Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops

Canadian Journal of Remote Sensing

Volumn 37, Issue 4, 2012, Pages 376-386

  Felderhof, L ^a   Gillieson, D ^a  

^a SkyView Solutions   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

AERIAL IMAGES; CANOPY CHLOROPHYLL; COST SAVING; FARM MANAGEMENT; FERTILIZER APPLICATIONS; FIELD SAMPLING; LEAF NITROGEN; NEAR INFRARED; NITROGEN APPLICATION RATES; SPECTRAL RADIOMETRY; TREE CANOPY; TREE CROPS; TREE HEALTH; UNMANNED AERIAL SYSTEMS; VARIABLE RATE; VEGETATION INDEX;

CHLOROPHYLL; COMPOSTING; FARMS; FORESTRY; SATELLITE IMAGERY;

NITROGEN FERTILIZERS;

CHLOROPHYLLS; COMPOSTING; FERTILIZERS; FORESTRY; IMAGE ANALYSIS; NITROGEN; SATELLITES;

EID: 84870008033     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m11-046     Document Type: Article

References (17)

1. Barnes, E.M., Clarke, T.R., and Richards, S.E. 2000. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In: Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, Minn.
2. Berni, J., Zarco-Tejada, P, Suarez, L., and Fereres, E. 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial system. IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 3, pp. 722-738. doi: 10.1109/TGRS.2008.2010457.
3. Enderle, B. 2002. Commercial applications of UASs in Japanese agriculture. 1st Unmanned Aerospace Systems, Systems, Technologies, and Operations Conference and Workshop, 20-23 May 2002, Portsmouth, Va.
4. Felderhof, L., Gillieson, D., Zadro, P, and van Boven, A. 2009. Preliminary Assessment of UAS Technology for the Macadamia Industry and Precision Agriculture in General, Final report for Horticulture Australia project MC07016, 43 pp.
5. Grenzdorffer, G.J., Engel, A., and Teichert, B. 2008. The photogrammetric potential of low-cost UAVs in forestry and agriculture, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1. Beijing.
6. Haboudane, D., Miller, J., Pattey, E., Zarco-Tejada, P, and Strachan I. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, Vol. 90, pp. 337-352. doi: 10.1016/j.rse.2003.12.013.
7. Herwitz, S., Johnson, L., Dunagan, S., Higgins, R., Sullivan, D., Zheng, J., Lobitz, B., Leung, J., Gallmeyer, B., Aoyagi, M., Slye, R., and Brass, J. 2004. Imaging from an unmanned aerial system: agricultural surveillance and decision support. Computers and Electronics in Agriculture, Vol. 44, pp. 49-61. doi: 10.1016/j.compag.2004.02.006.
8. Jensen, T., Apan, A., Young, F.R., Zeller, L., and Cleminson, K. 2003. Assessing grain crop attributes using digital imagery acquired from a low-altitude remote controlled aircraft. In: 2003 Spatial Sciences Institute Conference: Spatial Knowledge Without Boundaries (SSC2003) 22-27 Sept 2003, Canberra, Australia.
9. Jensen, T. 2008, Using remotely controlled platform to acquire low-altitude imagery for grain crop mapping. Unpublished PhD thesis, University of Southern Queensland, Australia.
10. Lan, Y., Thomson S.J., Huan Y., Hoffmann C., and Zhang, H. 2010. Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and Electronics in Agriculture, Vol. 74, pp. 34-38. doi: 10.1016/j.compag.2010.07.001.
11. Rodriguez, D., Fitzgerald, G.J., Belford, R., and Christensen, L. 2006. Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-biophysiological concepts. Australian Journal of Agricultural Research, Vol. 57, pp. 781 -789.
12. Rouse, J.W, Haas, R.H., Schell, J.A., and Deering, D.W 1973. Monitoring vegetation systems in the Great Plains with ERTS. In Third ERTS-1 Symposium, vol. 1, Section A. NASA, Washington, DC, pp. 309-317.
13. Sims, D.A., and Gamon, J.A. 2003. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, Vol. 84, pp. 526-537. doi: 10.1016/S0034-4257(02)00151-7.
14. Sullivan, D., Fulton, J., Shaw, J., and Bland, G. 2007. Evaluating the sensitivity of an unmanned thermal infrared aerial system to detect water stress in a cotton canopy. Transactions of the ASABE, Vol. 50, pp. 1955-1962.
15. Tilling, A.K., O’leary, G.J., Ferwerda, J.G., Jones, S.D., Fitzgerald, G.J., Rodriguez, D., and Belford, R. 2007. Remote sensing of nitrogen and water stress in wheat. Field Crops Research, Vol. 104, pp. 77-85. doi: 10.1016/j.fcr.2007.03.023.
16. Watts, A.C., Perry, J.H., Smith, S.E., Burgess, M.A., Wilkinson, B.E., Szantoi, Z., Ifju, PG., and Percival, H.F. 2010. Small unmanned aircraft systems for low-altitude aerial surveys. The Journal of Wildlife Management, Vol. 74, pp. 1614-1619.
17. Wilkinson, B.E., Dewitt, B.A., Watts, A.C., Mohamed, A.H., and Burgess, M.A. 2009. A new approach for pass-point generation from aerial video imagery. Photogrammetric Engineering & Remote Sensing, Vol. 75, pp. 1415-1423