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.

NPP-VIIRS夜间灯光视角下突发公共事件影响与时空动态变化分析

夜间灯光数据已成为反映包括社会经济和能源消耗在内的人类活动的一个重要指标。针对突发公共事件对全国影响的动态监测区域化研究不足的问题,本研究基于2022年1—6月美国夜光传感器可见光近红外成像辐射搭载国家极轨卫星(NPP-VIIRS)采集的夜间灯光数据,研究新冠疫情影响下夜间灯光的时空变化情况,揭示与分析疫情影响时空动态变化,并结合人口流动数据进行辅助验证。结果表明:以全国为空间尺度,夜间灯光强度与疫情发展呈负相关,且验证了基于NPP-VIIRS夜间灯光数据监测新冠疫情全国时空动态变化的可行性和准确性。本文研究成果可为相关部门应对今后可能突发的公共安全事件提供借鉴和参考。

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.

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.

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.

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.

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.

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.

基于NPP-VIIRS夜间灯光数据的河北省GDP空间化方法

为了研究省域尺度上像素级GDP空间化的方法,基于NPP-VIIRS夜间灯光数据,首先去除孤立极亮像元和背景噪声,将夜间灯光总强度、线性加权灯光指数和综合灯光指数分别与河北省11地市内的各区县GDP进行相关性分析,得知夜间灯光总强度与地市内各区县GDP相关性最显著,根据每个地市中最高相关系数对应的回归模型计算每个像素对应的GDP,经过线性纠正后,生成河北省GDP密度图.结果表明,全省172个区县的平均GDP相对误差为0.10%.该方法精度较高,生成的GDP密度图可以反映河北省经济发展的现状.

NPP-VIIRS夜间灯光数据的县级电力消费空间化研究

以NPP-VIIRS夜间灯光数据和Landsat-8影像为数据源,通过CART算法将Landsat-8影像分为人工表面地类和非人工表面地类,为非人工表面地类对应的夜间灯光区域赋值0,将人工表面地类对应到夜间灯光区域,并求取各个县级行政区域内的灯光值总和,结合廊坊市的电力消费统计数据建立夜间灯光总量与电力消费量之间的二次多项式回归模型。根据模型生成廊坊市500 m格网大小的电力消费空间分布图。研究结果表明:模拟的各县电力消费量的绝对误差和相对误差较小,相对误差最大为0.03%,这说明夜间灯光结合Landsat-8土地利用分类方法模拟的县级电力消费量的精度较高。全局Moran's I指数为0.63,表明廊坊市电力消费在空间分布上呈现较显著的聚集性;局部空间自相关分析表明廊坊市的电力消费具有一定的差异性。

由NPP-VIIRS影像估算广东省碳排放的尺度效应分析

碳排放估算是节能减排和全球气候变化研究的重要领域之一,NPP-VIIRS夜间灯光影像能够反映人类活动强度而被广泛应用于碳排放的空间估算分析。本文构建和对比了基于2015年NPP-VIIRS夜间灯光影像的广东省能源消耗碳排放估算拟合模型,并重点研究了NPP-VIIRS影像的尺度效应,探讨了500、1000、1500和2000 m分辨率的模型结果精度。研究显示:(1)二次多项式拟合模型是碳排放估算的较优化方法,广东省21个城市之间拟合结果差异较大;(2)1000 m分辨率的NPP-VIIRS夜间灯光影像的广东省碳排放估算结果均方根误差最小,2000 m分辨率的绝对误差较小,并通过升尺度提高了模型运算效率;(3)间隔100 m从500 m连续递增至2000 m的不同空间分辨率的夜间灯光影像碳排放估算结果具有波动性,在1000 m分辨率处趋于平衡。本文分析了基于NPP-VIIRS夜间灯光影像的广东省碳排放估算模型,揭示了不同空间分辨率影像的尺度效应规律,可为夜间灯光影像碳排放估算提供空间尺度优化和结果精化方面的参考。

DMSP-OLS与NPP-VIIRS夜间灯光遥感影像数据整合

DMSP-OLS夜间灯光遥感数据截至2013年,现已被NPP-VIIRS夜间灯光数据取代。因此,要获得长时间序列且稳定的夜间灯光数据集,需要整合两类夜间灯光数据。基于此,本文提出了基于重采样的两类数据整合方法,对2013—2020年NPP-VIIRS数据进行模拟,最终建立了1992—2020年长时间序列校正—模拟DMSP-OLS夜光遥感数据集。结果表明,基于重采样的整合方法效果良好(城市区域Pearson相关系数ρ=0.985 2,RMSE=3.460 7),整合数据集与相关社会经济参考量高度契合(影像DN值总和与GDP的相关系数ρ=0.946,与人口的相关系数ρ=0.971,二次多项式模型拟合R~2≈0.98,RMSE<5.55),优于已有研究。因此,利用该方法整合后的数据集能更好地支撑基于夜间灯光影像的长时间序列研究。

基于Sentinel-2A与NPP-VIIRS夜间灯光数据的城市建成区提取

利用夜间灯光数据(nighttime light data,NTL)与光学遥感影像提取城市建成区是当今的一个研究热点,其中基于植被校正的城市夜间灯光指数(vegetation adjusted NTL urban index,VANUI)被学者广泛利用,但它容易混淆城市边缘的建筑、水体,空间分辨率较低。对VANUI做出改进,提出基于建筑校正的城市夜间灯光指数(building adjusted NTL urban index,BANUI)。利用该指数对包头市南部的城市建成区进行提取,首先,借助Sentinel-2A遥感影像数据提取研究区的归一化建筑指数;然后,将其与NTL数据结合得到BANUI(空间分辨率为20 m),并由此得到空间分辨率更高、建筑信息更丰富的BANUI图像;最后,利用分水岭分割算法从BANUI,VANUI和NTL中提取出城市建成区并进行对比。结果表明,由BANUI提取的城市建成区总体精度可达93.61%,Kappa系数为0.7934,用户精度为81.34%,生产者精度为85.34%,提取结果与实际城市建成区的分布较吻合、提取精度较高,且优于另外2种数据。此方法可为NTL在城市建成区提取的研究中提供参考意见,也可用于对城市规划发展的监测。

基于NPP-VIIRS夜间灯光数据的福建省县域经济发展时空演化研究

基于福建省县域统计经济数据和校准预处理后的NPP-VIIRS夜间灯光数据,揭示县域统计经济数据与夜间灯光数据的关联,在此基础上探讨县域经济发展时空演化特征。结果表明:(1)夜间灯光总量与地区生产总值(GDP)高度相关,县域夜间灯光总量集中分布在东南沿海地区,沿海县(市)单位面积夜间灯光总量高于内陆县(市),夜间灯光总量空间分布呈现高度不均衡性;(2)县域GDP全局自相关指数以及夜间灯光全局自相关指数呈显著正相关水平,县域夜间灯光数据空间集聚水平低于县域GDP空间集聚水平,夜间灯光数据高高聚类区域相比县域GDP高高聚类区域呈扩张趋势;(3)夜间灯光数据和经济指数热点区域均分布在东南部沿海地区,冷点区域主要分布在三明和南平等地区部分县(市),夜间灯光数据和统计经济数据冷热点之间存在一致性。为缩小区域发展差异,需加大经济政策扶持,促进区域经济交流,带动经济同步增长,研究结果可为县域经济发展政策制定提供参考借鉴。

SNPP-VIIRS夜间灯光影像去噪方法研究

高精度的夜间灯光数据能够有效反映人类空间活动特征,传统的SNPP-VIIRS夜间灯光影像去噪方法容易忽略新增加或消失的有效灯光,且未处理灯光异常值,随着时间推移,去噪误差会逐渐增大。因此,本文提出了利用中值滤波与低阈值去噪相结合的方法过滤SNPP-VIIRS夜间灯光影像中的异常值及背景噪声,并将去噪后的夜间灯光总量与GDP进行相关性分析及精度验证。试验结果表明,通过中值滤波与低阈值去噪结合的方法提取的夜间灯光总量与GDP的相关性优于传统方法,利用该方法建立的模型估算出的亮度值精度更高,证明该方法具有更高的去噪精度。

基于NPP-VIIRS夜光遥感数据的京津冀城市群空间结构变化分析

本文基于NPP-VIIRS夜光遥感数据对京津冀城市群2015—2019年的空间结构进行分析,通过灯光值统计、城市位序-规模法则、空间关联测度等方法探究京津冀城市群空间结构及时空变化特征,结合城市总体规划浅析时空变化的原因。结果表明:(1)京津冀城市群灯光总量整体呈上升趋势,北京市和天津市的灯光增长率最高,其次为河北省廊坊市和石家庄市。(2)灯光集中分布在高位序城市北京和天津,中小城市不够发达。从时间序列上看,中小型城市快速发展,京津冀各城市有趋于ZIPF理想状态分布的态势。(3)随着区域交通一体化的推进,城市间的关联度整体增强,北京市与天津市的总体关联度占主导地位,但北部城市与中南地区的交通联系不足,关联度较弱。

基于NPP-VIIRS数据的山东省县级GDP分级空间建模

传统的统计方法对国内生成总值(GDP)进行统计耗时耗力费财。而夜间灯光遥感影像能对人类社会活动进行最直观的反映,为社会经济研究提供了新的思路方法和技术支持。本文以山东省作为研究区域,采用NPP-VIIRS灯光影像数据与山东省137个县级GDP数据进行分级多方式空间建模,以找到两者空间分布的最优规律。试验结果表明总GDP与总灯光亮度值的各级最优拟合优度R^2均在0.9左右。单位面积GDP与平均灯光亮度值的各级拟合优度R^2均在0.85左右。而且通过最优模型进行县级和市级GDP预测精度优于未分级的预测精度。

基于SNPP-VIIRS夜间灯光数据的GDP预测模型对比

由于基于夜光遥感数据的GDP预测多采用单一函数模型,缺乏对比和可行性验证,因此利用校正后的夜光影像对比多种函数模型的预测效果具有实际价值。鉴于此,选择SNPP-VIIRS作为基础数据,经过消除负值和背景噪声等校正处理,对线性函数等6种预测模型进行GDP估算效果对比。结果表明:在省级尺度下,二项式函数拟合优度最高,Gaussian函数预测效果最好;市级尺度下,二项式函数均优于其他函数模型。校正方法合理科学,校正后的影像可用于GDP预测研究。

DMSP-OLS与NPP-VIIRS夜间灯光数据测算统计指标能力评估——以京津冀地区县域GDP、人口及能源消耗为例

夜间灯光数据记录了地球表面的人造灯光强度,是估计社会统计指标的有效手段之一。为了评估DMSP-OLS和NPP-VIIRS2种夜间灯光数据对社会统计指标的模拟潜力,采用4种常用的灯光校正方法分别对2种夜间灯光数据进行灯光饱和性校正,根据校正后的夜间灯光数据分别建立与京津冀地区县域GDP、人口和能源消耗3种社会统计指标间的线性回归模型,从模型拟合的相关系数、F统计量值与概率p值中分析并评价了2种夜间灯光数据对GDP、人口和能源消耗3种社会统计指标的测算能力。本文研究结果表明:EANTLI法是2种夜间灯光数据的最佳校正方式,而HSI法不适用于夜间灯光数据校正后与县域社会统计指标的线性关系拟合2种夜间灯光数据对GDP的拟合效果都较好,NPP-VIIRS夜间灯光数据估算社会统计指标的拟合能力要优于DMSP-OLS数据。

西安及其附近地区城市空间演变分析——基于NPP-VIIRS夜间灯光数据

本文以西安市及其附近地区共计18个区县为研究对象,基于2010—2018年各年份的年均NPP-VIIRS夜间灯光数据,利用GIS平台基于阈值法的原理提取城市建成区实体范围。在此基础上,选取城市扩展度指标、空间结构特征指标、城市扩张方向3个因子进行量化分析,并分析城市空间变化的逐年规律,探究其内部成因。得出以下结论:(1) 2010—2018年,研究区域内提取的城市建成区面积逐年增长,并预测未来在一定时间内将会继续增长,但其扩展速度逐渐下降;(2) 2010—2018年,建成区空间结构的发展特征为“大城市大乡村”,演变过程体现出阶段性的特质;(3) 2010—2018年,建成区形成的标准差椭圆呈现西北—东南轴的分布状况,并沿短轴方向(东北—西南)不断增长,整体呈现上升的城市扩张趋势。