2b0dd699-9c23-40eb-b70f-1bcfdbc3f34a urn:uuid eng UTF-8 dataset Dataset 2019-04-03T03:09:56 creation ISO 19115 2016 NSW Urban Vegetation Cover to Modified Mesh Block 2016 2b0dd699-9c23-40eb-b70f-1bcfdbc3f34a The NSW Urban Vegetation Cover to Modified Mesh Block 2016 provides both an area and percentage of vegetation for city blocks and infrastructure corridors in the Sydney Greater Metropolitan Area as of 2016. With this dataset, users can estimate tree canopy and vegetation cover in urban areas at many scales, such as mesh block, precinct, or local government area. Having current and accurate estimates of tree canopy and vegetation like this supports citizens and governments to reliably identify areas of tree canopy and confidently develop urban greening and heat island mitigation strategies and action. This dataset provides the user with information of high spatial accuracy. The dataset uses vegetation information derived from high resolution aerial photography combined with boundary and land use information from the Australian Bureau of Statistics (ABS) Mesh Block polygon dataset augmented with road and railroad data from the NSW Digital Cadastral Database. The content was co-designed with state and local governments and developed using scientifically-rigorous methodologies. The extent of the dataset covers urban, major urban, peri-urban and other urban areas within the Sydney Greater Metropolitan. While the dataset provides wall to wall coverage of many councils, it does not include far outlying rural areas in local government areas with a largely rural component. HUMAN-ENVIRONMENT-Livability HUMAN-ENVIRONMENT-Planning HUMAN-ENVIRONMENT-Urban-Design LAND-Cadastre LAND-Cover LAND-Use VEGETATION green infrastructure tree canopy vegetation cover dataset 2019-01-15T00:00:00 The dataset has complete coverage for the designated Significant Urban Area of Sydney Greater Metropolitan Area and the major urban, urban, peri-urban and other urban areas of the included Local Government Areas. The Significant Urban Area sections that lie within the Lower Hunter, Central Coast, Greater Sydney and Illawarra Regions of Eastern/Coastal New South Wales, Australia are also included. dataset 2019-01-15T00:00:00 The dataset has complete coverage for the designated Significant Urban Area of Sydney Greater Metropolitan Area and the major urban, urban, peri-urban and other urban areas of the included Local Government Areas. As such it does not provide complete coverage to LGAs which have rural areas and may not include large areas devoted to national parks. This is due to the limits of the extent and completeness omissions of the Urban Vegetation Cover 2016 raster dataset. Therefore, the dataset cannot provide complete land cover/vegetation cover analysis for all areas within every LGAs in the study area. dataset 2019-01-15T00:00:00 Local Government Area users of the data will need to double check that their known boundaries are correctly identified. In the dataset, the LGA name that is given to a Modified Mesh Block may be inaccurate, due to the modified Mesh Blocks boundaries not nesting within the LGA boundaries completely. The LGA boundaries were overlaid with the Modified Mesh Blocks polygons and the LGA names were assigned to the Mesh Block with the greatest area within it. Note 2017 LGA boundaries were used. The knock on effect is that the Mesh Block may not correspond to the correct District. This issue most likely occurs with more rural Mesh Blocks at the boundaries, such as near Campbelltown, Wollondilly or Sutherland. It may also occur where roads form a boundary between LGAs. As adequate data validation was performed, this issue is likely minor and not affecting every LGA. dataset 2019-01-15T00:00:00 The dataset is deemed correct and consistent to the parameters of the Urban Monitor methodology, and the consistency of the NSW Digital Cadastral Database dataset 2019-01-15T00:00:00 98% of the features are expected to be within 2% of their true size and shape. dataset 2019-01-15T00:00:00 Non-quantitative attributes are deemed to be accurate as of the date of the review (15/01/2019) and by explanations provided in the source datasets.