Biodiversity Science ›› 2016, Vol. 24 ›› Issue (1): 85-94.doi: 10.17520/biods.2015150

• Orginal Article • Previous Article     Next Article

Trends and challenges of ecosystem observations in the age of big data

Shengqi Dai, Bin Zhao*()   

  1. Coastal Ecosystems Research Station of the Yangtze River Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Fudan University, Shanghai 200438
  • Received:2015-06-03 Accepted:2015-09-05 Online:2016-06-12
  • Zhao Bin

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

Table 1

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

Instrument name
Measure parameters
Operating frequency
Data flow
Carbon dioxide and vapor analyzer (LI-7500A)
CO2, 水汽浓度与气温等
Carbon dioxide, vapor solution, temperature, etc.
Measure frequency at 20 Hz
每秒15 KB
15 KB/s
Methane analyzer (LI-7700)
Methane solution in the air
Measure frequency at 50 Hz
每秒10 KB
10 KB/s
Gill 风速测量仪
Gill windmaster Pro
3-dimensional wind speed
Measure frequency at 10-20 Hz
每秒5 KB
5 KB/s
CMP3 & PQS1 radiation sensor
Solar radiation & photosynthetic active radiation
Measure once per 30 minutes
每天2 KB
2 KB/day
No. 109 soil temperature sensor
Multi-layer soil temperature
Measure once per 30 minutes
每天20 KB
20 KB/day
Phenological observation cameras
Phenological change around the station
Two shoots per day
每天6 MB
6 MB/day
Integrated data logger (CR5000, Li7550)
Collection of all observation data
Collection frequency at 10 Hz
每个月4 GB左右
Around 4 GB/month

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."

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."

Table 2

Outstanding projects that intended to integrate multiple-source data"

Project name
数据库状态 Database status
Data portal
Open access
Citing rules
The key metadata tag of data integration
The Long Term Ecological Research Network (LTER)
Single data portal
Completely open
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.
The National Ecological Observatory Network (NEON)
Single data portal
Completely open
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
Global Biodiversity Information Facility (GBIF)
Single data portal
Completely open
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.
Global Observing System Information System (GOSIC)
Multiple data portals
Partly open
Contact the dataset publisher
子项目名称, 数据集名称
Name of the child project, name of dataset

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."

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."

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] Zhang Ping, Hao Xiuying, Yu Ruifeng, Zhou Hongmei, Zhu Jianjun. (2018) A Tentative Method for Monitoring the Dynamic Features of Transpiration Regulation in Ferula krylovii Leaves . Chin Bull Bot, 53(3): 353-363.
[2] Jian Zhang. (2017) Biodiversity science and macroecology in the era of big data . Biodiv Sci, 25(4): 355-363.
[3] Qinghua Guo,Fangfang Wu,Tianyu Hu,Linhai Chen,Jin Liu,Xiaoqian Zhao,Shang Gao,Shuxin Pang. (2016) Perspectives and prospects of unmanned aerial vehicle in remote sensing monitoring of biodiversity . Biodiv Sci, 24(11): 1267-1278.
[4] Ke Guo,Changcheng Liu,Qingmin Pan. (2016) Methods of observing typical plant communities in the Steppe and Desert Biodiversity Observation Network, Sino BON . Biodiv Sci, 24(11): 1220-1226.
[5] Jian Zhang,Shengbin Chen,Bin Chen,Yanjun Du,Xiaolei Huang,Xubin Pan,Qiang Zhang. (2013) Citizen science: integrating scientific research, ecological conservation and public participation . Biodiv Sci, 21(6): 738-749.
[6] Qiang Fang, Shuangquan Huang. (2012) Progress in pollination networks: network structure and dynamics . Biodiv Sci, 20(3): 300-307.
[7] Wei Zhou Hong Wang . (2007) The physiological and molecular mechanisms of calcium uptake, transport, and metabolism in plants. . Chin Bull Bot, 24(06): 762-778.
Full text