An improved method of night-time light saturation reduction based on EVI

Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) night-time light (NTL) data have been widely applied to studies on anthropogenic activities and their interactions with the environment. Due to limitations of the OLS sensor, DMSP NTL data suffer from a saturation problem in central urban areas, which further affects studies based on nocturnal lights. Recently, the vegetation-adjusted NTL urban index (VANUI) has been developed based on the inverse correlation of vegetation and urban surfaces. Despite its simple implementation and ability to effectively increase variations in NTL data, VANUI does not perform well in certain rapidly growing cities. In this study, we propose a new index, denoted enhanced vegetation index (EVI)-adjusted NTL index (EANTLI), that was developed by reforming the VANUI algorithm and utilizing the EVI. Comparisons with radiance-calibrated NTL (RCNTL) and the new Visible Infrared Imager Radiometer Suite (VIIRS) data for 15 cities worldwide show that EANTLI reduces saturation in urban cores and mitigates the blooming effect in suburban areas. EANTLI’s similarity to RCNTL and VIIRS is consistently higher than VANUI’s similarity to RCNTL and VIIRS in both spatial distribution and latitudinal transects. EANTLI also yields better results in the estimation of electric power consumption of 166 Chinese prefecture-level cities. In conclusion, EANTLI can effectively reduce NTL saturation in urban centres, thus presenting great potential for wide-range applications.     

利用DMSP/OLS稳定夜间灯光数据提取城镇用地信息的分层阈值法研究

DMSP/OLS夜间灯光数据是提取大尺度城镇用地信息的有效数据手段。利用DMSP/OLS稳定夜间灯光数据,在传统二分比较法的基础上,发展一种提取城镇用地信息的新方法——分层阈值法。该方法采用分层分类的思想,不断地将灯光阈值相近的行政单元划分到相同的集合,直至集合内各行政单元的灯光阈值完全相同为止。基于分层阈值法,从2002年DMSP/OLS稳定夜间灯光数据中提取中国内地的城镇用地信息,并利用统计数据和Landsat ETM+数据分别从数量特征和空间格局上对提取结果进行精度评价。研究结果表明,分层阈值法不仅克服了传统二分比较法需要逐个行政单元求取阈值导致计算效率较低的缺陷,优化了阈值求解过程,提高了城镇用地信息提取效率,而且具有较高的可靠性和推广应用价值。     

基于城镇居民用地再分类的人口数据空间化方法研究——以长江中游4省为例

基于土地利用数据的人口统计数据空间化方法,在处理过程中会出现同一土地利用类型下人口难以细分的情况,从而影响人口空间数据精度。引入夜间灯光信息并提出了一种基于夜间灯光强度对城镇居民地再分类的人口空间化方法,以改善人口空间数据精度。基于DMSP/OLS夜间灯光及土地利用数据,以长江中游4省为研究区进行方法试验。研究结果显示:利用夜间灯光数据对城镇居民地再分类后,各分区模型的调整R2都提高到了0.8以上,人口空间数据总体平均相对误差较重分类前降低了12.32%。说明该方法在提高传统人口数据空间化模型精度的基础上能够细化城镇居民地人口空间分布。     

VANUI指数监测京津冀城市群区域2001～2012年城市化过程

快速城市化改变了下垫面物理性质和地气相互作用,影响了气候及环境变化,如何通过合理的时空尺度对我国城市过程进行监测与认知是我国城市化发展战略的迫切需求。以京津冀城市群为研究区,利用改进的夜晚灯光指数(VANUI)对京津冀城市群2001~2012年的城市化发展过程进行了监测,对比了VANUI指数与传统夜间灯光指数对城市区域的监测能力差异及其优势所在,为京津冀地区未来城市化发展战略规划服务。结果表明,VANUI城市指数能很好地弥补夜晚灯光指数在城市区域饱和的缺陷,并能够较好地反映城市区域的空间格局及相应的细节信息。根据VANUI结果,2001~2012年间京津冀城市群扩张了约一倍,其中城市扩张速度最快的为河北省唐山市。从整个研究区域来看,在此10年间,VANUI以接近线性的发展趋势增加,从统计结果来看,2004~2007年及2009~2012年为研究区域VANUI普遍增长的时期。     

Multi-scale Clustering Analysis on Chinese Urbans based on Nighttime Light Image

Understanding spatial distribution of urban clusters at regional and national scales is increasingly important for many fields especially urban planning.Previous Studies have demonstrated urban built\|up areas can be derived from stable nighttime light satellite (DMSP\|OLS) images.Population and economic variables (i.e.GDP) have been proved significant positive correlations with nocturnal light brightness.However,less studies focused on the spatial distribution of extracted urban built\|up area.an improved DBSCAN algorithm is proposed to cluster the urban objects extracted from nighttime light image in different scales based on density,of which our urban spatial clusters are proved corresponding with urban agglomerations identified by statistical data.The traditional DBSCAN method is based on points which is not the same case with urban objects.The inclusion relation is refined,assuming that only if all the vertexes of each polygon are within the given distance,it is included in the area.Moreover,the parameters for the DBSCAN clustering model are determined by valleys of distances of every objects to classify urban spatial clusters.Besides,in a larger scale,the clustering results imply the different patterns of urban agglomerations on both sides of the Huhuanyong Line.      

基于多源遥感数据的陕西2000～2015年城市扩展研究北大核心CSCD

夜间灯光数据已成为监测城市发展进程及空间格局演变的重要依据。利用覆盖陕西省的2000、2005、2010年3期DMSP/OLS数据及2015年无云复合NPP/VIIRS数据,结合MODIS等多源遥感数据构建VANUI、EANTLI两种指数模型,识别并提取陕西省城市建成区面积,经过对比分析后,选取较为精确的EANTLI指数重建陕西省2000～2015年城市扩展进程,并对西安市城市扩展的细节特征进行探究。结果表明:EANTLI指数在城市建成区识别与提取方面较VANUI指数稳定;2000～2015年陕西省城市扩展的不均衡性较为突出,关中各地市城市建成区面积增长速率最快,陕北次之,陕南地区较为缓慢。陕西省城市扩展的主要特征是以各地市中心城市为核心,呈同心圆或扇形模式向外蔓延;西安市城市扩展具有明显的外延性特征,城市重心逐渐向西移动,城市建成区向西北、西南方向持续扩展。     

基于DMSP/OLS影像的城市化水平遥感估算方法

提出了一种利用DMSP/OLS夜间稳定灯光影像定量估算区域城市化水平的方法。首先,从美国国防气象卫星计划的线性扫描业务系统(Defense Meteorological Satellite Program/Operational Linescan System,DM-SP/OLS)获取的夜间稳定灯光数据中提取了夜间灯光综合指数(Night Light Compositive Index,NLCI)。然后建立了基于社会经济统计数据的城市化水平指数(Urbanization Level Index,ULI)与NLCI间的统计学模型,并据此对中国大陆地区级尺度的城市化水平进行了估算。结果表明,ULI与NLCI间存在明显的线性关系,NLCI能较好地反映城市化水平,根据该模型得到的其他年份ULI估算值与基于统计数据的ULI实际值具有较强的一致性,故模型具有一定的可靠性和普适性。本文的创新之处在于对前人提出的NLCI公式进行了改进,并提出了公式中最优参数的搜寻算法。     

Analysis of the Development Trend of Global Papers Combining the DMSP-OLS Nighttime Light Data

The multi\|subject cooperation and intersection becomes the main development tendency of the modern sciences.In this study,the remote sensed nighttime light data from the Defense Meteorological Satellite Program(DMSP)Operational Linescan System(OLS)were combined with the global international papers for obtaining the temporal and spatial distribution characteristics and dynamic change characteristics at national and regional scale.The results indicate that most of the countries or regions have the increasing tendencies with the exponential models.There are good correlation between the nighttime light data and international paper for most of the countries or regions.In the 72 calculated countries,there are 53 countries with the correlation coefficients more than 0.5 when the significance level is more than 0.01.Furthermore,the developing countries have higher correlation coefficients than the developed countries.In the top 18 countries with higher correlation coefficients,16 are the developing countries.The night\|time light data can be potentially used to better predict the numbers of countries’ papers.     

基于夜间灯光数据的城市建成区提取方法评价与应用

DMSP/OLS夜间灯光数据已被广泛应用于城市建成区的提取。目前主要存在4类提取方法:经验阈值法、突变检测法、统计数据法和较高分辨率影像数据空间比较法。以上海为例,在2000年、2003年、2006年夜间灯光数据的基础上,利用4种方法完成了城市建成区的提取。通过对不同年份数据提取结果的比较,证明了相同的灰度分割阈值对不同年份的夜间灯光数据中不存在通用性;在对2003年夜间灯光数据的提取中,4类方法所得结果精确度从高到低依次为统计数据法、突变检测法、经验阈值法和较高分辨率影像数据空间比较法,相对误差分别为1.3%、2.1%、5.1%和11.2%,在对4种方法的便捷性和可实现性进行分析与评价后,使用突变检测法完成了上海市2000~2006年城市建成区的提取。     

Spatial improvement of human population distribution based on multi-sensor remote-sensing data: an input for exposure assessment

A spatial mismatch of hazard data and exposure data (e.g. population) exists in risk analysis. This article provides an integrated approach for a rapid and accurate estimation of population distribution on a per-pixel basis, through the combined use of medium and coarse spatial resolution remote-sensing data, namely the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night-time imagery, enhanced vegetation index (EVI), and digital elevation model (DEM) data. The DMSP/OLS night-time light data have been widely used for the estimation of population distribution because of their free availability, global coverage, and high temporal resolution. However, given its low-radiometric resolution as well as the overglow effects, population distribution cannot be estimated accurately. In the present study, the DMSP/OLS data were combined with EVI and DEM data to develop an elevation-adjusted human settlement index (EAHSI) image. The model for population density estimation, developed based on the significant linear correlation between population and EAHSI, was implemented in Zhejiang Province in southeast China, and a spatialized population density map was generated at a resolution of 250 m?×?250 m. Compared with the results from raw human settlement index (59.69%) and single night-time lights (35.89%), the mean relative error of estimated population by EAHSI has been greatly reduced (17.74%), mainly due to the incorporation of elevation information. The accurate estimation of population density can be used as an input for exposure assessment in risk analysis on a regional scale and on a per-pixel basis.     

Population spatialization in China based on night-time imagery and land use data

Population is a key indicator of socioeconomic development, urban planning and environmental protection, particularly for developing countries like China. But, census data for any given area are neither always available nor adequately reflect the internal differences of population. The authors tried to overcome this problem by spatializing the population across China through utilizing integer night-time imagery (Defense Meteorological Satellite Program/Operational Linescan System, DMSP/OLS) and land-use data. In creating the population linear regression model, night-time light intensity and lit areas, under different types of land use, were employed as predictor variables, and census data as dependent variables. To improve model performance, eight zones were created using night-time imagery clustering and shortest path algorithm. The population model is observed to have a coefficient of determination (R ²) ranging from 0.80 to 0.95 in the research area, which remained the same in different years. A comparison of the results of this study with those of other researchers shows that the spatialized population density map, prepared on the basis of night-time imagery, reflects the population distribution character more explicitly and in greater detail.     

Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects

A methodology is presented to accurately estimate electric power consumption from saturated night-time Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the build-up area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied in Japan and also in China, India and 10 other Asian countries. The correction method was then evaluated. This study confirms that electric power consumption can be estimated with high precision from the stable light.     

A dynamic model for population mapping: a methodology integrating a Monte Carlo simulation with vegetation-adjusted night-time light images

Population is attracting increasing attention as a driver of resource overexploitation, environmental degradation, loss of biodiversity, and other environmental challenges. Timely and accurately updating maps of population distribution are thus urgently needed. Images of night-time lights from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) have been used for years in population mapping as an alternative to human settlement distribution. The capacity of night-time light images for gridding populations, however, is compromised by the dual effects of saturation and overglow. Static models of the human settlement index (HSI), elevation-adjusted human settlement index (EAHSI), and vegetation-adjusted night-time light urban index (VANUI) have been developed to counteract these negative effects by using constant coefficients. The static models, however, retain disadvantages due to the negative effects of the high variation of socio-economic backgrounds in different study areas. In this study, we integrate Monte Carlo simulation with the above three static indices and propose the dynamic model VANUI Supported by Monte Carlo Simulation (VANUIMCS) for mapping the population of Liaoning Province, China. We assess the accuracy of the simulation using data for 60 counties and 1251 townships. The VANUIMCS improve the accuracy of population mapping, with the mean percentage errors of 19.43% at the county level and 43.19% at the township level.     

Assessing the impact of urban land development on net primary productivity in the southeastern United States

The southeastern United States (SE-US) has undergone one of the highest rates of landscape changes in the country due to changing demographics and land use practices over the last few decades. Increasing evidence indicates that these changes have impacted mesoscale weather patterns, biodiversity and water resources. Since the Southeast has one of the highest rates of land productivity in the nation, it is important to monitor the effects of such changes regularly. Here, we propose a remote sensing based methodology to estimate regional impacts of urban land development on ecosystem structure and function. As an indicator of ecosystem functioning, we chose net primary productivity (NPP), which is now routinely estimated from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We used the MODIS data, a 1992 Landsat-based land cover map and nighttime data derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) for the years 1992/1993 and 2000 to estimate the extent of urban development and its impact on NPP. The analysis based on the nighttime data indicated that in 1992/1993, urban areas amounted to 4.5% of the total land surface of the region. In the year 2000, the nighttime data showed an increase in urban development for the southeastern United States of 1.9%. Estimates derived from the MODIS data indicated that land cover changes due to urban development that took place during the 1992-2000 period reduced annual NPP of the southeastern United States by 0.4%. Despite the uncertainties in sensor fusion and the coarse resolution of the data used in this study, results show that the combination of MODIS products such as NPP with nighttime data could provide rapid assessment of urban land cover changes and their impacts on regional ecosystem resources.     

Quantifying urban land use and impact on soils in Egypt using diurnal satellite imagery of the Earth surface

Conversion of agricultural land to urban use represents a potential loss of agricultural productivity, especially in areas where arable land is in short supply. Using derived products from both daytime (Landsat sensor data) and night-time imaging systems (U.S. Air Force Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS)) we examined the impacts of urbanization on soils in Egypt; a country with very limited agricultural land. We concluded that urban land cover types to occupy 3.7% of the total area of Egypt and that over 30% of the soils most suitable for agriculture are under urban land cover. Analysis of multiyear historical DMSP/OLS data sets (digitized from paper images) proved unreliable for long-term urban growth estimates.     

A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery

“Urban Sprawl” is a growing concern of citizens, environmental organizations, and governments. Negative impacts often attributed to urban sprawl are traffic congestion, loss of open space, and increased pollutant runoff into natural waterways. Definitions of “Urban Sprawl” range from local patterns of land use and development to aggregate measures of per capita land consumption for given contiguous urban areas (UA). This research creates a measure of per capita land use consumption as an aggregate index for the spatially contiguous urban areas of the conterminous United States with population of 50,000 or greater. Nighttime satellite imagery obtained by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) is used as a proxy measure of urban extent. The corresponding population of these urban areas is derived from a grid of the block group level data from the 1990 U.S. Census. These numbers are used to develop a regression equation between Ln(Urban Area) and Ln(Urban Population). The ‘scale-adjustment’ mentioned in the title characterizes the “Urban Sprawl” of each of the urban areas by how far above or below they are on the “Sprawl Line” determined by this regression. This “Sprawl Line” allows for a more fair comparison of “Urban Sprawl” between larger and smaller metropolitan areas because a simple measure of per capita land consumption or population density does not account for the natural increase in aggregate population density that occurs as cities grow in population. Cities that have more “Urban Sprawl” by this measure tended to be inland and Midwestern cities such as Minneapolis-St. Paul, Atlanta, Dallas-Ft. Worth, St. Louis, and Kansas City. Surprisingly, west coast cities including Los Angeles had some of the lowest levels of “Urban Sprawl” by this measure. There were many low light levels seen in the nighttime imagery around these major urban areas that were not included in either of the two definitions of urban extent used in this study. These areas may represent a growing commuter-shed of urban workers who do not live in the urban core but nonetheless contribute to many of the impacts typically attributed to “Urban Sprawl”. “Urban Sprawl” is difficult to define precisely partly because public perception of sprawl is likely derived from local land use planning decisions, spatio-demographic change in growing urban areas, and changing values and social mores resulting from differential rates of international migration to the urban areas of the United States. Nonetheless, the aggregate measures derived here are somewhat different than similar previously used measures in that they are ‘scale-adjusted’; also, the spatial patterns of “Urban Sprawl” shown here shed some insight and raise interesting questions about how the dynamics of “Urban Sprawl” are changing.     

基于夜间灯光和土地利用数据的GDP空间化

GDP是社会经济发展、区域规划和资源环境保护的重要指标之一。然而,传统以各级行政单元为主的GDP统计资料无法显示区域内部GDP的差异,尝试通过GDP空间化来解决这个问题,以满足资源环境研究领域对空间型社会经济数据的需求。在分析总结国内外社会经济数据空间化技术方法的基础上,第一产业基于土地利用数据建模\,第二产业和第三产业基于DMSP/OLS与土地利用数据结合生成的土地灯光参数建模。为提高模型质量,全国按照省级行政边界分区,将因变量GDP分产业分区建模,第一产业绝大部分区域的模型精度在0.7~0.95之间,第二产业和第三产业绝大部分区域的模型精度在0.8~0.98之间。通过与其他GDP空间化技术和结果的比较分析,本研究中的GDP空间化方法无论是模型精度还是GDP密度分布结果都具有一定的优势。生成的GDP密度图能较完整地反映全国GDP分布细节以及宏观分布特征,可为将来经济策略和发展路线的绘制提供一定依据。     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

Study of Urban Expansion and Driving Factors in Xi’an City based on Nighttime Light Data

The dynamic monitoring of urban expansion information is of great significance to understand the social and economic activities of cities and the implementation and feedback of urban planning.Using DMSP / OLS night-time light image as data source,the urban expansion data were corrected and extracted by using rational function model and threshold dichotomy.By using the light index,urban spatial expansion rate and intensity index and the index of center of gravity migration,The results show that the urban built-up area in Xi’an has expanded 2.2 times from 148 km² in 1993 to 473 km² in 2013.The direction of urban expansion expands from southwest to south and north,and the spatial expansion mode also increases from the early stage The single-core concentric expansion mode gradually transformed into a multi-level nuclear growth expansion mode;thus using gray relational analysis to analyze the socio-economic indicators of the suburbs in Xi’an in 2008~2013 (Weiyang district,Yanta district,Baqiao district and Chang’an district) It is confirmed that secondary industry,population density,tertiary industry and fixed assets of the whole society are the main driving factors of urban expansion in four districts respectively.     

双拼组合宽角相机低空航测实验研究

介绍无人机载双拼组合相机低空航测的实验过程,包括双拼组合相机组成结构、原理以及利用该系统进行大比例尺测图的生产流程,对此过程中出现的误差进行了重点分析,并提出消除误差的方法。理论和实践证明,无人机载双拼相机低空航测可以满足1∶1000测图精度标准,比装备普通单相机工作效率有明显提高。