Combining multiple proximal sensors within a wireless sensor network (WSN) enhances our capacity to monitor vegetation, compared to using a single sensor or non-networked setup. Data from sensors with different spatial and temporal characteristics can provide complementary information. For example, point-based sensors such as multispectral sensors which monitor at high temporal frequency but, at a single point, can be complemented by array-based sensors such as digital cameras which have greater spatial resolution but may only gather data at infrequent intervals. In this article we describe the successful deployment of a prototype system for using multiple proximal sensors (multispectral sensors and digital cameras) for monitoring pastures. We show that there are many technical issues involved in such a deployment, and we share insights relevant for other researchers who may consider using WSNs for an operational deployment for pasture monitoring under often difficult environmental conditions. Although the sensors and infrastructure are important, we found that other issues arise and that an end-to-end workflow is an essential part of effectively capturing, processing and managing the data from a WSN. Our deployment highlights the importance of testing and ongoing monitoring of the entire workflow to ensure the quality of data captured. We demonstrate that the combination of different sensors enhances our ability to identify sensor problems necessary to collect accurate data for pasture monitoring.
References
[1]
Sanderson, M.A.; C Alan Rotz, S.W.F.; Rayburn, E.B. Estimating forage mass with a commercial capacitance meter, rising plate meter, and pasture ruler. Agron. J. 2001, 93, 1281–1281.
[2]
Hill, M.J.; Donald, G.E.; Hyder, M.W.; Smith, R.C.G. Estimation of pasture growth rate in the south west of Western Australia from AVHRR NDVI and climate data. Remote Sens. Environ. 2004, 93, 528–545, doi:10.1016/j.rse.2004.08.006.
[3]
Handcock, R.N.; Mata, G.; Donald, G.E.; Edirisinghe, A.; Henry, D.; Gherardi, S.G. The Spectral Response of Pastures in An Intensively Managed Dairy System. In Innovations in Remote Sensing and Photogrammetry; Jones, S., Reinke, K., Eds.; Springer: Berlin/Heidelberg, Germany, 2009; pp. 309–321.
[4]
Todd, S.W.; Hoffer, R.M.; Milchunas, D.G. Biomass estimation on grazed and ungrazed rangelands using spectral indices. Int. J. Remote Sens. 1998, 19, 427–438, doi:10.1080/014311698216071.
[5]
Zerger, A.; Viscarra Rossel, R.A.; Swain, D.L.; Wark, T.; Handcock, R.N.; Doerr, V.A.J.; Bishop-Hurley, G.J.; Doerr, E.D.; Gibbons, P.G.; Lobsey, C. Environmental sensor networks for vegetation, animal and soil sciences. Int. J. Appl. Earth Obs. 2010, 12, 303–316, doi:10.1016/j.jag.2010.05.001.
[6]
Hilker, T.; Lyapustin, A.; Hall, F.G.; Wang, Y.; Coops, N.C.; Drolet, G.; Black, T.A. An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance. Remote Sens. Environ. 2009, 113, 2463–2475.
[7]
Collins, S.L.; Bettencourt, L.M.; Hagberg, A.; Brown, R.F.; Moore, D.I.; Bonito, G.; Delin, K.A.; Jackson, S.P.; Johnson, D.W.; Burleigh, S.C.; et al. New opportunities in ecological sensing using wireless sensor networks. Front. Ecol. Environ. 2006, 4, 402–407.
[8]
Hamilton, M.P.; Graham, E.A.; Rundel, P.W.; Allen, M.F.; Kaiser, W.; Hansen, M.H.; Estrin, D.L. New approaches in embedded networked sensing for terrestrial ecological observatories. Environ. Eng. Sci. 2007, 24, 192–204.
[9]
Szewczyk, R.; Osterweil, E.; Polastre, J.; Hamilton, M.; Mainwaring, A.; Estrin, D. Habitat monitoring with sensor networks. Commun. ACM 2004, 47, 34–40.
[10]
Allen, M.F.; Vargas, R.; Graham, E.A.; Swenson, W.; Hamilton, M.; Taggart, M.; Harmon, T.C.; Rat’ko, A.; Rundel, P.; Fulkerson, B.; et al. Soil sensor technology: Life within a pixel. Bioscience 2007, 57, 859–867, doi:10.1641/B571008.
Zerger, A.; Gobbett, D.; Crossman, C.; Valencia, P.; Wark, T.; Davies, M.; Handcock, R.N.; Stol, J. Temporal monitoring of groundcover change using visible digital cameras within an environmental sensor network. Int. J. Appl. Earth Obs. 2012, 19, 266–275, doi:10.1016/j.jag.2012.05.016.
[14]
Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150.
[15]
Davidson, A.; Csillag, F. The influence of vegetation index and spatial resolution on a two-date remote sensing-derived relation to C4 species coverage. Remote Sens. Environ. 2001, 75, 138–151.
Turner, D.P. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens. Environ. 1999, 70, 52.
[18]
Lukina, E.V.; Stone, M.L.; Rann, W.R. Estimating vegetation coverage in wheat using digital images. J. Plant. Nutr. 1999, 22, 341–350, doi:10.1080/01904169909365631.
[19]
Richardson, A.D. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 2007, 152, 323–334.
[20]
Myneni, R.B. On the relationship between FAPAR and NDVI. Remote Sens. Environ. 1994, 49, 200, doi:10.1016/0034-4257(94)90016-7.
[21]
Tucker, C.J. Remote sensing of leaf water content in the near infrared. Remote Sens. Environ. 1980, 10, 23, doi:10.1016/0034-4257(80)90096-6.
[22]
Asner, G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 1998, 64, 234–253.
[23]
Mohd Shafri, H.Z.; Mohd Salleh, M.A.; Ghiyamat, A. Hyperspectral remote sensing of vegetation using red edge position techniques. Am. J. Appl. Sci. 2006, 3, 1864–1871.
[24]
Fitzgerald, G.; Rodriguez, D.; O'Leary, G. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI). Field Crop. Res. 2010, 116, 318–324, doi:10.1016/j.fcr.2010.01.010.
Br？ring, A.; Echterhoff, J.; Jirka, S.; Simonis, I.; Everding, T.; Stasch, C.; Liang, S.; Lemmens, R. New generation sensor web enablement. Sensors 2011, 11, 2652–2699, doi:10.3390/s110302652.
[27]
Balzarolo, M.; Anderson, K.; Nichol, C.; Rossini, M.; Vescovo, L.; Arriga, N.; Wohlfahrt, G.; Calvet, J.-C.; Carrara, A.; Cerasoli, S.; et al. Ground-based optical measurements at European flux sites: A review of methods, instruments and current controversies. Sensors 2011, 11, 7954–7981.
[28]
Ide, R.; Oguma, H. Use of digital cameras for phenological observations. Ecol. Inform. 2010, 5, 339–347, doi:10.1016/j.ecoinf.2010.07.002.
[29]
CHDK Team, Canon Hack Development Kit (CHDK). Available online: http://chdk.wikia.com/wiki/CHDK (accessed on 11 March 2013).
[30]
Pekin, B.; Macfarlane, C. Measurement of crown cover and leaf area index using digital cover photography and its application to remote sensing. Remote. Sens. 2009, 1, 1298–1320.
[31]
Lebourgeois, V.; Bégué, A.; Labbé, S.; Mallavan, B.; Prévot, L.; Roux, B. Can commercial digital cameras be used as multispectral sensors? A crop monitoring test. Sensors 2008, 8, 7300–7322.
[32]
Szewczyk, R.; Polastre, J.; Mainwaring, A.; Culler, D. Lessons from a Sensor Network Expedition. In Wireless Sensor Networks; Springer: Berlin/Heidelberg, Germany, 2004; Volume 2920, pp. 307–322.
[33]
Jain, A.; Chang, E.Y. Adaptive Sampling for Sensor Networks. In Proceeedings of the 1st International Workshop on Data Management for Sensor Networks: In Conjunction with VLDB 2004, Toronto, Canada, 30 August 2004; ACM: Toronto, ON, Canada, 2004.
Basu, S.; Meckesheimer, M. Automatic outlier detection for time series: An application to sensor data. Knowl. Inf. Syst. 2007, 11, 137–154.
[36]
Huemmrich, K.F.; Black, T.A.; Jarvis, P.G.; McCaughey, J.H.; Hall, F.G. High temporal resolution NDVI phenology from micrometeorological radiation sensors. J. Geophys. Res. 1999, 104, 27935–27944, doi:10.1029/1999JD900164.
[37]
Liu, H.; Shah, S.; Jiang, W. On-line outlier detection and data cleaning. Comput. Chem. Eng. 2004, 28, 1635–1647, doi:10.1016/j.compchemeng.2004.01.009.
[38]
Jeffery, S.R.; Alonso, G.; Franklin, M.J.; Wei, H.; Widom, J. A Pipelined Framework for Online Cleaning of Sensor Data Streams. In Proceedings of the 22nd International Conference on Data Engineering, ICDE’06, Atlanta, GA, USA, 3–7 April 2006; p. 140.
[39]
Zhang, Y.; Meratnia, N.; Havinga, P.J. Ensuring high sensor data quality through use of online outlier detection techniques. Int. J. Sens. Netw. 2010, 7, 141.
