Mapping China's Electronic Power Consumption Using Points of Interest and Remote Sensing Data

doi:10.3390/rs13061058

	Mapping China's Electronic Power Consumption Using Points of Interest and Remote Sensing Data
	Jin, Cheng 3; Zhang, Yili 4,5,6; Yang, Xuchao 3; Zhao, Naizhuo 7,8; Ouyang, Zutao 1; Yue, Wenze 2
刊名	REMOTE SENSING
	2021-03-01
卷号	13 期号:6 页码:17
关键词	electric power consumption points of interest nighttime light random forests China
DOI	10.3390/rs13061058
通讯作者	Yang, Xuchao(yangxuchao@zju.edu.cn)
英文摘要	Producing gridded electric power consumption (EPC) maps at a fine geographic scale is critical for rational deployment and effective utilization of electric power resources. Brightness of nighttime light (NTL) has been extensively adopted to evaluate the spatial patterns of EPC at multiple geographical scales. However, the blooming effect and saturation issue of NTL imagery limit its ability to accurately map EPC. Moreover, limited sectoral separation in applying NTL leads to the inaccurate spatial distribution of EPC, particularly in the case of industrial EPC, which is often a dominant portion of the total EPC in China. This study pioneers the separate estimation of spatial patterns of industrial and nonindustrial EPC over mainland China by jointly using points of interest (POIs) and multiple remotely sensed data in a random forests (RF) model. The POIs provided fine and detailed information about the different socioeconomic activities and played a significant role in determining industrial and nonindustrial EPC distribution. Based on the RF model, we produced industrial, non-industrial, and overall EPC maps at a 1 km resolution in mainland China for 2011. Compared against statistical data at the county level, our results showed a high accuracy (R-2 = 0.958 for nonindustrial EPC estimation, 0.848 for industrial EPC estimation, and 0.913 for total EPC). This study indicated that the proposed RF-based method, integrating POIs and multiple remote sensing data, can markedly improve the accuracy for estimating EPC. This study also revealed the great potential of POIs in mapping the distribution of socioeconomic parameters.
资助项目	Second Tibetan Plateau Scientific Expedition and Research program (STEP)[2019QZKK0603] ; National Natural Science Foundation of China[41971019] ; Open Research Fund of National Earth Observation Data Center[NODAOP2020018]
WOS关键词	NIGHTTIME LIGHT IMAGES ; ELECTRICITY CONSUMPTION ; SPATIOTEMPORAL DYNAMICS ; ENERGY-CONSUMPTION ; ECONOMIC-GROWTH ; LANDSAT IMAGES ; SOCIAL MEDIA ; TIME-SERIES ; SATURATION ; PATTERNS
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000651949300001
资助机构	Second Tibetan Plateau Scientific Expedition and Research program (STEP) ; National Natural Science Foundation of China ; Open Research Fund of National Earth Observation Data Center
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162748]
专题	中国科学院地理科学与资源研究所
通讯作者	Yang, Xuchao
作者单位	1.Stanford Univ, Dept Earth Syst Sci, Stanford, CA 94305 USA 2.Zhejiang Univ, Dept Land Management, Hangzhou 310058, Peoples R China 3.Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 5.CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China 6.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 7.Northeastern Univ, Sch Humanities & Law, Inst Land Resource Management, Shenyang 110169, Peoples R China 8.McGill Univ, Hlth Ctr, Div Clin Epidemiol, Montreal, PQ H3A 1A1, Canada
推荐引用方式 GB/T 7714	Jin, Cheng,Zhang, Yili,Yang, Xuchao,et al. Mapping China's Electronic Power Consumption Using Points of Interest and Remote Sensing Data[J]. REMOTE SENSING,2021,13(6):17.
APA	Jin, Cheng,Zhang, Yili,Yang, Xuchao,Zhao, Naizhuo,Ouyang, Zutao,&Yue, Wenze.(2021).Mapping China's Electronic Power Consumption Using Points of Interest and Remote Sensing Data.REMOTE SENSING,13(6),17.
MLA	Jin, Cheng,et al."Mapping China's Electronic Power Consumption Using Points of Interest and Remote Sensing Data".REMOTE SENSING 13.6(2021):17.