Captured as part of the 2012-13 CIP, this 15cm imagery product comprises 3 visible band (RGB) photography over selected areas within East Gippsland Shire.

This project consists of data that has been reprocessed by RPS and AAM for the purpose of creating an improved Victorian coastal DEM including contours based on the original data acquired in 2007. The purpose of this project is to reclassify the original level 2 classification LiDAR data into level 3 for input to a higher accuracy ICSM Level 3 classification (Level 3 DEM). LiDAR (Light Detection and Ranging) is an airborne remote sensing technique for rapid collection of terrain data. The sensor used for this LiDAR project collected XYZ and Intensity data for first and last return by bouncing a pulse from the aircraft to the surface that enables the height and intensity values to be calculated. Products derived from the following projects: 1. 2006-7 South West Elevation ¿ Coastal Elevation 2. 2007-8 Ninety Mile Beach LiDAR Project 3. West Gippsland LIDAR Project 4. 2007-8 South Gippsland and East Gippsland Coastal 9. 2009-10 Bunyip River Project Products: DEM, Contours, raw LiDAR

The 2012-13 Greater Geelong Townships Project is an aerial photography project to produce digital orthophotography over an area of 1,582 km2 covering Geelong and surrounds. The 10cm resolution digital orthophotography was produced from VisionMap A3 photography acquired in the period 10th of December 2012 to the 4th of January 2013.

This dataset provides bore controlled residual gravity bedrock depth. It contains the Victorian geological basement surface and the distribution of the basal aquifer derived using gravity data and controlled by all basement boreholes extracted from the Groundwater Management System (GMS) and Geological Exploration and Development Information System (GEDIS) databases. Depth is presented as depth below ground surface. This technique was applied state-wide to the Upland Valleys, Murray Basin and Western Volcanic Plains. More reliable seismic and borehole data was used to map bedrock in the Gippsland and Otway Basin. The dataset was compiled by GHD to inform the report 'Potential Influences of Geological Structures on Groundwater Flow Systems' for DEPI's Secure Allocation Future Entitlements (SAFE) Project.

This dataset comprises all basement boreholes reaching bedrock passing through basalt covered areas used for bore control of Free Air (FA) Gravity data. These boreholes have been used to give better control for interpolating to FA gravity data. The dataset was compiled by GHD to inform the report 'Potential Influences of Geological Structures on Groundwater Flow Systems' for DEPI's Secure Allocation Future Entitlements (SAFE) Project.

This dataset was derived using 50m gridded radiometrics data including: Plain raster images of individual channels (TC, K, Th & U) Individual channels (TC, K, Th & U) draped over topography RGB false colour images of K (red), Th (green) and U (blue) with and without topography and lineaments and compared to topography and lineaments. (where Total Count - TC, Potassium - K, Thorium - Th and Uranium -U) . The interpretation was completed on a regional or sub-regional scale using geophysical remote sensing techniques. By combining the radiometrics and topography datasets, a pseudo-geomorphology is created. The radiometrics respond to soil cover in first the 0.5m of depth. As soils may change across small fault boundaries, radiometric lineaments bear the best relationship with topographic lineaments. From the various radiometric outputs the following key observations have been made: The ENE-WSW lineaments are evident but not as extensively as in the interpreted topographic dataset; The NNW-SSE lineament datasets is most dominant in the radiometric data; In the Otway Basin, many radiometric lineaments are parallel to the cost and are due to strandlines or basin faults; In the Murray Basin, strandlines are obvious as they are evident in the topographic data; In some cases, the soil radiometric chemistry changes across topographic lineaments, supporting the interpretation of topographic lineaments as evidence of small palaeo-fault movement. The dataset was compiled by GHD to inform the report 'Potential Influences of Geological Structures on Groundwater Flow Systems' for DEPI's Secure Allocation Future Entitlements (SAFE) Project.

This CIP project is for the capture of 10cm photography for local government, state government and water authorities will be used for a range of analytical and mapping purposes.

RapidEye Satellite Imagery over prescribed burns located in the north west of the state: Murray Sunset & Big Desert.

This dataset is a raster layer depicting the thickness of the Golden Beach Formation, Otway Basin. A number of processes were applied to the Top and Base to derive the Thickness of Golden Beach Formation. The dataset was compiled by GHD to inform the report 'Potential Influences of Geological Structures on Groundwater Flow Systems' for DEPI's Secure Allocation Future Entitlements (SAFE) Project.

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the West Gippsland Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this. The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon. In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater. Separate datasets and associated metadata records have been created for GDE species tolerance.