Land cover across the southern Australian temperate agricultural
region comprises primarily of native pasture,
introduced improved pastures and crops for livestock production
and also perennial remnant vegetation. A feed-base
pasture audit was carried out throughout southern Australia
commencing mid-year 2011 (Donald and Burge 2012; Donald
et al. 2012). The purpose of the audit was to map and
analyse information obtained about the pasture feed-base
for livestock production by surveying Statistical Local
Areas (SLAs) across the southern states. The purpose of
this Feed-Base audit was to survey pastures within agricultural
NSW, Victoria, Tasmania, South Australia and South-
Western Australia, collate these data into an organised database,
and prepare a short report and summarise by
tabulating and mapping pasture species abundance and distribution.
Data collected were based on “desk-top
estimates” by state district agronomists and agricultural
consultants. In this paper a method using satellite imagery
is described on how more objective assessments of pasture
types can be provided as a means to discriminate between
the SLA’s major pasture classes far more objectively than
by visual assessment. Satellite remote sensing may be used
to define landcover classes for large regional areas. A number
of procedures have been developed to discriminate
between pastures, crop and woody vegetation (for example
Hill et al. 1997, Emelyanova et al. 2008). In the Hill study
(Hill et al. 1997) NOAA AVHRR NDVI provided spatial
land cover maps of pasture cover at 1 km resolution. The
classifications results in that study showed that satellite
information may be used to help in the interpretation of
pasture survey results, and in turn, the survey data can provide
some validation data for the pasture types ascribed to
the remotely sensed classes.
In this study daily temporal continental scale imagery
from 250m2 resolution TERRA and AQUA satellite Moderate
Resolution Imaging Spectroradiometer (MODIS)
normalised difference vegetation index (NDVI) composited
into weekly continental images provided a means to assess
temporal profile of spectral greenness over the growing
season.