Internet of Things to network smart devices for ecosystem monitoring

被引:62
作者
Li, Xin [1 ,2 ]
Zhao, Ning [3 ]
Jin, Rui [2 ,3 ]
Liu, Shaomin [4 ]
Sun, Xiaomin [5 ]
Wen, Xuefa [5 ]
Wu, Dongxiu [6 ]
Zhou, Yan [7 ]
Guo, Jianwen [3 ]
Chen, Shiping [6 ]
Xu, Ziwei [4 ]
Ma, Mingguo [8 ]
Wang, Tianming [9 ,10 ,11 ]
Qu, Yonghua [12 ]
Wang, Xinwei [7 ]
Wu, Fangming [13 ]
Zhou, Yuke [5 ]
机构
[1] Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China
[2] Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
[3] Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China
[4] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
[5] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
[6] Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
[7] Chinese Acad Sci, Inst Semicond, Optoelect Syst Lab, Beijing 100083, Peoples R China
[8] Southwest Univ, Sch Geog Sci, Minist Nat Resources, Res Base Karst Ecoenvironm Nanchuan Chongqing, Chongqing 400715, Peoples R China
[9] Beijing Normal Univ, Minist Educ, Key Lab Biodivers Sci & Engn Monitoring, Beijing 100875, Peoples R China
[10] Beijing Normal Univ, Res Ctr Amur Tigers & Amur Leopards, Natl Forestry & Grassland Adm, Beijing 100875, Peoples R China
[11] Beijing Normal Univ, Coll Life Sci, Beijing 100875, Peoples R China
[12] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[13] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
关键词
Ecosystem monitoring; Fragile ecosystem; Internet of Things; Wireless sensor network; Smart device; WIRELESS SENSOR NETWORK; SAP FLOW MEASUREMENT; ENVIRONMENTAL INTERNET; WATER; OPPORTUNITIES; SPECTROSCOPY; CALIBRATION; OBJECTIVES; MANAGEMENT; FRAMEWORK;
D O I
10.1016/j.scib.2019.07.004
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Smart, real-time, low-cost, and distributed ecosystem monitoring is essential for understanding and managing rapidly changing ecosystems. However, new techniques in the big data era have rarely been introduced into operational ecosystem monitoring, particularly for fragile ecosystems in remote areas. We introduce the Internet of Things (IoT) techniques to establish a prototype ecosystem monitoring system by developing innovative smart devices and using IoT technologies for ecosystem monitoring in isolated environments. The developed smart devices include four categories: large-scale and nonintrusive instruments to measure evapotranspiration and soil moisture, in situ observing systems for CO2 and delta C-13 associated with soil respiration, portable and distributed devices for monitoring vegetation variables, and Bi-CMOS cameras and pressure trigger sensors for terrestrial vertebrate monitoring. These new devices outperform conventional devices and are connected to each other via wireless communication networks. The breakthroughs in the ecosystem monitoring IoT include new data loggers and long-distance wireless sensor network technology that supports the rapid transmission of data from devices to wireless networks. The applicability of this ecosystem monitoring IoT is verified in three fragile ecosystems, including a karst rocky desertification area, the National Park for Amur Tigers, and the oasis-desert ecotone in China. By integrating these devices and technologies with an ecosystem monitoring information system, a seamless data acquisition, transmission, processing, and application IoT is created. The establishment of this ecosystem monitoring IoT will serve as a new paradigm for ecosystem monitoring and therefore provide a platform for ecosystem management and decision making in the era of big data. (C) 2019 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.
引用
收藏
页码:1234 / 1245
页数:12
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