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Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

Satellite derived maps of pasture growth status: association of classification with botanical composition

P. J. Vickery, M. J. Hill and G. E. Donald

Australian Journal of Experimental Agriculture 37(5) 547 - 562
Published: 1997

Abstract

Summary. Spectral data from the green, red and near-infrared bands of Landsat MSS and Landsat TM satellite imagery acquired in mid-spring were classified into 3 and 6 pasture growth classes respectively. The classifications were compared with a site database of botanical composition for the Northern Tablelands of New South Wales to examine the association between spectral growth class and pasture composition. Pastures ranged in composition from unimproved native perennial grasses through semi-improved mixtures of native and naturalised grasses and legumes to highly improved temperate perennial grasses and legumes. For 3 years of MSS data, the fast growth class had a mean botanical composition of about 80% improved perennial grass and 0% native; medium growth class averaged 46% improved perennial grass and 14% native; while the slow growth class had about 60% native and 1% improved perennial grass when averaged over 3 years of MSS data. For the 6 class TM data from a single year, a predictive logistic regression of cumulative probability was developed for percentage of ‘very fast’ growth pixels and ordered 10 percentile categories of improved perennial grass or native grass. Differences in patch characteristics between classes with MSS disappeared with TM reclassified to the same 3 class level. Most probable pasture type was inferred from 3 class MSS and TM data using Bayesian probability analysis. The resulting maps were similar in general appearance but detail was better with the TM data. The pasture growth classification identified highly improved perennial grass pastures and native pastures but sensitivity to intermediate pasture types was poor. Future improvement will come from direct measurement of biophysical characteristics using vegetation indices or inversion of reflectance models.

http://dx.doi.org/10.1071/EA97014

© CSIRO 1997


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