Is ground cover a useful indicator of grazing land condition?
Terrence S. Beutel
A G * , Robert Shepherd B , Robert A. Karfs C , Brett N. Abbott D , Teresa Eyre E , Trevor J. Hall F and Emily Barbi A
+ Author Affiliations
- Author Affiliations
A Queensland Department of Agriculture and Fisheries, Parkhurst, Qld 4702, Australia.
B Queensland Department of Agriculture and Fisheries, Charters Towers, Qld 4820, Australia.
C Queensland Department of Agriculture and Fisheries, Dutton Park, Qld 4102, Australia.
D CSIRO Land and Water, Townsville, Qld 4811, Australia.
E Queensland Department of Environment and Science, Toowong, Qld 4066, Australia.
F Queensland Department of Agriculture and Fisheries, Toowoomba, Qld 4350, Australia.
G Corresponding author. Email: terry.beutel@daf.qld.gov.au
The Rangeland Journal 43(1) 55-64 https://doi.org/10.1071/RJ21018
Submitted: 24 March 2021 Accepted: 30 August 2021 Published: 14 September 2021
Abstract
Remotely sensed ground cover data play an important role in Australian rangelands research development and extension, reflecting broader global trends in the use of remotely sensed data. We tested the relationship between remotely sensed ground cover data and field-based assessments of grazing land condition in the largest quantitative analysis of its type to date. We collated land condition data from 2282 sites evaluated between 2004 and 2018 in the Burdekin and Fitzroy regions of Queensland. Condition was defined using the Grazing Land Management land condition framework that ranks grazing land condition on a four point ordinal scale based on dimensions of vegetation composition, ground cover level and erosion severity. Nine separate ground cover derived indices were then calculated for each site. We found that all indices significantly correlated with grazing land condition on corresponding sites. Highest correlations occurred with indices that benchmarked ground cover at the site against regional ground cover assessed over several years. These findings provide some validation for the general use of ground cover data as an indicator of rangeland health/productivity. We also constructed univariate land condition models with a subset of these indices. Our models predicted land condition significantly better than random assignment though only moderately well; no model correctly predicted land condition class on >40% of sites. While the best models predicted condition correctly at >60% of A and D condition sites, condition at sites in B and C condition sites was poorly predicted. Several factors limit how well ground cover levels predict land condition. The main challenge is modelling a multidimensional value (grazing land condition) with a unidimensional ground cover measurement. We suggest that better land condition models require a range of predictors to address this multidimensionality but cover indices can make a substantial contribution in this context.
Keywords: degradation, grazing lands, ground cover, land condition, remote sensing, rangelands.
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