Search

Vegetation changes over time are important indicators of condition, and are particularly important as targets or triggers for management. Satellite image data have unique capacities to provide information on changes in vegetation. In particular, Landsat imagery has the spatial resolution and a historical archive that make it relevant to providing information for understanding and management of native vegetation at a range of scales from small remnant to region. Regional and national vegetation monitoring programs based on time series Landsat imagery are now operational in Australia. These programs and their data have huge potential to provide information for conservation and natural resource management questions. They have already found multiple applications, including applications to biodiversity assessment and planning. This paper presents some examples of the delivery and application of satellite image monitoring information in the context of vegetation management. [ABSTRACT FROM AUTHOR]

The assessment of vegetation condition is seen as an increasingly important requirement for effective biodiversity conservation in Australia. Condition assessments that operate at the scale of the site are well established. However, there is a need for mapped representations of vegetation condition at regional scales to: (i) assist with regional planning and target setting; (ii) provide regional context for site-based assessment; and (iii) monitor the change in vegetation condition at multiple scales. This paper describes a methodology for converting site condition data collected in plots into maps of vegetation condition across entire regions using a predictive statistical modelling framework (Generalized Additive Modelling) combined with a GIS. The research demonstrates how explanatory variables including topographic position, terrain roughness, landscape connectivity and remote sensing derived indices can be used to map the condition of native vegetation at the scale of a subcatchment. The inclusion of indices derived from remotely sensed imagery (SPOT4) as explanatory variables in the modelling is a novel component of this research. Although the methodology generates statistically and ecologically plausible models of vegetation condition, there are nevertheless limitations associated with the way plot data were collected and some of the explanatory variables, which impacts upon model utility. We discuss how these problems can be minimized when embarking upon studies of this type. We demonstrate how maps produced from exercises such as this could be used for conservation planning and discuss the limitations of these data for monitoring. [ABSTRACT FROM AUTHOR]