基于NPP-VIIRS夜间灯光数据的北京市GDP空间化方法

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李峰, 米晓楠, 刘军, 刘小阳. 基于NPP-VIIRS夜间灯光数据的北京市GDP空间化方法[J]. 国土资源遥感, 2016,28(3): 19-24
LI Feng, MI Xiaonan, LIU Jun, LIU Xiaoyang. Spatialization of GDP in Beijing using NPP-VIIRS data[J]. REMOTE SENSING FOR LAND & RESOURCES,2016,28(3): 19-24 复制到剪切板

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《国土资源遥感》编辑部

基于NPP-VIIRS夜间灯光数据的北京市GDP空间化方法

李峰¹, 米晓楠², 刘军¹, 刘小阳¹

1.防灾科技学院,三河 065201

2.山西省气候中心,太原 030002

第一作者简介: 李峰(1979- ),男,讲师,工程师,工学博士,主要从事测量与遥感方面的教学与研究工作。Email:lif1223@aliyun.com

收稿日期: 2015-02-04

基金项目: 河北省高等学校科学研究计划重点项目和中央高校基本科研业务费项目“京津冀地区多维经济统计信息的可视化挖掘方法研究”(编号: ZD2014203)共同资助

摘要

为了分析像素级社会经济活动的空间分布状况,以Landsat8 和NPP-VIIRS夜间灯光影像为数据源,分别对北京市第一产业和第二、三产业GDP进行空间化操作。利用分类回归树(classification and regression tree,CART)算法,通过Landsat8影像生成北京市的土地利用图,在分析第一产业GDP与土地利用类型面积相关性的基础上,构建了第一产业GDP与耕地面积的线性回归模型。建立了5种灯光指标与第二、三产业GDP的数学关系,通过相关性和回归分析确定第二、三产业GDP与综合灯光指数呈明显的幂函数关系。根据以上2种模型分别生成对应2类产业的像素级GDP密度图,再分别对其进行线性纠正并求和后制作出北京市500 m格网尺寸的GDP密度图。误差分析发现,第一产业GDP、第二、三产业GDP和GDP总量与实际统计值的平均相对误差分别为0.86%,0.61%和1.37%。结果表明,结合土地利用数据的NPP-VIIRS夜间灯光GDP空间化方法可以精确估算北京市GDP产值,反映北京市经济空间分布特征。

关键词: NPP-VIIRS; 夜间灯光; 省域GDP; 土地利用; GDP空间化

中图分类号:TP 79 文献标志码:A 文章编号:1001-070X(2016)03-0019-06 doi: 10.6046/gtzyyg.2016.03.04

Spatialization of GDP in Beijing using NPP-VIIRS data

LI Feng¹, MI Xiaonan², LIU Jun¹, LIU Xiaoyang¹

1. Institute of Disaster Prevention, Sanhe 065201, China

2. Shanxi Climate Center, Taiyuan 030002, China

Abstract

In order to analyze spatial distributions of socioeconomic activities at pixel scale, the authors used Landsat8 and NPP-VIIRS night-time light images as data sources and produced spatialization maps of primary industry GDP and the secondary, tertiary industry GDP in Beijing. The land use map of Beijing for the spatialization was produced from Landsat8 image with CART decision-tree algorithm. According to the correlation results between the primary industry GDP and areas of land use, a linear regression model was built based on the primary industry GDP and areas of plough. By analyzing the correlation relationships between five light indexes and the secondary, tertiary industry GDP, compounded night light index (CNLI) and the secondary, tertiary industry GDP presented apparent power function’s correlation relationship. Using linear corrections and summation of two types of pixel level’s GDP density maps produced both modes listed above, and a total GDP density map was generated with the resolution of five hundred meters in Beijing. The results of GDP relative errors show that the primary industry GDP and the secondary, tertiary industry GDP were 0.86%, 0.61% and 1.37% respectively. This suggests that this approach of pixel level’s GDP spatialization can be applied to estimate Beijing’s GDP and reflect characteristics of its economic distribution.

Keyword: NPP-VIIRS; night-time light; provincial GDP; land use; GDP spatialization

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0 引言

国民生产总值(gross domestic product, GDP)是反映一个国家或地区全部生产活动最终成果的重要指标, 用来衡量国家或地区的经济实力和发展状况。长期以来, 社会经济数据常基于统计单元(行政区)建立多边形数据库, 以点值内插法进行全区域统计数据的分析和操作。但因数据分辨率低而不能充分揭示统计数据的空间差异性。随着对高分辨率GDP密度数据需求的日益增加, 格网生成技术被用于社会经济统计数据的格网化。刘红辉等^[1]利用遥感数据建立了分产业GDP与土地利用类型的空间关系, 实现1 km格网的社会经济数据的空间定量模拟; 易玲等^[2]分别按照人口和土地利用数据构造了GDP空间化模型, 分析认为土地利用GDP模型精度明显高于人口分配GDP模型。随后, GIS和栅格数字模拟技术进一步促进了社会经济数据空间化的发展。自20世纪90年代起, 美国国防气象卫星计划(defense meteorological satellite program’ s operational linescan system, DMSP-OLS)夜间灯光数据因能探测城市灯光甚至小规模低强度灯光, 而被逐渐用于1 km格网的GDP空间化和城市化的研究中^{[3, 4, 5]}。Elvidge等^{[6, 7]}在研究全球21个和200个国家的GDP与DMSP-OLS灯光数据后发现, 两者分别呈现高度的对数和线性关系; Henderson等^[8]根据DMSP-OLS稳定灯光和辐射定标灯光数据的最佳阈值, 提取了旧金山、北京和拉萨经济发展水平有差异地区的空间信息; Doll等^[9]依据DMSP-OLS夜间灯光与GDP的关系绘制了区域经济活动图; 韩向娣等^[10]利用土地利用图对全国第一产业GDP建模, 结合DMSP-OLS和土地利用数据对全国第二、三产业GDP建模, 生成的GDP密度图比单一数据的精度要高。但是, DMSP-OLS数据中的像素过饱和和溢出现象削弱了灯光数据与社会经济数据的相关性, 而新一代的夜间灯光数据可见光红外成像辐射仪(national polar-orbiting partnership’ s visible infrared imaging radiometer suite, NPP-VIIRS)却不存在上述问题。Li等^[11]和Shi等^[12]在采用这种夜间灯光数据研究中国大陆的GDP后发现, 相比DMSP-OLS数据, 夜间灯光总量与GDP之间有更高的线性关系。随着经济和社会科学的发展, 在全球或国家尺度上的社会经济数据空间化, 更多以行政单元均值的形式来表现, 无法全面准确反映地区的GDP分布状况。因而, 更多的研究重心开始转向省域、县域甚至乡镇级GDP空间化的工作中^{[13, 14]}。

为了深入提高省域GDP空间化的精度, 本文以北京市为例, 在综合分析其经济统计数据的基础上, 通过NPP-VIIRS灯光数据建立其与第二、三产业GDP的数学模型, 通过Landsat8影像生成北京市的土地利用图, 分析并构建各土地利用类型与第一产业GDP的回归模型, 从而建立适用于省域的像素级GDP空间化模型, 更加详实准确地模拟省域GDP分布状况, 为政府经济政策的制定和GDP产值估计提供可靠的决策依据。

1 数据源

NPP-VIIRS夜晚灯光数据来源于NOAA/NGDC(http://ngdc.noaa.gov/eog/viirs/download monthly.html)网站, 是由 Suomi-NPP 卫星利用VIIRS在2012年4— 10月间拍摄, 距地表约 824 km, 采用极地轨道, 由多幅无云影像拼接得到。与DMSP-OLS数据不同, NPP-VIIRS数据并没有过滤火光、气体燃烧、火山或极光, 背景噪声也未剔除, 但是其影像分辨率达到了15″(约450 m), 所采用的广角辐射探测仪消除了灯光过饱和现象, 增强了探测敏感度, 其在轨检校程序也进一步提高了影像的清晰度。图1显示了2012年北京市NPP-VIIRS夜间灯光数据。

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	图1 2012年北京市NPP-VIIRS夜间灯光数据Fig.1 NPP-VIIRS nigh-time light data of Beijing in 2012

原始数据文件采用WGS84椭球下的地理坐标系, 为使得投影变形最小, 投影转换成Albers投影, 以双线性采样的方法重采样成500 m格网大小。

为了降低NPP-VIIRS的噪声, 采用2012年北京市DMSP-OLS夜间平均稳定灯光数据提取NPP-VIIRS的有效灯光数据。DMSP-OLS数据清除了云层的影响, 包含城市、乡镇以及永久性光源地, 排除了偶然灯光噪声, 背景噪声用0值替代, DN值在1~63之间。

由于2012年Landsat5传感器失效, 但是据统计年鉴数据可知2012年第一产业GDP与2013年相比变化较小。因此, 本文利用2013年10月3日Landsat8卫星OLI多光谱数据获得的北京市土地利用类型图建立2012年第一产业GDP模型。

2 省域GDP空间化建模方法

本文的目的在于通过夜间灯光数据和土地利用数据建立与地区生产总值(GDP)、第一产业产值(GDP₁)以及第二、三产业产值(GDP₂₃)之间的数学模型。作为第二产业的工业、建筑业与以服务业为主的第三产业之间的城市灯光差异很小, 因此综合第二、三产业的夜间灯光数据的建模结果要优于单独建模的结果。夜间灯光数据很难准确探测到城市以外的农村地区, 第一产业的产值与灯光数据的关系也不明显, 而与耕地等农业用地关系密切, 土地利用数据可以为第一产业产值建模提供便利^[15]。

2.1 第一产业产值建模

北京市土地利用分类数据通过采用分类回归树(classification and regression tree, CART)算法基于Landsat8影像分类获得^[16]。与其他决策树相比, CART算法简单、结构清晰、运行速度快, 能最大限度地降低各个步骤的不纯洁度, 有效处理大量的高维数据。

参加分类的变量包括Landsat8影像中的第1— 7个多光谱波段、NDVI值、MNDWI值及北京市30 m空间分辨率DEM。鉴于草地与耕地的光谱差异极小, 且北京市草地面积仅占总面积的0.13%, 本文将草地归入耕地类别。选择林地、耕地(含草地)、水域、建设用地和其他(裸地和阴影)共5种土地类型的6 092个训练样本, 通过学习得到56个叶结点的决策树结构。根据高空间分辨率Google Earth影像随机提取的276个检验样本建立混淆矩阵。检验结果显示, CART算法的总体分类精度达95.65%, Kappa系数为0.94。虽然北京市林地面积占全市总面积的53.31%, 但是并非所有的林地都能为第一产业贡献产值, 故通过GIS的分析功能提取坡向在90° ~270° 且坡度小于20° 的经济林地来分析其对第一产业的贡献。

根据北京市16个区县的土地利用类型面积(耕地、水域和经济林地)和对应第一产业的产值, 分析各土地利用类型面积与GDP₁的相关性, 发现呈多元线性关系, 对应的耕地(含草地)、水域和经济林地面积的模型回归系数分别为m₁=0.026 1, m₂=0.073 3和m₃=-0.000 4, 相关系数为0.689。单独分析耕地面积(S_c)与GDP₁的相关性, 其回归关系如图2所示。

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	图2 耕地面积与GDP₁的关系Fig.2 Relationship of agricultural acreage and GDP₁

很明显, 就GDP₁而言, 与耕地面积(S_c)的相关性明显高于其他各土地利用类型面积, 因此本文采用式(1)来模拟北京市GDP₁的产值, 即

GDP₁=0.047S_c 。 (1)

2.2 第二、三产业产值建模

2.2.1 NPP-VIIRS数据去噪

假设2012年的NPP-VIIRS数据和2012年的DMSP-OLS稳定灯光数据有着同样的灯光范围, 因而可以通过同年份的DMSP-OLS数据制作掩模提取NPP-VIIRS中的有效灯光数据。去噪的DMSP-OLS数据仍然存在灯光过饱和及溢出现象, 根据2.1节的土地利用类型结果, 将分类的建筑用地与DMSP-OLS灯光数据叠加来确定最佳灯光阈值(DN值范围在14~63), 通过去饱和灯光的DMSP-OLS数据掩模NPP-VIIRS数据来消除其背景噪声和偶然灯光噪声^[17]。为了去除NPP-VIIRS数据中孤立的极亮像元, 将全部像元值按升序排列, 取像元数99.9%处的像元值作为阈值DN_M(本文为89.145), 高于此阈值的像元值均赋值为DN_M。

2.2.2 灯光数据与GDP₂₃的回归分析

目前常用的灯光数据指标有总灯光强度(total night-time light, TNL)、平均灯光强度(I)、灯光面积比(S)、线性加权综合灯光指数(L)和综合灯光指数(compounded night light index, CNLI)。TNL是指行政单元内灯光DN值的总和。上述指标的表达式分别^{[18, 19]}为

I= $\begin{matrix} \overset{D N_{M}}{\sum_{i = 1}} \end{matrix}$ (DN_in_i)/(DN_MN) , (2)

S=A_N/A , (3)

L=Ip₁+Sp₂ , (4)

CNLI=IS , (5)

式中: DN_i和n_i 分别表示行政单元内第i级灰度像元值和像元数; N和A_N分别代表行政单元内位于[1, DN_M]区间的像元总数和所占据的面积; A表示行政单元面积; L为I和S的函数; p₁和p₂分别为指标I和S的权重, 通过多元线性拟合的方式求出, 故利用Matlab软件求出p₁和p₂的值分别为0.9和0.1, 即

L=0.9I+0.1S 。 (6)

分别统计并生成北京市16个区县的TNL, I, S, L和CNLI这5种灯光数据指标与对应GDP₂₃的散点图, 如图3所示, 分析其与GDP₂₃的相关性, 确定最佳回归模型。

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	图3 5种灯光指标与GDP₂₃的回归关系Fig.3 Relationships of five light indexes and GDP₂₃

经过实验发现, 上述5种指标均与GDP₂₃呈现幂函数关系。选取其中相关系数最大的CNLI灯光指标建立与GDP₂₃的最优回归模型, 采用

GDP₂₃=4 258.251CNLI⁰^.⁶⁴³ (7)

来模拟北京市GDP₂₃的产值。为了估计行政单元内的任意像元所代表的GDP₂₃值, 式(7)可改写为

GDP₂₃=4 258.251(DN_i/DN_M)⁰^.⁶⁴³ 。 (8)

虽然式(8)为北京市GDP₂₃的最优回归模型, 但它是通过各区县的灯光数据与实际统计数据回归分析得到, 直接用任意灯光像元计算得到的GDP₂₃的误差依然较大, 因此需要对模拟的GDP₂₃进行分区县重新纠正。

2.3 GDP空间化

对社会经济数据空间化时, 格网尺度大小对最终模拟结果有着重要的影响, 格网尺度并非越小越好。研究表明, 国家和全球格网尺度为1 km或5 km, 省市级区域的格网尺度在100~1 000 m之间为佳。确定格网尺度的原则为: 在顾及地理数据尺度的前提下, 既要反映社会经济数据的原始分布状态, 又要反映出其连续分布的现实^{[20, 21]}。

本文在模拟GDP₁和GDP₂₃时分别采用了30 m和500 m空间分辨率的影像, 考虑到北京市山地面积和城市用地面积分别占全市总面积的55%和37%, 过小的格网会导致没有GDP分布, 过大的格网会忽略GDP分布的差异性。为此, 本次研究选用了500 m格网尺度。为了避免重采样造成的精度损失, 通过将模拟的500 m空间分辨率的GDP₂₃图上每个像元的地理坐标映射到同范围30 m空间分辨率的土地利用图上, 并统计其中耕地像元的面积, 再利用式(1)计算出500 m空间分辨率每个格网内的GDP₁。

直接采用式(1)和式(8)模拟的GDP₁和GDP₂₃误差仍较大, 故需要分别利用第一和第二、三产业实际产值通过线性调整的方式来纠正。对每个区县, 在保证各区县GDP₁和GDP₂₃总量为实际统计值的前提下, 采用式(9)来分别逐像元纠正模拟的GDP₁和GDP₂₃值, 即

GDP_T=GDP_j(GDP_t/GDP_all) , (9)

式中, GDP_T为纠正后的GDP产值; GDP_j为格网j模拟的GDP产值; GDP_t为该区县统计的实际GDP值; GDP_all为该区县模拟的GDP值。

将纠正后的GDP₁和GDP₂₃直接求和可得到该地区的总产值, 生成的2012年北京市GDP图如图4所示。

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	图4 2012年北京市500 m空间分辨率的GDP图Fig.4 Beijing GDP map with 500 m resolution in 2012

3 结果与分析

对北京市各区县GDP₁, GDP₂₃和GDP的模拟结果与实际统计值进行误差分析, 结果如表1所示。

表1 北京市各产业产值模拟结果与实际统计值的相对误差 Tab.1 Relative errors of simulated three kinds of GDP in Beijing(%)

在第一产业产值的相对误差中, 东城区、西城区和石景山区的误差为0, 主要是由于这3个城区的第二、三产业相当发达, 行政单元内绝大部分区域被建筑用地所覆盖, 耕地、草地和林地面积相对较少, 经过线性纠正后的GDP₁模拟值与实际统计结果一致。其中, 朝阳区、门头沟区、密云县和延庆县的GDP₁相对误差超过了2%; 朝阳区的第二、三产业很发达而第一产业的比重较低, 在使用Landsat8影像分类时不可避免地把不会产生第一产业产值的公园草地归类为耕地, 造成该区耕地面积被高估, 使得第一产业误差增大; 门头沟区、密云县和延庆县均为山区, 耕地面积较少, 除耕地外, 占大部分面积的林地、密云水库等对第一产业产值的贡献更大, 因此只用耕地来预估GDP₁会低估其产值, 从而增大GDP₁的相对误差。第二、三产业产值的相对误差均小于2%, 这表明利用NPP-VIIRS夜间灯光数据来模拟GDP₂₃的精度较高。总产值的相对误差中, 除密云县和延庆县外, 其余区的GDP相对误差都低于2%, 这很可能是因为这2个县的GDP₁模拟误差较大所造成。GDP₁, GDP₂₃和GDP的平均相对误差分别为0.86%, 0.61%和1.37%, 三者的平均相对误差都很小, 各类GDP预测精度较高。

4 结论

利用NPP-VIIRS夜间灯光和Landsat8数据对北京市的各产业GDP进行空间化处理, 研究结果表明: 耕地面积与第一产业GDP的相关性要高于其他土地利用类型; 第二、三产业GDP与区县NPP-VIIRS综合灯光指数呈现较强的幂函数关系; 基于像素级的第一产业GDP和第二、三产业GDP的回归模型直接计算的GDP产值误差较大, 需要进行线性纠正才能达到较高的精度; 从模拟的各产业GDP平均相对误差上看, 各产业GDP的平均误差均小于2%, 模拟精度较高, 生成的像素级GDP分布可完整地反映北京市经济分布的实际状况。

NPP-VIIRS夜间灯光数据在2012年开始对外提供数据, 当前针对这种新数据源的研究成果较少, 如何有效过滤其背景噪声和偶然灯光因素, 优化回归模型构建方法, 分析社会经济数据的时空变化情况, 将成为后续深入研究的重点。

The authors have declared that no competing interests exist.

参考文献

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[1]	刘红辉, 江东, 杨小唤, 等. 基于遥感的全国GDP 1km格网的空间化表达[J]. 地球信息科学, 2005, 7(2): 120-123. Liu H H, Jiang D, Yang X H, et al. Spatialization approach to 1km grid GDP supported by remote sensing[J]. Geo-Information Science, 2005, 7(2): 120-123. [本文引用:1]
[2]	易玲, 熊利亚, 杨小唤. 基于GIS技术的GDP空间化处理方法[J]. 甘肃科学学报, 2006, 18(2): 54-58. Yi L, Xiong L Y, Yang X H. Method of pixelizing GDP data based on the GIS[J]. Journal of Gansu Sciences, 2006, 18(2): 54-58. [本文引用:1]
[3]	徐梦洁, 陈黎, 刘焕金, 等. 基于DMSP/OLS夜间灯光数据的长江三角洲地区城市化格局与过程研究[J]. 国土资源遥感, 2011, 23(3): 106-112. doi: DOI: 106046/gtzyyg. 2011. 03. 19. Xu M J, Chen L, Liu H J, et al. Pattern and process of urbanization in the Yangtze Delta based on DMSP/OLS data[J]. Remote Sensing for Land and Resources, 2011, 23(3): 106-112. doi: DOI:10.6046/gtzyyg.2011.03.19. [本文引用:1]
[4]	王晓慧, 肖鹏峰, 冯学智, 等. 基于DMSP/OLS数据的中国大尺度城镇用地信息提取[J]. 国土资源遥感, 2013, 25(3): 159-164. doi: DOI: 106046/gtzyyg. 2013. 03. 26. Wang X H, Xiao P F, Feng X Z, et al. Extraction of large-scale urban area information in China using DMSP/OLS nighttime light data[J]. Remote Sensing for Land and Resources, 2013, 25(3): 159-164. doi: DOI:10.6046/gtzyyg.2013.03.26. [本文引用:1]
[5]	陈征, 胡德勇, 曾文华, 等. 基于TM图像和夜间灯光数据的区域城镇扩张监测——以浙江省为例[J]. 国土资源遥感, 2014, 26(1): 83-89. doi: DOI: 106046/gtzyyg. 2014. 01. 15. Chen Z, Hu D Y, Zeng W H, et al. TM image and nighttime light data to monitoring regional urban expansion: A case study of Zhejiang Province[J]. Remote Sensing for Land and Resources, 2014, 26(1): 83-89. doi: DOI:10.6046/gtzyyg.2014.01.15. [本文引用:1]
[6]	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. [本文引用:1]
[7]	Elvidge C D, Imhoff M L, Baugh K E, et al. Night-time lights of the world: 1994-1995[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2001, 56(2): 81-99. [本文引用:1]
[8]	Henderson M, Yeh E T, Gong P, et al. Validation of urban boundaries derived from global night-time satellite imagery[J]. International Journal of Remote Sensing, 2003, 24(3): 595-609. [本文引用:1]
[9]	Doll C N H, Muller J P, Morley J G. Mapping regional economic activity from night-time light satellite imagery[J]. Ecological Economics, 2006, 57(1): 75-92. [本文引用:1]
[10]	韩向娣, 周艺, 王世新, 等. 基于夜间灯光和土地利用数据的GDP空间化[J]. 遥感技术与应用, 2012, 27(3): 396-405. Han X D, Zhou Y, Wang S X, et al. GDP spatialization in China based on DMSP/OLS data and land use data[J]. Remote Sensing Technology and Application, 2012, 27(3): 396-405. [本文引用:1]
[11]	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. [本文引用:1]
[12]	Shi K F, Yu B L, Huang Y X, et al. Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data[J]. Remote Sensing, 2014, 6(2): 1705-1724. [本文引用:1]
[13]	梁友嘉, 徐中民. 基于夜间灯光辐射数据的张掖市甘州区GDP空间分布建模[J]. 冰川冻土, 2013, 35(1): 249-254. Liang Y J, Xu Z M. Modeling the spatial distribution of GDP based on night light radiation: A case study in Ganzhou district, Zhangye municipality[J]. Journal of Glaciology and Geocryology, 2013, 35(1): 249-254. [本文引用:1]
[14]	杨妮, 吴良林, 邓树林, 等. 基于DMSP/OLS夜间灯光数据的省域GDP统计数据空间化方法——以广西壮族自治区为例[J]. 地理与地理信息科学, 2014, 30(4): 108-111. Yang N, Wu L L, Deng S L, et al. Spatialization method of provincial statistical GDP data based on DMSP/OLS night lighting data: A case study of Guangxi Zhuang Autonomous Region[J]. Geography and Geo-Information Science, 2013, 30(4): 108-111. [本文引用:1]
[15]	韩向娣, 周艺, 王世新, 等. 夜间灯光遥感数据的GDP空间化处理方法[J]. 地球信息科学学报, 2012, 14(1): 128-136. Han X D, Zhou Y, Wang S X, et al. GDP spatialization in China based on nighttime imagery[J]. Journal of Geo-information Science, 2012, 14(1): 128-136. [本文引用:1]
[16]	陈云, 戴锦芳, 李俊杰. 基于影像多种特征的CART决策树分类方法及其应用[J]. 地理与地理信息科学, 2008, 24(2): 33-36. Chen Y, Dai J F, Li J J. CART-based decision tree classifier using multi-feature of image and its application[J]. Geography and Geo-Information Science, 2008, 24(2): 33-36. [本文引用:1]
[17]	米晓楠, 白林燕, 谭雪航, 等. 基于DMSP/OLS数据的城市中心城区提取新方法[J]. 地球信息科学学报, 2013, 15(2): 255-261. Mi X N, Bai L Y, Tan X H, et al. A new method of extracting areas of center city regions based on DMSP/OLS data[J]. Journal of Geo-Information Science, 2013, 15(2): 255-261. [本文引用:1]
[18]	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. Chen J, Zhuo L, Shi P J, et al. The study on urbanization process in China based on DMSP/OLS data: Development of a light index for urbanization level estimation[J]. Journal of Remote Sensing, 2003, 7(3): 168-175. [本文引用:1]
[19]	卓莉, 史培军, 陈晋, 等. 20世纪90年代中国城市时空变化特征——基于灯光指数CNLI方法的探讨[J]. 地理学报, 2003, 58(6): 893-902. Zhuo L, Shi P J, Chen J, et al. Application of compound night light index derived from DMSP/OLS data to urbanization analysis in China in the 1990s[J]. Acta Geographica Sinica, 2003, 58(6): 893-902. [本文引用:1]
[20]	叶靖, 杨小唤, 江东. 乡镇级人口统计数据空间化的格网尺度效应分析——以义乌市为例[J]. 地球信息科学学报, 2010, 12(1): 40-47. Ye J, Yang X H, Jiang D. The grid scale effect analysis on town leveled population statistical data spatialization[J]. Journal of Geo-information Science, 2010, 12(1): 40-47. [本文引用:1]
[21]	李月娇, 杨小唤, 王静. 基于景观生态学的人口空间数据适宜格网尺度研究——以山东省为例[J]. 地理与地理信息科学, 2014, 30(1): 97-100. Li Y J, Yang X H, Wang J. Grid size suitability of population spatial distribution in Shand ong Province based on land scape ecology[J]. Geography and Geo-Information Science, 2014, 30(1): 97-100. [本文引用:1]

2005

0.0

... 刘红辉等^[1]利用遥感数据建立了分产业GDP与土地利用类型的空间关系,实现1 km格网的社会经济数据的空间定量模拟 ...

2006

0.0

易玲, 熊利亚, 杨小唤. 基于GIS技术的GDP空间化处理方法[J]. 甘肃科学学报, 2006, 18(2): 54-58.

, Xiong L

, Yang X

Method of pixelizing GDP data based on the GIS[J]. Journal of Gansu Sciences, 2006, 18(2): 54-58.

利用基于LANDSAT TM信息获取的1∶10万比例尺的土地利用/覆盖数据,建立与统计型GDP数据的多元相关关系模型,计算各种土地利用类型中的GDP系数,在GIS支持下计算出西部12省区1 km2格网GDP空间分布数据.

... 易玲等^[2]分别按照人口和土地利用数据构造了GDP空间化模型,分析认为土地利用GDP模型精度明显高于人口分配GDP模型 ...

2011

0.0

徐梦洁, 陈黎, 刘焕金, 等. 基于DMSP/OLS夜间灯光数据的长江三角洲地区城市化格局与过程研究[J]. 国土资源遥感, 2011, 23(3): 106-112. doi: DOI: 106046/gtzyyg. 2011. 03. 19.

Xu M

, Chen

, Liu H

, et al. Pattern and process of urbanization in the Yangtze Delta based on DMSP/OLS data[J]. Remote Sensing for Land and Resources, 2011, 23(3): 106-112. doi: DOI:10.6046/gtzyyg.2011.03.19.

The lighted area, night light intensity, compounded night light index (CNLI) and total night light index (TNLI) of 16 cities in the Yangtze Delta were derived from the DMSP/OLS night light data in 2008, and their correlations with urbanization statistics were calculated respectively. Of these factors, the night light intensity proved to be an effective indicator for assessing the urbanization level. Construction land information of urban agglomerations in 1998, 2003 and 2008 was also extracted from DMSP/OLS night light data after the determination of the threshold value. Then the pattern and process of urbanization in the Yangtze Delta were analyzed and the expansion process of buildup area was categorized into several types. During the study period, urban agglomerations in the Yangtze Delta had three typical spatial expansion modes, of which the polygon mode could be observed in all the cities, the linear mode exists along main highways and railways, and the point mode occurs mainly around the less important cities. In spite of the imbalance of urbanization level within the study area, the regional differentiation was lessened gradually. According to the variation of the threshold value from 1998 to 2008, 11 cities, namely Zhenjiang, Changzhou, Yangzhou, Wuxi, Suzhou, Nantong, Huzhou, Jiaxing, Shaoxing, Zhoushan and Taizhou, belonged to the filling-in dominant type, Nanjing, Hangzhou and Shanghai belonged to the first-extension-then-filling-in dominant type, and Taizhou and Ningbo belonged to the first-filling-in-then-extension dominant type.

College of Public Adminstration, Nanjing Agricultural University, Nanjing 210095, China

利用1998、2003和2008年的DMSP/OLS夜间灯光数据,结合相关统计数据,分析灯光数据与城市化进程中经济、人口和土地利用等方面的关系,构建基于灯光数据的城市化水平指数,分析长三角城市群城市化进程和空间模式; 通过与统计数据对比,按照最小误差原则确定市辖区建设用地信息提取的阈值,根据各城市在1998~2003年和2003~2008年两个阶段建设用地信息提取阈值的变化,划分城市建设用地的主导扩张类型。研究表明: 长三角城市群表现为核心城市的面状扩张、沿交通网络的线状扩张和非核心城市的点状扩张模式,城市群的特征逐渐体现; 区域内部城市化发展水平不均衡,但空间差异逐渐缩小; 镇江、常州、扬州、无锡、苏州、南通、湖州、嘉兴、绍兴、舟山和台州等市属于填充增强主导扩展类型; 南京、杭州和上海则属于外延-填充增强主导扩展类型; 泰州和宁波属于填充增强-外延主导扩展类型。

... s operational linescan system,DMSP-OLS)夜间灯光数据因能探测城市灯光甚至小规模低强度灯光,而被逐渐用于1 km格网的GDP空间化和城市化的研究中^[3,4,5] ...

2013

0.0

王晓慧, 肖鹏峰, 冯学智, 等. 基于DMSP/OLS数据的中国大尺度城镇用地信息提取[J]. 国土资源遥感, 2013, 25(3): 159-164. doi: DOI: 106046/gtzyyg. 2013. 03. 26.

Wang X

, Xiao P

, Feng X

, et al. Extraction of large-scale urban area information in China using DMSP/OLS nighttime light data[J]. Remote Sensing for Land and Resources, 2013, 25(3): 159-164. doi: DOI:10.6046/gtzyyg.2013.03.26.

Nighttime light data have been widely used for mapping urban land. Previous application of the TM-assisted thresholding method mainly determined threshold of the nighttime light data in each city, which is not suitable for the study at a large scale due to its low processing efficiency and high data acquisition cost. In this paper, based on the TM-assisted thresholding method, the authors put forward the partition idea to extract the urban area using the DMSP/OLS nighttime light data. China was divided into 7 major regions based on economic partition and then TM-assisted thresholding method was employed to extract urban land areas. Finally, applying real urban land use pixels judgment, the authors obtained China’s urban land information of the years 1992, 1995 and 2000. A comparison with the previous method shows that the performance of the method based on partition idea is more efficient and can ensure overall accuracy.

1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China; 2. Department of Geographical Information Science, Nanjing University, Nanjing 210093, China

DMSP/OLS夜间灯光数据已在城镇用地信息提取中得到较多应用,其中,较高分辨率数据比较法是一种较为客观的方法,但现有研究主要通过逐个城市确定灯光数据的阈值提取城镇用地信息,这对于大尺度的城镇研究所需成本较大、处理效率较低。为此,提出了一种基于分区的较高分辨率数据比较法,将中国按经济发展程度划分为7大区,分别对各区提取城镇用地信息,并对提取结果进行真实城镇像元判定,得到1992,1995,2000年中国的城镇用地信息。结果表明,采用分区的方法不仅提高了较高分辨率数据比较法的提取效率,反映了特定区域的城镇整体状况,而且在总体区域上保证了提取精度。

... s operational linescan system,DMSP-OLS)夜间灯光数据因能探测城市灯光甚至小规模低强度灯光,而被逐渐用于1 km格网的GDP空间化和城市化的研究中^[3,4,5] ...

2014

0.0

陈征, 胡德勇, 曾文华, 等. 基于TM图像和夜间灯光数据的区域城镇扩张监测——以浙江省为例[J]. 国土资源遥感, 2014, 26(1): 83-89. doi: DOI: 106046/gtzyyg. 2014. 01. 15.

Chen

, Hu D

, Zeng W

, et al. TM image and nighttime light data to monitoring regional urban expansion: A case study of Zhejiang Province[J]. Remote Sensing for Land and Resources, 2014, 26(1): 83-89. doi: DOI:10.6046/gtzyyg.2014.01.15.

Acquiring urban information and dynamically monitoring urban expansion forms constitute important parts of remote sensing technique in the field of resource and environment application. As a coastal province, Zhejiang has experienced a rapid economic development in the past 20 years. At the same time, its urban expansion phenomenon is significant. With Zhejiang Province as the study area, the authors obtained the spatial distribution information of urban land from Landsat TM data by using the CART algorithm. Under the condition of getting accurate classification results, the urban expansion spatial-temporal features of Zhejiang Province from 1995 to 2010 were analyzed.Applying CART to extracting urban information from TM data and optimized with NTL is an effective and adaptable method for monitoring regional urban expansion.During the past 15 years, the expansion acceleration of most cities is greater than zero, except for Dongtou, Qingyuan, Wencheng, Yunhe, Lanxi, Longquan and Shaoxing. From 1995 to 2010, Xiaoshan, Yuhang, Ningbo remained the first three cities in this aspect.It is shown that the urbanization levels of various cities are significantly different from each other. The urbanization level of coastal cities and cities in the terrain flat area is higher than that of the non-coastal cities and cities in the complicated topography area. As a result, the first three large coastal cities, which are Hangzhou, Shaoxing and Wenzhou, and the zone of small and medium sized cities in the non-costal area around Jinhua have been developed.

1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China; 2. Zhejiang Surveying and Mapping Data Archives, Hangzhou 310012, China

以浙江省为研究区，利用收集到的Landsat TM图像、DMSP/OLS夜间灯光数据及其他辅助数据，开展区域城镇扩张监测方法的研究，并在此基础上分析了浙江省1995—2010年间城镇扩张的时空特征。结果表明：基于分类回归树算法，综合利用TM数据和DMSP/OLS夜间灯光数据，可精确稳定地提取城镇信息；1995—2010年间，浙江省城镇用地持续增加，除洞头县、庆元县、文成县、云和县、兰溪市、龙泉市和绍兴市等7个县市扩张加速度有所减缓外，全省其余市、县均处于加速扩张阶段；浙江省城镇扩张地域差异明显，表现为沿海地区的城市化水平整体高于内陆地区，地势平坦地区高于地形复杂地区，从而形成了杭州、绍兴、温州3大沿海城市群和以金华为中心的内陆地区中小城市带。

... s operational linescan system,DMSP-OLS)夜间灯光数据因能探测城市灯光甚至小规模低强度灯光,而被逐渐用于1 km格网的GDP空间化和城市化的研究中^[3,4,5] ...

1997

0.0

... Elvidge等^[6,7]在研究全球21个和200个国家的GDP与DMSP-OLS灯光数据后发现,两者分别呈现高度的对数和线性关系 ...

2001

0.0

... Elvidge等^[6,7]在研究全球21个和200个国家的GDP与DMSP-OLS灯光数据后发现,两者分别呈现高度的对数和线性关系 ...

2003

0.0

... Henderson等^[8]根据DMSP-OLS稳定灯光和辐射定标灯光数据的最佳阈值,提取了旧金山、北京和拉萨经济发展水平有差异地区的空间信息 ...

2006

0.0

... Doll等^[9]依据DMSP-OLS夜间灯光与GDP的关系绘制了区域经济活动图 ...

2012

0.0

韩向娣, 周艺, 王世新, 等. 基于夜间灯光和土地利用数据的GDP空间化[J]. 遥感技术与应用, 2012, 27(3): 396-405.

Han X

, Zhou

, Wang S

, et al. GDP spatialization in China based on DMSP/OLS data and land use data[J]. Remote Sensing Technology and Application, 2012, 27(3): 396-405.

GDP is a key indicator of socioeconomic development,urban planning,and environmental protection,accurate estimates of the magnitude and spatial distribution of economic activity have many useful applications in resources and environmental sciences.Developing alternative methods may prove to be useful for making estimates of gross domestic product when other measures are of suspect accuracy or unavailable.Based on the summary and analysis of existing economic activity spatialization approaches,this paper explored the potential for spatializing GDP through China using night-time satellite imagery(DMSP/OLS) and land-use data.In creating the GDP linear regression model of secondary industry and tertiary industry,night-time light intensity and lit areas,under different types of land use,were employed as predictor variables,and the GDP statistical data was as dependent variable,meanwhile,model of primary industry based on the landuse data.To improve model performance,31 zones were created according to provincial administrative boundary.The model of primary industry is observed to have a correlation(R2) ranging from 0.7 to 0.95 in majority zones and R2 of secondary industry and tertiary industry modle is ranging from 0.8 to 0.98 in majority zones.A comparison of the results of this research with other researches shows that spatialized GDP density map,prepared on night-time imagery and land-use data,which reflects the GDP distribution characteristics more explicitly and greater detail.Meantime,the density map is significant sustainable economic development policies and basically explores the relationship between socioeconomic and regional ecological environment interaction.

(1.The State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing Application Chinese Academy of Sciences,Beijing 100101,China;2.Graduate University of Chinese Academy of Sciences,Beijing 100049,China)

GDP是社会经济发展、区域规划和资源环境保护的重要指标之一。然而,传统以各级行政单元为主的GDP统计资料无法显示区域内部GDP的差异,尝试通过GDP空间化来解决这个问题,以满足资源环境研究领域对空间型社会经济数据的需求。在分析总结国内外社会经济数据空间化技术方法的基础上,第一产业基于土地利用数据建模\,第二产业和第三产业基于DMSP/OLS与土地利用数据结合生成的土地灯光参数建模。为提高模型质量,全国按照省级行政边界分区,将因变量GDP分产业分区建模,第一产业绝大部分区域的模型精度在0.7~0.95之间,第二产业和第三产业绝大部分区域的模型精度在0.8~0.98之间。通过与其他GDP空间化技术和结果的比较分析,本研究中的GDP空间化方法无论是模型精度还是GDP密度分布结果都具有一定的优势。生成的GDP密度图能较完整地反映全国GDP分布细节以及宏观分布特征,可为将来经济策略和发展路线的绘制提供一定依据。

... 韩向娣等^[10]利用土地利用图对全国第一产业GDP建模,结合DMSP-OLS和土地利用数据对全国第二、三产业GDP建模,生成的GDP密度图比单一数据的精度要高 ...

2013

0.0

... Li等^[11]和Shi等^[12]在采用这种夜间灯光数据研究中国大陆的GDP后发现,相比DMSP-OLS数据,夜间灯光总量与GDP之间有更高的线性关系 ...

2014

0.0

... Li等^[11]和Shi等^[12]在采用这种夜间灯光数据研究中国大陆的GDP后发现,相比DMSP-OLS数据,夜间灯光总量与GDP之间有更高的线性关系 ...

2013

0.0

梁友嘉, 徐中民. 基于夜间灯光辐射数据的张掖市甘州区GDP空间分布建模[J]. 冰川冻土, 2013, 35(1): 249-254.

Liang Y

, Xu Z

Modeling the spatial distribution of GDP based on night light radiation: A case study in Ganzhou district, Zhangye municipality[J]. Journal of Glaciology and Geocryology, 2013, 35(1): 249-254.

Spatial and temporal distributions of both physical and socioeconomic phenomena can be approximated by functions depended on location in a multi-dimensional space as multivariate scalar, vector or tensor fields. Collection of spatial GDP data is problematic, because of various shortcomings in the data collection process. A model is developed for creating a disaggregated map of estimated GDP for towns and then a distribution map of GDP is got. Firstly, a regression model is developed to calibrate the official GDP at township level in the study area with the sum of light emissions, and subsequently unique coefficients are derived.The spatial population grid and nighttime light satellite imagery are used to represent primary industry and other industries. Multiplying the unique coefficients with the sum of light emissions provides an estimate of total economic activity, from which a spatially disaggregated GDP map of total economic activity may generate based on the grid of 500 m×500 m. The average GDP value of a township is 3.72 million Yuan, with a Standard Deviation of 0.34 million Yuan.

Key Laboratory of Ecohydrology of Inland River Basin, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou Gansu 730000, China

尽管近年来统计数据的生成技术有了很大提高, 但可用的详细GDP空间分布数据始终难以得到满足. 以夜间灯光辐射数据、官方统计数据和已有空间化人口分布数据构建了一种GDP空间化建模方法, 得到张掖市甘州区2000年的GDP空间分布. 首先, 分析基于乡镇单元的GDP与对应的夜间灯光辐射数据DN值之间的关系, 得到GDP模拟模型; 然后结合人口空间分布数据和三次产业的比例, 得到基于500 m×500 m栅格的GDP空间分布图. 各乡镇的GDP平均值为372×10 4 元, 标准差为34×10 4 元.

... 因而,更多的研究重心开始转向省域、县域甚至乡镇级GDP空间化的工作中^[13,14] ...

2014

0.0

... 因而,更多的研究重心开始转向省域、县域甚至乡镇级GDP空间化的工作中^[13,14] ...

2012

0.0

韩向娣, 周艺, 王世新, 等. 夜间灯光遥感数据的GDP空间化处理方法[J]. 地球信息科学学报, 2012, 14(1): 128-136.

Han X

, Zhou

, Wang S

, et al. GDP spatialization in China based on nighttime imagery[J]. Journal of Geo-information Science, 2012, 14(1): 128-136.

Resources and environmental sciences require greatly the spatialized socio-economic data sets which are always obtained from administrative regions at national or provincial level, and accurate estimates of the magnitude and spatial distribution of economic activity have many useful applications. Developing alternative methods for making estimates of gross domestic product (GDP) may prove to be useful when other measures are of suspect accuracy or unavailable. Based on the summary and analysis of existing economic activity spatialization approaches and nighttime imagery applications in economic activity, this research explores the potential for estimating the GDP using relationship between the spatial patterns of nighttime satellite imagery and GDP in China by correlation analysis and regression analysis using concerned data processing software. With the regional differences of China's economic development, logarithmic regression models have been established between different night light indexes and GDP, primary industry, secondary industry, tertiary industry and the sum of secondary industry and tertiary industry at the provincial level. A clear logarithmic linear relationship between nightlight imagery and GDP, especially the correlation coefficient of night light index and the sum of secondary industry and tertiary industry is 0.824 and R 2 of them is 0.679 at national level, suggests that this method is available and feasible to estimate the spatial distribution of economic activity such as GDP. The result, 1-km grid GDP map of China based on nighttime light data, by comparing with the other GDP spatialization approaches, shows the obvious advantage to reflect complete details and characteristics of the national secondary industry and tertiary industry distribution, which is extending the field of nighttime light data research and applications for the socio-economic data in resources and environmental sciences.

1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, CAS, Beijing 100101, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China

随着夜间灯光遥感数据的应用日渐成熟和资源环境研究领域,对空间型社会经济数据的需求增加,利用相关分析和回归分析的方法,首次定量探讨夜间灯光数据与统计型的社会经济数据的空间关系。为提高模型精度,按照我国省级行政边界分区建模,分析全国县级的地区生产总值、第一产业、第二产业、第三产业分别与夜间灯光指数的空间相关关系,最终建立全国的1km GDP密度图。结果表明,全国范围的夜间灯光数据与第一产业的相关性不明显,相关系数0.554,模型拟合效果差,R 2 为0.306;夜间灯光数据与地区生产总值、第二产业、第三产业均有明显的对数线性关系,尤其是与第二产业和第三产业之和,相关系数为0.824,R 2 为0.679。利用分区模型估算,生成的GDP密度图能较完整地反映全国社会经济分布详况,以及宏观分布特征。

... 夜间灯光数据很难准确探测到城市以外的农村地区,第一产业的产值与灯光数据的关系也不明显,而与耕地等农业用地关系密切,土地利用数据可以为第一产业产值建模提供便利^[15] ...

2008

0.0

... 1 第一产业产值建模北京市土地利用分类数据通过采用分类回归树(classification and regression tree,CART)算法基于Landsat8影像分类获得^[16] ...

2013

0.0

米晓楠, 白林燕, 谭雪航, 等. 基于DMSP/OLS数据的城市中心城区提取新方法[J]. 地球信息科学学报, 2013, 15(2): 255-261.

Mi X

, Bai L

, Tan X

, et al. A new method of extracting areas of center city regions based on DMSP/OLS data[J]. Journal of Geo-Information Science, 2013, 15(2): 255-261.

In recent years, the DMSP/OLS nighttime light data have been widely applied to various fields such as urban space and urbanizations, human activity and effects, economic level, ecological effects, electric energy consumption and hazards analysis. In order to get the research results of high-precision, accurate center city regions should be generally obtained before researching. This study utilized DMSP/OLS stable lights imagery to estimate the areas of center city regions. Firstly, we set the minimum threshold of center city regions according to the spatial contrast method based on auxiliary data. Subsequently we extracted every maximum of urban nighttime light and the values which closed to each maximum in a certain range from the DMSP data. These regions should be corresponding to center city regions. However, because of the problem of“lighting overflow”and other influences, the results were not very accurate. Trying to make a correlation analysis about center city regions's areas between DMSP data and MODIS land cover data, the results showed that they were significantly related. Then an center city regions model could be established between them. Considering the different levels of urban development, there may be two different relationships between center city regions' areas which respectively extracted from DMSP data and MODIS land cover data. Therefore, cities were divided into two classes. The maximum of urban light in one class was equal to 63 and it was less than 63 in the other one. As a result, two different models were established between MODIS land cover data and DMSP data based on the maximum value of urban light, thus, it is feasible to accurately estimate the center city regions' areas by DMSP data. At last, the results of verification showed that there was a high percent of prediction fitness (82.06%) and the mean relative error was 8.47%, which showed the reliability of the linear models. So, this paper introduced a new method which could simultaneously and accurately extract the center city regions with different levels of urban development. This work is an important preparation for further studies in urban development and so on.

1. China University of Mining & Technology, Beijing 100083, China; 2. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; 3.Ningbo University, Ningbo 315211, China

近年来, DMSP/OLS夜间灯光数据已被广泛应用于城市空间格局和城市化过程、人类活动及效应、经济水平、生态环境影响、电力能源消耗量和灾害分析等领域。本文利用DMSP/OLS的稳定灯光数据产品, 首先, 以辅助资料的空间对比法确定城市中心城区范围最小阈值, 然后, 提取DMSP数据中的每个极大值及一定范围内的临近值, 作为城市中心城区范围, 并将提取的范围与作为辅助资料的MODIS土地覆盖数据中相应的城市中心城区范围作线性回归分析, 进而获得估算城市中心城区范围的线性模型。经验证其结果预测吻合度达到82.06%, 平均相对误差为8.47%, 说明利用该方法提取城市中心城区范围具有一定的可行性, 可较准确地提取发展水平不同的城市的中心城区范围。

... 1节的土地利用类型结果,将分类的建筑用地与DMSP-OLS灯光数据叠加来确定最佳灯光阈值(DN值范围在14~63),通过去饱和灯光的DMSP-OLS数据掩模NPP-VIIRS数据来消除其背景噪声和偶然灯光噪声^[17] ...

2003

0.0

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.

Chen

, Zhuo

, Shi P

, et al. The study on urbanization process in China based on DMSP/OLS data: Development of a light index for urbanization level estimation[J]. Journal of Remote Sensing, 2003, 7(3): 168-175.

Urbanization, stimulated by striking economic development, has been proceeded in China on a large scale and with striking rapidity in the past two decades. It is necessary to monitor and model urbanization process of China for its sustainable development. This paper presents a new light index for regional urbanization level estimation considering the light spatial distribution and intensity based on DMSP/OLS data, which was pre-processed by Japan National Institute of Environmental Studies. The correlation analysis between light index and composite urbanization index was carried out in province scale. The result shows that there is significant relationship between two indexes. The regression model for composite urbanization index estimation using light index was also developed with R 2 equal to 0.793. It suggests that light index is an effective and applicable index for regional urbanization analysis and monitoring. Through the analysis of light index change in China during 1992 to 1998, it is shown that the urbanization level in China is different from high level in East China to low level in West China, and urbanization level was improved largely during 1992 to 1998, especially in provinces of East China.

基于DMSP/OLS非辐射定标的夜间灯光平均强度数据构建了一个反映区域城市化水平的灯光指数 ,并分析了该灯光指数与城市化水平在省级尺度上的相关性。研究表明 ,灯光指数与反映城市化水平的复合指数存在较高的相关关系 ,可以用于我国城市化水平及其时空分异分析和城市化进程监测。

... 上述指标的表达式分别^[18,19]为 ...

2003

0.0

卓莉, 史培军, 陈晋, 等. 20世纪90年代中国城市时空变化特征——基于灯光指数CNLI方法的探讨[J]. 地理学报, 2003, 58(6): 893-902.

Zhuo

, Shi P

, Chen

, et al. Application of compound night light index derived from DMSP/OLS data to urbanization analysis in China in the 1990s[J]. Acta Geographica Sinica, 2003, 58(6): 893-902.

This paper presented compounded night light index (CNLI) derived from DMSP/OLS data which were pre-processed by Japan National Institute of Environmental Studies for regional urbanization level estimation considering the light spatial distribution and intensity. The correlation analyses between light indices and composite urbanization indices were carried out at provincial level and county level. They can also be carried out in other scales if appropriate census data are available. The results show that there were significant relationships between the two kinds of indices. The regression models at provincial scale and county scale for composite urbanization index estimation using CNLI were also developed. It suggests that CNLI is an effective and applicable index for regional urbanization analysis and monitoring. Analysis of the changes of CNLI, S and I in China during the period 1992-1998 showed that the imbalance of urbanization level in China was obvious. It was higher in the east and lower in the west. Urbanization level was improved largely during 1992-1998, but the speed and the type of development were different.

1. Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education of China, Beijing Normal University, Beijing 100875, China; 2. Japan National Institute of Environmental Studies, 16-2 Onogawa, Tsukuba, 305-8506, Japan

用DMSP/OLS夜间平均灯光强度图像在区域上的扩展特征和聚集特征构建了一反映区域城市化综合水平的灯光指数CNLI (compounded night light index) , 并分别在省级和县级尺度上对CNLI与基于统计数据的城市化水平指数做了相关分析，结果表明CNLI与基于统计数据的城市化水平复合指数显著相关，最后用CNLI分析了中国省际城市化水平的时空特征。CNLI为客观、实时监测城市化水平的空间格局及其发展变化提供了新的指标和方法。

... 上述指标的表达式分别^[18,19]为 ...

2010

0.0

叶靖, 杨小唤, 江东. 乡镇级人口统计数据空间化的格网尺度效应分析——以义乌市为例[J]. 地球信息科学学报, 2010, 12(1): 40-47.

, Yang X

, Jiang

The grid scale effect analysis on town leveled population statistical data spatialization[J]. Journal of Geo-information Science, 2010, 12(1): 40-47.

The gird scale effect is one of the basic issues on population data spatialization.For the demand of all kinds of spatial population data in the fields of resources and environment and global change models,a lot of researches have been done based on remote sensing and GIS technology both at home and abroad.But the models used are mostly on global(such as GPW,1995,5km),national(such as national population database,2000,1km) or provincial scale,and their resolution ranges from 1km to several kilometers.In recent years,there are studies on local distribution of population by using of high-resolution images.For all the researches,both the method of data source selection according to specific application and the analysis on production suitability are deficient.So,many uncertainties exit in population data application,especially in county level and secondary or tertiary rivers.To solve the problems mentioned above,in this article we mainly propose the method of scale effect analysis on population data spatialization.Taking Yiwu City,Zhejiang Province as the study area,using CBERS and IRS-P5 images we extract land use information and build a spatialization model to the statistical population data of rural towns,then get a set of population data gird ranging from 20m to 1km.Moreover,by comparing population data by grid and statistical population data in rural towns,the grid scale effect analysis is made;by comparing population data by grid and statistical population data in villages,the remote sensing data source scale effect analysis is made.The result of scale effect analysis shows: by using CBERS as data source,the suitable grid scale of production is 200m and its precision is 76%;by using P5 as a data source,the suitable grid scale of production is 100m and its precision is 84%.The method of scale effect analysis in spatial distribution of statistical population is argued in this paper and it can provide basic technical solutions and examples to optimum scale selection in the process of humanistic factors(such as population) spatialization.

Institute of Geographical Sciences &Natural Resources Research, CAS, Beijing 100101, China

格网尺度效应是统计型人口数据空间化研究的基础性问题之一。针对资源环境研究领域和全球变化区域模型对各种尺度的空间型人口数据的需求,人们对人口数据空间化进行了大量研究。综观现有研究成果,缺乏针对具体应用需求的数据源选择方法和对数据产品适宜性的分析。因此,数据在实际应用中,特别是县市、二、三级流域等尺度上的应用中,存在较多的不确定性。本文重点研究人口数据空间化的格网尺度效应分析方法。以浙江省义乌市为例,利用CBERS、IRS-P5卫星影像,提取了研究区土地利用信息;在地理信息系统技术支持下,对乡镇统计人口进行空间化建模,生成20m至1km系列的格网人口数据;通过比较不同格网人口与乡镇统计人口的误差,分析人口数据空间化的格网尺度效应。分析结果表明,采用CBERS数据源进行人口数据空间化,其数据产品即格网人口的适宜尺度是200m,抽样精度为76%;采用P5数据源进行人口数据空间化,其格网人口的适宜尺度是100m,抽样精度为84%。

... 确定格网尺度的原则为: 在顾及地理数据尺度的前提下,既要反映社会经济数据的原始分布状态,又要反映出其连续分布的现实^[20,21] ...

2014

0.0

... 确定格网尺度的原则为: 在顾及地理数据尺度的前提下,既要反映社会经济数据的原始分布状态,又要反映出其连续分布的现实^[20,21] ...