Coastal environments are intrinsically dynamic and respond to a wide array of natural and anthropogenic drivers across a broad range of time steps. In addition, coastal environments are under increasing pressure from land use intensification and climate change. The development of the Australian Geoscience Data Cube has delivered an unprecedented capability to support environmental change monitoring applications through rapid processing and analysis of standardised Earth Observation (EO) time-series data in a High Performance Computing environment. Standardised long-term EO data records provide the capacity to monitor coastal changes processes and understand current changes from a historical perspective. The ability to visualise environmental changes in a spatio-temporal context provides the opportunity to assess whether the change phenomena are rapid / gradual onset, and/or episodic / cyclical in nature. Understanding the spatio-temporal nature of the changes also enables the attribution of observed changes to the potential causes. Hovmöller diagrams, typically used to plot meteorological data, can be applied for visualising large datasets in a meaningful way. In this study, we apply Hovmöller plots to examine coastal change processes and estuarine dynamics, based on a time-series of Landsat based surface reflectance data over a 27-year period (1987-2014), within the Australian Geoscience Data Cube. The Hovmöller plot in Figure 1 highlights the timing of a sea wall installation and associated land reclamation processes near Fremantle, Western Australia (see PDF attachment).Three coastal change processes are illustrated in this study: 1. The opening, closing and migration of the mouth of the Glenelg River in Victoria; the Hovmöller plots show that the river mouth moves on an episodic basis and remains closed for periods of time. 2. The installation of a sea wall and subsequent land reclamation near Fremantle in Western Australia; the results illustrate rapid anthropogenic change in the coastal zone and highlight the timing of the sea wall installation and land reclamation processes. 3. The migration of coastal dune fields north of Perth in Western Australia; the results show slow coastal change processes through the gradual northward migration of the dune field over multi-decadal time scales. The availability of standardised long-term Landsat data, in conjunction with new data becoming available from the Copernicus Sentinel-2 missions, point to the need for cross calibrated multi-sensor data, to enrich the global long-term EO record, in support of the detection and characterisation of coastal change phenomena. Presented at the 2016 Living Planet Symposium (LPS16) Prague, Czech Republic

Interferometric Synthetic Aperture Radar (InSAR) is a proven geodetic imaging technique that makes use of remotely sensed radar imagery to map spatial patterns of ground surface movement and their temporal evolution. One application of the InSAR technique is to monitor human interactions with the landscape, such as the extraction of resources from the crust. The increasing demand for gas in Australia has led to increased extraction of unconventional coal seam gas (CSG) reserves, particularly in the Surat Basin in south-east Queensland. Proved and Probable reserves of CSG now exceed 32,000 Petajoules, making the Surat Basin the largest onshore gas reserve in Australia. The geological target of CSG extraction in the Surat Basin is the Walloon subgroup of the Jurassic period, which is typically between 300 to 600 metres depth. Production of CSG from the Walloon subgroup began in 2006 and reserves are currently being extracted by several operators, with combined extraction exceeding 160 Petajoules in 2013-2014. Predictions of the magnitude of subsidence in the Surat Basin based on analytical poroelastic models and quoted CSG production rates indicate that total subsidence on the order of a decimetre may occur. In this contribution we will present new InSAR analysis of the Surat Basin using multi-sensor SAR imagery spanning the 2006-2015 time period. Should patterns of subsidence be detected over the producing gas fields, we will use a geophysical inversion scheme to characterise the objective function between the spatial InSAR observations and predictions of a simple analytical model. Our methodology will make use of a Monte-Carlo sampling algorithm run on High Performance Computing architecture to efficiently sample the multi-dimensional parameter space. The homogenous poroelastic model we employ has dependence on the depth and thickness of the target geological unit as well as on the unit’s rock properties (porosity, Young’s Modulus, Poisson’s Ratio and Shear Modulus). Given that limited information about these properties is generally publically available for the Surat Basin, the geophysical inversion scheme will enable a sensitivity analysis to be conducted that will allow us to understand uncertainties and what parameters have the most significant impact on the system. This in turn will enable more accurate predictions of future subsidence using the poroelastic model. In 2014, Geoscience Australia installed a regional geodetic network over a sub-region of the north-eastern Surat Basin in the vicinity of the towns of Dalby, Miles and Chinchilla in Queensland. The network covers a region of approximately 20,000 km2 and consists of 40 co-located corner reflectors and survey marks. Ongoing SAR imaging of the corner reflectors and periodic campaign GNSS surveys on the survey marks will enable InSAR analysis to be combined with ground-based geodetic measurements and as a result, refine the geodetic reference datum in this region. Preliminary analysis of the persistent scatterer response of the corner reflector network will form a part of this contribution. A dense archive of Interferometric-Wide-Swath (IWS) and Extra-Wide-Swath (EWS) Sentinel-1A images is currently being acquired over the region since the permanently deployed corner reflectors are being used as targets for ongoing geometric and radiometric calibration of the Sentinel-1A SAR sensor. InSAR analysis of this Sentinel-1A data will also form a part of this contribution. Presented at the 2016 Living Planet Symposium (LPS16) Prague, Czech Republic