大数据时代下的生态系统观测发展趋势与挑战

doi:10.17520/biods.2015150

摘要/Abstract

摘要：

随着观测技术的发展, 生态学研究尺度不断扩大。生态系统观测从小规模合作、短时间个人观测向大规模、长时间、跨学科、多因子联合观测转变。传感器技术的革新带来了生态观测在时空尺度的扩展与精确度上的提升, 致使生态学观测数据的容量、产生速度与数据种类飞速增长。对生态系统数据获取、存储与管理的传统方法无疑不再能满足现代生态学研究的要求。因此, 我们建议以大数据时代的数据存储、管理与处理技术为基础, 整合生态物联观测网络(Internet of Ecology)、公民科学观测网络以及基于标准化数据管理的研究者网络互联, 建立整合生态系统观测平台来应对这一困境。为生态学研究者打造一站式生态观测服务, 是大数据时代下生态系统观测的大势所趋。

关键词: 传感器, 观测网络, 物联网, 公民科学, 社交网络

Abstract

With the development of observation technology, the scale of ecological research is increasing. Ecological observations have turned from small-scale, short-time individual observations into broad-scale, long-term, interdisciplinary, multi-factor group observations. Innovation in sensor techniques has led to a profound evolution in time and space precision of ecological observations, while the volume, type, and generating speed of these observational data are increasing, which indicates that traditional ecological data acquisition, storage and management methods cannot afford the demands of modern ecological research. With the assistance of new big data storage, management and processing techniques, integrated with the Internet of Ecology, a citizen science observational network and standardized data management network, we can build an ecological observation system to resolve these issues. The concept to provide a one-station ecological observation service to researchers represents the general trend of the development of ecological observations in the age of big data.

Key words: sensors, observation network, internet of things, citizen science, social network

戴圣骐, 赵斌 (2016) 大数据时代下的生态系统观测发展趋势与挑战. 生物多样性, 24, 85-94. DOI: 10.17520/biods.2015150.

Shengqi Dai, Bin Zhao (2016) Trends and challenges of ecosystem observations in the age of big data. Biodiversity Science, 24, 85-94. DOI: 10.17520/biods.2015150.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2015150

https://www.biodiversity-science.net/CN/Y2016/V24/I1/85

图/表 6

表1 典型的涡度相关碳通量观测站仪器搭载, 测量参数以及数据流量概览

Table 1 Introduction to typical instruments carried by an eddy carbon flux observation tower, their measure parameters and data flux

仪器名称 Instrument name	测量参数 Measure parameters	工作频率 Operating frequency	数据流量 Data flow
二氧化碳与水汽浓度测量仪 Carbon dioxide and vapor analyzer (LI-7500A)	CO₂, 水汽浓度与气温等 Carbon dioxide, vapor solution, temperature, etc.	每秒采集20次 Measure frequency at 20 Hz	每秒15 KB 15 KB/s
甲烷浓度测量仪 Methane analyzer (LI-7700)	CH₄浓度 Methane solution in the air	每秒采集50次 Measure frequency at 50 Hz	每秒10 KB 10 KB/s
Gill 风速测量仪 Gill windmaster Pro	三维风速 3-dimensional wind speed	每秒采集10-20次 Measure frequency at 10-20 Hz	每秒5 KB 5 KB/s
CMP3与PQS1辐射测量探头 CMP3 & PQS1 radiation sensor	太阳辐射与光合有效辐射 Solar radiation & photosynthetic active radiation	30分钟采集1次 Measure once per 30 minutes	每天2 KB 2 KB/day
109号土温测量探头 No. 109 soil temperature sensor	多层土壤温度 Multi-layer soil temperature	30分钟采集1次 Measure once per 30 minutes	每天20 KB 20 KB/day
物候观测摄像头 Phenological observation cameras	站点周围物候变化 Phenological change around the station	每天拍摄2次 Two shoots per day	每天6 MB 6 MB/day
复合数据采集器 Integrated data logger (CR5000, Li7550)	收集所有仪器数据 Collection of all observation data	每秒汇总10次 Collection frequency at 10 Hz	每个月4 GB左右 Around 4 GB/month

图1 传感器阵列从自然界获取数据示意图。多功能传感器组成的传感阵列拥有极强的数据抓取能力, 使单位时间内获取的数据量剧增, 进而为下游数据操作带来挑战。

Fig. 1 Sketch map for the sensor matrix that acquires data from nature. The sensor matrix contains multifunctional sensors which can efficiently fetch environmental data. For its high data collection speed, this data collection system has brought challenges to the downward data processing works.

图2 全球数据总量与数据存储能力对比。早在2008年传感器获取的数据就已经大到不能被完全存储(Baraniuk, 2011), 预计2015年总数据量将超越存储能力两倍甚至更多。

Fig. 2 Global total data volume in contrast with the ability of data storage. Early in 2008, the data collected by sensors could not be completely stored (Baraniuk, 2011). It is estimated that the total data volume will be twice bigger than the storage ability in 2015.

表2 实施多源数据整合的代表性项目

Table 2 Outstanding projects that intended to integrate multiple-source data

项目名称 Project name	数据库状态 Database status
项目名称 Project name	数据接口 Data portal	开放获取 Open access	引用规则 Citing rules	整合数据集的关键元数据标签 The key metadata tag of data integration
长期生态学研究网络 The Long Term Ecological Research Network (LTER)https://www.lternet.edu/	单个接口 Single data portal	完全开放 Completely open	引用数据集DOI Cite the DOI of dataset	站点名称, 数据包编号, 地理位置, 发布单位等 Site name, package identifier, spatial location, publisher name, etc.
国家生态学观测网络 The National Ecological Observatory Network (NEON) https://www.neoninc.org/	单个接口 Single data portal	完全开放 Completely open	引用NEON名称 Cite the name of NEON	日期, 站点名称, 行政州名, NEON地域划分, 数据集主题 Date, site name, state name, NEON domain, dataset subject
全球生物多样性信息中心 Global Biodiversity Information Facility (GBIF) https://www.gbif.org/	单个接口 Single data portal	完全开放 Completely open	引用数据集DOI Cite the DOI of dataset	数据集名称, 关键词, 发布单位, 国家等 Dataset name, key words, publisher, country, etc.
全球观测系统信息中心 Global Observing System Information System (GOSIC) https://www.ncdc.noaa.gov/gosic	多个接口 Multiple data portals	部分开放 Partly open	联系数据集发布者 Contact the dataset publisher	子项目名称, 数据集名称 Name of the child project, name of dataset

图3 传统无线传感器网络与生态物联网络对比, 图中不同类型圆圈代表不同传感器。传统无线传感器网络数据流呈树形分层, 其数据单向流动且传感器功能单一。生态物联网络支持传感器间的双向数据交流, 其中物联网传感器具备优秀信息预处理能力与网络信息共享能力, 能够自动调节测量参数并预处理数据。

Fig. 3 Traditional Wireless Sensor Networks (WSNs) and the Internet of Ecology (IoE). In this figure, different types of circles stand for different types of sensors. Traditional WSNs have its data flow in hierarchical tree shape, with single data direction and function sensors inside. The IoE supports mutual communication between the sensors, these internet based sensors are equipped with comprehensive data processing functions, which can share information throughout the network, feedback regulate the parameters in measurement and pre-process the data.

图4 生态系统观测数据生命周期示意图。生态物联网络、公民科学、通用数据格式、数据开放获取社交网络与数据版本控制系统等设想的实施可以改变生态观测数据的生命周期, 这将形成有利于生态学发展的良性循环。

Fig. 4 The life cycle of ecosystem observation data. The adoption of the IoE, citizen science, universal data format, social network for data open-access and the control system of data version can improve the life cycle of ecosystem observation data, which also builds a virtuous circle for the development of ecology.

参考文献 59

1	Anthoni PM, Unsworth MH, Law BE, Irvine J, Baldocchi DD, van Tuyl S, Moore D (2002) Seasonal differences in carbon and water vapor exchange in young and old-growth ponderosa pine ecosystems. Agricultural and Forest Meteorology, 111, 203-222.
2	Baldocchi DD, Falge E, Gu L, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee X, Malhi Y, Meyers T, Munger W, Oechel W, Paw KT, Pilegaard K, Schmid HP, Valentini R, Verma S, Vesala T, Wilson K, Wofsy S (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82, 2415-2434.
3	Baldocchi DD (2014) Measuring fluxes of trace gases and energy between ecosystems and the atmosphere—the state and future of the eddy covariance method. Global Change Biology, 20, 3600-3609.
4	Baraniuk RG (2011) More is less: signal processing and the data deluge. Science, 331, 717-719.
5	Barlow KE, Briggs PA, Haysom KA, Hutson AM, Lechiara NL, Racey PA, Walsh AL, Langton SD (2015) Citizen science reveals trends in bat populations: the National Bat Monitoring Programme in Great Britain. Biological Conservation, 182, 14-26.
6	Barseghian D, Altintas I, Jones MB, Crawl D, Potter N, Gallagher J, Cornillon P, Schildhauer M, Borer ET, Seabloom EW (2010) Workflows and extensions to the Kepler scientific workflow system to support environmental sensor data access and analysis. Ecological Informatics, 5, 42-50.
7	Belhumeur P, Jacobs D, Kress J (2011) Introduction to Leafsnap.
8	Bonney R, Shirk JL, Phillips TB, Wiggins A, Ballard HL, Miller-Rushing AJ, Parrish JK (2014) Next steps for citizen science. Science, 343, 1436-1437.
9	Butler D (2014) Earth observation enters next phase. Nature, 508, 160-161.
10	Chattopadhyay I, Lipson H (2015) Data smashing: uncovering lurking order in data. Journal of the Royal Society, 11, 1-11.
11	Collins SL, Bettencourt LMA, Hagberg A, Brown RF, Moore DI, Bonito G, Delin KA, Jackson SP, Johnson DW, Burleigh SC, Woodrow RR, McAuley JM (2006) New opportunities in ecological sensing using wireless sensor networks. Frontiers in Ecology and the Environment, 4, 402-407.
12	Conant R (2006) Wireless sensor networks: driving the new industrial revolution. Industrial Embedded Systems, 1, 8-11.
13	de Boer J (2012) Introduction to iSPEX.
14	Elmendorf SC, Moore KA (2008) Use of community composition data to predict the fecundity and abundance of species. Conservation Biology, 22, 1523-1532.
15	Field C, DeFries R, Foster D, Grove M, Jackson R, Law B, Lodge D, Peters D, Schimel D (2006) Integrated science and education plan for the National Ecological Observatory Network.
16	Gantz J, Reinsel D (2011) Extracting value from chaos. International Data Corporation Interview, 11, 9-10.
17	Gantz J, Reinsel D (2012) The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. International Data Corporation iView: IDC Analyze the Future, 20, 1-16.
18	Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S (2008) Measuring and modelling biodiversity from space. Progress in Physical Geography, 32, 203-221.
19	Goldman SL (2014) Reinventing discovery: the new era of networked science. The European Legacy, 19, 392-393.
20	Gong P (2010) Progress in recent environmental applications of wireless sensor networks. Journal of Remote Sensing, 14, 387-395. (in Chinese with English abstract)
	[宫鹏 (2010) 无线传感器网络技术环境应用进展. 遥感学报, 14, 387-395.]
21	Goring SJ, Utz RM, Weathers KC, Dodds WK, Soranno PA, Sweet LCC, Kominoski JS, Rüegg J, Thorn AM (2014) Improving the culture of interdisciplinary collaboration in ecology by expanding measures of success. Frontiers in Ecology and the Environment, 12, 39-47.
22	Hampton SE, Strasser CA, Tewksbury JJ, Gram WK, Budden AE, Batcheller AL, Duke CS, Porter JH (2013) Big data and the future of ecology. Frontiers in Ecology and the Environment, 11, 156-162.
23	Hart JK, Martinez K (2006) Environmental sensor networks: a revolution in the earth system science? Earth-Science Reviews, 78, 177-191.
24	Heffernan JB, Xiao J, Harms TK, Goring SJ, Koenig LE, McDowell WHP, Richardson AD, Stow CA, Vargas R, Weathers KC, Soranno PA, Angilletta MJ, Buckley LB, Gruner DS, Keitt TH, Kellner JR, Kominoski JS, Rocha AV (2014) Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment, 12, 5-14.
25	Horning N, Robinson J, Sterling E, Turner W, Spector S (2010) Remote Sensing for Ecology and Conservation. Oxford University Press, London.
26	Huang C, Kim S, Song K, Townshend JR, Davis P, Altstatt A, Rodas O, Yanosky A, Clay R, Tucker CJ (2009) Assessment of Paraguay’s forest cover change using Landsat observations. Global and Planetary Change, 67, 1-12.
27	Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S, Zhu Y (2000) Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128, 109-120.
28	Jones MB, Schildhauer MP, Reichman OJ, Bowers S (2006) The new bioinformatics: integrating ecological data from the gene to the biosphere. Annual Review of Ecology, Evolution, and Systematics, 37, 519-544.
29	Jones MB (2003) SEEK EcoGrid: Integrating Data and Computational Resources for Ecology.
30	Jones N (2014) Mini satellites prove their scientific power. Nature, 508, 300-301.
31	Jung M, Reichstein M, Bondeau A (2009) Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6, 2001-2013.
32	Kampe TU, Johnson BR, Kuester M, Keller M (2010) NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure. Journal of Applied Remote Sensing, 4, 510-524.
33	Mairota P, Cafarelli B, Labadessa R, Lovergine FP, Tarantino C, Nagendra H, Didham RK (2015) Very High Resolution Earth Observation features for testing the direct and indirect effects of landscape structure on local habitat quality. International Journal of Applied Earth Observation and Geoinformation, 34, 96-102.
34	Melbourne BA, Cornell HV, Davies KF, Dugaw CJ, Elmendorf S, Freestone AL, Hall RJ, Harrison S, Hastings A, Holland M (2007) Invasion in a heterogeneous world: resistance, coexistence or hostile takeover? Ecology Letters, 10, 77-94.
35	Michener WK, Beach J, Bowers S, Downey L, Jones M, Ludäscher B, Pennington D, Rajasekar A, Romanello S, Schildhauer M (2005) Data integration and workflow solutions for ecology. In: Data Integration in the Life Sciences (ed. Bodenreider O), pp. 321-324. Springer, Berlin.
36	Michener WK, Jones MB (2012) Ecoinformatics: supporting ecology as a data-intensive science. Trends in Ecology & Evolution, 27, 85-93.
37	National Ecological Observatory Network (2011) History of National Ecological Observatory Network.
38	NSSL (2013) Introduction to mPING.
39	Palazzo S, Spampinato C, Giordano D (2014) Large scale data processing in ecology: a case study on long-term underwater video monitoring. In: Parallel, Distributed and Network-Based Processing (PDP), 22nd Euromicro International Conference, pp. 312-316. IEEE Computer Society, Washington.
40	Pennekamp F, Schtickzelle N (2013) Implementing image analysis in laboratory-based experimental systems for ecology and evolution: a hands-on guide. Methods in Ecology and Evolution, 4, 483-492.
41	Porter JH, Arzberger P, Braun H, Bryant P, Gage S, Hansen T, Hanson P, Lin C, Lin F, Kratz T, Michener W, Shapiro S, Williams T (2005) Wireless sensor networks for ecology. BioScience, 55, 561-572.
42	Porter JH, Hanson PC, Lin C (2012) Staying afloat in the sensor data deluge. Trends in Ecology & Evolution, 27, 121-129.
43	Reichman OJ, Jones MB, Schildhauer MP (2011) Challenges and opportunities of open data in Ecology. Science, 331, 703-705.
44	Rüegg J, Gries C, Bond-Lamberty B, Bowen GJ, Felzer BS, McIntyre NE, Soranno PA, Vanderbilt KL, Weathers KC (2014) Completing the data life cycle: using information management in macrosystems ecology research. Frontiers in Ecology and the Environment, 12, 24-30.
45	Rundel PW, Graham EA, Allen MF, Fisher JC, Harmon TC (2009) Environmental sensor networks in ecological research. New Phytologist, 182, 589-607.
46	Schatz G (2014) The faces of Big Science. Nature Reviews Molecular Cell Biology, 15, 423-426.
47	Shamoun-Baranes J, Chapman JW, Alves JA, Bauer S, Dokter AM, Hüppop OKJ, Jarmo K, Leijnse H, Liechti F, van Gasteren H (2014) Continental-scale radar monitoring of the aerial movements of animals. Movement Ecology, 2, 9.
48	Stankovic JA (2014) Research directions for the internet of things. IEEE Internet of Things Journal, 1, 3-9.
49	Sullivan BL, Aycrigg JL, Barry JH, Bonney RE, Bruns N, Cooper CB, Damoulas T, Dhondt AA, Dietterich T, Farnsworth A, Fink D, Fitzpatrick JW, Fredericks T, Gerbracht J, Gomes C, Hochachka WM, Iliff MJ, Lagoze C, La Sorte FA, Merrifield M, Morris W, Phillips TB, Reynolds M, Rodewald AD, Rosenberg KV, Trautmann NM, Wiggins A, Winkler DW, Wong W, Wood CL, Yu J, Kelling S (2014) The eBird enterprise: an integrated approach to development and application of citizen science. Biological Conservation, 169, 31-40.
50	Tulloch AIT, Possingham HP, Joseph LN, Szabo J, Martin TG (2013) Realising the full potential of citizen science monitoring programs. Biological Conservation, 165, 128-138.
51	Turner W (2014) Sensing biodiversity. Science, 346, 301-302.
52	Turner W, Rondinini C, Pettorelli N, Mora B, Leidner AK, Szantoi Z, Buchanan G, Dech S, Dwyer J, Herold M, Koh LP, Leimgruber P, Taubenboeck H, Wegmann M, Wikelski M, Woodcock C (2015) Free and open-access satellite data are key to biodiversity conservation. Biological Conservation, 182, 173-176.
53	Velicogna I, Wahr J (2006) Measurements of time-variable gravity show mass loss in Antarctica. Science, 311, 1754-1756.
54	Wallis JC, Rolando E, Borgman CL (2013) If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology. PLoS ONE, 8, e67332.
55	Wang Y, Liu XN, Ju XH (2007) The difference and relativity between rainfall by automatic recording and manual observation. Journal of Applied Meteorological Science, 18, 412-413. (in Chinese with English abstract)
	[王颖, 刘小宁, 鞠晓慧 (2007) 自动观测与人工观测差异的初步分析, 应用气象学报, 18, 412-413.]
56	Watt KE (2013) Systems Analysis in Ecology. Elsevier, Amsterdam.
57	Willis KS (2015) Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233-242.
58	Zhao B (2014) Ecology should evolve to facing global problems. Science and Technology Review, 32(12), 12. (in Chinese)
	[赵斌 (2014) 生态学必须进化以应对全球性重大问题. 科技导报, 32(12), 12.]
59	Zulueta RC, Oechel WC, Loescher HW, Lawrence WT (2011) Aircraft-derived regional scale CO2 fluxes from vegetated drained thaw-lake basins and interstitial tundra on the Arctic coastal plain of Alaska. Global Change Biology, 17, 2781-2802.

[1]	张敏, 田春坡, 车先丽, 赵岩岩, 陈什旺, 周霞, 邹发生. 广东省鸟类新记录及其与自然和社会经济因素的关联性[J]. 生物多样性, 2022, 30(5): 21396-.
[2]	阿卜杜赛麦提·买尔迪亚力, 王云, 陶双成, 孔亚平, 王昊, 吕植. 我国道路对野生动物影响研究的现状与挑战[J]. 生物多样性, 2022, 30(11): 22209-.
[3]	万雅琼, 李佳琦, 杨兴文, 李晟, 徐海根. 基于红外相机的中国哺乳动物多样性观测网络建设进展[J]. 生物多样性, 2020, 28(9): 1115-1124.
[4]	郭庆华, 吴芳芳, 胡天宇, 陈琳海, 刘瑾, 赵晓倩, 高上, 庞树鑫. 无人机在生物多样性遥感监测中的应用现状与展望[J]. 生物多样性, 2016, 24(11): 1267-1278.