摘要
我国海岸带区域是台风、风暴潮、地震海啸和海岸侵蚀等海洋灾害的重灾区,精细空间分辨率的人口数据,能够有效服务海岸带灾害风险管理。本文基于我国第六次人口普查资料、OLS/DMSP和NPP/VIIRS DNB两种夜间灯光数据及Landsat卫星遥感影像,综合利用遥感与地理信息系统理论与技术,进行了我国海岸带人口空间化方法与应用研究。利用建筑物与裸地增强指数法(EBBI)基于Landsat卫星遥感影像提取了我国沿海区县建成区数据,作为人口分布的空间控制因素,以普查人口数与夜间灯光数据回归函数关系为依据,对人口进行空间化处理。反演得到了我国海岸带区县1km×1km和0.5km×0.5km两个空间尺度的人口格网数据。并利用福建省沿海乡镇人口数据对人口空间化结果进行了精度评价。研究结果表明NPP/VIIRS DNB夜间灯光数据适用于人口空间化研究,且其反演精度整体优于基于DMSP/OLS传统夜间灯光数据反演的人口格网模型。通过本文实践,可以发现NPP/VIIRS DNB夜间灯光数据具有实现人口和社会经济数据空间化的巨大潜力。
The densely populated low-lying coastal zone of China is a multiple marine disaster-prone region. Disaster risk management in this region could be improved with fine spatial resolution data on the human population. Here, we carried out population spatial processing utilizing the theory and technology of GIS and RS. Data sources included demographic data, Landsat satellite remote sensing data, and remote sensing data of night light including the operational linescan system sensors (OLS) on board of the defense meteorological satellite program (DMSP) and day/night band of visible infrared imaging radiometer suite (DNB VI1RS) on board the national polar-orbiting partnership (NPP). The residential area was used for spatial scope of population spatial processing, acquired by the method of enhanced built-up and bareness index based on Landsat remote sensing data. Combining the regression functions of population and night light data, we arrived at two sets of gridded population data with a spatial resolution of lkm lkm and 0.Skmx 0.Skm, respectively. The accuracy of the two-gridded population dataset was estimated using demographic data from coastal towns in Fujian. Accuracy assessment results show that the night light of NPP/VIIRS DBN is superior to DMSP/OLS in population spatial processing research. We also show that the recently published NPP/VIIRS DNB night light data is suitable for research on spatial processing of demographic data. The night light data of NPP/VIIRS DNB has enormous potential value for the spatial processing of demographic and socioeconomic data in the future.
出处
《资源科学》
CSSCI
CSCD
北大核心
2013年第12期2517-2523,共7页
Resources Science
基金
国家海洋局海洋公益性行业专项:"重点区域精细化风暴潮预报技术及海峡西岸示范应用"
"沿海重点保障区域精细化综合预报系统研制与应用"(编号:200905013
201305031)
国家科技支撑计划项目(编号:2012BAC19B08
2011BAC03B03)
自然科学基金项目(编号:41201442)