基于珞珈一号和随机森林的兰州市GDP空间化北大核心CSCD

GDP空间化是反映区域经济内部差异的重要途径,目前的GDP空间化大多基于县级及以上行政单元的统计数据实现,准确性偏低。该文基于兰州市118个乡镇街道单元进行GDP的1 km×1 km格网空间化。实验采用了珞珈一号夜间灯光数据和其他辅助数据(地表温度、路网密度、平地占比、建筑用地占比和归一化植被指数),建模工具使用了多元线性回归和随机森林回归。实验结果显示:乡镇、街道尺度上GDP模拟模型的决定系数可达0.85,高于大多文献得到的基于更大行政单元的模型决定系数;随机森林回归的拟合精度高于多元线性回归;珞珈一号夜间灯光数据在GDP空间化中具有很大的应用潜力;兰州市GDP高密度区集中在沿黄河干流两岸的建成区,次密度区主要位于黄河支流和铁路沿线的地形平缓区以及兰州新区(全国第五个、西北第一个国家级新区)。     

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。     

地理加权回归在人口空间分布研究中的应用

针对基于多元线性回归的人口数据空间化方法忽略研究区间空间异质性的问题,采用分区回归和地理加权回归改进。以西南地区为例,以县级为研究单位,利用全球地表覆盖数据(GlobeLand30),对西南地区2010年县级人口统计数据进行空间化模型研究。采用二种不同方式进行改进,其一是利用影响人口空间分布的因素进行人口一致性分区,依据分区结果分别进行基于地表覆盖类型的统计分析建模;其二是引入局部回归建模思想的地理加权回归分析方法,利用局部最小二乘方法进行逐点估计。最后,基于误差理论,对人口数据空间化的结果进行误差比较和模型精度分析。研究结果表明,运用分区回归模型和地理加权回归模型提高了人口数据空间化的数值精度和空间精度。     

利用DMSP/OLS夜间灯光影像模拟中国经济参量

鉴于传统统计调查方法难以准确、高效和客观揭示经济参量时空信息,提出结合DMSP/OLS夜间灯光数据和统计年鉴数据模拟中国经济参量。选取省级尺度地区生产总值(GDP)、第一产业生产总值(PI)、第二产业生产总值(SI)、第三产业生产总值(TI)4个经济参量,分别构建1992年、2002年和2012年3期DMSP/OLS夜间灯光总值参量与4个经济参量一元线性回归模型,遴选夜间灯光-经济敏感参量,构建空间化模型模拟经济参量。结果表明,地区生产总值(GDP)和第二产业生产总值为敏感参量,模拟结果表明过去21年中国经济参量主要围绕中心城市(省会城市和直辖市)快速增长,其中三大城市群(长三角城市群、珠三角城市群和京津冀城市群)形成高值绵延区,与实际情况相符合,展现了利用DMSP/OLS模拟时序经济参量的优势。     

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。     

多源信息耦合的GDP空间化研究—以北京市为例

针对统计数据对G D P空间分布信息表达存在的局限性,耦合G D P统计数据、城市兴趣点数据、夜间灯光数据以及土地利用数据,提出了一种G D P空间化方法.以北京市为实验区,首先,利用土地利用类型与GDP的关系,实现了第一产业GDP的100 m网格空间化;其次,通过计算兴趣点密度分布情况,并耦合夜间灯光指数以及土地利用...     

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。     

融合灯光强度和斑块空间分布特征的贫困区域识别模型构建——以山西省为例

贫困区域识别对于国家实施精准扶贫方略具有重要作用。基于山西省2013~2017年NPP-VIIRS夜间灯光数据,提取灯光总强度、平均灯光强度、灯光斑块面积、最大斑块灯光强度、灯光斑块聚集度等参数,应用方差分析方法检验贫困县与非贫困县的参数差异;以2013年NPP-VIIRS数据构建贫困区域识别模型,并应用于2014~2017年的贫困县识别。结果表明：模型的综合识别准确率为71.43%~77.31%,贫困县识别精度较高,为79.31%~86.21%,非贫困县识别精度为59.02%~73.77%。除了灯光强度参数,模型中包含灯光斑块空间分布特征参数能够提高总体精度。进一步分析贫困概率与GDP关系、不同类型县的贫困概率年际变化,可以认为：夜间灯光数据能够用于贫困区域识别和退出评估,融合灯光强度与灯光斑块空间分布特征有助于提高贫困区域识别精度。     

基于遥感和GIS的人口数据空间化研究进展及案例分析

人口数据对于全球、洲际、区域尺度的人与环境交互作用研究的重要性已经被广泛地认知。但基于行政单位的人口统计数据难以与基于自然单位的环境数据相匹配,必须通过建模对人口数据进行空间化分布。人口数据空间化建模的基本思路是将人口数据和地球表面的地理因子关联起来,遥感和地理信息系统提供了有效的工具。介绍了国内外基于遥感和GIS的人口空间化研究的主要项目和方法思路。以黑河流域为研究区,在流域尺度上把典型的人口估计结果GPW、UNEP/GRID、LandScan和中国1 km格网的人口资料与政府人口统计数据进行比较分析,可以看出国内外的研究机构和研究人员已做了大量的相关工作。国外研究主要包括从遥感解译信息反演人口数据、从DMSP-OLS夜间灯光数据反演人口数据和从遥感获取的光谱特征直接反演人口数据。国内研究尽管起步较晚但发展很快,主要是根据土地利用数据和其它地理因子(如高程、道路、居民区等)建立回归模型。黑河流域人口空间分布的比较结果表明中国1 km格网人口数据是几种数据中与实践情况最相符合的。     

基于Kriging方法的动态地形反向多级细化研究

针对层次细节模型（LOD）只能实现数据精度低于原始DEM数据的动态地形建模过程,提出了基于Kriging方法的动态地形反向细化方法（Levels Of Backward Detail,LOBD）。该方法提取出需反向细化区域,判断多级细化级别,分析区域的空间属性相关性,根据最优条件拟合变异函数,根据Kriging方法对目标区域进行插值,生成精度高于原始DEM数据精度的区域数据。设计了按独立树结构的插值数据动态存储方式。建模实验表明,新的动态建模方法的细节层次可以比LOD方法至少增加两级,并且保持渲染效率基本不变。     

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.     

Improved GDP spatialization approach by combining land-use data and night-time light data: a case study in China’s continental coastal area

Gross domestic product (GDP) reflects a nation or region’s economic growth as a whole, and is the sum of product in the primary, secondary, and tertiary sectors of the economy in the area. However, statistical GDP data is problematic in integrated application with geographical data. The GDP spatialization data, which shows the GDP in grid cells and often is obtained by operating a spatialization model, is more useful than its officially published statistical data recorded by administrative units in both spatial representation and application. Thus, there is a need to improve the GDP spatialization models, and to present these models in a way as clear and transparent as possible. In this article, by taking China’s continental coastal area as a case study area, we combined economic census data, land-use data, and night-time light data together, and developed a technique that we call the ‘dynamic regionalization’ method to improve the GDP spatialization products. We then created GDP spatialization models for three sectors of the economy (i.e. the primary, the secondary, and the tertiary sector) in 2000, 2005, and 2010, respectively. We find the following. (1) Because the ‘overglow’ effect of night-time light data has a bad influence on spatialization models, we used land-use data to distinguish the distribution plots of the tertiary sector on night-time light images. Compared with setting a threshold merely, land-use data can more effectively remove the ‘overglow’ effect. (2) Owing to the prominent spatial heterogeneity of GDP distribution in China’s continental coastal area, building one spatialization model for the whole area would probably produce the estimated products with poor accuracy, so the ‘dynamic regionalization’ method was adopted to dynamically divide the whole study area into several subregions, and build separate spatialization models for each subregion. The accuracy assessment showed that the new method improved the accuracy of GDP spatialization data, especially in the area with high spatial heterogeneity.     

Monitoring land-use change in the Pearl River Delta using Landsat TM

The Pearl River Delta in the People's Republic of China is experiencing rapid rates of economic growth. Government directives in the late 1970s and early 1980s spurred economic development that has led to widespread land conversion. In this study, we monitor land-use through a nested hierarchy of land-cover. Change vectors of Tasseled Cap brightness, greenness and wetness of Landsat Thematic Mapper (TM) images are combined with the brightness, greenness, wetness values from the initial date of imagery to map four stable classes and five changes classes. Most of the land-use change is conversion from agricultural land to urban areas. Results indicate that urban areas have increased by more than 300% between 1988 and 1996. Field assessments confirm a high overall accuracy of the land-use change map (93.5%) and support the use of change vectors and multidate Landsat TM imagery to monitor land-use change. Results confirm the importance of field-based accuracy assessment to identify problems in a land-use map and to improve area estimates for each class.     

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.     

Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery

When mapping land cover with satellite imagery in montane tropical regions, varying illumination angles and ecological zones can obscure the differences between spectral responses of old-growth forest, secondary forest and agricultural lands. We used multi-date, Landsat Thematic Mapper (TM) imagery to map secondary forests, agricultural lands and old-growth forests in the Talamanca Mountain Range in southern Costa Rica. With stratification by illumination and ecological zone, the overall accuracy for this classification was 87% with a Kappa coefficient of 0.83. We also examined spectral responses to forest successional stage, ecological zone and aspect illumination for the TM Tasselled Cap indices, TM (2 x 6)/7, TM 4/5 and TM difference bands, and whether using digital data from multiple decades improved classification accuracy. Digital maps of ecological zones should be useful for large-scale mapping of land use and forest successional stage in complex montane regions such as those in Central America.     

Evaluating Algeria’s social and economic development using a series of night-time light images between 1992 to 2012

ABSTRACT

After social and economic problems, Algeria regained its night-time light (NL) brightness as this analysis of space-borne images shows. We evaluate the advancement of artificial light sources and the socioeconomic development between 1992 to 2012 for all 48 provinces. Image calibration improved the consistency and comparability of the DMSP-OLS data. Light detected from gas flaring has been separated to focus on light from human settlements. We then estimated the relationship between night-time light and selected socioeconomic parameters (SEP). Night-time light growth (NLG) and night-time light annual growth (NLAG) indices are calculated at the national, regional and provincial level. Considering the Oil price crash and the Algerian Civil Concord, we take a detailed look at these historic events in Algeria. Our main findings are: (A) The total night-time light (TNL) for human settlements grew nationwide by 128%, (B) night-time light data correlates with socioeconomic parameters such as population, and electric power consumption, (C) the oil price crash resulted in slower night-time light growth, while the Algerian Civil Concord and the end of the crises led to increased TNL, (D) because of government investment, the High Plateaus are the fastest growing region since the end of the crises, (E) space-borne night-time light data can consistently fill the information gap when data from official sources are not available. Government and policy-making bodies may benefit from the presented findings allowing them to oversee and address provincial inequalities and economic development.     

Population estimation from Landsat imagery

The paper introduces a method of population estimation using the Landsat MSS data. The radiance in the four spectral bands, detected by the multi spectral scanner (MSS) depends upon the ground covering materials, albeit the land use of the area. A mathematical model is set up to express the relation between the reflected electromagnetic energy of sample areas and their population distribution. Landsat 1 and Landsat 3 data of the Kanto area (including Tokyo Metropolitan), acquired in 1972 and 1979, are used along with ground-based census data of 1970 and 1975 to monitor the population distribution and its temporal changes. The method provided a reliable assessment of the population density in residential zones, however land-use classification using MSS imagery previous to the modeling is expected to improve the results.     

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.     

Bathymetric and bottom effects on CZCS chlorophyll-like pigment estimation: Data from the Kerkennah Shelf (Tunisia)

The Coastal Zone Color Scanner (CZCS)-retrieved chlorophyll-like pigments for the Mediterranean Sea show zones of high concentrations (10mgm-3) such as the shallow Kerkennah Shelf, off the southern Tunisian coast. This occurrence pointing to probable erroneous estimates, exaggerated by the effect of the bottom signal is discussed in the general framework of the applicability of water colour techniques to coastal areas, as a source of environmental data suitable for inclusion in a multi-purpose Geographic Information System (GIS). The state of knowledge on the Kerkennah Shelf test site, organized into a regional geo-referenced data base (including CZCS and TM imagery), is presented. A two-flow model has been applied to in situ reflectance measurements, to derive some information on the interplay between water colour and bottom typology. The results have been used to evaluate nature, intensity and extent of the sedimentary and vegetated sea bottom effect on the chlorophyll-like pigment concentrations derived from CZCS in the Kerkennah area. Finally, the applicability of satellite-derived ocean colour data to coastal zone mapping is discussed in some detail.     

A study of urban expansion of prefectural-level cities in South China using night-time light images

Because of its rapid economic development, China has been undergoing a dramatic urbanization process in recent decades. Such a process can be reflected by urban expansion, which can be represented by the change in urban built-up areas. In the literature, very little has been discussed on the extraction of built-up areas with a high frequency of acquisition for a large region. This article introduces a methodology to extract built-up areas using night-time stable light data. An improved calibration method is formulated to first eliminate the discrepancies across different satellites and years. A thresholding technique utilizing the sudden jump method is then employed to extract built-up areas for each prefectural city in each year. This method selects the best threshold by scrutinizing the sudden jump in the natural logarithms of the areas under different digital number (DN) thresholds. Moreover, the urbanization process of South China is examined using the extracted time series of built-up areas. The results show that such extracted time series represent changes in urban areas rather well when compared with the Thematic Mapper (TM) images, and a significant linear relationship between the extracted built-up areas and those of the land-use map and the China City Statistical Yearbook (CCSY) has also been established. Moreover, the empirical analyses also reveal that urban expansion took place in all cities from 1992 to 2010, especially in coastal cities, capital cities, and cities in the special economic zones.