interpreted in the context of geographical and management information. It is now widely accepted that 
Geographical Information Systems (GIS). when incorporated with remotely-sensed data, can offer farm 
managers a useful tool for monitoring environmental conditions and allocating livestock in a spatially 
efficient manner under rangeland conditions. This is particularly important to assist farmers to manage 
their land during ‘critical' periods, eg. during severe droughts which have now become a national-wide 
environmental and economic concern. 
One of the obstacles that limit remote sensing application to rangeland farm management is the 
poor computational models which link spectral characteristics recorded by remote sensors to vital 
parameters for rangeland management, such as quantitative measurements of vegetation cover and 
biomass. This is particularly a significant problem in Australian rangelands where 'mixed' land cover 
types (ie. mixture of live and died vegetation, bare soil and stony materials) are typical within the 
resolution of available remotely sensed data (eg. Landsat TM). For years, research efforts have been 
made in Australian rangelands (eg. Graetz and Gentle, 1982; Pech, et al, 1986; Graetz, et al.; 1988; 
Pickup, et al., 1993; and Williamson and Eldridge, 1993) and elsewhere (eg. Smith, et al., 1990; 
Dymond, et al., 1992; and Anderson, et al.. 1993), and encouraging results w'ere obtained. Tasks remain, 
however, to model sub-pixel components and their contributions to spectral measurements of satellite 
images, to improve the relatively low accuracy in quantifying vegetation cover or biomass, and to 
overcome difficulties in transferring the techniques on an operational scale. 
Accurate field estimation of vegetation cover and biomass is essential for any improvements in 
rangeland vegetation modelling and monitoring. It is highly arguable on the accuracy and correctness of 
some current field techniques for vegetation cover and biomass estimation (Wilson, et al., 1987) due to 
the common constraints of available time and fund. In addition to the sample size issues outlined by 
Curran and Williamson (1986), it has also been argued that some commonly used field estimation 
methods can produce highly subjective and inconsistent results in the ‘ground truthingf making the 
subsequent image processing of remotely sensed data a meaningless effort. 
This paper reports a recent research project which focuses on improved field techniques for 
rangeland vegetation investigation. A semi-arid rangeland field station has been selected for intensive 
field testing of a number of field techniques employed by pasture management and remote sensing. The 
field sampling results from visual estimation, line intercept, quadrat and 2-D crown cover model have 
been compared with orthographic digital images which were acquired from a digital camera. The 
statistical relationships between results obtained from different methods and observers were analysed for 
their suitability for ground truthing of rangeland remote sensing. 
2.0 METHODOLOGY 
The study was undertaken in Fowlers Gap Arid Zone Research Station where bladder saltbush 
(.Atriplex vesicaria ) dominated rangeland is typical for western New South Wales, Australia. A 
comprehensive GIS data base has been established for the Station since 1987 and subsequently updated. 
Image and GIS data processing were conducted using the research infrastructure supported in the School 
of Geography and Centre for Remote Sensing and GIS at the University of New South Wales.