Satellite imagery of night-time lights provided by the US Air Force Defense Meteorological Satellite Program (DMSP), using the Operational Linescan System (OLS), has been used to estimate the spatial distribution of electricity consumption throughout Australia. For the period 1997 to 2002, there was very high correlation between state electricity consumption and night-time lights with an R ² value of 0.9346 at the state and territory spatial resolution. To increase the accuracy at which electricity consumption can be estimated at greater spatial resolution, an Overglow Removal Model (ORM) was developed to overcome the overglow effect caused by the dispersion of light into surrounding areas. The ORM makes use of the relationship between light source strength and the overglow/dispersion distance from the light source. As electricity consumption statistics at a greater spatial resolution than the state or territory level are not publically available in Australia, population statistics at the statistical local area (SLA) were used to demonstrate the increased accuracy of the ORM at returning the overglow light to its source, and, in turn, the accuracy of measuring electricity consumption. The ORM enabled an estimation of the electricity consumption of SLAs, greater than 10 km², with an R ² value of 0.8732, which is a 25.4% increase in accuracy over untreated data before applying the ORM. The increase in accuracy of the location of the origin of night-time lights can enable better georeferencing of satellite imagery of night-time lights and greater accuracy in locating population centres and centres of economic development, and assist with electricity infrastructure planning in regions of the world where statistics are not readily available. The result of the ORM is a map of Australian electricity consumption, and an estimation of the regional electricity consumption for all SLAs greater than 10 km² in size is included.

A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICI_ρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8.

Groundwater dependent ecosystems (GDEs) are vulnerable to groundwater regime changes. However, their protection is often hampered by challenges in their identification. Within is presented a remote sensing-based GDE potential mapping approach based on the persistency of relevant vegetation parameters during prolonged dry periods as an indicator of potential ‘consistency’ of water supply (e.g. groundwater). The study uses a novel approach to characterising persistency for selected vegetation parameters based on a normalised difference measure and an adaptation of the coefficient of variation statistic. Aggregation of parameters was facilitated through the analytic hierarchy process providing a structured weighting approach to minimise parameter bias. The approach is demonstrated in the semi-arid Flinders Ranges of South Australia where new groundwater resources are being sought to support local domestic and industry needs. Variations in GDE potential were mapped to better target areas where exploration of groundwater should be avoided. Mapping results indicated a high-level of agreement of 77% with an independent springs dataset, along with an 87% agreement with areas coinciding with known phreatophyte species and depths to groundwater. The index-based mapping approach has potential applicability across landscapes, as it normalises for variations in vegetation cover, minimises technical overheads, and employs continental-wide remote sensing data-products.