a b s t r a c tPopular pastoral species, Buffel grass (Cenchrus ciliaris) is receiving long overdue attention as an invasive weed that poses serious threats to biodiversity conservation worldwide. Most research focuses on the species as forage plant and is largely published in agricultural and grey literature. Meanwhile, there is a dearth of information about the species ecology in natural landscapes despite strong evidence from field workers and managers that the species is an aggressive invader and threat to biodiversity in many environments. We present a comprehensive review of the ecology, distribution and biodiversity impacts of Buffel grass when behaving as an invasive species. Foundations are laid for research into localised habitat requirements of the species that will aid in the management of Buffel grass invasions now and into the future.
Commission VIII, WG VIII/6 KEY WORDS: Vegetation, Mapping, Classification, Targets, Multispectral ABSTRACT:Our goals is to determine if Worldview-2 8-band multispectral imagery can be used to discriminate an invasive grass species namely, Buffel grass (Cenchrus ciliaris) in the subtropical arid parts of central Australia and whether it offers a tangible improvement on 4-band (visible and near infra red) multispectral imagery. A Worldview-2 scene was acquired for a 10*10km area just west of Alice Springs in central Australia following heavy rains of early Summer. Mixture Tuned Matched Filtering was used to classify the image. Target and background spectra were selected in the field and extracted from the image. Linear discriminate analysis (LDA) was used to examine the spectral separability of each group of the target/ background spectra. The importance of the additional spectral bands on the image classification was assessed by running LDA for both 8 and 4 bands (red, green, blue and NIR). LDA did not indicate improved separability between groups when additional spectral bands were applied. Preliminary classification results indicate that Buffel grass (Cenchrus ciliaris) is detected with an omission error 44%, commission error of 11.8% and overall accuracy of 59.5%. We were surprised that the additional spectral bands did not improve spectral separability and in part attribute this to the high degree of variance we observed within groups of spectra, which was particularly observable in the NIR2 and Yellow bands. The analyses may be significantly improved by acquiring imagery following the first big rains at the end of the dry season. At this time, phonological differences between our focal species and the surrounding native vegetation should be maximised. We suspect that Worldview-2 will offer even greater potential for the discrimination of Buffel grass under these conditions, being able to fully utilise the yellow-band in particular.
Understanding factors determining the distribution of a species is critical for developing strategies and policies in natural resources management. The southern hairy-nosed wombat (Lasiorhinus latifrons) is an excellent model species to examine species distribution patterns because of its conspicuous burrowing behaviour, making it possible to obtain highly accurate distribution maps. The aim of this study was to evaluate the relative importance of biophysical factors impacting on the species’ distribution at regional and continental scales. At the fine scale, we digitised the distribution of individual warrens within a population, whereas at the continental scale we utilised the entire species’ distribution. At the regional level, the strongest predictors of burrowing activity were soil characteristics and geology with little influence of climate. In contrast, at the continental scale, species distribution was most strongly influenced by climatic variables, with most of the distribution located in regions with narrow ranges of mean annual maximum temperature (23−25°C) and mean annual rainfall (200–300 mm). This discrepancy suggests that the species’ distribution is limited to small geographic areas where both a suitable climate and appropriate soil and geology exist and, consequently, that conservation strategies need to adopt a long-term view considering the combined effect of both regional and continental factors.
We assess the feasibility of using airborne imagery for Buffel grass detection in Australian arid lands and evaluate four commonly used image classification techniques (visual estimate, manual digitisation, unsupervised classification and normalised difference vegetation index (NDVI) thresholding) for their suitability to this purpose. Colour digital aerial photography captured at approximately 5 cm of ground sample distance (GSD) and four-band (visible–near-infrared) multispectral imagery (25 cm GSD) were acquired (14 February 2012) across overlapping subsets of our study site. In the field, Buffel grass projected cover estimates were collected for quadrates (10 m diameter), which were subsequently used to evaluate the four image classification techniques. Buffel grass was found to be widespread throughout our study site; it was particularly prevalent in riparian land systems and alluvial plains. On hill slopes, Buffel grass was often present in depressions, valleys and crevices of rock outcrops, but the spread appeared to be dependent on soil type and vegetation communities. Visual cover estimates performed best (r2 0.39), and pixel-based classifiers (unsupervised classification and NDVI thresholding) performed worst (r2 0.21). Manual digitising consistently underrepresented Buffel grass cover compared with field- and image-based visual cover estimates; we did not find the labours of digitising rewarding. Our recommendation for regional documentation of new infestation of Buffel grass is to acquire ultra-high-resolution aerial photography and have a trained observer score cover against visual standards and use the scored sites to interpolate density across the region.
Invasive plants are a major threat to environmental conservation and are costly to control. To effectively mitigate invasion natural resource managers need to anticipate potential damage, develop policies to prevent introduction as well as mitigate spread. Weed distribution modeling provides managers with the objective information required to strategically direct control efforts. However, often the empirical species distribution data needed to model habitat susceptibility to invasion are limited. For this reason, the benefits of mechanistic models (predictions based on knowledge of species environmental tolerances) are gaining recognition and acceptance. In a recent publication by Smith et al. (2012) a framework for estimating weed invasion potential that utilized expert knowledge of dispersal, establishment and persistence was presented. Here, we construct a model for the contentious weed species, Buffel grass in accordance with the theoretical framework proposed by Smith et al. (2012). This framework distinguishes between habitat suitability and susceptibility. In our study, maps for habitat suitability and susceptibility that incorporate both expert opinion and objective empirical modeling of 2010 Buffel grass roadside survey data are created. Presented are spatially explicit models of introduction pathways, habitat suitability and landscape susceptibility for Buffel grass invasion in the arid zone of South Australia. Results show the relative susceptibility of arid South Australia to Buffel grass invasion. The inclusion of empirical data in this modeling framework presented several challenges, such as the "persistence" indicator, which requires a time component, difficult to quantify empirically. The use of this theoretical framework for spatially explicit modeling requires more thought on how to tackle scale, particularly regarding how the scale of the expert observation lines up with the scale of available environmental data layers, and this is the focus of our discussion.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.