Please wait a minute...
img

官方微信

遥感技术与应用  2021, Vol. 36 Issue (3): 502-510    DOI: 10.11873/j.issn.1004-0323.2021.3.0502
森林遥感专栏     
基于无线传感器网络的森林生态系统观测试验平台构建
黄艳1,2,3(),郑玮4
1.金陵科技学院 软件工程学院,江苏 南京 211169
2.金陵科技学院 智能人机交互研究中心,江苏 南京 211169
3.金陵科技学院 南京大数据研究院,江苏 南京 211169
4.金陵科技学院 计算机工程学院,江苏 南京 211169
Design of Forest Ecosystem Observation Experiment Platform based on Wireless Sensor Networks
Yan Huang1,2,3(),Wei Zheng4
1.School of Software Engineering,Jinling Institute of Technology,Nanjing 211169,China
2.Center for Intelligent Computer Human Interaction,Jinling Institute of Technology,Nanjing 211169,China
3.Nanjing Institute of Big Data,Jinling Institute of Technology,Nanjing 211169,China
4.School of Computer Engineering,Jinling Institute of Technology,Nanjing 211169,China
 全文: PDF(2644 KB)   HTML
摘要:

在长时间尺度上监测和评价森林生态过程的状态变量是当前森林生态系统观测研究的热点问题之一。针对森林生态系统观测站观测数据实时传输存储不畅、数据共享度低、数据碎片化严重、大数据分析平台建设薄弱、森林火灾实时预测预警缺乏等问题,下蜀林场综合观测试验平台结合遥感技术、涡度相关技术、样方调查技术和无线传感器网络技术实现了森林生态系统水、土、气、生各要素长期的连续的综合观测,提供了森林生态系统与大气之间的碳收支数据、水通量数据、能量通量数据、气象数据的实时自动存储及显示,森林生态系统服务功能及价值量的存储及显示,实现了对森林火灾的监控以及森林火险的预测预警。试验平台可为森林生态系统长期自动化观测提供借鉴。

关键词: 森林生态系统遥感涡度相关无线传感器网络样方调查数据可视化平台    
Abstract:

Monitoring and evaluating the state variables and fluxes of forest ecological processes on a long-term scale is one of the hot topics of current forest ecosystem observation research. In order to solve the problems of real-time transmission and storage of observation data of forest ecosystem observation stations, low data sharing, severe data fragmentation, weak construction of big data analysis platforms, and lack of real-time forest fire prediction and warning system, this paper used the remote sensing, eddy covariance technique, field investigation and Wireless Sensor Networks (WSN) technology to observe the spatial and temporal dynamics of carbon budget, water flux, and energy flux in Xiashu Forest Farm. We also developed a Data Visualization Platform Software (DVPS) to store and show the forest ecosystem observation data. Based on those data, DVPS can fulfill the monitoring of forest fires and the forecast and early warning of forest fire risks. DVPS also provides real-time display of carbon balance data, water flux data, energy flux data, and meteorological data between the forest ecosystem and the atmosphere, and the display of forest ecosystem service functions and its value. This experiment provides reference for the integrating of WSN, remote sensing and eddy correlation technology in long-term comprehensive observation experiments of forest ecosystems.

Key words: Forest ecosystem    Femote sensing    Eddy covariance    Wireless Sensor Networks    Field investigation    Data visualization platform software
收稿日期: 2020-02-26 出版日期: 2021-07-22
ZTFLH:  S718.5  
基金资助: 江苏省高校哲学社会科学基金项目(2020SJA0541)
作者简介: 黄艳(1981-),女,江苏泰兴人,讲师,主要从事生态系统遥感方面的研究。E?mail:huangyan@jit.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
黄艳
郑玮

引用本文:

黄艳,郑玮. 基于无线传感器网络的森林生态系统观测试验平台构建[J]. 遥感技术与应用, 2021, 36(3): 502-510.

Yan Huang,Wei Zheng. Design of Forest Ecosystem Observation Experiment Platform based on Wireless Sensor Networks. Remote Sensing Technology and Application, 2021, 36(3): 502-510.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.3.0502        http://www.rsta.ac.cn/CN/Y2021/V36/I3/502

图1  下蜀林场空间分布区域、观测样地及超级观测站分布
图2  平台总体框架
观测地观测数据类型变量/参数地面测量时间分辨率
样地观测数据气象数据气温、气压、降水、风速、风向、光照、相对湿度、光合有效辐射自动气象站*30 min
植被生态数据林龄、林分密度、郁闭度、胸径、树高、树冠长度、叶面积指数、凋落物厚度样方调查**逐月
土壤参数数据土壤温湿度土壤温湿度传感器*30 min
土壤热通量土壤热通量板*30 min
土壤氮磷钾含量土壤养分速测仪**逐月
土壤有机质含量重铬酸钾容量法**逐月
超级站观测数据通量数据CO2通量、CO2浓度、H2O浓度、潜热通量、显热通量开路/闭路涡度相关系统*30 min
气象数据风速、风向三维超声风速仪、三层风速传感器*30 min
气温和空气相对湿度大气温湿度传感器、红外温度仪*30 min
降水雨量传感器测量*30 min
向下的短波辐射、向上的短波辐射、向下的长波辐射、向上的长波辐射四分量净辐射仪*30 min
植被状态数据胸径胸径传感器*30 min
凋落物厚度凋落物量传感器*30 min
森林火灾监控红外热成像仪*实时
土壤参数数据土壤温湿度土壤温湿度传感器*30 min
土壤热通量土壤热通量板*30 min
表1  核心变量/参数的地面观测
  图3观测系统网络拓扑图
1 MacDougall A S, McCann K S, Gellner G, et al. Diversity Loss with Persistent Human Disturbance Increases Vulnerability to Ecosystem Collapse[J]. Nature, 2013, 494(7435): 86-89. doi: 10.1038/nature11869.
doi: 10.1038/nature11869
2 Yu Guirui, He Honglin, Zhou Yuke. Ecosystem Observation and Research Under Background of Big Data[J]. Bulletin of Chinese Academy of Sciences, 2018, 33(8): 832-837.
2 于贵瑞, 何洪林, 周玉科. 大数据背景下的生态系统观测与研究[J]. 中国科学院院刊, 2018, 33(8): 832-837.
3 Kampe T U, Johnson B R, Kuester M A, et al. NEON: The First Continental-scale Ecological Observatory with Airborne Remote Sensing of Vegetation Canopy Biochemistry and Structure[J]. Journal of Applied Remote Sensing, 2010, 4(1): 043510. doi: 10.1117/1.3361375.
doi: 10.1117/1.3361375
4 Mirtl M, Borer E T, Djukic I, et al. Genesis, Goals and Achievements of Long-term Ecological Research at the Global Scale: A Critical Review of ILTER and Future Directions[J]. Science of the Total Environment,2018,626:1439-1462. doi: 10.1016/j.scitotenv.2017.12.001.
doi: 10.1016/j.scitotenv.2017.12.001
5 Li X, Cheng G, Liu S, et al. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design[J]. Bulletin of the American Meteorological Society, 2013, 94: 1145-1160. doi: 10.1175/BAMS-D-12-00154.1.
doi: 10.1175/BAMS-D-12-00154.1
6 Liu S, Li X, Xu Z, et al. The Heihe Integrated Observatory Network: A Basin-scale Land Surface Processes Observatory in China[J]. Vadose Zone Journal, 2018, 17: 180072. doi: 10.2136/vzj2018.04.0072.
doi: 10.2136/vzj2018.04.0072
7 Fu Bojie, Niu Dong, Yu Guirui. The Roles of Ecosystem Observation and Research Network in Earth System Science[J]. Progress in Geography, 2007, 26(1): 1-16.
7 傅伯杰, 牛栋, 于贵瑞. 生态系统观测研究网络在地球系统科学中的作用[J]. 地理科学进展, 2007, 26(1): 1-16.
8 Lu Kangning, Duan Jinghua, Ji Ping, et al. A Brief Review of Progress of Terrestrial Ecosystem Observation Research Network of China[J]. Journal of Temperate Forestry Research, 2019, 2(3): 13-17.
8 卢康宁, 段经华, 纪平, 等. 国内陆地生态系统观测研究网络发展概况[J]. 温带林业研究, 2019, 2(3): 13-17.
9 Zhou Yuke. Development of Integrated Management Information System for Ecological Observing Stations[J]. Geomatics & Spatial Information Technology, 2019, 42(8): 11-14.
9 周玉科. 生态观测台站综合管理信息系统研发[J]. 测绘与空间地理信息, 2019, 42(8): 11-14.
10 Dai Shengqi, Zhao Bin. Trends and Challenges of Ecosystem Observations in the Age of Big Data[J]. Biodiversity Science, 2016, 24(1): 85-94.
10 戴圣骐, 赵斌. 大数据时代下的生态系统观测发展趋势与挑战[J]. 生物多样性, 2016, 24(1): 85-94.
11 Tan Xing, Feng Pengfei, Zhang Xu, et al. Application State and Development Strategy of Internet of Things Technology in Intelligent Forestry in China[J]. World Forestry Research, 2019, 32(5): 57-62.
11 谭星, 冯鹏飞, 张旭, 等. 物联网技术在我国智慧林业建设中的应用现状及发展策略[J]. 世界林业研究, 2019, 32(5): 57-62.
12 Li Xin, Liu Shaomin, Sun Xiaomin, et al. Innovative Development of Equipments and Internet-of-things Techniques for Ecosystem Monitoring and Its Demonstration[J]. Acta Ecologica Sinica, 2016, 36(22): 7023-7027.
12 李新, 刘绍民, 孙晓敏, 等. 生态系统关键参量监测设备研制与生态物联网示范[J]. 生态学报, 2016, 36(22): 7023-7027.
13 Bustamante M M, Roitman I, Aide T M, et al. Toward an Integrated Monitoring Framework to Assess the Effects of Tropical Forest Degradation and Recovery on Carbon Stocks and Biodiversity[J]. Global Change Biology, 2016, 22(1): 92-109. doi: 10.1111/gcb.13087.
doi: 10.1111/gcb.13087
14 Xie X, Li A, Tan J, et al. Assessments of Gross Primary Productivity Estimations with Satellite Data-driven Models Using Eddy Covariance Observation Sites over the Northern Hemisphere[J]. Agricultural and Forest Meteorology, 2020, 280: 107771. doi: 10.1016/j.agrformet.2019.107771.
doi: 10.1016/j.agrformet.2019.107771
15 Chen S, Jiang H, Chen Y, et al. Spatial-temporal Patterns of Net Primary Production in Anji(China) between 1984 and 2014[J]. Ecological Indicators,2020,110:105954. doi:10.1016/j.ecolind.2019.105954.
doi: 10.1016/j.ecolind.2019.105954
16 Wen Xiaorong, Jiang Lixiu, Liu Lei, et al. Estimation of forest Biomass, Net Primary Production and Analysis on Spatial Distribution Pattern for Jiangsu Province[J]. Journal of Northwest Forestry University, 2014, 29(1): 36-40.
16 温小荣, 蒋丽秀, 刘磊, 等. 江苏省森林生物量与生产力估算及空间分布格局分析[J]. 西北林学院学报, 2014, 29(1): 36-40.
17 Chen Shulin, Wen Zuomin. Estimating Forest Ecosystem Service Function and Its Value of Soil Carbon Sequestration Under Different Tree Species in Guangxi Province: Take Raw Material Forest of Stora Enso bases for Example[J]. Issues of Forestry Economics, 2017, 37(2): 35-38.
17 陈书林, 温作民. 桉树人工林不同树种土壤固碳价值评估——以广西斯道拉恩索公司两个原料林基地为例[J]. 林业经济问题, 2017, 37(2): 35-38.
18 Li X, Gentine P, Lin C, et al. A Simple and Objective Method to Partition Evapotranspiration into Transpiration and Evaporation at Eddy-covariance Sites[J]. Agricultural and Forest Meteorology,2019,265:171-182. doi:10.1016/j.agrformet. 2018. 11.017.
doi: 10.1016/j.agrformet. 2018. 11.017
19 Wang L, Zhao Q, Wen Z, et al. RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression[J]. Sustainability, 2018, 10(12): 4620-4636. doi: 10.3390/su10124620.
doi: 10.3390/su10124620
20 Li Ainong, Bian Jinhu, Zhang Zhengjian, et al. An Integrated Multi-scale Remote Sensing Experiment on Carbon Budget Parameters in the Zoige Plateau: Scientific Objectives and Experiment Design[J]. Remote Sensing Technology and Application, 2016, 31(3): 405-416.
20 李爱农, 边金虎, 张正健, 等. 若尔盖高原区域碳收支参量多尺度遥感综合观测试验:科学目标与试验设计[J]. 遥感技术与应用, 2016, 31(3): 405-416.
21 Qian Zhihong, Wang Yijun. Internet of Things-oriented Wireless Sensor Networks Review[J]. Journal of Electronics & Information Technology, 2013, 35(1): 215-227.
21 钱志鸿, 王义君. 面向物联网的无线传感器网络综述[J]. 电子与信息学报, 2013, 35(1): 215-227.
22 Li Xin, Liu Shaomin, Ma Mingguo, et al. HiWATER: An Integrated Remote Sensing Experiment on Hydrological and Ecological Processes in the Heihe River Basin[J]. Advances in Earth Science, 2012, 27(5): 481-498.
22 李新, 刘绍民, 马明国, 等. 黑河流域生态—水文过程综合遥感观测联合试验总体设计[J]. 地球科学进展, 2012, 27(5): 481-498.
23 Zhang Z, Glaser S D, Watteyne T, et al. Long-term Monitoring of the Sierra Nevada Snowpack Using Wireless Sensor Networks[J]. IEEE Internet of Things Journal, 2020, doi:10.1109/JIOT.2020.2970596.
doi: 10.1109/JIOT.2020.2970596
24 Liu Shirong, Dai Limin, Wen Yuanguang, et al. A Review on Forest Ecosystem Management Towards Ecosystem Services: Status, Dhallenges, and Future Perspectives. Acta Ecologica Sinica, 2015, 35(1): 1-9. [刘世荣, 代力民, 温远光,等. 面向生态系统服务的森林生态系统经营:现状、挑战与展望[J]. 生态学报, 2015, 35(1): 1-9.]
25 Chen Shulin, Wen Zuomin. Estimating Forest Ecosystem Service Function of Carbon Sequestration and Oxygen Release in Guangxi Province[J]. Journal of Agro-forestry Economics and Management, 2016, 15(5): 557-563.
25 陈书林, 温作民. 广西人工林植被固碳释氧服务功能及其价值量评估[J]. 农林经济管理学报, 2016, 15(5): 557-563.
26 Chen Shulin. Estimating Forest Ecosystem Service Function of Water Conservation based on TVDI[J]. Ecological Economy, 2016, 32(12): 182-186.
26 陈书林. 基于TVDI模型的森林生态系统水源涵养服务功能研究[J]. 生态经济, 2016, 32(12): 182-186.
27 Chen Shulin, Wen Zuomin. Estimating Forest Ecosystem Service Function of Soil Conservation in Guangxi-taking Stora Enso's Raw Material Forest base as Example[J]. Forestry Economics, 2016, 38(11): 104-107.
27 陈书林, 温作民. 广西人工林保育土壤服务功能价值评估-以斯道拉恩索原料林基地为例[J]. 林业经济, 2016, 38(11): 104-107.
[1] 罗婕纯一,秦龙君,毛鹏,熊育久,赵文利,高辉辉,邱国玉. 水质遥感监测的关键要素叶绿素a的反演算法研究进展[J]. 遥感技术与应用, 2021, 36(3): 473-488.
[2] 刘鹤,顾玲嘉,任瑞治. 基于无人机遥感技术的森林参数获取研究进展[J]. 遥感技术与应用, 2021, 36(3): 489-501.
[3] 陈敏,潘佳威,李江杰,徐璐,刘加敏,韩健,陈奕云. 结合VGGNet与Mask R-CNN的高分辨率遥感影像建设用地检测[J]. 遥感技术与应用, 2021, 36(2): 256-264.
[4] 陈妮,应丰,王静,李健. 基于U-Net的高分辨率遥感图像土地利用信息提取[J]. 遥感技术与应用, 2021, 36(2): 285-292.
[5] 林娜,冯丽蓉,张小青. 基于优化Faster-RCNN的遥感影像飞机检测[J]. 遥感技术与应用, 2021, 36(2): 275-284.
[6] 李庆,陈俊杰,李庆亭,李柏鹏,卢凯旋,昝露洋,陈正超. 基于SSD模型的京津冀地区尾矿库检测[J]. 遥感技术与应用, 2021, 36(2): 293-303.
[7] 祝一诺,高婷,王术东,周磊,杜明义. 基于迁移学习再训练模型和高分遥感数据的建筑垃圾自动识别方法[J]. 遥感技术与应用, 2021, 36(2): 314-323.
[8] 帅艳民,杨健,吴昊,邵聪颖,徐辛超,刘明岳,刘涛,梁继. 基于无人机观测的水稻冠层样方多角度反射特点分析[J]. 遥感技术与应用, 2021, 36(2): 342-352.
[9] 邵文静,孙伟伟,杨刚. 高光谱遥感影像纹理特征提取的对比分析[J]. 遥感技术与应用, 2021, 36(2): 431-440.
[10] 杨昊翔,张丽,闫敏,林光辉. 基于高时空分辨率融合影像的红树林总初级生产力遥感估算[J]. 遥感技术与应用, 2021, 36(2): 453-462.
[11] 金亚秋. 空间微波遥感研究与应用—丛书述评[J]. 遥感技术与应用, 2021, 36(1): 1-10.
[12] 李佳,辛晓洲,彭志晴,李小军. 地表蒸散发遥感产品比较与分析[J]. 遥感技术与应用, 2021, 36(1): 103-120.
[13] 刘良云,白雁,孙睿,牛振国. “全球生态系统碳循环关键参数立体观测与反演”项目概述与研究进展[J]. 遥感技术与应用, 2021, 36(1): 11-24.
[14] 熊育久,冯房观,方奕舟,邱国玉,赵少华,姚云军. 蒸散发遥感反演产品应用关键问题浅议[J]. 遥感技术与应用, 2021, 36(1): 121-131.
[15] 张茂,张霞,胡光成,王楠. 遥感干旱指数在洛川苹果干旱监测中的适用性分析[J]. 遥感技术与应用, 2021, 36(1): 187-197.