factors mediating these relationships.We found that without considering other factors,the nighttime lights only revealed up to 51.2%of TC,followed by 41.7%of IC and 22.7%of SC.When controlling for city characteristic variables,the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC,IC,and TC,and that nighttime lights have an Inverted-U relationship with SC.The Suomi NPP-VIIRS data are more suitable for revealing SC,TC,and IC in medium-sized and large-sized cities than in small-sized cities and megacities.

Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin,China

Regional sustainable development necessitates a holistic understanding of spatiotemporal variations in ecosystem carbon storage(ECS),particularly in ecologically sensitive areas with arid and semi-arid climate.In this study,we calculated the ECS in the Ningxia Section of Yellow River Basin,China from 1985 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model based on land use data.We further predicted the spatial distribution of ECS in 2050 under four land use scenarios:natural development scenario(NDS),ecological protection scenario(EPS),cultivated land protection scenario(CPS),and urban development scenario(UDS)using the patch-generating land use simulation(PLUS)model,and quantified the influences of natural and human factors on the spatial differentiation of ECS using the geographical detector(Geodetector).Results showed that the total ECS of the study area initially increased from 1985 until reaching a peak at 402.36×10^(6) t in 2010,followed by a decreasing trend to 2050.The spatial distribution of ECS was characterized by high values in the eastern and southern parts of the study area,and low values in the western and northern parts.Between 1985 and 2020,land use changes occurred mainly through the expansion of cultivated land,woodland,and construction land at the expense of unused land.The total ECS in 2050 under different land use scenarios(ranked as EPS>CPS>NDS>UDS)would be lower than that in 2020.Nighttime light was the largest contributor to the spatial differentiation of ECS,with soil type and annual mean temperature being the major natural driving factors.Findings of this study could provide guidance on the ecological construction and high-quality development in arid and semi-arid areas.

Changes in human settlement environments and their drivers in valley cities located in arid and semi-arid regions:A case study of Lanzhou in Western China

Development of urban human settlement environments(HSEs)is an integral part of promoting high-quality and sustainable regional development and constructing a beautiful China.The city of Lanzhou,located at the geometric center of China,is the only provincial capital traversed by the Yellow River.Given the constraints posed by the valley topography and the need for economic development,the development of this HSE,which is located within an arid region,poses considerable challenges.Evidently,an understanding of the evolution of HSEs and drivers of changes in them contributes to high-quality,sustainable urban development in arid and semi-arid regions.An analytical model was developed using the parameters of relief degree of land surface,human comfort days,the land cover index,nighttime light index,and precipitation.This model was used in combination with population density and the gross domestic product to analyze the spatial distribution of Lanzhou's HSE and its drivers.The results showed that landscapes in Lanzhou underwent significant changes between 2000 and 2022,with an increase in building-up land(+0.946%),cultivated land(+0.134%),and forest land(+0.018%)and a decrease in grassland(-1.10%).There was significant outward expansion of the main urban zone of Lanzhou and of various county towns,with the increase in building-up land being most prominent.During this period,there were significant changes in the periphery of the core urban area and county towns in Lanzhou,with decreases moving from the urban center(the highest value)to the surrounding areas(Yongdeng County had the lowest value).The correlation between the HSE and population density grew stronger in Anning and Chengguan Districts but became weaker in Xigu and Qilihe Districts.Spatiotemporal variations in the HSE were primarily caused by climate change,followed by human activities,and were also influenced by the valley topography.Overall,the spatial distribution of population density and the HSE in Lanzhou demonstrated good consistency under the in-fluence of economic development and urbanization.

Spatial Identification of Housing Vacancy in China

The housing vacancy rate(HVR)is an important index in assessing the healthiness of residential real estate market.In China,it is hardly to take advantage of the basic data of real estate information due to the opaque of those data.In this paper,the HVR is estimated to two scales.At the grid level,urban area ratio was calculated by nighttime images after eliminating outliers of nighttime images and night light intensity of non-residential pixels in mixed pixels by a proposed modified optimal threshold method,and built-up areas in each pixel were extracted from the land-cover data.Then,the HVR is calculated by comparing the light intensity of specific grid with the light intensity of full occupancy rate regions.At the administrative scale,the GCI(‘ghost city’index)is constructed by calculating the ratio of the total light radiation intensity of a city to the total construction land area of the city.The overall spatial differentiation pattern of the vacant houses in the national prefecture level administrative regions is analyzed.The following conclusions were drawn:vacant housing is rare in certain eastern coastal cities and regions in China with relatively fast economic development.Cities based on exhausted resources,some mountainous cities,and cities with relatively backward economic development more typically showed high levels of housing vacancy.The GCI of prefecture-level administrative units gradually declined from north to south,whereas the east-west distribution showed a parabolic shape.As city level decreased,the GCI registered a gradual upward trend.China’s urban housing vacancy can be divided into five categories:industry or resources driven,government planned,epitaxy expansionary,environmental constraint and speculative activate by combining the spatial distribution of housing vacancy with the factors of natural environment,social economic development level,and population density into consideration.