Abstract: Land Surface Temperature (Ts) is an important boundary condition in many land surface modelling schemes. It is also important in other application areas such as, hydrology, urban environmental monitoring, agriculture, ecological and bushfire monitoring. Many studies have shown that it is possible to retrieve Ts on a global scale using thermal infrared data from satellites. Development of standard methodologies that generate Ts products routinely would be of broad benefit to the application of remote sensing data in areas such as hydrology and urban monitoring. AVHRR and MODIS datasets are routinely used to deliver Ts products. However, these data have 1km spatial resolution, which is too coarse to detect the detailed variation of land surface change of concern in many applications, especially in heterogeneous areas. Higher resolution thermal data from Landsat is a possible option in such cases. To derive Ts, two scientific problems need to be resolved: to remove the atmospheric effects and derive surface brightness temperature (TB) and to separate the emissivity and Ts effects in the surface brightness temperature (TB). To derive TB, for single thermal band sensors such as, Landsat 5, 7 and (due to a faulty dual-band thermal instrument) on Landsat-8, the split window methods, such as those used for NOAAAVHRR data (Becker & Li, 1990), and the day/night pairs of thermal infrared data in several bands, as used for MODIS (Wan et al., 2002) are not available for correcting atmospheric effects. The retrieval of surface brightness temperature TB from Landsat data therefore needs more care, as the accuracy of the TB retrieval depends critically on the ancillary data, such as atmospheric water vapour data (precipitable water). In this paper, a feasible operational method to remove the atmospheric effects and retrieve surface brightness temperature from Landsat data is presented. The method uses the MODTRAN 5 radiative transfer model and global atmospheric profile data sets, such as NASA MERRA (The Modern Era Retrospective-Analysis for Research and Applications) atmospheric profiles, NOAA NCEP (National Center for Environmental Prediction) reanalysis product and ECMWF (The European Centre for Medium-Range Weather Forecasts) to correct for the atmospheric effects. The results derived from the global atmospheric profiles are assessed against the TB product estimated by using (accurate) ground based radiosonde data (balloon data). The results from this study have found: The global data sets NCEP1, NCEP2, MERRA and ECMWF can all generally give satisfactory TB products and can meet the levels of accuracy demanded by many practitioners, such as 1º K. Among global data sets, ECMWF data set performs best. The root mean square difference (RMSD) for the 9 days and 3 test sites are all within 0.4º K when compared with the TB products estimated using ground radiosonde measurements.

We have developed a Building Fire Impact Model to evaluate the probability that a building located in a peri-urban region of a community is affected/destroyed by a forest fire. The methodology is based on a well-known mathematical technique called Event Tree (ET) modeling, which is a useful graphical way of representing the dependency of events. The tree nodes are the event itself, and the branches are formed with the probability of the event happening. If the event can be represented by a discrete random variable, the number of possible realisations of the event and their corresponding probability of occurring, conditional on the realisations of the previous event, is given by the branches. As the probability of each event is displayed conditional on the occurrence of events that precede it in the tree, the joint probability of the simultaneous occurrence of events that constitute a path is found by multiplication (Hasofer et al., 2007). BFIM contains a basic implementation of the main elements of bushfire characteristics, house vulnerability and human intervention. In the first pass of the BFIM model, the characteristics of the bushfire in the neighboring region to the house is considered as well as the characteristics of the house and the occupants of the house. In the second pass, the number of embers impacting on the house is adjusted for human intervention and wind damage. In the third pass, the model examines house by house conditions to determine what houses have been burnt and their impact on neighboring houses. To illustrate the model application, a community involved in the 2009 Victorian bushfires has been studied and the event post-disaster impact assessment is utilized to validate the model outcomes. MODSIM 2013 Conference

Surface Bidirectional Reflectance Distribution Function (BRDF) correction is important for time series based analysis, such as dynamic landcover mapping and monitoring climate change etc. It is thus important to understand characteristics of BRDF and its variation under different cover and climate conditions and its seasonal and annual variation. Many studies suggested that BRDF is related to the characteristics of landcover types, vegetation structure (height and cover) and climate patterns. In this study, 10 years of MODIS BRDF data sets (MOD43A1) from 2001 to 2011 are used to conduct the analysis using landcover data in Australia derived in the same period. The study found that BRDF spectral shape is strongly correlated with the Normalized Difference Vegetation Index (NDVI), but BRDF shape varies significantly between landcover classes, vegetation structure and climate regions. Intra-annual variation of BRDF spectral shape is stronger than the inter-annual variation and seasonal patterns of spectral BRDF shape are different from those of NDVI.

Imagine you are an incident controller viewing a computer screen which depicts the likely spread of a bushfire that's just started. The display shows houses and other structures in the fire's path, and even the demographics of the people living in the area, such as the number of people, their age spread, whether households have independent transport, and whether English is their second language. In addition, imagine that you can quantify and display the uncertainty in both the fire weather and also the type and state of the vegetation, visualising the sensitivity of the expected fire spread and impact to these uncertainties. It will be possible to consider 'what if' scenarios as the event unfolds, and reject those scenarios that are no longer plausible. The advantages of such a simulation system in making speedy, well-informed decisions has been considered by a group of Bushfire CRC researchers who have collaborated to produce a 'proof of concept' for such a system, demonstrated initially on three case studies. The 'proof of concept' system has the working name FireDST (Fire Impact and Risk Evaluation Decision Support Tool). FireDST links various databases and models, including the Phoenix RapidFire fire prediction model and building vulnerability assessment models, as well as infrastructure and demographic databases. The information is assembled into an integrated simulation framework through a geographical information system (GIS) interface. Pre-processed information, such as factors that determine the local and regional wind, and also the typical response of buildings to fire, are linked through a database, along with census-derived social and economic information. This presentation provides an overview of the FireDST simulation 'proof of concept' tool and walks through a sample probabilistic simulation constructed using the tool. Handbook MODSIM2013 Conference

The Collaborative Australian Protected Areas Database (CAPAD) 2012 provides both spatial and text information about government, Indigenous and privately protected areas for continental and marine Australia. State and Territory conservation agencies supplied data, current to 31 December 2012, to Australian Government Department of the Environment.

This report provides background information about the Ginninderra controlled release Experiment 2 including a description of the environmental and weather conditions during the experiment, the groundwater levels and a brief description of all the monitoring techniques that were trialled during the experiment. Release of CO2 began 26 October 2012 at 2:25 PM and stopped 21 December 2012 at 1:30 PM. The total CO2 release rate during Experiment 2 was 218 kg/d CO2. The aim of the second Ginninderra controlled release was to artificially simulate the leakage of CO2 along a line source, to represent leakage along a fault. Multiple methods and techniques were then trialled in order to assess their abilities to: - detect that a leak was present - pinpoint the location of the leak - identify the strength of the leak - monitor how the CO2 behaves in the sub-surface - assess the effects it may have on plant health Several monitoring and assessment techniques were trialled for their effectiveness to quantify and qualify the CO2 that was release. This experiment had a focus on plant health indicators to assess the aims listed above, in order to evaluate the effectiveness of monitoring plant health and the use of geophysical methods to identify that a CO2 leak may be present. The methods are described in this report and include: - soil gas - airborne hyperspectral surveys - plant health (PhenoMobile) - soil CO2 flux - electromagnetic (EM-31) - electromagnetic (EM-38) - ground penetrating radar (GPR) This report is a reference guide to describe the Ginninderra Experiment 2 details. Only methods are described in this report with the results of the study published in conference papers and future journal articles.

This report provides background information about the Ginninderra controlled release Experiment 1 including a description of the environment and weather during the experiment, the groundwater conditions and a brief description of all the monitoring techniques that were trialled during the experiment. Release of CO2 began 28 March 2012 at 10:30 AM and stopped 30 May 2012 4:15 PM. The total CO2 release rate during Experiment 1 was 144 kg/d CO2. Krypton gas was also released as a tracer gas at a rate of 10 mL/min Kr in one section of the release well only. The aim of the Ginninderra Experiment 1 controlled release was to artificially simulate the leakage of CO2 along a line source, to represent leakage along a fault. Multiple methods and techniques were then trialled in order to assess their abilities to: - detect that a leak was present - pinpoint the location of the leak - identify the strength of the leak - monitor how the CO2 behaves in the sub-surface - assess the effects it may have on soil ecology Several monitoring and assessment techniques were trialled for their effectiveness to quantify and qualify the CO2 that was release. The methods are described in this report and include: - soil gas - CO2 carbo-cap (GMP343) - eddy covariance - groundwater levels and chemistry - soil microbial samples - soil flux - krypton in air - electromagnetic (EM-31) - meteorology - CO2 isotopes in tank This report is a reference guide to describe the Ginninderra Experiment 1 details. Only methods are described in this report with the results of the study published in conference papers and future journal articles.

Wildfires are one of the major natural hazards facing the Australian continent. Chen (2004) rated wildfires as the third largest cause of building damage in Australia during the 20th Century. Most of this damage was due to a few extreme wildfire events. For a vast country like Australia with its sparse network of weather observation sites and short temporal length of records, it is important to employ a range of modelling techniques that involve both observed and modelled data in order to produce fire hazard and risk information/products with utility. This presentation details the use of statistical and deterministic modelling of both observations and synthetic climate model output (downscaled gridded reanalysis information) in the development of extreme fire weather potential maps. Fire danger indices such as the McArthur Fire Forest Danger Index (FFDI) are widely used by fire management agencies to assess fire weather conditions and issue public warnings. FFDI is regularly calculated at weather stations using measurements of weather variables and fuel information. As it has been shown that relatively few extreme events cause most of the impacts, the ability to derive the spatial distribution of the return period of extreme FFDI values contributes important information to the understanding of how potential risk is distributed across the continent. The long-term spatial tendency FFDI has been assessed by calculating the return period of its extreme values from point-based observational data. The frequency and intensity as well as the spatial distribution of FFDI extremes were obtained by applying an advanced spatial interpolation algorithm to the recording stations' measurements. As an illustration maps of 50 and 100-year return-period (RP) of FFDI under current climate conditions are presented (based on both observations and reanalysis climate model output). MODSIM 2013 Conference

In many areas of the world, vegetation dynamics in semi-arid floodplain environments have been seriously impacted by increased river regulation and groundwater use. With increases in regulation along many rivers in the Murray-Darling Basin, flood volume, seasonality and frequency have changed which has in turn affected the condition and distribution of vegetation. Floodplain vegetation can be degraded from both too much and too little water due to regulation. Over-regulation and increased use of groundwater in these landscapes can exacerbate the effects related to natural climate variability. Prolonged flooding of woody plants has been found to induce a number of physiological disturbances such as early stomatal closure and inhibition of photosynthesis. However, drought conditions can also result in leaf biomass reduction and sapwood area decline. Depending on the species, different inundation and drought tolerances are observed. Identification of groundwater-dependent terrestrial vegetation, and assessment of the relative importance of different water sources to vegetation dynamics, typically requires detailed ecophysiological studies over a number of seasons or years as shown in Chowilla, New South Wales [] and Swan Coastal Plain, Western Australia []. However, even when groundwater dependence can be quantified, results are often difficult to upscale beyond the plot scale. Quicker, more regional approaches to mapping groundwater-dependent vegetation have consequently evolved with technological advancements in remote sensing techniques. Such an approach was used in this study. LiDAR canopy digital elevation model (CDEM) and foliage projected cover (FPC) data were combined with Landsat imagery in order to characterise the spatial and temporal behaviour of woody vegetation in the Lower Darling Floodplain, New South Wales. The multi-temporal dynamics of the woody vegetation were then compared to the estimated availability of different water sources in order to better understand water requirements.

Wind multipliers are factors that transform regional wind speeds into local wind speeds, accounting for the local effects which include topographical, terrain and shielding influences. Wind multipliers have been successfully utilized in various wind related activities such as wind hazard assessment (engineering building code applications), event-based wind impact assessments (tropical cyclones), and also national scale wind risk assessment. The work of McArthur in developing the Forest Fire Danger Index (FFDI: Luke and McArthur, 1978) indicates that the contribution of wind speed to the FFDI is about 45% of the magnitude, indicating the importance of determining an accurate local wind speed in bushfire hazard and spread calculations. For bushfire spread modeling, local site variation (@ 100 metre and also 25 metre horizontal resolution) have been considered through the use of wind multipliers, and this has resulted in a significant difference to the currently utilized regional '10 metre height' wind speed (and further to the impact analysis). A series of wind multipliers have been developed for three historic bushfire case study areas; the 2009 Victorian fires (Kilmore fire), the 2005 Wangary fire (Eyre Peninsula), and the 2001 Warragamba - Mt. Hall fire (Western Sydney). This paper describes the development of wind multiplier computation methodology and the application of wind multipliers to bushfire hazard and impact analysis. The efficacy of using wind multipliers within a bushfire spread hazard model is evaluated by considering case study comparisons of fire extent, shape and impact against post-disaster impact assessments. The analysis has determined that it is important to consider wind multipliers for local wind speed determination in order to achieve reliable fire spread and impact results. From AMSA 2013 conference