基于夜间灯光数据的人口空间分布研究综述

doi:10.6046/gtzyyg.2019.03.02

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摘要

人口空间分布信息是地理学科、资源学科和社会学科等研究中的基础信息,在城市规划和应急救援等实际应用中具有十分重要的意义。利用自然地理和社会经济等多种辅助数据,可以很好地模拟人口分布情况。夜间灯光数据综合反映着人口分布活动情况,与传统遥感数据相比,具有数据获取方便、数据量小、覆盖范围广和数据更新快等优点。随着美国军事气象卫星计划搭载的线性扫描业务系统(defense meteorolgical satellite program-operational linescan system,DMSP-OLS)、美国国家极地轨道卫星搭载的新一代可见光及近红外辐射仪(national polar orbiting partnership-visible infrared imaging radiometer suite,NPP-VIIRS)及其他平台的发展,连续获取夜间灯光数据成为可能,基于这类数据的人口空间分布研究受到学者的重视,在区域尺度的人口估算和格网尺度的人口分布模拟方面取得了丰富的研究成果。但其中也存在着数据、尺度、模型方法及精度验证等方面的问题。因此,在详细阐述了夜间灯光数据特征及获取平台的基础上,对已有基于夜间灯光数据的人口空间分布研究方法及模型进行了对比总结,分析了研究中存在的各种问题及现有解决方法,并对未来的重点发展方向进行了探讨。

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	肖东升
	杨松

关键词 ：夜间灯光, 人口分布, 数据校正, 格网尺度, 精度验证

Abstract：

Population spatial distribution information is the basic information in the study of geography, resources, sociology and other disciplines, and hence is of great significance in practical applications such as urban planning and emergency rescue. The population distribution can be well simulated by using the auxiliary data of physical geography and social economy data. Nighttime light data reflect the distribution of population comprehensively. Compared with traditional remote sensing data, it has the advantages of convenient data acquisition, small data volume, wide coverage and fast data update. With the development of DMSP-OLS, NPP-VIIRS and other platforms, the study of population spatial distribution based on continuous archiving nighttime light data has attracted the attention of scholars, and a rich research result has been formed at regional-scale population estimates and grid-scale simulations of population distribution. Nevertheless, there are also problems in data correction, data fusion, scale selection and precision verification. Therefore, with the expectation of providing references for other researchers, this paper elaborates the nighttime light data characteristics and access platforms, summarizes the methods and models of population spatial distribution based on night lighting data, and analyzes the problems and solutions in the research. Finally, the important development directions in the future are discussed.

Key words： nighttime light data population distribution data correction grid scale accuracy verification

收稿日期: 2018-08-03 出版日期: 2019-08-30

TP79

基金资助:国家自然科学基金项目“基于人类动力学的面向震后救援的人员在地理建筑空间的分布规律研究”(51774250);成都理工大学地质灾害防治与地质环境保护国家重点实验室开放基金项目“5·12汶川地震前后的震中地区建筑空间人员分布规律研究及反演”共同资助(SKLGP2016K017)

通讯作者: 杨松

作者简介: 肖东升(1974-),男,博士,教授,主要从事空间信息技术与防灾减灾方面的研究。Email: xiaodsxds@163.com.。

引用本文:

肖东升, 杨松. 基于夜间灯光数据的人口空间分布研究综述[J]. 国土资源遥感, 2019, 31(3): 10-19.
Dongsheng XIAO, Song YANG. A review of population spatial distribution based on nighttime light data. Remote Sensing for Land & Resources, 2019, 31(3): 10-19.

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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.03.02 或 https://www.gtzyyg.com/CN/Y2019/V31/I3/10

Tab.1 DMSP-OLS和NPP-VIIRS主要参数对比

Tab.2 基于夜间灯光像元特征的主要方法

所需数据^①	方法	模型	优缺点
夜间灯光数据、归一化植被指数(normalized difference vegetation index,NDVI)和人口统计数据	按灯光斑块面积比例对县域分级,城镇地区采用灯光强度与人口密度的回归方法,乡村地区采用人口密度距离衰减模型和电场叠加理论,人口稀少地区采用平均分配方法^[31]	灯光强度与人口密度的三次回归模型,人口密度距离衰减模型	在城乡地区分别采用不同的模型建模,是对中国区域人口格网化的早期探索; 缺点是计算较为复杂,没有进行模型结果验证分析
夜间灯光数据、数字高程模型(digital elevation model,DEM)、人口统计数据、路网密度和土地覆被数据	利用基础数据提取灯光强度、坡度、路网密度和高程等13个因子作为影响因子,基于随机森林模型建立人口密度与影响因子的关系^[2]	随机森林模型	考虑因子较全面,能避免过度拟合,对异常值和噪声有很好的容忍度,能够度量影响因子的重要性; 缺点是在人口密度较低和较高的地区模拟精度欠佳
夜间灯光数据、人口统计数据和建筑物数据	基于灯光数据和建筑物数据,通过空间分析方法求得格网单元权重,进行人口密度空间化^[39]	多因子加权平均模型	考虑了建筑物分布,计算方便; 但是模型相对粗糙,尺度精细,但精度一般
夜间灯光数据、人口统计数据、DEM、土地利用数据和河流路网数据	考虑夜间灯光、坡度、河流路网和土地利用等因子的影响,采用专家打分和层次分析法对因子赋权重^[40]	多因子加权平均模型	考虑因子更为全面; 但是计算复杂,权重打分主观性强,城市间精度差异大
夜间灯光数据、NDVI、增强植被指数(enhanced vegetation index,EVI)、DEM和人口统计数据	在城市地区利用灯光数据与人口的显著相关性进行回归建模; 在乡村地区,提出新方法进行建模^[41,42]	多元回归模型	在城乡地区分别采用不同方法建模,削弱灯光饱和及溢出影响,模型适用性较强

Tab.3 基于多源数据融合的主要模型方法

Tab.4 主要构建指数及优缺点

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