中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives

文献类型:期刊论文

作者Zhao, Min1,6,7,8; Zhou, Yuyu1; Li, Xuecao1; Cao, Wenting1; He, Chunyang5; Yu, Bailang4; Li, Xi3; Elvidge, Christopher D.2; Cheng, Weiming7; Zhou, Chenghu6,7
刊名REMOTE SENSING
出版日期2019-09-01
卷号11期号:17页码:35
关键词nighttime light advances challenges strategic directions review
DOI10.3390/rs11171971
通讯作者Zhou, Yuyu(yuyuzhou@iastate.edu)
英文摘要Nighttime light observations from remote sensing provide us with a timely and spatially explicit measure of human activities, and therefore enable a host of applications such as tracking urbanization and socioeconomic dynamics, evaluating armed conflicts and disasters, investigating fisheries, assessing greenhouse gas emissions and energy use, and analyzing light pollution and health effects. The new and improved sensors, algorithms, and products for nighttime lights, in association with other Earth observations and ancillary data (e.g., geo-located big data), together offer great potential for a deep understanding of human activities and related environmental consequences in a changing world. This paper reviews the advances of nighttime light sensors and products and examines the contributions of nighttime light remote sensing to perceiving the changing world from two aspects (i.e., human activities and environmental changes). Based on the historical review of the advances in nighttime light remote sensing, we summarize the challenges in current nighttime light remote sensing research and propose four strategic directions, including: Improving nighttime light data; developing a long time series of consistent nighttime light data; integrating nighttime light observations with other data and knowledge; and promoting multidisciplinary and interdisciplinary analyses of nighttime light observations.
WOS关键词ELECTRIC-POWER CONSUMPTION ; BREAST-CANCER INCIDENCE ; CARBON-DIOXIDE EMISSIONS ; DMSP-OLS ; TIME-SERIES ; ARTIFICIAL-LIGHT ; CO2 EMISSIONS ; SPATIOTEMPORAL VARIATIONS ; ENERGY-CONSUMPTION ; IMPERVIOUS SURFACE
资助项目College of Liberal Arts and Science's (LAS) Dean's Emerging Faculty Leaders award at the Iowa State University ; China Scholarships Council[201806190136] ; China Scholarships Council[201706320298]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000486874300015
出版者MDPI
资助机构College of Liberal Arts and Science's (LAS) Dean's Emerging Faculty Leaders award at the Iowa State University ; China Scholarships Council
源URL[http://ir.igsnrr.ac.cn/handle/311030/69563]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Zhou, Yuyu
作者单位1.Iowa State Univ, Dept Geol & Atmospher Sci, Ames, IA 50011 USA
2.Colorado Sch Mines, Payne Inst, Earth Observat Grp, 1500 Illinois St, Golden, CO 80401 USA
3.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China
4.East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
5.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Ctr Human Environm Syst Sustainabil, Beijing 100875, Peoples R China
6.Collaborat Innovat Ctr South China Sea Studies, Nanjing 210023, Jiangsu, Peoples R China
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
8.Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714
Zhao, Min,Zhou, Yuyu,Li, Xuecao,et al. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives[J]. REMOTE SENSING,2019,11(17):35.
APA Zhao, Min.,Zhou, Yuyu.,Li, Xuecao.,Cao, Wenting.,He, Chunyang.,...&Zhou, Chenghu.(2019).Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives.REMOTE SENSING,11(17),35.
MLA Zhao, Min,et al."Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives".REMOTE SENSING 11.17(2019):35.

入库方式: OAI收割

来源:地理科学与资源研究所

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