中巴经济走廊DMSP/OLS与NPP/VIIRS夜光数据辐射一致性校正

由美国国防气象卫星搭载的可见光成像线性扫描业务系统(DMSP/OLS)和国家极轨卫星搭载的可见光近红外成像辐射仪(NPP/VIIRS)获取的夜间灯光影像是监测人类社会经济活动和自然现象(如林火、油气燃烧等)的主要数据源。然而,现有的夜间灯光数据存在缺乏星上的辐射定标、像元饱和、时间尺度不连续、多源夜间灯光影像辐射不一致等问题。基于此,本文以中巴经济走廊区域为研究区,提出了一种基于线性拟合提取不变目标区域的方法,实现了DMSP/OLS影像间、DMSP/OLS与NPP/VIIRS两种数据间的相互校正。然后对中巴经济走廊的校正结果在不同空间尺度上选用区域灰度总量、标准化差异指数以及标准化差异指数和作为评价指标进行检验。结果表明:两种校正模型的拟合优度均在0.78以上,校正后的DMSP/OLS影像灰度总量与GDP和人口数据的相关性显著提高(GDP:R~2=0.7689;人口:R~2=0.9033),且标准化差异指数明显降低;NPP/VIIRS影像经过与DMSP/OLS互校正后在辐射亮度、时空分布上与DMSP/OLS更加一致,空间细节信息更加突出,从而增强了多源夜间灯光影像的一致性,更加适合用于长时间序列社会经济要素发展趋势的分析。     

POI与NPP/VIIRS夜光数据空间耦合关系下的城市空间结构分析——以武汉市主城区为例

城市兴趣点（POI）和夜光遥感影像能够直观反映城市社会经济等实体要素的空间分布特征,在城市空间结构研究中发挥着重要作用。本文首先选取长江中游城市群的典型城市代表——武汉市作为研究区,选用研究区2016年POI和NPP/VIIRS夜光遥感数据作为基础研究数据,采用GIS分析工具对POI数据进行了空间核密度分析;然后分别对POI核密度分析结果和NPP/VIIRS夜光遥感数据进行了空间网格化处理;最后采用双因素制图和栅格叠加分析方法对两类数据的空间耦合关系进行了探讨,并在此基础上进一步分析了城市空间结构特征。研究表明,武汉市POI数据和夜光遥感数据的空间耦合性整体较好,空间耦合相一致区域占比为82.15%;但POI数据和夜光遥感数据的空间耦合性在长江沿岸地区也存在部分差异,如硚口区、汉阳区夜光遥感数据和POI数据多以低—中的空间耦合模式为主,而青山区、武昌区和汉口区则多以中—低的空间耦合模式为主。武汉市作为中原城市群的核心城市之一,其城市内部空间结构与长江经济带发展关联密切,通过对POI和夜光遥感数据的空间耦合关系探讨,能够对武汉市空间实体要素的空间结构特征有更加深入的了解。本文结果可为沿江城市内部空间结构的研究提供一种崭新的视角。     

NPP卫星VIIRS微光资料反演夜间PM2.5质量浓度

为研究城市夜间PM_(2.5)质量浓度,利用NPP卫星上可见光红外成像辐射套件(VIIRS)DNB通道的微光辐射数据,以辐射传输理论为基础,建立了夜间城市灯光辐射强度与地面表层PM_(2.5)质量浓度的关系,并基于支持向量机方法建立了夜间城市PM_(2.5)质量浓度反演模型。以北京市作为研究对象,选取2015-03—2015-05期间无月、无云且晴朗夜空条件下4个PM_(2.5)监测站点的微光辐射数据与时空匹配的PM_(2.5)质量浓度数据对模型进行验证。研究结果表明:夜间城市灯光辐射强度与地面表层PM_(2.5)质量浓度呈现负相关性,相关系数最高的定陵站点达到–0.83。基于支持向量机方法建立的PM_(2.5)反演模型获得的PM_(2.5)质量浓度与实际PM_(2.5)质量浓度的相关系数达到0.95,反演结果较优,为进一步大范围监测PM_(2.5)质量浓度空间分布以及改善城市夜间空气质量状况评估方法提供了可行性参考。     

夜间灯光数据支持下的广州市人口空间化

了解区域人口的空间分布,能够为城市精细化管理和城市规划提供有力的支撑数据,对提高城市发展水平有重要的现实意义。为此,本文以“珞珈一号”(LJ-1 01)夜间灯光(NTL)影像为基础数据,以广州市为研究区,结合人口估算模型等方法,开展基于NTL数据的人口空间化研究。结果表明:LJ-1 01 NTL数据能够有效地应用于人口空间化研究。人口空间化后,广州市人口分布具有显著的“一主中心,多核心”的特征,各区域内部的人口分布差异可被清晰地展示与区分。本文结果对城市规划与城市管理具有一定价值的辅助支撑作用。     

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales. However, the rapid growth of urban areas exerts effects on climate, biodiversity, hydrology, and natural ecosystems worldwide. Therefore, regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local, regional, and potentially global scales. This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data, including Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013, population density of 2012, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights of 2012, and constructed Impervious Surface Area (ISA) data of 2010. The Eurasian urban map was created using the threshold method for these data, combined with references of fine resolution Landsat and Google Earth imagery. The resultant map was compared with nine global urban maps and was validated using random sampling method. Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%. This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R² value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.