基于多源遥感信息的电力消费量估算与影响因素分析——以浙江省为例

Electricity Consumption Estimation Using Multi-sensor Remote Sensing Data: A Case Study of Zhejiang Province

利用DMSP/OLS遥感夜间灯光数据进行电力消费量等社会经济数据的空间化时,往往受到像元过饱和、像元溢出现象的影响。利用夜间灯光数据和植被指数(NDVI)之间的互补性构建人居指数,与NDVI的融合有效减少了夜灯数据的过饱和现象。在人居指数的计算中使用阈值法有效减少了夜灯数据像元溢出效应的影响,并对其进行了海拔修正。借助修正后的人居指数与电力消费量之间很强的相关关系建立电力消费量空间化模型,获得了2010年浙江省1 km×1 km分辨率下电力消费量的空间分布。模拟结果显示,利用修正后的人居指数对浙江省电力消费量模拟的平均相对误差为26%,表明利用多源遥感数据融合后的人居指数对电力消费量进行空间化的精度较高。

The satellite-measured DMSP/OLS nighttime light data was widely used for regional level mapping of socioeconomic activities due to its high temporal resolution,free availability and wide swath.However,the use of DMSP/OLS nighttime light data as covariates for mapping socioeconomic activities faces numbers of problems.One of these is the spatial resolution of the available data.Although the DMSP/OLS sensor has a nominal resolution of 1 km,this has been resampled from the 2.7 km native resolution of the sensor.The second difficulty is caused by＂overglow＂due to surface reflection and scattering and refraction in the atmosphere which results in the overestimation of lighted areas.The third problem relates to low radiometric resolution of 6 bits（i.e.the digital number value ranges from 0 and 63） which results in data saturation over brightly light built-up areas.Vegetation indexes like NDVI are negatively correlated with the impervious surfaces and can be used for estimation of built-up areas.The incorporation of NDVI can reduce the errors occurring in estimating built-up areas from the DMSP/OLS nighttime light imagery due to data saturation and other factors.In present study,the DMSP/OLS nighttime light data was combined with SPOT NDVI data to develop an index called human settlement index（HSI）,which estimated the fraction of built-up area on a per pixel basis.Due to the complementary characteristics between DMSP/OLS data and NDVI,the resultant HSI image conveys more information than both the individual datasets.The model for electricity consumption estimation was developed based on the significant correlation between the HSI and electricity consumption in Zhejiang Province in the article.Preliminary modeling results show general overestimation of electricity consumption,especially in high altitude area in southwest Zhejiang Province.The HSI was further corrected by thresholding method to overcome the overglow effect and elevation effect correction was also conducted.The modified HSI image was then used for mapping the electricity consumption in 2010 in Zhejiang Province at a resolution of 1 km×1 km.The results show that the correction of HSI results in a significant increase in accuracy in mapping the electricity consumption.The mean relative error is 26% when modified HSI was used to estimated the electricity consumption of Zhejiang province,which is much smaller than previous studies.The spatial distribution of electricity consumption is well in line with the economic development level.In addition,more than 75% of the electricity consumption located in area below 50 m in Zhejiang Province.The present research provides an integrated approach for rapid and accurate estimation of electricity consumption in regional scale on a per pixel-basis,which can be very useful for mapping socioeconomic activities from medium coarse resolution data at regional level within limited time and minimal cost.