Publisher : The Korean Society of Grassland and Forage Science
DOI : 10.5333/KGFS.2015.35.3.225
Title & Authors
Mapping Herbage Biomass on a Hill Pasture using a Digital Camera with an Unmanned Aerial Vehicle System Lee, Hyowon; Lee, Hyo-Jin; Jung, Jong-Sung; Ko, Han-Jong;
Improving current pasture productivity by precision management requires practical tools to collect site specific pasture biomass data. Recent developments in unmanned aerial vehicle (UAV) technology provide cost effective and real time applications for site specific data collection. For the mapping of herbage biomass (BM) on a hill pasture, we tested a UAV system with digital cameras (visible and near-infrared (NIR) camera). The field measurements were conducted on the grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17 and June 27, 2014. Plant samples were obtained from 28 sites. A UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number (DN) values of Red and NIR channels were extracted from the aerial photos and a normalized differential vegetation index using DN () was calculated. The results show that the correlation coefficient between BM and was 0.88. For the precision management of hilly grazing pastures, UAV monitoring systems can be a quick and cost effective tool to obtain site-specific herbage BM data.
Digital camera;Herbage biomass;Hilly pasture;Mapping;Unmanned aerial vehicle;
Beeri, O., Phillips, R., Hendrickson, J., Frank, A.B. and Kronberg, S. 2007. Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment. 110:216-225.
Betteridge, K., Schnug, E. and Haneklaus, S. 2008. Will site specific nutrient management live up to expectation?. Agriculture and Forestry Research. 58:283-294.
Bouma, J. 1997. Precision agriculture: introduction to the spatial and temporal variability of environmental quality. Lake, J.V., Bock, G. R. and Goode, J. A. Eds. pp. 5-17. John Wiley and Sons, Wageningen, The Netherlands.
Di Bella, Faivre, C., Ruget, R., Seguin, F., Guerif, M., Combal, B., Weiss, M. and Rebella, C. 2004. Remote sensing capabilities to estimate pasture production in France. International Journal of Remote Sensing. 25:5359-5372.
Edirisinghe, A., Hill, M.J., Donald, G.E. and Hyder, M. 2011. International Journal of Remote Sensing. 32(10):2699-2724.
Hill, M.J., Donald, G.E., Vickery, P.J., Moore, A.D. and Donnelly, J.R. 1999. Combining satellite data with a simulation model to describe spatial variability in pasture growth at a farm scale. Australian Journal of Experimental Agriculture. 39:285-300.
Inoue, Y., Morinaga, S. and Tomita, A. 2000. A blimp-based remote sensing system for low altitude monitoring of plant variables: A preliminary experiment for agricultural and ecological applications. International Journal of Remote Sensing. 21:379-385.
Jung, Y.K. 2003. Effects of the kieserite application on the seedling vigour and yield of grass/clover mixed swards on newly reclaimed hilly soil (in Korean). Journal of the Korean Society of Grassland and Forage Science. 23(1):31-36.
Kawamura, K., Sakuno, Y., Tanaka, Y., Lee, H.J., Lim, J., Kurokawa, Y. and Watanabe, N. 2011. Mapping herbage biomass and nitrogen status in an Italian ryegrass (Lolium multiflorum L.) field using a digital video camera with balloon system. Journal of Applied Remote Sensing. 5(1):053562-053562.
Laliberte, A. and Rango, A. 2011. Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands. GIScience & Remote Sensing. 48(1):4-23.
Lamb, D.W. and Brown, R.B. 2001. PA precision agriculture: Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research. 78:117-125.
Mutanga, O. and Skidmore, A.K. 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing. 25:3999-4014.
Quantum GIS (QGIS). Version 2.6.1. 2014. QGIS Development Team. http://www.qgis.org/.
Rovira-M'as, F., Zhang, Q. and Reid, J.F. 2005. Creation of three-dimensional crop maps based on aerial stereoimages. Biosystems Engineering. 90:251-259.
Schanda, E. 1978. Remote sensing of the environment. Naturwissenschaften. 65:169-173.
Sellers, P.J. 1987. Canopy reflectance, photosynthesis, and transpiration, II. The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment. 21:143-183.
Starks. J.P., Zhao, D., Phillips, A.W. and Coleman, S.W. 2006. Herbage mass, nutritive value and canopy spectral reflectance of bermudagrass pastures. Grass and Forage Science. 61:101-111.
Sugiura, R., Noguchi, N. and Ishii, K. 2005. Remote-sensing technology for vegetation monitoring using an unmanned helicopter. Biosystems Engineering. 90:369-379.
Sung, K.I., Kim, G.S., Lee, J.W., Kim, B.W., Lee, J.K. and Jung, J.W. 2005. Effects of cutting frequency and level of fertilizer application on forage productivity at alpine grassland of 600 m altitude (in Korean). Journal of the Korean Society of Grassland and Forage Science. 25(2):137-142.
Suzuki, Y., Tanaka, K., Kato, W., Okamoto, H., Kataoka, T., Shimada, H., Sugiura, T. and Shima, E. 2008. Field mapping of chemical composition of forage using hyperspectral imaging in a grass meadow. Grassland Science. 54:179-188.
Tucker, J.C. 1979. Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment. 8:127-150.
Wallace, J.F., Caccetta, P.A. and Kiiveri, H.T. 2004. Recent developments in analysis of spatial and temporal data for landscape qualities and monitoring. Austral Ecology. 29:100-107.
Wessels, K.J., Prince, S.D., Zambatis, N., Macfadyen, S., Frost, P.E. and Van Zyl, D. 2006. Relationship between herbaceous biomass and 1-$km^2$ Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa. International Journal of Remote Sensing. 27:951-973.
White, J. and Hodgson, J. 1999. New Zealand pasture and crop science. NZ: Oxford University Press, Auckland.
Yoder, B.J. and Waring, R.H. 1994. The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations. Remote Sensing of Environment. 49:81-91.
Zhao, D., Starks, P.J., Brown, M.A., Phillips, W.A. and Coleman, S.W. 2007. Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassland Science. 53:39-49.