Booth, D. T., Cox, S. E., Fifield, C., Phillips, M., and Willlamson, N.: Image analysis compared with other methods for measuring ground cover, Arid Land Res. Manag., 19, 91–100, 2005.
Chen, B., Coops, N. C., Fu, D., Margolis, H. A., Amiro, B. D., Black, T. A., Arain, M. A., Barr, A. G., Bourque, C. P. A., Flanagan, L. B., Lafleur, P. M., McCaughey, J. H., and Wofsy, S. C.: Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian Carbon Program Network using remote sensing and footprint analysis, Remote Sens. Environ., 124, 742–755, https://doi.org/10.1016/j.rse.2012.06.007, 2012.
Collins, S. L., Bettencourt, L. M. A., Hagberg, A., Brown, R. F., Moore, D. I., Bonito, G., Delin, K. A., Jackson, S. P., Johnson, D. W., Burleigh, S. C., Woodrow, R. R., and McAuley, J. M.: New opportunities in ecological sensing using wireless sensor networks, Front. Ecol. Environ., 4, 402–407, https://doi.org/10.1890/1540-9295(2006)4[402:noiesu]2.0.co;2, 2006.
Colomina, I. and Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm., 92, 79–97, https://doi.org/10.1016/j.isprsjprs.2014.02.013, 2014.
Eklundh, L., Jin, H., Schubert, P., Guzinski, R., and Heliasz, M.: An optical sensor network for vegetation phenology monitoring and satellite data calibration, Sensors, 11, 7678–7709, https://doi.org/10.3390/s110807678, 2011.
Flynn, E. S., Dougherty, C. T., and Wendroth, O.: Assessment of pasture biomass with the normalized difference vegetation index from active ground-based sensors, Agron. J., 100, 114–121, https://doi.org/10.2134/agrojnl2006.0363, 2008.
Friedel, M. H., Chewings, V. H., and Bastin, G. N.: The Use of Comparative Yield and Dry-Weight-Rank Techniques for Monitoring Arid Rangeland, J. Range Manage., 41, 430–435, https://doi.org/10.2307/3899584, 1988.
Gamon, J. A.: Reviews and Syntheses: optical sampling of the flux tower footprint, Biogeosciences, 12, 4509–4523, https://doi.org/10.5194/bg-12-4509-2015, 2015.
Gobbett, D., Handcock, R. N., Zerger, A., Crossman, C., Valencia, P., Wark, T., and Davies, M.: Prototyping an Operational System with Multiple Sensors for Pasture Monitoring, Journal of Sensor and Actuator Networks, 2, 388–408, 2013.
González, L. A., Bishop-Hurley, G., Henry, D., and Charmley, E.: Wireless sensor networks to study, monitor and manage cattle in grazing systems, Anim. Prod. Sci., 54, 1687–1693, https://doi.org/10.1071/AN14368, 2014.
Guerschman, J. P., Hill, M. J., Renzullo, L. J., Barrett, D. J., Marks, A. S., and Botha, E. J.: Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors, Remote Sens. Environ., 113, 928–945, 2009.
Hamilton, M. P., Graham, E. A., Rundel, P. W., Allen, M. F., Kaiser, W., Hansen, M. H., and Estrin, D. L.: New Approaches in Embedded Networked Sensing for Terrestrial Ecological Observatories, Environ. Eng. S., 24, 192–204, https://doi.org/10.1089/ees.2006.0045, 2007.
Handcock, R. N., Mata, G., and Gherardi, S. G.: Combining spectral information aggregated to the paddock scale with knowledge of on-farm practices will enhance remote sensing methods for intensively managed dairy pastures, 14th Australian Remote Sensing and Photogrammetry Conference, Darwin, Australia, 29 September to 3 October 2008.
Handcock, R. N.: Animation of 545 days of daily digital camera images of tropical pastures from the fenced node at the CSIRO Lansdown Research Farm, Queensland, Australia, Animation, Perth, Australia: Commonwealth Scientific and Industrial Research Organisation, https://doi.org/10.5446/19349, 2016.
Harrell Jr., F. E., Lee, K. L., and Mark, D. B.: MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS, Stat. Med., 15, 361–387, https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4, 1996.
Harris, A., Gamon, J. A., Pastorello, G. Z., and Wong, C. Y. S.: Retrieval of the photochemical reflectance index for assessing xanthophyll cycle activity: a comparison of near-surface optical sensors, Biogeosciences, 11, 6277–6292, https://doi.org/10.5194/bg-11-6277-2014, 2014.
Holben, B. N.: Characteristics of maximum-value composite images from temporal AVHRR data, Int. J. Remote Sens., 7, 1417–1434, https://doi.org/10.1080/01431168608948945, 1986.
Huemmrich, K. F., Black, T. A., Jarvis, P. G., McCaughey, J. H., and Hall, F. G.: High temporal resolution NDVI phenology from micrometeorological radiation sensors, J. Geophys. Res.-Atmos., 104, 27935–27944, 1999.
Jeffery, S. R., Alonso, G., Franklin, M. J., Wei, H., and Widom, J. A.: Pipelined Framework for Online Cleaning of Sensor Data Streams, 22nd International Conference on Data Engineering, ICDE'06, Atlanta, GA, USA, 3–7 April 2006.
Johnson, D., Vulfson, M., Louhaichi, M., and Harris, N.: Vegmeasure v1.6 user's manual, Department of Rangeland Resources, Oregon State University, Corvallis, Oregon, USA, 2003.
King, W., Rennie, G. M., Dalley, D. E., Dynes, R. A., and Upsdell, M. P.: Pasture mass estimation by the C-DAX pasture meter: regional calibrations for New Zealand, Proceedings of the Australasian Dairy Science Symposium, Caxton Press, 233–238, 2010.
Lo, F., Wheeler, M. C., Meinke, H., and Donald, A.: Probabilistic forecasts of the onset of the north Australian wet season, Mon. Weather Rev., 135, 3506–3520, https://doi.org/10.1175/mwr3473.1, 2007.
Macfarlane, C. and Ogden, G. N.: Automated estimation of foliage cover in forest understorey from digital nadir images, Methods in Ecology and Evolution, 3, 405–415, https://doi.org/10.1111/j.2041-210X.2011.00151.x, 2012.
McCoy, R. M.: Field methods in remote sensing, Book, Whole, Guilford Press, New York, 2005.
Myneni, R. B. and Williams, D. L.: On the relationship between FAPAR and NDVI, Remote Sens. Environ., 49, 200–211, https://doi.org/10.1016/0034-4257(94)90016-7, 1994.
Ni, K., Ramanathan, N., Chehade, M. N. H., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., and Srivastava, M.: Sensor network data fault types, ACM T. Sensor Network., 5, 1–29, 2009.
Orchard, B. A., Cullis, B. R., Coombes, N. E., Virgona, J. M., and Klein, T.: Grazing management studies within the Temperate Pasture Sustainability Key Program: Experimental design and statistical analysis, Aust. J. Exp. Agr., 40, 143–154, https://doi.org/10.1071/EA98005, 2000.
O'Reagain, P., Scanlan, J., Hunt, L., Cowley, R., and Walsh, D.: Sustainable grazing management for temporal and spatial variability in north Australian rangelands – A synthesis of the latest evidence and recommendations, Rangeland J., 36, 223–232, https://doi.org/10.1071/RJ13110, 2014.
Peddle, D. R., Peter White, H., Soffer, R. J., Miller, J. R., and LeDrew, E. F.: Reflectance processing of remote sensing spectroradiometer data, Comput. Geosci., 27, 203–213, https://doi.org/10.1016/S0098-3004(00)00096-0, 2001.
Pullanagari, R. R., Yule, I. J., Tuohy, M. P., Hedley, M. J., Dynes, R. A., and King, W. M.: In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture, Precis. Agric., 13, 351–369, https://doi.org/10.1007/s11119-011-9251-4, 2012.
FutureBeef: Pasture photo-standards – FutureBeef, https://futurebeef.com.au/knowledge-centre/pastures-forage-crops/pasture-photo-standards/, last access: 14 August 2016.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/ (last access: 14 August 2016), 2013.
Richardson, A. D., Jenkins, J. P., Braswell, B. H., Hollinger, D. Y., Ollinger, S. V., and Smith, M. L.: Use of digital webcam images to track spring green-up in a deciduous broadleaf forest, Oecologia, 152, 323–334, 2007.
Richter, K., Atzberger, C., Hank, T. B., and Mauser, W.: Derivation of biophysical variables from Earth observation data: validation and statistical measures, J. Appl. Remote Sens., 6, 063557, https://doi.org/10.1117/1.JRS.6.063557, 2012.
Sakowska, K., Vescovo, L., Marcolla, B., Juszczak, R., Olejnik, J., and Gianelle, D.: Monitoring of carbon dioxide fluxes in a subalpine grassland ecosystem of the Italian Alps using a multispectral sensor, Biogeosciences, 11, 4695–4712, https://doi.org/10.5194/bg-11-4695-2014, 2014.
Sanderson, M. A., Rotz, C. A., Fultz, S. W., and Rayburn, E. B.: Estimating Forage Mass with a Commercial Capacitance Meter, Rising Plate Meter, and Pasture Ruler, Agron. J., 93, 1281, https://doi.org/10.2134/agronj2001.1281, 2001.
Serrano, J. M., Shahidian, S., and Marques da Silva, J. R.: Monitoring pasture variability: optical OptRx
® crop sensor versus Grassmaster II capacitance probe, Environ. Monitor. Assess., 188, 1–17, https://doi.org/10.1007/s10661-016-5126-5, 2016.
Skye-Instruments: Application Notes Sensors for NDVI Calculations, 21, Ddole Enterprise Park, Llandrindod Wells, Powys LD1 6DF, UK, 1, 2012a.
Skye-Instruments: SKR 1850D & 1850ND, SKR 1850D/A & 1850ND/A 4 Channel Sensor, 21, Ddole Enterprise Park, Llandrindod Wells, Powys LD1 6DF, UK, 1, 2012b.
Skye-Instruments: 4 Channel Sensor SKR 1860D & SKR 1860ND, 21, Ddole Enterprise Park, Llandrindod Wells, Powys LD1 6DF, UK, 1, 2013.
Sonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A. M., Friedl, M., Braswell, B. H., Milliman, T., O'Keefe, J., and Richardson, A. D.: Digital repeat photography for phenological research in forest ecosystems, Agr. Forest Meteorol., 152, 159–177, https://doi.org/10.1016/j.agrformet.2011.09.009, 2012.
Steyerberg, E. W., Harrell Jr., F. E., Borsboom, G. J. J. M., Eijkemans, M. J. C., Vergouwe, Y., and Habbema, J. D. F.: Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis, J. Clin. Epidemiol., 54, 774–781, https://doi.org/10.1016/S0895-4356(01)00341-9, 2001.
Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., and Estrin, D.: Habitat monitoring with sensor networks, New York, ACM, https://doi.org/10.1145/990680.990704, 2004.
t'Mannetje, L. and Haydock, K. P.: The dry-weight-rank method of botanical analysis of pasture, Grass Forage Sci., 18, 268–275, https://doi.org/10.1111/j.1365-2494.1963.tb00362.x, 1963.
Toomey, M., Friedl, M. A., Frolking, S., Hufkens, K., Klosterman, S., Sonnentag, O., Baldocchi, D. D., Bernacchi, C. J., Biraud, S. C., Bohrer, G., Brzostek, E., Burns, S. P., Coursolle, C., Hollinger, D. Y., Margolis, H. A., McCaughey, H., Monson, R. K., Munger, J. W., Pallardy, S., Phillips, R. P., Torn, M. S., Wharton, S., Zeri, M., and Richardson, A. D.: Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis, Ecol. Appl., 25, 99–115, https://doi.org/10.1890/14-0005.1, 2015.
Tothill, J. and Partridge, I. (Eds.): Monitoring grazing lands in northern Australia, Tropical Grassland Society of Australia, Brisbane, http://www.tropicalgrasslands.asn.au/Monitoring book/Contents.htm (last access: 14 August 2016), Occasional Publication No. 9, 98, 1998.
Trotter, M. G., Lamb, D. W., Donald, G. E., and Schneider, D. A.: Evaluating an active optical sensor for quantifying and mapping green herbage mass and growth in a perennial grass pasture, Crop Pasture Sci., 61, 389–398, https://doi.org/10.1071/CP10019, 2010.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
Tucker, C. J.: Remote sensing of leaf water content in the near infrared, Remote Sens. Environ., 10, 23–32, 1980.
Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., and Briggs, J. M.: Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites, Remote Sens. Environ., 70, 52–68, 1999.
von Bueren, S. K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M. P., and Yule, I. J.: Deploying four optical UAV-based sensors over grassland: challenges and limitations, Biogeosciences, 12, 163–175, https://doi.org/10.5194/bg-12-163-2015, 2015.
Weber, C., Schinca, D. C., Tocho, J. O., and Videla, F.: Passive field reflectance measurements, J. Optics A-Pure Appl. Opt., 10, 104020–104027, https://doi.org/10.1088/1464-4258/10/10/104020, 2008.
Wood, S. N.: Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, J. Roy. Stat. Soc. B Met., 73, 3–36, https://doi.org/10.1111/j.1467-9868.2010.00749.x, 2011.
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., and Lobsey, C.: Environmental sensor networks for vegetation, animal and soil sciences, Int. J. Appl. Earth Obs., 12, 303–316, https://doi.org/10.1016/j.jag.2010.05.001, 2010.
Zerger, A., Gobbett, D., Crossman, C., Valencia, P., Wark, T., Davies, M., Handcock, R. N., and Stol, J.: Temporal monitoring of groundcover change using digital cameras, Int. J. Appl. Earth Obs., 19, 266–275, 2012.
Zhao, D., Starks, P. J., Brown, M. A., Phillips, W. A., and Coleman, S. W.: Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance, Grassland Sci., 53, 39–49, https://doi.org/10.1111/j.1744-697X.2007.00072.x, 2007.