Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems

Abstract Timely and accurate detection of microclimates is extremely valuable for monitoring and stimulating exchanges of mass and energy in forest ecosystems under climate change. Recently, the rapid growth of wireless sensor networks (WSNs) has provided a new approach for detecting microclimates i...

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Main Authors: Jiaxin Jin, Ying Wang, Hong Jiang, Xiaofeng Chen
Format: Article
Language:English
Published: Nature Publishing Group 2018-11-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-018-34832-7
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spelling doaj-240b0d8c69c54d1da9bc0def83dd53162020-12-08T04:25:09ZengNature Publishing GroupScientific Reports2045-23222018-11-01811910.1038/s41598-018-34832-7Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest EcosystemsJiaxin Jin0Ying Wang1Hong Jiang2Xiaofeng Chen3School of Earth Sciences and Engineering, Hohai UniversityCollege of Culture Industry and Tourism Management, Sanjiang UniversityInternational Institute for Earth System Science, Nanjing UniversityBeijing Pri-eco Technology Co., LTDAbstract Timely and accurate detection of microclimates is extremely valuable for monitoring and stimulating exchanges of mass and energy in forest ecosystems under climate change. Recently, the rapid growth of wireless sensor networks (WSNs) has provided a new approach for detecting microclimates in a complex environment at multiple temporal and spatial scales. However, applications of wireless sensors in forest microclimate monitoring have rarely been studied, and the corresponding observation accuracy, error sources and correction methods are not well understood. In this study, through field experiments in two typical subtropical forest ecosystems in Zhejiang Province, China, the accuracy of the temperature and humidity observed by the wireless sensors was evaluated against standard meteorological data. Furthermore, the observation error sources were analyzed and corresponding correction models were established. The results showed that the wireless sensor-based temperature and humidity values performed well within the total observation accuracy. However, the observation errors varied with season, daily periodicity and weather conditions. For temperature, the wireless sensor observations were overestimated during the daytime while they were underestimated during the nighttime. For humidity, the data observed by the wireless sensors generally appeared as overestimates. Adopting humidity as the corrected factor, correction models were established and effectively improved the accuracy of the microclimatic data observed by the wireless sensors. Notably, our error analysis demonstrated that the observation errors may be associated with the shell material of the wireless sensor, suggesting that shading measures for the wireless sensors should be considered for outdoor work.https://doi.org/10.1038/s41598-018-34832-7Wireless SensorSubtropical Forest TypesTemperature-based SensorsShading MeasuresObservation Error
collection DOAJ
language English
format Article
sources DOAJ
author Jiaxin Jin
Ying Wang
Hong Jiang
Xiaofeng Chen
spellingShingle Jiaxin Jin
Ying Wang
Hong Jiang
Xiaofeng Chen
Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems
Scientific Reports
Wireless Sensor
Subtropical Forest Types
Temperature-based Sensors
Shading Measures
Observation Error
author_facet Jiaxin Jin
Ying Wang
Hong Jiang
Xiaofeng Chen
author_sort Jiaxin Jin
title Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems
title_short Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems
title_full Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems
title_fullStr Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems
title_full_unstemmed Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems
title_sort evaluation of microclimatic detection by a wireless sensor network in forest ecosystems
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2018-11-01
description Abstract Timely and accurate detection of microclimates is extremely valuable for monitoring and stimulating exchanges of mass and energy in forest ecosystems under climate change. Recently, the rapid growth of wireless sensor networks (WSNs) has provided a new approach for detecting microclimates in a complex environment at multiple temporal and spatial scales. However, applications of wireless sensors in forest microclimate monitoring have rarely been studied, and the corresponding observation accuracy, error sources and correction methods are not well understood. In this study, through field experiments in two typical subtropical forest ecosystems in Zhejiang Province, China, the accuracy of the temperature and humidity observed by the wireless sensors was evaluated against standard meteorological data. Furthermore, the observation error sources were analyzed and corresponding correction models were established. The results showed that the wireless sensor-based temperature and humidity values performed well within the total observation accuracy. However, the observation errors varied with season, daily periodicity and weather conditions. For temperature, the wireless sensor observations were overestimated during the daytime while they were underestimated during the nighttime. For humidity, the data observed by the wireless sensors generally appeared as overestimates. Adopting humidity as the corrected factor, correction models were established and effectively improved the accuracy of the microclimatic data observed by the wireless sensors. Notably, our error analysis demonstrated that the observation errors may be associated with the shell material of the wireless sensor, suggesting that shading measures for the wireless sensors should be considered for outdoor work.
topic Wireless Sensor
Subtropical Forest Types
Temperature-based Sensors
Shading Measures
Observation Error
url https://doi.org/10.1038/s41598-018-34832-7
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