Predictive mapping of blackberry in the Condamine Catchment using logistic regression and spatial analysis

Abstract

[Abstract]: The development of control strategies for noxious weeds depends on reliable information about the location and extent of weed species. Consequently, there is a need to develop mapping and monitoring techniques that are accurate, costeffective and reliable. This paper investigated predictive modelling and mapping techniques for blackberry (Rubus fruticosus agg.) weed in the Condamine Catchment. In all, 19 bio-physical factors were assessed, of which a subset was analysed by logistic regression using SPSS. The model calculated the probability of a binary dependent variable (i.e. “presence of weed” vs. “absence of weed”) in response to the above independent (bio-physical) variables. The output model was brought into ArcGIS’s Spatial Analyst to produce the predictive map. The factors found to be significant in the model were a) distance from stream, b) foliage projective cover, c) elevation, and d) distance from NSW border. The use of logistic regression generated maps depicting the probability of blackberry occurrence with a model accuracy of greater than 90%. The predicted maps offer relevant information that could be useful to land planners and decision-makers on where to target or prioritise weed control strategies, or for other aspects of weed management.