不同地形背景下的NPP/VIIRS影像特征与估算模型对比分析
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摘要
以2015年NPP/VIIRS夜光遥感影像为信息源,从夜光空间分布、典型城市剖面、区域建筑密度指数三方面对比分析不同地形背景下的山区与平原地区夜光分布的空间特征,构建县级尺度夜光强度指数与年末总人口、地区生产总值的回归模型,对比分析两种地形背景下模型的精度,并探讨了NDVI、坡度、海拔对山区夜光分布的影响。结果表明:平原地区夜光聚集效应更加显著,夜间成像效果优于山区;通过比较模型的相关系数、平均误差等多个指标,平原地区的估算模型明显优于山区县级尺度的回归模型,直接通过夜光强度指数建模估算山区人口与经济水平误差较大;贵州山区夜光分布受NDVI、坡度、海拔的影响显著,夜光强度与NDVI、坡度呈显著负相关关系,随着海拔的升高夜光像元分布呈现先上升后逐渐下降的趋势。
Based on the NPP/VIIRS nighttime lighting data in 2015, the spatial characteristics of the distribution of luminous intensity in mountainous and plain areas under different topographical backgrounds were compared and analyzed from three aspects(luminous space distribution, typical urban profiles, and regional building density index).The regression model of the scale-intensity luminous intensity index and the total population and regional GDP at the end of the year, and the accuracy of the model under two terrain backgrounds are compared and analyzed. The influence of NDVI, slope and altitude on the luminous distribution of mountainous areas is discussed. The results show that:(1) The effect of night light accumulation in the plain area is more significant, and the imaging effect at night is better than that in mountainous areas.(2) In comparison of models correlation coefficient, R~2, average error, and other indicators, it is believed that the plain region estimation model is obviously depended by the county-level regression model in mountain areas.The error of the population and economic level in mountainous areas is directly estimated by the luminous intensity index modeling.(3) The distribution of luminous intensity in Guizhou mountainous area was significantly affected by NDVI, slope, and elevation. There was a significant negative correlation among luminous intensity,NDVI and slope. With the increase of altitude, the distribution of luminous intensity increased first and then decreased gradually.
Based on the NPP/VIIRS nighttime lighting data in 2015, the spatial characteristics of the distribution of luminous intensity in mountainous and plain areas under different topographical backgrounds were compared and analyzed from three aspects(luminous space distribution, typical urban profiles, and regional building density index).The regression model of the scale-intensity luminous intensity index and the total population and regional GDP at the end of the year, and the accuracy of the model under two terrain backgrounds are compared and analyzed. The influence of NDVI, slope and altitude on the luminous distribution of mountainous areas is discussed. The results show that:(1) The effect of night light accumulation in the plain area is more significant, and the imaging effect at night is better than that in mountainous areas.(2) In comparison of models correlation coefficient, R~2, average error, and other indicators, it is believed that the plain region estimation model is obviously depended by the county-level regression model in mountain areas.The error of the population and economic level in mountainous areas is directly estimated by the luminous intensity index modeling.(3) The distribution of luminous intensity in Guizhou mountainous area was significantly affected by NDVI, slope, and elevation. There was a significant negative correlation among luminous intensity,NDVI and slope. With the increase of altitude, the distribution of luminous intensity increased first and then decreased gradually.
引文
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[21]杨续超,高大伟,丁明军,等.基于多源遥感数据及DEM的人口统计数据空间化——以浙江省为例 [J].长江流域资源与环境,2013,22(6):729-734.
[22]高义,王辉,王培涛,等.基于人口普查与多源夜间灯光数据的海岸带人口空间化分析 [J].资源科学,2013,35(12):2517-2523.
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[25]程志刚,杨欣悦,董思言,等.利用城市夜间灯光资料和NDVI研究成都地区植被覆盖度变化 [J].气象科技进展,2016,6(1):14-20.
[26]李旭东,张善余.贵州喀斯特高原人口分布的自然环境因素——Ⅰ.主要影响因素研究 [J].干旱区研究,2007,24(1):120-125.
[2]李德仁,李熙.论夜光遥感数据挖掘 [J].测绘学报,2015,44(6):591-601.
[3]Elvidge C D,Baugh K E,Kihn E A,et al.Relation between satellite observed visible-near infrared emissions,population,economic activity and electric power consumption [J].International Journal of Remote Sensing,1997,18(6):1373-1379.
[4]Li X,Xu H M,Chen X L,et al.Potential of NPP-VIIRS nighttime light Imagery for modeling the regional economy of China [J].Remote Sensing,2013,5(6):3057-3081.
[5]王珂靖,蔡红艳,杨小唤.多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用 [J].地理科学进展,2016,35(12):1494-1505.
[6]Letu H,Hara M,Yagi H,et al.Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects [J].International Journal of Remote Sensing,2010,31(16):4443-4458.
[7]Doll C N H,Muller J-P,Elvidge C D.Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions[J].Ambio,2000,29(3):157-162.
[8]苏泳娴,陈修治,叶玉瑶,等.基于夜间灯光数据的中国能源消费碳排放特征及机理 [J].地理学报,2013,68(11):1513-1526.
[9]陈晋,卓莉,史培军,等.基于DMSP/OLS数据的中国城市化过程研究——反映区域城市化水平的灯光指数的构建 [J].遥感学报,2003,7(3):168-175,241.
[10]吴健生,刘浩,彭建,等.中国城市体系等级结构及其空间格局——基于DMSP/OLS夜间灯光数据的实证[J].地理学报,2014,69(6):759-770.
[11]何洋,程辉,唐亮.基于DMSP/OLS数据的我国省级经济发展水平研究 [J].地理空间信息,2014,12(2):79-82.
[12]周垠,李磊,廖菲.成都平原城市群城镇化时空格局——基于DMSP/OLS夜间灯光数据的研究 [J].城市发展研究,2015,22(3):28-32.
[13]卓莉,陈晋,史培军,等.基于夜间灯光数据的中国人口密度模拟 [J].地理学报,2005,60(2):266-276.
[14]唐梁博,崔海山.基于NPP-VIIRS夜间灯光数据和Landsat-8数据的城镇建筑用地提取方法改进——以广州市为例 [J].测绘与空间地理信息,2017,40(9):69-73.
[15]柴子为,王帅磊,乔纪纲.基于夜间灯光数据的珠三角地区镇级GDP估算 [J].热带地理,2015,35(3):379-385.
[16]郑辉,曾燕,王勇,等.基于VIIRS夜间灯光数据的城市建筑密度估算——以南京主城区为例 [J].科学技术与工程,2014,14(18):68-75.
[17]熊念.基于灯光数据的人口空间化 [J].测绘与空间地理信息,2017,40(10):105-107.
[18]江威,何国金,刘慧婵.NPP/VIIRS 和 DMSP/OLS 夜光数据模拟社会经济参量对比 [J].遥感信息,2016,31(4):28-34.
[19]李翔,陈振杰,吴洁璇,等.基于夜间灯光数据和空间回归模型的城市常住人口格网化方法研究 [J].地球信息科学学报,2017,19(10):1298-1305.
[20]李峰,卫爱霞,米晓楠,等.基于 NPP-VIIRS 夜间灯光数据的河北省 GDP 空间化方法 [J].信阳师范学院学报(自然科学版),2016,29(1):152-156.
[21]杨续超,高大伟,丁明军,等.基于多源遥感数据及DEM的人口统计数据空间化——以浙江省为例 [J].长江流域资源与环境,2013,22(6):729-734.
[22]高义,王辉,王培涛,等.基于人口普查与多源夜间灯光数据的海岸带人口空间化分析 [J].资源科学,2013,35(12):2517-2523.
[23]苏维词,朱文孝.贵州喀斯特山区生态环境脆弱性分析 [J].山地学报,2000,18(5):429-434.
[24]王世杰.喀斯特石漠化——中国西南最严重的生态地质环境问题 [J].矿物岩石地球化学通报,2003,22(2):120-126.
[25]程志刚,杨欣悦,董思言,等.利用城市夜间灯光资料和NDVI研究成都地区植被覆盖度变化 [J].气象科技进展,2016,6(1):14-20.
[26]李旭东,张善余.贵州喀斯特高原人口分布的自然环境因素——Ⅰ.主要影响因素研究 [J].干旱区研究,2007,24(1):120-125.
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