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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 10-19     DOI: 10.6046/gtzyyg.2019.03.02
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A review of population spatial distribution based on nighttime light data
Dongsheng XIAO1,2, Song YANG1()
1. School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500,China
2. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology, Chengdu 610059, China
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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.
Keywords nighttime light data      population distribution      data correction      grid scale      accuracy verification     
:  TP79  
Corresponding Authors: Song YANG     E-mail: yangsurvey@126.com
Issue Date: 30 August 2019
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Cite this article:   
Dongsheng XIAO,Song YANG. A review of population spatial distribution based on nighttime light data[J]. Remote Sensing for Land & Resources, 2019, 31(3): 10-19.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.02     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/10
参数 DMSP-OLS NPP-VIIRS
条带宽度/ km 3 000 3 000
空间分辨率/(″) 30 15
夜间过境时间 19: 30 01: 30
微光成像波段(全色)/μm 0.5~0.9 0.5~0.9
最低辐亮度/(W·cm-2·sr-1) ~5E-10 ~2E-11
定标与否 否 星上定标
量化等级/bit 6 14
饱和情况 存在 不存在
Tab.1  Comparison of main parameters between DMSP-OLS and NPP-VIIRS
特征参数 方法 模型 优缺点
灯光面积、灯光体积、像素均值、光面积百分比 在省、市、县尺度上选择显著参数进行人口与人口密度估算[26] 异速增长模型、线性回归模型 多空间尺度,多特征参数选择; 缺点是不能反映精细尺度的人口空间分布情况,没有分区建模
灯光频率 利用灯光频率与人口密度的相关性[27] 传递函数和质量守恒算法 对全球数据集进行了改正; 但灯光频率与人口密度模型粗糙,精度不高
灯光面积(像素数量) 与城市人口数量进行回归分析[28,29] 线性回归 模型简单易于应用; 缺点是精度不高,无法反映城市内部特征
灯光强度 在区县或城市尺度上与人口密度进行回归分析[30,31,32] 线性回归、三次多项式回归 模型简单易用; 缺点是没有考虑灯光溢出和饱和问题
灯光体积 与城市人口数量进行回归分析[33,34] 线性回归 比灯光面积更能反映与人口分布的相关性,模型简单易于推广
光面积百分比 在区县或城市尺度上与人口密度进行回归分析[33] 线性回归 在区县或城市尺度上应用较为简单; 但并不适用于精细尺度
Tab.2  Main methods based on nighttime light pixel feature
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  Main model methods of multi-source data fusion
构建指数 所需数据 优缺点
基于植被调整的夜间灯光城市指数VANUI[52] NDVI和夜间灯光数据 增强城市内部变化特征; 但不适合沙漠地区,短期发展城市的表现欠佳
人类居住指数HSI[53] NDVI和夜间灯光数据 削弱饱和影响,增强变化性; 但城市核心周边区域存在过分校正
植被温度灯光指数VTLI[54] 夜间灯光数据、NDVI和地表温度数据 收敛速度快于VANUI,削弱灯光饱和和溢出影响; 但中小城市应用效果较差,部分地区存在分类错误
基于温度和植被调整的夜间灯光城市指数TVANUI[55] NDVI、夜间灯光数据和地表温度数据 削弱灯光饱和和溢出影响,增强城市特征,提高了制图精度; 但计算略复杂
基于EVI调整的夜间灯光指数EANTLI[46] 夜间灯光数据和EVI 削弱城市内部灯光饱和和溢出影响; 但在水体附近表现不理想
基于高程调整的人类居住指数EAHSI[41] EVI、夜间灯光数据和DEM 削弱饱和影响,考虑高程因素,适合于农村地区
基于蒙特卡罗模拟支持的VANUI指数VANUIMCS[56] NDVI、夜间灯光数据、道路数据和居民点数据 采用多种数据,削弱饱和,提高了精度; 在不发达地区适用性欠佳,数据量较大
去除水体的夜间灯光指数RwNTLI[57] 夜间灯光数据、NDVI和水体数据 改善了VANUI在水体提取中的不足,有效缓解饱和影响,增强地物识别能力
Tab.4  Main index models and evaluation
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