×F5. Novel methods for data collection and predictive modelling in remote, vast, data sparse environments

Model predictions or estimates of some future state require a degree of accuracy to be useful. This is, in turn strongly dependent available upon available data, either as a model input, a source of model validation, or even to better understand the dominant processes before any model formulation has been finalised. However, there are large parts of the world where there are few data on which to parameterise or calibrate models. Often sparsely inhabited these regions typically have relatively undisturbed environments; however, they are increasingly becoming contested spaces due to population, climate and resource development pressures. Northern Australia is one such area, with only about 1 million people across 3 million square kilometres it is home to the world’s largest intact tracks of tropical savannas and many of the world’s last large unregulated river systems. Managing the resources of these regions, however, is challenging as inevitably they are poorly resourced and consequently lack data fundamental to confidently make decisions and undertake predictive modelling. Low cost collection of new data in these regions is challenging because the areas are typically vast in scale, remote, lacking in infrastructure, relatively inhospitable to humans and development pressures often mean there is limited time to acquire new data. To meet the needs of decision makers and stakeholders in data sparse environments such as northern Australia requires novel methods to efficiently collect new data in remote and inhospitable locations, and modelling methods that can extending or extrapolate these sparse data across space and/or time.

We invite papers that address novel ways of either collecting data, particularly in remote and inhospitable environments, novel new sources of data and/or novel ways of coping with data sparsity within the predictive process.

Key topics: Remote areas, Northern Australia, Data sparse, Modelling