Quantitative mapping of pasture biomass using satellite imagery

A knowledge of the amount of pasture biomass available in farm paddocks is crucial for improving utilization and productivity in the Australian grazing industry. A method to quantitatively map the biomass of annual pastures under grazing has been developed using the Normalized Difference Vegetation Index (NDVI) derived from high-resolution satellite imagery. Relationships between field-measured pasture biomass and the NDVI were examined for different transects in paddocks under different grazing regimes across three geographically dispersed farm sites. A significant linear relationship (R 2 = 0.84) was observed when the NDVI was regressed against biomass. The slope of the relationship between the NDVI and biomass declined in a highly predictable (R 2 = 0.82) exponential form as the growing season progressed and this pattern was consistent across four separate seasons. This knowledge was used to formulate a reliable model to predict paddock average pasture biomass using the NDVI. The model estimates were validated against observed biomass in the range 500–4000 kilograms of dry matter per hectare (kg DM ha–1) with R 2 = 0.85 and a standard error of 315 (kg DM ha–1).

[1]  Ghassem R. Asrar,et al.  Assessing grassland biophysical characteristics from spectral measurements , 1986 .

[2]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[3]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[4]  R. M. Hoffer,et al.  Biomass estimation on grazed and ungrazed rangelands using spectral indices , 1998 .

[5]  A. Thompson,et al.  Effects of stocking rate in spring on liveweight and wool production of sheep grazing annual pastures , 1994 .

[6]  J. K. Aase,et al.  Radiometric reflectance measurements of northern Great Plains rangeland and crested wheatgrass pastures. , 1987 .

[7]  Pierre Hiernaux,et al.  A regional Sahelian grassland model to be coupled with multispectral satellite data. II: Toward the control of its simulations by remotely sensed indices , 1995 .

[8]  Markus Reichstein,et al.  Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland , 2004 .

[9]  Daoyi Chen,et al.  Satellite-sensed distribution and spatial patterns of vegetation parameters over a tallgrass prairie , 1998 .

[10]  J. Cayley,et al.  Techniques for measuring pastures , 1991 .

[11]  A. Skidmore,et al.  Narrow band vegetation indices overcome the saturation problem in biomass estimation , 2004 .

[12]  N. Campbell,et al.  The visual assessment of pasture yield , 1973 .

[13]  J. Everitt,et al.  Estimating grassland phytomass production with near-infrared and mid-infrared spectral variables , 1989 .

[14]  R. Person.  Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado , 1972 .

[15]  J. Cihlar,et al.  Relation between the normalized difference vegetation index and ecological variables , 1991 .

[16]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[17]  L. D. Miller,et al.  Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado , 1972 .

[18]  G. Donald,et al.  Estimation of pasture growth rate in the south west of Western Australia from AVHRR NDVI and climate data , 2004 .

[19]  Rasmus Fensholt,et al.  Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment , 2005 .

[20]  A. Skidmore,et al.  Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions , 2004 .

[21]  J. King,et al.  A comparison of spectral reflectance and sward surface height measurements to estimate herbage mass and leaf area index in continuously stocked ryegrass pastures , 1986 .

[22]  K. Haydock,et al.  The comparative yield method for estimating dry matter yield of pasture , 1975 .

[23]  A. L. Dick,et al.  Chloride and Bromide Loss from Sea-Salt Particles in Southern Ocean Air , 1999 .

[24]  M. S. Rasmussen.  Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information. Part II: Crop yield assessment , 1998 .

[25]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[26]  S. T. Gower,et al.  Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .

[27]  Miller,et al.  Spectral Mapping of Shortgrass Prairie Biomass , 2008 .

[28]  J. Bédard,et al.  The estimation of dry green biomass in hayfields from canopy spectroreflectance measurements , 1987 .

[29]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[30]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[31]  J. Singh,et al.  The structure and function of ten Western North American grasslands: II. Intra-Seasonal dynamics in primary producer compartments , 1978 .

[32]  J. Michaelsen,et al.  Estimating grassland biomass and leaf area index using ground and satellite data , 1994 .

[33]  James H. Everitt,et al.  Radiometric estimation of biomass and nitrogen content of Alicia grass , 1983 .

[34]  J. Everitt,et al.  Using spectral vegetation indices to estimate rangeland productivity , 1992 .