基于夜间灯光数据的六盘山连片特困区贫困空间分异特征分析

针对当前精准扶贫过程中,传统数据统计口径、计算方法、空间尺度不统一的问题,以六盘山连片特困区为例,借助灰色关联模型、DMSP-OLS与NPP-VIIRS夜间灯光数据,构建综合贫困指数(IPI)、区域灯光指数(ALID、ALIN),比对选取最优指数模型,生成基于栅格的连片特困区综合贫困指数(MPI),从而精准地识别山区贫困。结果表明:灯光与贫困模拟模型中NPP-VIIRS数据构建的ALIN指数优于ALID指数,其灯光与贫困的决定系数R2超过0.88,平均相对误差小于3.49%;连片特困区贫困空间化中识别出36个极贫困、21个高度贫困,高度贫困率高达73.08%,表明六盘山贫困面积广、贫困程度深且呈现集中态势。     

夜间灯光数据下的中国贫困县发展状况

针对贫困县长时间跨度的社会经济数据统计时低效以及准确性得不到保障等问题,将DMSP/OLS夜间灯光数据引入贫困问题的研究。通过对2003—2012年全国的592个贫困县进行分析,构建基于夜间灯光数据的贫困县社会经济发展指数,使用二次多项式模型从时间和空间角度揭示贫困县夜间灯光强度的变化趋势。结果表明,贫困县的平均灯光强度呈现出增长速度持续加快的状态;缓慢增长型贫困县主要集中在我国的中部、东部和西南部地区,快速增长型主要位于东北部和西部;东北部和西部贫困县的平均灯光强度增长速度超过了中部和东部;西部贫困县的夜间灯光强度增长速度与其省会城市差距较小,而东部和东北部区域的差距在逐渐增大。     

夜间灯光数据下的中国贫困县发展状况

针对贫困县长时间跨度的社会经济数据统计时低效以及准确性得不到保障等问题,将DMSP/OLS夜间灯光数据引入贫困问题的研究。通过对2003—2012年全国的592个贫困县进行分析,构建基于夜间灯光数据的贫困县社会经济发展指数,使用二次多项式模型从时间和空间角度揭示贫困县夜间灯光强度的变化趋势。结果表明,贫困县的平均灯光强度呈现出增长速度持续加快的状态;缓慢增长型贫困县主要集中在我国的中部、东部和西南部地区,快速增长型主要位于东北部和西部;东北部和西部贫困县的平均灯光强度增长速度超过了中部和东部;西部贫困县的夜间灯光强度增长速度与其省会城市差距较小,而东部和东北部区域的差距在逐渐增大。     

基于夜间灯光数据的陕西省县域相对贫困水平时空差异分析

2020年中国消除了绝对贫困,但相对贫困还继续存在且长期存在,研究相对贫困对于巩固拓展脱贫攻坚成果和有效衔接乡村振兴具有重要意义。针对已有贫困研究中仍存在的贫困分类研究、动态研究等方面不足,从相对贫困角度入手,选取陕西省107个区县为研究对象,以2011—2020年为研究时段,基于夜间灯光数据、NDVI数据和社会经济统计数据,构建以夜间灯光指数为自变量的多维贫困指数估算模型来量化识别相对贫困县域,并综合运用锡尔指数、空间局部自相关等分析方法,对量化识别出的县域相对贫困水平时空动态差异进行研究。研究结果表明：(1)基于夜间灯光数据可以有效进行多维贫困指数估算且估算精度达84.62%,利用多维贫困指数均值序列的前50%作为区域相对贫困划分标准,适用于描述区域相对贫困水平,有利于探索建立解决相对贫困的长效机制。(2)时间上,从2011年到2020年陕西省相对贫困县个数总体下降,省内各市区间相对贫困水平的区域间差异增大直接导致了贫困县两极分化程度增强。(3)空间上,陕西省相对贫困县域呈现“陕南贫困程度深范围广,关中渭河沿岸次之,陕北无定河沿岸以北零星分布”的格局。     

NPP-VIIRS年度夜间灯光数据的合成方法与验证

针对缺乏NPP-VIIRS年度夜间灯光数据的情况,提出基于NPP-VIIRS月度夜间灯光数据合成年度夜间灯光数据的方法。用DMSP/OLS数据中的非负DN值与均值滤波消除数据中的负值与极高值并求出年度均值图像;将稳定夜间灯光数据二值化后与2016年度均值图像相乘,得到稳定的2016年度夜间灯光数据;用不变目标区域法进行相对辐射校正,以2016年稳定夜间灯光数据为基准数据进行逐年校正,利用各年二值图像消除不稳定光源,从而保证各年数据的可比性、稳定性,得到2012年到2016年的合成夜间灯光数据。验证表明,合成年度夜间灯光数据像元变化符合实际,经济参数拟合度在省、市级尺度上分别达到81.26%、75.90%,明显高于1月份的61.86%、54.81%,12月份的60.28%、57.71%,说明年度夜间灯光数据合成方法具有一定的科学性与必要性,可用于后续科学研究中。     

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。     

基于NPP/VIIRS和LJ1-01夜间灯光数据的人口空间化对比研究——以北京市为例

为探究珞珈一号01星(LJ1-01)获取的最新一代夜间灯光数据在人口空间化研究的适用性,采用NPP/VIIRS夜间灯光数据、LJ1-01夜间灯光数据、WorldPop人口数据、土地利用数据等,在乡镇(街道)尺度上分别建立线性回归模型、GWR模型和RFR模型,并对建模结果进行评价。结果表明:线性回归模型和GWR模型在人口密度大的区域模拟效果较好,RFR模型在整个区域表现更稳定;GWR模型的精度与空间自相关程度有关,在分区构建GWR模型时还要考虑空间因素的影响;基于LJ1-01夜间灯光数据的人口空间化结果在拟合度以及精度方面均优于NPP/VIIRS。根据实验结果可知,由LJ1-01数据制作的人口空间分布图能够清晰地展示城市内部人口空间分布的差异,在人口空间化研究方向具有巨大潜力。     

NPP-VIIRS年度夜间灯光数据合成方法

夜间灯光数据能有效反映人类夜间活动强度,但由于年度NPP-VIIRS夜间灯光数据的缺乏,很大程度上限制了其应用发展。传统的年度数据合成是直接基于年度均值影像进行数据校正合成,但这会使误差累积。因此,文章提出结合建设用地数据利用阈值法和中值法对NPP-VIIRS月度影像进行逐月校正,剔除背景噪声和极高值后再合成年度数据,并利用GDP数据对其进行精度评定。结果表明,合成年度夜间灯光数据与GDP相关性达到0.996,其数据的可靠性得到了很大提高,同时也说明该合成方法更具科学性。     

基于地表覆盖及夜间灯光的分区人口空间化——以京津冀地区为例

高精度的格网人口数据能够详细地表达人口分布的实际情况,是分析区域人口活动和社会经济发展的重要数据。文章以地形复杂且人口空间分布异质性较高的京津冀地区为例,利用地表覆盖数据和NPP/VIIRS夜间灯光数据,综合考虑地形和人口密度因素对研究区进行分区,并基于区县常住人口统计数据建立逐步回归模型,模拟京津冀地区2017年500m格网人口数据。结果表明:基于地形和人口密度因子的分区建模可提升人口格网化精度,模拟的格网人口数据与乡镇统计人口数据的平均相对误差为32.71%;79.28%的乡镇的人口空间化相对误差小于0.5,精度优于WorldPop人口数据。该方法对地形复杂且人口空间分布异质性较高区域的人口格网化研究具有借鉴意义。     

SMAP射频干扰与夜间灯光分布关系初探北大核心CSCD

文章对SMAP射频干扰和灯光分布的关系进行了初步探索。为检测和衡量人类活动对SMAP射频干扰(SMAP-RFI)造成的影响,以夜间灯光数据作为衡量人类活动强度的标准,探究RFI的强度和分布规律。结果显示:RFI源往往会伴随着夜间高灯光区出现,夜间灯光数据与RFI强度负相关系数达0.55;在个别低灯光区也观测到很强的RFI,RFI源具有辐射特性,多个RFI源在低灯光区辐射叠加产生了很强的RFI;夜间灯光数据能够识别催生RFI源因素的分布,但无法量化这些因素,不能准确识别工厂、机场、基站等,这会对RFI和夜间灯光值的关系造成影响。     

基于VIIRS夜间灯光数据的山东半岛城市群发展特征研究

NPP-VIIRS夜间灯光数据是在中大尺度上开展城市发展变化研究的稳定数据源。基于2012~2018年NPP-VIIRS夜间灯光数据,以山东半岛城市群为研究对象,采用参考比较法提取城市建成区图斑,选取9个景观格局指数对山东半岛城市群的城市化发展特征进行定量分析。结果表明：①整体上,域内斑块总面积以4.5%的速度增长,边缘总长度和边缘密度年均增长3.15%,斑块数量和密度增长较快（分别为1.95%和1.98%）,表明山东半岛城市群整体城市面积增长迅速,城市范围持续扩张;②从不同指标变化趋势来看,各城市斑块总面积增长最快的是青岛市和东营市（分别为9.66%和6.01%）;青岛市的斑块数量和密度增速最快（分别为9.54%和8.55%）,日照市的斑块数量和密度均以3.65%的速率显著降低;景观形状指数整体增速缓慢;平均回旋半径在日照市具有较高的年均增长速度（5.99%）;③从各城市发展特征的差异性来看,青岛市的平均斑块面积和回旋半径分别以0.56%和1.53%的速度降低,其他各指标均显著增加,表明青岛市出现了较多的新兴城镇,城区面积不断扩大;济南、日照和东营市的城区面积增长较快,斑块数量、景观形状指数等指标增长缓慢,城市发展以旧城区的扩张为主;潍坊、淄博和烟台市在2015年和2016年前后经历了新兴城镇出现,城镇融合的阶段,城市发展较快。总体而言,山东半岛城市群城市化发展较快,但空间差异性明显。     

夜间灯光数据估算中国省域碳排放与国际碳数据库分配的碳排放比较

快速准确获取省域碳排放数据是实时制定差异化碳减排政策的前提。基于DMSP/OLS和NPP-VIIRS夜间灯光数据,采用统计数据比较法提取1997～2017年中国大陆各省域（不包括西藏）建成区的夜间灯光总值（用TDN表示）,并利用1997～2014年各省域的TDN值与同期核算的碳排放量建立各省域碳排放预测模型。然后,以2015～2017年的TDN值为自变量估算中国各省域的碳排放量;同时,利用熵值法和碳排放分配模型将四大国际权威数据库（IEA、EIA、EDGAR和CEADs）发布的中国碳排放量分配至各省;最后,将估算结果与四大典型碳数据库分配的省域碳排放值进行比较。研究表明：估算的省域碳排放量与分配的省域碳排放量大体一致,平均绝对百分比误差（MAPE）为6.45%～9.12%,并且基于夜间灯光数据估算的省域碳排放量与IEA和EIA数据库分配的碳排放量更为接近;各省域估算的碳排放量与分配的碳排放量均落在1∶1线附近;单个省域的MAPE值变化在0.68%～14.85%,且多数省域的MAPE值均在10.0%以内。上述结果证明,基于夜间灯光数据通过提取TDN值估算省域碳排放量具有可行性和准确性。     

Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping

The accurate mapping of small, often fragmented and linear vegetation patches is of key importance for natural resource management because of their ecological significance. However, due to their small size and the quantised nature of remote sensing imagery they may be under-represented in the landscape when mapped using earth observation. This paper investigates the effect of patch area and patch elongation on the accurate mapping of these vegetation patches. Using synthetic images to simulate sub-pixel patch location, we investigated classification accuracy and extraction probability resulting from differences in the geometric properties of the raster grid and the feature alone. We simulated the effect of grid position, detectability, feature size and shape on classification. This represents the highest achievable accuracy using the remote sensing raster grid, where other factors influencing classification such as classification algorithm, radiometric calibration and sensor characteristics are excluded. We found that mapping error was highest when the scale of the feature and the raster grid coincided. We showed that the spatial resolution of the grid should be many times finer in order to extract these features accurately. For square patches with a mean classification accuracy of 75%, the grid pixel area was 11 times smaller than patch size. When patches were small and/or elongated, the probability of extraction was reduced, mapping accuracies decreased and variability in accuracy due to the effects of grid position increased. For example, a square shaped patch needed an area of at least 11 pixels to achieve a mean accuracy of 75%, whilst a linear patch with a width to length ratio of 4 needed an area of 12.3 pixels. This paper quantifies the limitations of remote sensing for the accurate detection of small and linear features and provides guidelines on the appropriate spatial resolution required to map these features. Using our results, map users can estimate the probability of a map classifying small and linear features independently of the error matrix. Furthermore, we provide a more precise estimate of the size of the smallest discernable feature taking into account the random position of the remote sensing grid with respect to the feature as well as its shape. An understanding of this phenomenon is critical for making good land management decisions based on a thorough understanding of the limitations of remote sensing data.     

基于遥感和微博签到数据的住房空置空间识别

房屋空置率是衡量房地产市场健康与否的重要指标。基于夜间灯光遥感数据和全国土地覆盖数据,对房屋空置率进行空间识别。利用微博签到数据,通过基于密度的聚类算法和热力分析对居民活动空间强度进行分析,从“鬼城”指数排名靠前的100个城市中随机选择30个样本城市,进行城市内部房屋空置空间识别。结果表明：2013年全国地级及以上城市基于像元的平均房屋空置率为27.3%。东部地区房屋空置率较低,中西部地区房屋空置现象明显;房屋空置在中小型城市更加突出。     

Study on the Spatial Identification of Housing Vacancy

Housing Vacancy Rate (HVR) is an important index in assessing the healthiness of residential real estate market. Due to lack of clear and effectively evaluation criterion, the understanding of housing vacancy in China is then rather limited. This paper quantitatively analyzed spatial identification and difference pattern of house vacancy at different scale in China by using nighttime light data and micro-blog check-in data, in order to make up the deficiency of traditional methods in the aspects of data missing and differential approach. The nighttime light intensity for non-vacancy area is estimated after removing the nighttime light intensity from non-residential sources of NPP-VIIRS light data and difference of nighttime light caused by the different urban area ratio. Then, the HVR is calculated for the spatial pattern analysis. This paper analyzed the spatial strength of residents activities by using micro-blog check-in data, based on density-based spatial clustering of applications with noise and heat map. The 30 sample cities were selected to identify house vacancy from 100 cities which ghost city index were high. The following conclusions were drawn through the study: The HVR of eastern coastal cities and regions with rapid development of economy were lower, while the phenomenon of house vacancy in central and western regions were more obvious. The HVR increased from eastern coastal regions to inland areas. What’s more, the phenomenon of house vacancy in middle and small cities were more distinct from the aspect of urban scale. The house vacancy of China were divided into five types: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by taking the factors of natural environment, social economic development level, and population density into consideration. This may shed light on policy implications for Chinese urban development.     

A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery

The goal of the present study is to demonstrate that high-poverty counties and robust classification features can be identified by machine learning approaches using only DMSP/OLS night-time light imagery. To accomplish this goal, a total of 96 high-poverty and 96 non-poverty counties were classified using 15 statistical and spatial features extracted from night-time light imagery in China in 2010 formed a training set for identifying high-poverty counties. Seven machine learning approaches were adopted to classify high-poverty counties, and five feature importance measures were used to select robust features. The resulting metrics, including the user’s (>63%), producer’s (>66%) and overall (>82%) accuracies of the poor county identification (probability of poverty greater than 0.6), show that the seven machine learning approaches used in this paper exhibit good performance, although some differences exist among the approaches. The order of feature importance reveals that the relative importance of each feature differs among the models; however, the important features remain consistent. The nine most important features ranked in each approach are relatively robust for poverty identification at the county level. Both spatial feature and statistical features calculated in part from the central tendency, degree of dispersion, and the distribution of the night-time light data were identified as indispensable robust features in all the approaches, indicating that the complex social phenomenon of poverty requires analysis from different aspects. Previous studies that utilized primarily night-time light imagery applied single features related to the central tendency or the distribution features of the imagery; this study provides a new method and can act as a reference for feature selection and identification of high-poverty counties using night-time light imagery and has potential applications across several scientific domains.     

Modelling the population density of China at the pixel level based on DMSP/OLS non‐radiance‐calibrated night‐time light images

The spatial distribution of population density is crucial for analysing the relationships among economic growth, environmental protection and resource use. In this study we simulated China's population density in 1998 at 1 km×1 km resolution by integrating DMSP/OLS non‐radiance‐calibrated night‐time images, SPOT/VGT 10‐day maximum NDVI composite, population census data and vector county boundaries. Population density, both inside and outside of light patches, was estimated for four types of counties, which were classified according to their light characteristics. The model for estimating population density inside the light patches was developed based on a significant correlation between light intensity and population, while the model for estimating population density outside of light patches was constructed by combining Coulomb's law with electric field superposition principle. Our method was simpler and less expensive than existing methods for spatializing population density. The results were consistent with other estimates but exhibited more spatial heterogeneity and richer information.     

基于分形的海南岛东寨港红树林景观空间结构变化研究

红树林湿地在维护近岸海域的生态环境,抵御海潮、台风自然灾害等方面具有重要作用。利用海南岛红树林遥感专题信息产品,基于分形理论中的豪斯道夫维数、景观稳定性指数和聚集维数,分析了1987—2017年海南岛东寨港自然保护区红树林景观空间结构的变化特征：(1)1987—2017年间,红树林景观面积整体增加,2017年总面积达到18.05 km²,且扩展方式主要为边缘扩展和内部联通。(2)红树林景观分布复杂度和稳定性在1987—2003年间整体下降,波动性较强,景观结构不稳定;在2003—2013年间稳步上升,斑块持续扩展,景观结构稳定发展;2013年后略有下降。(3)红树林景观增长有聚集中心,聚集维数为1.47,对周围红树林的增长有辐射性。东寨港红树林聚集分布区已达景观稳定状态,在保持抚育的基础上可适度开发其审美、生态功能,针对尚处于扩张等状态的红树林斑块,可持续进行扩种、抚育。