本文關(guān)鍵詞：基于多源遙感數(shù)據(jù)及DEM的人口統(tǒng)計(jì)數(shù)據(jù)空間化——以浙江省為例，由筆耕文化傳播整理發(fā)布。

基于多源遙感數(shù)據(jù)及DEM的人口統(tǒng)計(jì)數(shù)據(jù)空間化——以浙江省為例

MODELING POPULATION DENSITY USING MULTI-SENSOR REMOTE SENSING DATA AND DEM. A CASE STUDY OF ZHEJIANG PROVINCE

[1] [2] [3] [4]

YANG Xu-chao1 , GAO Da-wei2 , DING Ming-jun3 , LIU Lin-shan4 （1. Zhejiang Institute of Meteorological Sciences, Hangzhou 310008, China; 2. Zheiiang Province Climate Center,

[1]浙江省氣象科學(xué)研究所,浙江杭州310008; [2]浙江省氣候中心,浙江杭州310017; [3]江西師范大學(xué)鄱陽湖濕地與流域研究教育部重點(diǎn)實(shí)驗(yàn)室,江西南昌330022; [4]中國科學(xué)院地理科學(xué)與資源研究所,北京100101

文章摘要：利用DMSP／OLS遙感夜間燈光數(shù)據(jù)進(jìn)行人口等社會經(jīng)濟(jì)數(shù)據(jù)的空間化時，往往受到其較低的空間分辨率、像元過飽和以及像元溢出現(xiàn)象的影響。植被指數(shù)（如NDVI）與不透水面呈負(fù)相關(guān)關(guān)系，與夜間燈光數(shù)據(jù)在反映人類活動、提取建成區(qū)方面可以互補(bǔ)，將這兩種數(shù)據(jù)融合可以有效減少夜燈數(shù)據(jù)像元過飽和等因素引起的誤差。通過進(jìn)一步融合DEM數(shù)據(jù)對基于DMSP／OLS夜間燈光數(shù)據(jù)和NDVI構(gòu)建的人居指數(shù)進(jìn)行了海拔修正，基于修正后的人居指數(shù)與統(tǒng)計(jì)人口之間很強(qiáng)的線性相關(guān)建立人口空間化模型，獲得2010年浙江省1kmXlkm分辨率下的人口密度空間分布。模擬結(jié)果顯示，，浙江省平均人口密度為515人／km。，模擬的平均相對誤差為18．3％，相比海拔訂正前的模擬誤差減少約5％，表明利用多源遙感數(shù)據(jù)融合后的人居指數(shù)在省級尺度上模擬人口空間分布的精度較高。

Abstr：In order to bridge the gap between aggregated census data and geocoded data,different dasymetric mapping techniques were developed to disaggregate census data. The satellite-measured DMSP/OLS night- time light data was widely used for regional level mapping of socioeconomic activities due to its high tempo- ral resolution,free availability and wide swath. However,due to the coarse resolution, data saturation and overglow effects of DMSP/OLS data,any application need to take into account the limitations of using this data source. Firstly,although the DMSP/OLS sensor has a nominal resolution of 1 km, this has been resam- pled from the 2.7 km native resolution of the sensor. The coarse resolution of the nighttime lights data lower the accuracy of dasymetric mapping. Secondly,the overglow effect due to surface reflection and scat- tering and refraction in the atmosphere results in the overestimation of lighted areas. Thirdly,the low ra- diometric resolution of 6 bits （i. e. the digital number value ranges from 0 and 63） results in data saturation over brightly light built-up areas. Vegetation indexes like Normalized Difference Vegetation Index （NDVI） are negatively correlated with the impervious surfaces and can be used for estimation of built-up areas. The incorporation of vegetation index （NDVI） can reduce the errors occurring in estimating human settlements from the DMSP/OLS nighttime light imagery due to data saturation and other factors. In addition, elevation is an important variable in population distribution modeling because most human settlements occur on low- er elevation in China. This paper provides an approach for rapid and accurate estimation of population on a per pixel-basis using a integration of tw

文章關(guān)鍵詞：

Keyword：:spatialization of statistical population DMSP/OLS NDVI human settlement index ZhejiangProvince

課題項(xiàng)目：中國氣象局氣候變化專項(xiàng)（ccsF一201336）;浙江省科技廳公益技術(shù)研究社會發(fā)展項(xiàng)目（2011C23051）;國家自然科學(xué)基金項(xiàng)目（41001023）

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  本文關(guān)鍵詞：基于多源遙感數(shù)據(jù)及DEM的人口統(tǒng)計(jì)數(shù)據(jù)空間化——以浙江省為例，由筆耕文化傳播整理發(fā)布。