Data

Probable Vegetation Groundwater Dependent Ecosystems - Sydney Metro

data.nsw.gov.au
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://data.nsw.gov.au/data/dataset/probable-vegetation-gde-sydney-metro&rft.title=Probable Vegetation Groundwater Dependent Ecosystems - Sydney Metro&rft.identifier=http://data.nsw.gov.au/data/dataset/probable-vegetation-gde-sydney-metro&rft.publisher=data.nsw.gov.au&rft.description=The purpose of this project is to create a data set that identifies vegetation communities that have a probability of being a groundwater dependent ecosystem within NSW. Probability categories are high, medium, low and non GDE. The data is required to meet legislative requirements for water sharing plans and water resource plans. This is a base data layer that can be used to aid research projects, assessment of state significant development applications, licence applications and compliance issues. For ease of data modelling and management, the dataset was divided into the following catchment management areas; Border Rivers-Gwydir, Central Tablelands, Central West, Hawkesbury-Nepean, Hunter-Central Rivers, Lachlan, Lower Murray Darling, Murray, Murrumbidgee, Namoi, Northern Rivers, Southern Rivers, Southern Tablelands, Sydney Metro and Western Division. \r\n\r\nPlease note: Groundwater Dependent Ecosystem mapping used the NSW DPE Environment and Heritage vegetation mapping product as the underlying data source. NSW DPE Water activities such as bore applications that are examined at a site specific scale may need subsequent verification to confirm.\r\n\r\nMethod report is available via the DoI Water website - https://water.dpie.nsw.gov.au/__data/assets/pdf_file/0010/151894/High-Probability-GDE-method-report.pdf&rft.creator=Anonymous&rft.date=2025&rft.coverage=150.76635,-34.2694 150.76635,-33.54797 151.34237,-33.54797 151.34237,-34.2694 150.76635,-34.2694&rft_rights=Creative Commons Attribution http://www.opendefinition.org/licenses/cc-by&rft_subject=DCCEEW - Water&rft_subject=DCCEEW - Water Science&rft_subject=Groundwater&rft_subject=Groundwater Dependent Ecosystems&rft_subject=Remote Sensing&rft_subject=Sydney Metro&rft_subject=Vegetation&rft.type=dataset&rft.language=English Access the data
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Brief description

The purpose of this project is to create a data set that identifies vegetation communities that have a probability of being a groundwater dependent ecosystem within NSW. Probability categories are high, medium, low and non GDE. The data is required to meet legislative requirements for water sharing plans and water resource plans. This is a base data layer that can be used to aid research projects, assessment of state significant development applications, licence applications and compliance issues. For ease of data modelling and management, the dataset was divided into the following catchment management areas; Border Rivers-Gwydir, Central Tablelands, Central West, Hawkesbury-Nepean, Hunter-Central Rivers, Lachlan, Lower Murray Darling, Murray, Murrumbidgee, Namoi, Northern Rivers, Southern Rivers, Southern Tablelands, Sydney Metro and Western Division.

Please note: Groundwater Dependent Ecosystem mapping used the NSW DPE Environment and Heritage vegetation mapping product as the underlying data source. NSW DPE Water activities such as bore applications that are examined at a site specific scale may need subsequent verification to confirm.

Method report is available via the DoI Water website - https://water.dpie.nsw.gov.au/__data/assets/pdf_file/0010/151894/High-Probability-GDE-method-report.pdf

150.76635,-34.2694 150.76635,-33.54797 151.34237,-33.54797 151.34237,-34.2694 150.76635,-34.2694

151.05436,-33.908685

Subjects
DCCEEW - Water | DCCEEW - Water Science | Groundwater | Groundwater Dependent Ecosystems | Remote Sensing | Sydney Metro | Vegetation |

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Other Information
NSW Department of Climate Change, Energy, the Environment and Water

url : http://data.nsw.gov.au/data/organization/nsw-department-of-climate-change-energy-the-environment-and-water

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