Abstract
A Generalised Additive Modelling (GAM) approach is used to predict weed occurrence across the Top End of the Northern Territory, Australia. The availability of new toolsets such as GRASP (Generalised Regression Analysis and Spatial Predictions) and the application of novel GIS variables, including remotely sensed Radiometric data, Infrastructure density (roads and fences) mapping and Climate layers, provide a useful framework for mapping regions at high risk of infestation. An independent set of weed locations provides support for the regional accuracy of the modelling. Infrastructure density is a significant factor in the prediction of weed occurrence in this study. Coastal areas with high levels of infrastructure appear most susceptible to weed infestation, although 8 Biogeographic Regions are considered currently at risk.
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Acknowledgements
The authors would like to thank Frances Perrett, Greg Wallis, Tom Vigilante and Ron Firth for their tireless efforts in the field and Woodside and Epic Energy for funding the field work. Keith Ferdinands provided useful comments and staff from the Biodiversity North Unit of NRETAS provided access to GIS layers.
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Hempel, C., Preece, N., Harvey, K., Woinarski, J. (2009). Modelling Weed Distribution Across the Northern Australia Using Very Extensive Transects. In: Jones, S., Reinke, K. (eds) Innovations in Remote Sensing and Photogrammetry. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93962-7_29
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