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Volume 10
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Addendum to Remote Sens. 2017, 9(10), 1076.
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Addendum

Addendum: Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China. Remote Sens. 2017, 9, 1076

by
Pengbo Yu
1,†,
Yidan Li
2,3,†,
Bo Xu
4,
Jing Wei
1,
Shen Li
1,
Jianhua Dong
1,
Jianhui Qu
5,
Jing Xu
1,
Zheng Y.X. Huang
6,
Chaofeng Ma
7,
Jing Yang
2,
Guogang Zhang
8,
Bin Chen
2,
Shanqian Huang
2,
Chunming Shi
2,
Hongwei Gao
9,
Feng Liu
1,
Huaiyu Tian
2,*,
Nils Chr. Stenseth
10,*,
Bing Xu
4,* and
Jingjun Wang
1,*add Show full author list remove Hide full author list
1
Shaanxi Provincial Centre for Disease Control and Prevention, Xi’an 710054, China
2
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
3
College of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
4
Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, School of Environment, Tsinghua University, Beijing 100084, China
5
Hu County Centre for Disease Control and Prevention, Xi’an 710302, China
6
College of Life Sciences, Nanjing Normal University, Nanjing 225300, China
7
Xi’an Centre for Disease Control and Prevention, Xi’an 710054, China
8
Key Laboratory of Forest Protection of State Forestry Administration, National Bird Banding Center of China, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China
9
Institute of Disaster Medicine and Public Health, Affiliated Hospital of Logistics University of Chinese People’s Armed Police Force (PAP), Tianjin 300162, China
10
Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, PO Box 1066 Blindern, N-0316 Oslo, Norway
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2018, 10(1), 20; https://doi.org/10.3390/rs10010020
Submission received: 7 December 2017 / Revised: 21 December 2017 / Accepted: 21 December 2017 / Published: 23 December 2017
Versions Notes
After publication of the research paper [1], it was found that the affiliation of the 15th author, Chunming Shi, was wrong, which should be corrected as follows: State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China. Personal website: http://www.gcess.cn/yjspy/dsjj/152442.html.
The authors would like to apologize for any inconvenience caused. The change does not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this addendum.

References

  1. Yu, P.; Li, Y.; Xu, B.; Wei, J.; Li, S.; Dong, J.; Qu, J.; Xu, J.; Huang, Y.Z.; Ma, C.; et al. Using satellite data for the characterization of local animal reservoir populations of hantaan virus on the weihe plain, china. Remote Sens. 2017, 9, 1076. [Google Scholar] [CrossRef]

Share and Cite

MDPI and ACS Style

Yu, P.; Li, Y.; Xu, B.; Wei, J.; Li, S.; Dong, J.; Qu, J.; Xu, J.; Huang, Z.Y.X.; Ma, C.; et al. Addendum: Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China. Remote Sens. 2017, 9, 1076. Remote Sens. 2018, 10, 20. https://doi.org/10.3390/rs10010020

AMA Style

Yu P, Li Y, Xu B, Wei J, Li S, Dong J, Qu J, Xu J, Huang ZYX, Ma C, et al. Addendum: Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China. Remote Sens. 2017, 9, 1076. Remote Sensing. 2018; 10(1):20. https://doi.org/10.3390/rs10010020

Chicago/Turabian Style

Yu, Pengbo, Yidan Li, Bo Xu, Jing Wei, Shen Li, Jianhua Dong, Jianhui Qu, Jing Xu, Zheng Y.X. Huang, Chaofeng Ma, and et al. 2018. "Addendum: Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China. Remote Sens. 2017, 9, 1076" Remote Sensing 10, no. 1: 20. https://doi.org/10.3390/rs10010020

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