In 1986, the River Murray Riparian Vegetation Survey was initiated by the Murray-Darling Basin Commission to assess the present status of the vegetation along the River Murray, to identify causes of degradation, and to develop solutions for its rehabilitation and long term stability. The Study area was the floodplain of the River Murray and its anabranches, including the Edward-Wakool system, from below Hume Dam to the upper end of Lake Alexandrina, a total of nearly 9,000 square kilometres (900,000 hectares). The survey was conducted by Margules and Partners Pty Ltd, P and J Smith Ecological Consultants, and the then Victorian Department of Conservation, Forests and Lands (DCFL). The results were then compiled by DCFL, a report published (see References) and a GIS was constructed. Please note that the vegetation mapping uses a mixed floristic/structural classification. VIS_ID 3964

Native vegetation mapping of the Murray River National Park (Vic), covering part of Barmah Forest area.; ; This map was prepared to specifically show the spatial delineation of stem density and canopy condition categories of vegetation in the Barmah and Millewa Forest areas. This map was prepared using aerial photo interpretation (API) of ADS40 digital aerial photography captured in 2010. ; ; The map was prepared by updating (linework and attribution) of existing vegetation mapping produced in 2005 by Doug Frood (Frood 2007).; ; Barmah National Park (Vic) and the Murray River Park (Vic) are collectively known as the Barmah Forest. VIS_ID 3868

Native vegetation was delineated into stands using feature recognition software. A hybrid classification method that combined spatial modelling and visual interpretation was used to combine the features and create a vegetation map. SPOT 5 and Landsat satellite imagery was used in the creation of image objects. The spectral response of individual SPOT 5 scenes varied widely across the catchment so it was not used in the classification of vegetation type. Spatial layers used in the classification included a Digital Elevation Model (DEM), Landsat reflectance data, radiometric data and soil and climate layers, all of which are available for the entire State. Over 340 new full floristic survey sites were commissioned and the results were combined with 900 existing survey site records to create training areas for spatial modelling. Each survey site was assigned a New South Wales Vegetation Classification and Assessment (NSWVCA) vegetation type. The relationship between survey sites and spatial layers was explored by using machine learning software and vegetation type was classified using an object-based nearest neighbour approach. The catchment was divided into three discrete spatial models with separate training and validation survey sites. Model performance was assessed on the basis of the number of NSWVCA types mapped correctly in five classes of precision. The percentage of correctly modelled vegetation types ranged between 58% and 68%. Several vegetation community types were not able to be modelled (e.g. chenopods) or were poorly modelled due to lack of sample data. These communities were added or amended based on the visual interpretation of remotely sensed data. The amended map was assessed against a limited subset of independent survey data. The percentage of correctly mapped vegetation types in five classes of precision ranged between 72% and 78%. The mapping was presented in a geodatabase, which allows for user-generated updates so that the product can evolve as more field data are collected. ROFF, A., SIVERTSEN, D., AND DENHOLM, B. 2010. The Native Vegetation of the Murray Catchment Management Authority Area, NSW Department of Environment, Climate Change and Water, Sydney, Australia. VIS_ID 3808 VIS_ID 3809 VIS_ID 3810 VIS_ID 3811

The composition and extent of the present vegetation within the Mutawintji Lands, comprising Mutawintji National Park, Historic Site and Nature Reserve, is described and mapped at 1: 100 000 scale from intensive quadrat samples, field traverses and aerial photograph interpretation. Five broad vegetation communities are described and 530 taxa of vascular plants recorded from 100 sample sites, opportunistic records and previous surveys, nine of which are rare or threatened taxa and 33 of which are regionally significant. Reference: Vegetation Map of the Mutawintji Lands area (including Mutawintji Historic Site, National Park and Nature Reserve). VIS_ID 823

Data forms part of a project undertaken for the Resource and Conservation Assessment Council as part of the regional assessments of western New South Wales - Joint Vegetation Mapping Project (JVMP), Brigalow Belt South Bioregion (BBS). Murrurrundi 1:100K mapsheet was part of the "targeted API" program, whereby information on the structural characteristics and overstorey composition was obtained via aerial photo interpretation (API) of 1:50000 or 1:25000 aerial photography.; ; Joint Vegetation Mapping Project. NSW Western Regional Assessments. Final Report, Dec 2003. Resource & Conservation Assessment Council. Project No. WRA 24.Hill, L. (2000) Goulburn River National Park and Munghorn Nature Reserve - Vegetation Survey for Fire Management Purposes. Volume 1: Main Report. Upper Hunter District. NSW National Parks and Wildlife Service. Hill, L. (1999) Goulburn River National Park and Munghorn Nature Reserve - Vegetation Survey for Fire Management Purposes.Volume 2: Community Profiles. Upper Hunter District. NSW National Parks and Wildlife Service.

This study was undertaken to assist in flood preparedness and risk management activities for the River Murray. The study predicts the flood extent for a range of flows when measured at the border to South Australia which are expressed in megalitres per day (ML/d). Scenario datasets released include: lOOGL,120GL,130GL,140GL, 160GL, 180GL, 200GL, 250GL, 300GL, 341GL.

The data and interpretations presented are based on firsthand experience, being compiled by the Department of Conservation and Land Management’s regional nature conservation staff between July 2001 and January 2002. Note: to access the data, select the data source link located on the right-hand side.