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.

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

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