Nighttime Light Images Research Articles

Monitoring Urban Clusters Expansion in the Middle Reaches of the Yangtze River, China, Using Time-Series Nighttime Light Images

The urban clusters in the Middle Reaches of the Yangtze River (MRYR) in China include the Chang-Zhu-Tan urban agglomeration, the Wuhan metropolitan area, and the Poyang Lake urban agglomeration. While previous studies of urban expansion in China focused mainly on the coastal regions, this study aimed to investigate urban expansion patterns and factors in the MRYR, which are crucial for urban development in Central China. A neighborhood statistics analysis (NSA) method and a local-optimized threshold method were used to detect urban changes during 1992–2011 from the time-series Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime light (NTL) images. The evolution of urban expansion intensity and landscape metrics were analyzed at multiple spatial scales, including the whole region, urban agglomeration, and city scales. Finally, the expanded STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was built to explore the factors that controlled NTL intensity. The results revealed that urban areas extracted from the NTL data were consistent with those extracted from the Landsat Thematic Mapper data, with an overall accuracy of 81.74% and a Kappa of 0.40. A relatively slow urbanization pace was observed from 1992 to 2002 in the MRYR region, which then accelerated in the period of 2002 to 2007 and then slowed down between 2007 and 2011. Additionally, urban expansion exhibited a radial pattern. The results further indicated that major factors controlling NTL intensity were gross domestic product, followed by total investment in fixed assets, tertiary industry, urban construction area, non-agricultural population, and industrial output in the city clusters. The study provides important insights for further studies on the urbanization processes in the MRYR region.

SPATIOTEMPORAL EVOLUTION OF THE IMBALANCED REGIONAL DEVELOPMENT IN MAINLAND CHINA USING DMSP-OLS DATA

Abstract. The Defense Meteorological Satellite Programs Operational Linescan System (DMSP-OLS) nighttime lights imagery has been widely used to monitor economic activities and regional development in recent decades. In this paper, we firstly processed the nighttime light imageries of the Mainland China from 1992 to 2013 due to the radiation or geometric errors. Secondly, by dividing the Mainland China into seven regions, we found high correlation between the sum light values and GDP of each region. Thirdly, we extracted the economic centers of each region based on their nighttime light images. Through the analysis, we found the distribution of these economic centers was relatively concentrated and the migration of these economic centers showed certain directional trend or circuitous changes, which suggested the imbalanced socio-economic development of each region. Then, we calculated the Regional Development Gini of each region using the nighttime light data, which indicated that social-economic development in South China presents great imbalance while it is relatively balanced in Southwest China. This study would benefit the macroeconomic control to regional economic development and the introduction of appropriate economic policies from the national level.

ASSESSMENT OF LIGHT POLLUTION IMPACT ON PROTECTED AREAS IN CHINA

Abstract. The Protected Areas (PAs) with natural, ecological and cultural value play important role in biological processes, biodiversity and ecosystem service. During the past years of rapid urban expansion in China, the spatial range and intensity of light pollution unprecedented increase. Historically, optical remote sensing and field survey data had been used to reveal that human activities impacted on PAs for individual areas and few papers documented the issue of light pollution impact on PAs at national scale. Here, time series night-time light satellite images of Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) were selected to assess the light pollution impacted on PAs in China. The method we proposed can be effectively applied to assess the impact of light pollution on PAs and the percent of dark PAs decreased by 35.38 % from 1992 to 2012 at nationwide. The trend of light pollution of most PAs in stable, however, light pollution of the local area is increase significantly, especially in northern Xinjiang, Gansu, Xizang, Yunnan, Jiangsu and Shandong. Considering the current status of light pollution encroach into PAs, two strategies of appropriate buffer zone and wide measured for light pollution are also recommend.

Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light

The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process flow to correct NPP-VIIRS nighttime light from April 2012 to March 2017 by employing the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light image. The time series analyses at national scales show that there is a sharp decline in the study period from February 2015 to June 2015 and that the total nighttime light (TNL) of Yemen decreased by 71.60% in response to the decline period. The nighttime light in all provinces also showed the same decline period, which indicates that the Saudi-led airstrikes caused widespread and severe humanitarian crisis in Yemen. Spatial pattern analysis shows that the areas of declining nighttime light are mainly concentrated in Sana’a, Dhamar, Ibb, Ta’izz, ’Adan, Shabwah and Hadramawt. According to the validation with high-resolution images, the decline in nighttime light in Western cities is caused by the damage of urban infrastructure, including airports and construction; moreover, the reason for the decline in nighttime light in eastern cities is the decrease in oil exploration. Using nighttime light remote sensing imagery, our findings suggest that war made Yemen dark and provide support for international humanitarian assistance organizations.

A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL

With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS NTL) have long been utilized to monitor urban areas and their expansion characteristics since this system detects variation in NTL emissions. However, the pixel saturation phenomenon leads to a serious limitation in mapping luminance variations in urban zones with nighttime illumination levels that approach or exceed the pixel saturation limits of OLS sensors. Consequently, we propose an NTL-based city index that utilizes the Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) images to regulate and compensate for desaturation on NTL images acquired from corresponding urban areas. The regulated results achieve good performance in differentiating central business districts (CBDs), airports, and urban green spaces. Consequently, these derived imageries could effectively convey the structural details of urban cores. In addition, compared with the Vegetation Adjusted NTL Urban Index (VANUI), LST-and-EVI-regulated-NTL-city index (LERNCI) reveals superior capability in delineating the spatial structures of selected metropolis areas across the world, especially in the large cities of developing countries. The currently available results indicate that LERNCI corresponds better to city spatial patterns. Moreover, LERNCI displays a remarkably better “goodness-of-fit” correspondence with both the Version 1 Nighttime Visible Infrared Imaging Radiometer Suite Day/Night Band Composite (NPP/VIIRS DNB) data and the WorldPop population-density data compared with the VANUI imageries. Thus, LERNCI can act as a helpful indicator for differentiating and classifying regional economic activities, population aggregations, and energy-consumption and city-expansion patterns. LERNCI can also serve as a valuable auxiliary reference for decision-making processes that concern subjects such as urban planning and easing the central functions of metropolis.

Saturation Correction for Nighttime Lights Data Based on the Relative NDVI

DMSP/OLS images are widely used as data sources in various domains of study. However, these images have some deficiencies, one of which is digital number (DN) saturation in urban areas, which leads to significant underestimation of light intensity. We propose a new method to correct the saturation. With China as the study area, the threshold value of the saturation DN is screened out first. A series of regression analyses are then carried out for the 2006 radiance calibrated nighttime lights (RCNL) image and relative NDVI (RNDVI) to determine a formula for saturation correction. The 2006 stable nighttime lights (SNL) image (F162006) is finally corrected and evaluated. It is concluded that pixels are saturated when the DN is larger than 50, and that the saturation is more serious when the DN is larger. RNDVI, which was derived by subtracting the interpolated NDVI from the real NDVI, is significantly better than the real NDVI for reflecting the degree of human activity. Quadratic functions describe the relationship between DN and RNDVI well. The 2006 SNL image presented more variation within urban cores and stronger correlations with the 2006 RCNL image and Gross Domestic Product after correction. However, RNDVI may also suffer “saturation” when it is lower than −0.4, at which point it is no longer effective at correcting DN saturation. In general, RNDVI is effective, although far from perfect, for saturation correction of the 2006 SNL image, and could be applied to the SNL images for other years.

Urban roughness parameters estimation from globally available datasets for mesoscale modeling in megacities

Approaches to understanding urban atmospheric phenomena in cities include mesoscale models coupled with urban canopy models (UCMs). To improve the accuracy of these models, urban morphological parameters of higher spatial resolution are necessary. In this paper, we introduce an approach to derive 1-km scale urban parameters from globally available satellite images of Landsat 8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and nighttime light (NL) images from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) for use in numerical weather models. Empirical equations derived from linear fittings were used to calculate urban geometric parameter plane area index from satellite images, which were then compared with values derived from real plane area index. To calculate urban geometric parameters average building height, an artificial neural network fitting was conducted. From the basic urban geometric parameters, other necessary urban parameters were derived. After validating the method, a weather simulation was conducted using the derived parameters for Jakarta, Indonesia. Results showed that urban parameters made from global datasets could improve the performance of the single-layer urban canopy model; and these estimated parameters can be used as a substitute when actual building information are lacking.

A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013)

The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed a stepwise calibration approach to generate a temporally consistent NTL time series from 1992 to 2013. First, the temporal inconsistencies in the original NTL time series were identified. Then, a stepwise calibration scheme was developed to systematically improve the over- and under- estimation of NTL images derived from particular satellites and years, by making full use of the temporally neighbored image as a reference for calibration. After the stepwise calibration, the raw NTL series were improved with a temporally more consistent trend. Meanwhile, the magnitude of the global sum of NTL is maximally maintained in our results, as compared to the raw data, which outperforms previous conventional calibration approaches. The normalized difference index indicates that our approach can achieve a good agreement between two satellites in the same year. In addition, the analysis between the calibrated NTL time series and other socioeconomic indicators (e.g., gross domestic product and electricity consumption) confirms the good performance of the proposed stepwise calibration. The calibrated NTL time series can serve as useful inputs for NTL related dynamic studies, such as global urban extent change and energy consumption.

Estimating Population Density Using DMSP-OLS Night-Time Imagery and Land Cover Data

Population density is an essential indicator of human society. Night-time light (NTL) data provided by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) has been widely used in estimating population distribution, due to its capability of indicating human activity. The overglow effect of the DMSP-OLS NTL image caused by reflection of light from adjacent areas and the different population distribution patterns between urban and rural areas have limited its application in estimating population density. Therefore, a method was proposed to reduce the overglow effect and to model urban and rural population densities separately. Moderate resolution imaging spectroradiometer (MODIS) land cover product was applied to reduce the overglow effect and to separate urban and rural areas. In urban area, the extracted urban DMSP-OLS NTL image was used to model population density. In rural area, a slope adjusted human settlement index (SAHSI), based on digital elevation model, MODIS enhanced vegetation index (EVI), and the DMSP-OLS NTL data, was proposed to estimate rural population density. Guangdong Province of China was taken as the study area for it has diverse population densities. The estimation in urban area was compared with population densities derived from normalized difference vegetation index adjusted NTL urban index (VANUI) and EVI adjusted NTL urban index (VANUI-EVI). Population density in the rural area was compared with results from EVI adjusted human settlement index (HSI-EVI) and the NTL data. The mean relative error of the proposed method was 55.14% in urban areas, which was better than VANUI (60.10%) and VANUI-EVI (60.16%), and was 71% in rural areas, which was 6% lower than HSI-EVI and 3% lower than NTL data. The result indicates that the proposed method has the ability to reduce the overglow effect of DMSP-OLS NTL image and to correct the impact of terrain on rural population density estimation.

A Map Spectrum-Based Spatiotemporal Clustering Method for GDP Variation Pattern Analysis Using Nighttime Light Images of the Wuhan Urban Agglomeration

Estimates of gross domestic product (GDP) play a significant role in evaluating the economic performance of a country or region. Understanding the spatiotemporal process of GDP growth is important for estimating or monitoring the economic state of a region. Various GDP studies have been reported, and several studies have focused on spatiotemporal GDP variations. This study presents a map spectrum-based clustering approach to analyze the spatiotemporal variation patterns of GDP growth. First, a sequence of nighttime light images (from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS)) is used to support the spatial distribution of statistical GDP data. Subsequently, the time spectrum of each spatial unit is generated using a time series of dasymetric GDP maps, and then the spatial units with similar time spectra are clustered into one class. Each category has a similar spatiotemporal GDP variation pattern. Finally, the proposed approach is applied to analyze the spatiotemporal patterns of GDP growth in the Wuhan urban agglomeration. The experimental results illustrated regional discrepancies of GDP growth existed in the study area.

Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War

ABSTRACTMonthly composites of night-time light acquired from the Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) had been used to evaluate socio-economic dynamics and human rights during the Syrian Civil War, which started in March 2011. However, DMSP/OLS monthly composites are not available subsequent to February 2014, and the only available night-time light composites for that period were acquired from the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (Suomi NPP/VIIRS). This article proposes an intercalibration model to simulate DMSP/OLS composites from the VIIRS day-and-night band (DNB) composites, by using a power function for radiometric degradation and a Gaussian low pass filter for spatial degradation. The DMSP/OLS data and the simulated DMSP/OLS data were combined to estimate the city light dynamics in Syria’s major human settlement between March 2011 and January 2017. Our analysis shows that Syria’s major human settlement lost about 79% of its city light by January 2017, with Aleppo, Daraa, Deir ez-Zor, and Idlib provinces losing 89%, 90%, 96%, and 99% of their light, respectively, indicating that these four provinces were most affected by the war. We also found that the city light in Syria and 12 provinces rebounded from early 2016 to January 2017, possibly as a result of the peace negotiation signed in Geneva.

Improving accuracy of economic estimations with VIIRS DNB image products

ABSTRACTA new-generation of night-time lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly composites, have been produced and released by the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information. Compared with the last generation NTL image products, the Defense Meteorological Satellite Program’s Operational Linescan System stable light composites, the new NTL image products have finer spatial resolution with compatible radiance values across different month/year images. However, the current defects in VIIRS DNB monthly composites show ephemeral lights, relatively high radiance in winter months, and missing data over the high-latitude regions of the northern hemisphere in summer months. This study presents a method to improve the accuracy of the new NTL image products by statistically modelling the time series VIIRS NTL images and uses the improved imagery to estimate socio-economic factors. In this method, we first estimate radiance for each pixel with ‘no data’ in May, June, July, and August images and then exponentially smooth the monthly time series images to produce a 2014 annual VIIRS DNB image for the contiguous USA. Sum radiance derived from the smoothed annual image shows stronger correlations with gross domestic product at the state level and smaller standard errors of the estimate at the metropolitan and county levels compared with that extracted from the annual image produced by simply averaging the original monthly DNB composites. Such results infer that exponential smoothing effectively improves the quality of the VIIRS DNB images for annual economic estimation.

Building classification in Yangon City, Myanmar using Stereo GeoEye images, Landsat image and night-time light data

Yangon, the former capital of Myanmar, is the biggest city in the country with more than five million people and it is also the major country's economic areas. These areas are complex with three classes of residential, commercial and industrial buildings. Understanding building uses in the city with the information of the locations and the quantitative measurement is very important to support urban management and development with various aspects. This research proposed a methodology to classify types of buildings with three classes in Yangon, Myanmar by using remotely sensed data. In this research, stereo GeoEye images, multi-spectral Landsat image and night-time light (NTL) image from Visible Infrared Imaging Radiometer Suite (VIIRS) were employed to extract types of buildings. The Stereo GeoEye images were used to obtain the heights of buildings, the Landsat image was classified to provide land cover areas, and NTL image was applied to separate NTL activities. By using the hierarchy classification with (1) the heights of buildings, (2) land cover areas and (3) NTL consumptions, the buildings were classified into three classes with (1) residential, (2) commercial, and (3) industrial buildings. In the experiments, the estimated building type map by our proposed method was compared with a land use map and surveying building data. The comparing results indicated that our methodology classified types of buildings in efficiency with the accuracy of 76% and the Kappa coefficient of 0.58.

Spatial differentiation and morphologic characteristics of China’s urban core zones based on geomorphologic partition

Based on a previous study that used the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light images to partition different types of night-lit areas within individual cities, 456 urban core zones in China, representing highly developed areas in 2012, were extracted. Then, several morphologic indices were selected for characterizing for each urban core zone, and the spatial differentiation and morphologic characteristics of urban core zones located within different geomorphologic regions in China were quantitatively analyzed. The results showed that urban core zones were most widely distributed in the eastern region comprising hilly plains, with a decreasing distribution trend from northeast to southwest, and the least distribution was in the Tibetan Plateau. The contours of most of these zones appeared to be relatively simple and compact, and evidenced seven shapes. Regions at lower altitudes with flat terrains were more likely to demonstrate a wide range of urban core zones, especially those with complex shapes. This study represents a preliminary effort toward the construction of an interactive coupling mechanism for urban and geomorphologic environments (e.g., altitude, relief of land surface, geomorphologic types, geomorphologic region). Its findings contribute to enhancing understanding of the spatial morphologic characteristics of highly developed areas in China.

Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data

With the rapid urbanization of China, plenty of new urban lands have been developed with the great expectation to deal with all kinds of issues in old urban areas such as high population density, great demand on limited land resources, and decaying environment. However, a great proportion of vacancy in these newly developed units leads to the undesired observation of ghost cities. Lacking of clear and effectively evaluation criterion, the understanding of ghost cities in China is then rather limited. Considering the fact of ghost cities, we borrow the theory of urban vitality to identify and evaluate ghost cities in this paper. We argue that ghost cities are associated with very low urban vitality. In the light of big/open data, we are able to profile ghost cities of China based on 535,523 recent project-level residential developments from 2002 to 2013. We use the national-wide and million magnitude road junctions, points of interest and location based service records of 2014/2015 for measuring the morphological, functional and social vitality of each residential project. We then aggregate the project level evaluation results into the city level and thirty ghost cities are then identified by comparing the residential projects' vitality in the old (developed before or in 2000) and new (developed after 2000) urban areas in each city. Our profiling results illustrate the big picture of China's past residential developments, and then of ghost cities. We find the average vitality of residential projects in new urban areas is only 8.8% of that in old urban areas, denoting the potential existence of ghost cities in newly developed areas in Chinese cities. We have also benchmarked our identified ghost cities with existing rankings, the Baidu searching engine and night-time light images. Although we admit that ghost cities may exist in the particular urbanizing phase of China and that some ghost cities now may be well developed in the future, this study provides a thorough evaluation on the ghost city condition in China. This may shed light on policy implications for Chinese urban development.

Forecasting China’s GDP at the pixel level using nighttime lights time series and population images

China’s rapid economic development greatly affected not only the global economy but also the entire environment of the Earth. Forecasting China’s economic growth has become a popular and essential issue but at present, such forecasts are nearly all conducted at the national scale. In this study, we use nighttime light images and the gridded Landscan population dataset to disaggregate gross domestic product (GDP) reported at the province scale on a per pixel level for 2000–2013. Using the disaggregated GDP time series data and the statistical tool of Holt–Winters smoothing, we predict changes of GDP at each 1 km × 1 km grid area from 2014 to 2020 and then aggregate the pixel-level GDP to forecast economic growth in 23 major urban agglomerations of China. We elaborate and demonstrate that lit population (brightness of nighttime lights × population) is a better indicator than brightness of nighttime lights to estimate and disaggregate GDP. We also show that our forecast GDP has high agreement with the National Bureau of Statistics of China’s demographic data and the International Monetary Fund’s predictions. Finally, we display uncertainties and analyze potential errors of this disaggregation and forecast method.

城市扩张背景下城市群环境问题的区域性特征分析

PDF HTML阅读 XML下载 导出引用 引用提醒 城市扩张背景下城市群环境问题的区域性特征分析 DOI: 10.5846/stxb201705250968 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金（41571148）；福建省自然科学基金（2014J01161） Regional characteristics analysis of environmental problems in urban agglomerations undergoing urban expansion Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:作为区域空间组织的重要形式，城市群已经成为促进城市化快速发展的重要地区，同时也逐渐成为生态环境问题高度集中的地区。采用夜晚灯光影像在分析京津冀、长江三角洲和珠江三角洲城市群空间扩张进程的基础上，从空气质量、工业固体废物处置利用情况以及植被覆盖度3个方面对三大城市群环境问题的区域性特征及其敏感区进行分析。结果表明：三大城市群空间扩张进程不断加快，并逐渐形成城市用地连绵区，长江三角洲城市群城市用地连绵区范围最大，珠江三角洲城市群城市用地连绵区范围高于京津冀城市群；从城市群层面看，三大城市群在空气质量、工业固体废物综合利用率以及植被覆盖度演变方面已呈区域性特征，城市化水平与工业固体废物综合利用率呈正相关，与城市群植被覆盖度呈负相关；从城市层面来看，空间扩张较快的宁波市和北京市为长江三角洲和京津冀城市群在空气质量、工业固体废物综合利用率以及植被覆盖度方面的敏感区，中山市为珠江三角洲在工业固体废物综合利用率以及植被覆盖度方面的敏感区。环境问题的区域特征以及环境敏感区分析对环境治理工作具有一定的指导作用，对区域的可持续发展具有一定的现实意义。 Abstract:As an important spatial form of regional spatial organizations, urban agglomeration has become an important aspect of rapid urbanization because it gradually leads to a highly concentrated region with large eco-environmental problems. Night time light image was adopted to analyze spatial expansion in the Beijing-Tianjin-Hebei, Yangtze River Delta, and Zhujiang River Delta urban agglomerations. We also analyzed the regional characteristics and areas that are sensitive to environmental problems, including air quality, comprehensive use of industrial solid wastes, and vegetation coverage. Spatial expansion of the three urban agglomerations is increasing and interlocking regions of urban land have gradually formed. The interlocking regions were largest in the Yangtze River Delta and smallest in Beijing-Tianjin-Hebei area. The regional characteristics for the three urban agglomerations are presented in terms of air quality, comprehensive use of industrial solid wastes, and vegetation coverage. The air quality in the Zhujiang River Delta cities was highest and lowest in the cities that make up Beijing-Tianjin-Hebei. The comprehensive use of industrial solid waste in the Yangtze River Delta cities was highest, but was lowest in in Beijing-Tianjin-Hebei. Vegetation degradation in the Yangtze River Delta was most serious, but was least serious in Beijing-Tianjin-Hebei. The air quality and comprehensive use of industrial solid waste regional characteristics were correlated with spatial expansion in the three urban agglomerations. There was a positive correlation between urbanization level and the comprehensive use of industrial solid waste as well as between urbanization level and vegetation coverage. Areas that are sensitive to poor air quality, the comprehensive use of industrial solid waste, and vegetation coverage were identified, and the sensitive areas were correlated with spatial expansion characteristics in the three urban agglomerations. Spatial expansion was faster in Ningbo and Beijing (cities in the Yangtze River Delta and Beijing-Tianjin-Hebei, respectively), which were sensitive to poor air quality, industrial solid waste, and vegetation coverage. Zhongshan in the Zhujiang River Delta was sensitive to the comprehensive use of industrial solid waste and vegetation coverage. The analysis of regional characteristics and environmentally sensitive areas is important if environmental protection and sustainable development is to improve. 参考文献 相似文献 引证文献

Measuring urban sprawl in China by night time light images

In the process of urbanization, a phenomenon called “urban sprawl” usually occurs. This phenomenon may exaggerated the negative effects of urbanization on environment, public and social health, energy efficiency, and maintenance of farmland. Therefore, the understanding of this phenomenon is urgently required for us to achieve sustainable development. This study proposed a group of night time lights (NTL) indicators of urban sprawl, which intend to use the distribution of lightness to quantify urban sprawl. These measures are proved to be efficient in describing urban sprawl. In addition, they are consistent and easy calculating, making comparison analysis easy to be done. These indicators are used to study urban sprawl in China during the year 2000 to 2010, the results show that in the last ten years, metropolitan areas in the northern part of China have undergone a more sprawl-like urban growth compared with other parts of China.

Updating urban extents with nighttime light imagery by using an object-based thresholding method

Abstract To address the problem of assessing large-scale urban dynamics from incompatible time series DMSP/OLS night-time light (NTL) data, this study proposed an Object-based Urban Thresholding method for NTL image data (i.e., NTL-OUT method) to estimate the optimal thresholds of urban objects in different NTL images. The optimal threshold for an urban object was determined by comparing the reference threshold with that of a target year through object-based normalization. Threshold normalization assumed the stability or limited change of NTL intensity over time for pseudo invariant pixels. An experiment was conducted in China to detect its urban areas in 2005 and 2010 from DMSP/OLS F152005 and F182010 NTL images by using the estimated object-based thresholds predicted from DMSP/OLS F152000 NTL data and reference urban data. Results showed that the proposed method can successfully relate thresholds among years. The estimated thresholds were highly correlated with the reference, with R 2 of 0.96 and 0.92 and RMSE of 2.9 and 4.1 pixels for 2005 and 2010, respectively. At the object level, R 2 and RMSE of the estimated vs. the reference urban areas yielded 0.94 and 12.5 pixels and 0.88 and 21.2 pixels for 2005 and 2010, respectively. At the provincial level, R 2 reached 0.94 and 0.91 with RMSE of 230.7 and 336.7 pixels for 2005 and 2010, respectively. The assessment of derived urban maps for 141 sample cities yielded an overall accuracy of 95.19% and 94.09% and Kappa of 0.56 and 0.53 in 2005 and 2010, respectively. The proposed method shows great potentials to update urban extents in a timely and cost-effective manner and to detect global urban dynamics.

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

ABSTRACTGross 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.