The Peel 2008 LiDAR data was captured over the Peel region during February, 2008. The data was acquired by AAMHatch (now AAMGroup) and Fugro Spatial Solutions through a number of separate missions as part of the larger Swan Coast LiDAR Survey that covers the regions of Perth, Peel, Harvey, Bunbury and Busselton. The project was funded by Department of Water, WA for the purposes of coastal inundation modelling and a range of local and regional planning. The data are made available under licence for use by Commonwealth, State and Local Government. The data was captured with point density of 1 point per square metre and overall vertical accuracy has been confirmed at <15cm (68% confidence). The data are available as a number of products including mass point files (ASCII, LAS) and ESRI GRID files with 1m grid spacing. A 2m posting hydrologically enforced digital elevation model (HDEM) and inundation contours has also been derived for low lying coastal areas.

Elevation data and products such as Digital Elevation Models derived from these data comprise an essential layer within the National Spatial Data Infrastructure. Historically the creation of these datasets has been the domain of National and State mapping agencies. However, in recent years the rapid development of survey technologies and industry capability, the need for high resolution elevation data to meet a range of purposes, and the nature of government funding arrangements has resulted in significant project-based investment.

SRTM Documentation (best viewed with mono-spaced font, such as courier) 1.0 Introduction The SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Imagery and Mapping Agency (NIMA), as well as the participation of the German and Italian space agencies, to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. The SRTM instrument consisted of the Spaceborne Imaging Radar-C (SIR-C) hardware set modified with a Space Station-derived mast and additional antennae to form an interferometer with a 60 meter long baseline. A description of the SRTM mission, can be found in Farr and Kobrick (2000). Synthetic aperture radars are side-looking instruments and acquire data along continuous swaths. The SRTM swaths extended from about 30 degrees off-nadir to about 58 degrees off-nadir from an altitude of 233 km, and thus were about 225 km wide. During the data flight the instrument was operated at all times the orbiter was over land and about 1000 individual swaths were acquired over the ten days of mapping operations. Length of the acquired swaths range from a few hundred to several thousand km. Each individual data acquisition is referred to as a "data take." SRTM was the primary (and pretty much only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. Following several hours for instrument deployment, activation and checkout, systematic interferometric data were collected for 222.4 consecutive hours. The instrument operated virtually flawlessly and imaged 99.96% of the targeted landmass at least one time, 94.59% at least twice and about 50% at least three or more times. The goal was to image each terrain segment at least twice from different angles (on ascending, or north-going, and descending orbit passes) to fill in areas shadowed from the radar beam by terrain. This 'targeted landmass' consisted of all land between 56 degrees south and 60 degrees north latitude, which comprises almost exactly 80% of the total landmass.

The Australian tidal error model is the first attempt to define uncertainties in Mean Sea Level (MSL) around the Australian coastline between tide gauges. Tide gauge observations for the Australian coast span from less than 1 month to greater than 100yrs of observations. The high quality, decade and longer observation tide gauges are used in the production of the frequency dependant error surface. The observed hourly data are analysed using the National Tidal Centre TANS analysis package, resulting in harmonic constituents (used for prediction), a MSL determination, a fitted linear trend and a residual. The power spectrum of the residual is then separated into a predefined set of frequency bins, representing the noise levels of the sea surface proportional to frequency. The longer span observations fill more of this predefined spectrum, specifically the lower frequency errors, which contribute a significant proportion of error. Spatial interpolation around Australia is performed individually for each frequency bin. Using this method allow regions with shorter span observations to have lower frequency error added, creating a synthetic spectrum at the interpolation point. The synthetic spectrums are then used to determine confidence intervals of MSL around the coastline of Australia. The understanding of these errors is an important step for combining bathymetry and topography datasets ultimately creating a seamless national digital elevation model.

Removing the topographic effect from satellite images is a very important step in order to obtain comparable surface reflectance in mountainous areas and to use the images for different purposes on the same spectral base. The most common method of normalising for the topographic effect is by using a Digital Surface Model (DSM) and / or a Digital Elevation Model (DEM). However, the accuracy of the correction depends on the accuracy, scale and spatial resolution of DSM data as well as the co-registration between the DSM and satellite images. A physics based BRDF and atmospheric correction model in conjunction with a 1-second SRTM (Shuttle Radar Topographic Mission) derived DSM product released by Geoscience Australia in 2010 were used to conduct the analysis reported in this paper. The results show that artefacts in the DSM data can cause significant local errors in the correction. For some areas, false shadow and over corrected surface reflectance factors have been observed. In other areas, the algorithm is unable to detect shadow or retrieve an accurate surface reflectance factor in the slopes away from the sun. The accuracy of co-registration between satellite images and DSM data is crucial for effective topographic correction. A mis-registration error of one or two pixels can lead to large error of retrieved surface reflectance factors in the gully and ridge areas (retrieved reflectance factors can change from 0.3 to 0.5 or more). Therefore, accurate registrations for both satellite images and DSM data are necessary to ensure the accuracy of the correction. Using low resolution DSM data in conjunction with high resolution satellite images can fail to correct some significant terrain effects. A DSM resolution appropriate to the scale of the resolution of satellite image is needed for the best results.

The 3 second (~90m) Smoothed Digital Elevation Model (DEM-S) Version 1.0 was derived from resampling the 1 second SRTM derived DEM-S (gridded smoothed digital elevation model; ANZCW0703014016). The DEM represents ground surface topography, excluding vegetation features, and has been smoothed to reduce noise and improve the representation of surface shape. The DEM-S was derived from the 1 second Digital Surface Model (DSM; ANZCW0703013336) and the Digital Elevation Model Version 1.0 (DEM; ANZCW0703013355) by an adaptive smoothing process that applies more smoothing in flatter areas than hilly areas, and more smoothing in noisier areas than in less noisy areas. This DEM-S supports calculation of local terrain shape attributes such as slope, aspect and curvatures that could not be reliably derived from the unsmoothed 1 second DEM because of noise. A full description of the methods is in progress (Gallant et al., in prep) and in the 1 second User Guide. The 3 second DEM was produced for use by government and the public under Creative Commons attribution. The 1 second DSM and DEM that forms the basis of the product are also available as 3 second products (DSM; ANZCW0703014216, DEM; ANZCW0703014182, DEM-S; ANZCW0703014217). <strong>Please note that all 1 second products are available for GOVERNMENT USERS ONLY.</strong>

The 3 second (~90m) Shuttle Radar Topographic Mission (SRTM) derived Digital Surface Model (DSM) Version 1.0 was derived from resampling the 1 arc second (~30m) gridded DSM (ANZCW0703013336) that represents ground surface topography as well as features above the ground such as vegetation and man-made structures. The 1 second DSM was derived from the SRTM data acquired in February 2000, supported by the GEODATA 9 second DEM in void areas and the SRTM Water Body Data. Stripes and voids have been removed from the 1 second SRTM data to provide an enhanced and complete DSM for Australia and near-shore islands. A full description of the methods is in progress (Read et al., in prep). The 3 second DEM was produced for use by government and the public under Creative Commons attribution. Further information can be found in the User Guide. The 1 second DSM forms the source for the 1 second DEM with vegetation offsets removed (ANZCW0703013355) and the smoothed version (ANZCW0703014016). All 1 second products resampled to 3 seconds are available (DSM; ANZCW0703014216, DEM; ANZCW0703014182, DEM-S; ANZCW0703014217). <strong>Please note that all 1 second products are available for GOVERNMENT USERS ONLY.</strong>

The Harvey 2008 LiDAR data was captured over the Harvey region during February, 2008. The data was acquired by AAMHatch (now AAMGroup) and Fugro Spatial Solutions through a number of separate missions as part of the larger Swan Coast LiDAR Survey that covers the regions of Perth, Peel, Harvey, Bunbury and Busselton. The project was funded by Department of Water, WA for the purposes of coastal inundation modelling and a range of local and regional planning. The data are made available under licence for use by Commonwealth, State and Local Government. The data was captured with point density of 1 point per square metre and overall vertical accuracy has been confirmed at <15cm (68% confidence). The data are available as a number of products including mass point files (ASCII, LAS) and ESRI GRID files with 1m grid spacing. A 2m posting hydrologically enforced digital elevation model (HDEM) and inundation contours has also been derived for low lying coastal areas.

The Perth 2008 LiDAR data was captured over the Perth region during February, 2008. The data was acquired by AAMHatch (now AAMGroup) and Fugro Spatial Solutions through a number of separate missions as part of the larger Swan Coast LiDAR Survey that covers the regions of Perth, Peel, Harvey, Bunbury and Busselton. The project was funded by Department of Water, WA for the purposes of coastal inundation modelling and a range of local and regional planning. The data are made available under licence for use by Commonwealth, State and Local Government. The data was captured with point density of 1 point per square metre and overall vertical accuracy has been confirmed at <15cm (68% confidence). The data are available as a number of products including mass point files (ASCII, LAS) and ESRI GRID files with 1m grid spacing. A 2m posting hydrologically enforced digital elevation model (HDEM) and inundation contours has also been derived for low lying coastal areas.

The quality and type of elevation data used in tsunami inundation models can lead to large variations in the estimated inundation extent and tsunami flow depths and speeds. In order to give confidence to those who use inundation maps, such as emergency managers and spatial planners, standards and guidelines need to be developed and adhered to. However, at present there are no guidelines for the use of different elevation data types in inundation modelling. One reason for this is that there are many types of elevation data that differ in vertical accuracy, spatial resolution, availability and expense; however the differences in output from inundation models using different elevation data types in different environments are largely unknown. This study involved simulating tsunami inundation scenarios for three sites in Indonesia, of which the results for one of these, Padang, is reported here. Models were simulated using several different remotely-sensed elevation data types, including LiDAR, IFSAR, ASTER and SRTM. Model outputs were compared for each data type, including inundation extent, maximum inundation depth and maximum flow speed, as well as computational run-times. While in some cases, inundation extents do not differ greatly, maximum depths can vary substantially, which can lead to vastly different estimates of impact and loss. The results of this study will be critical in informing tsunami scientists and emergency managers of the acceptable resolution and accuracy of elevation data for inundation modelling and subsequently, the development of elevation data standards for inundation modelling in Indonesia.