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Account-ready data: ecosystem extent in the Murray-Darling Basin. A data collection from the Regional Ecosystem Accounting Pilot projects

CSIRO RDS Repository

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Title Account-ready data: ecosystem extent in the Murray-Darling Basin. A data collection from the Regional Ecosystem Accounting Pilot projects
 
Creator Sana Khan
 
Subject Sustainability accounting and reporting
 
Description This collection comprises account-ready data for ecosystem extent in the Murray-Darling Basin, developed in the ‘Ecosystem Accounts for the Murray-Darling Basin’ project, one of two Regional Ecosystem Accounting Pilot (REAP) projects delivering ecosystem accounts at sub-national scales. The account-ready data in this collection are used to compile ecosystem extent accounts for the Murray-Darling Basin. These data describe spatial distribution of 16 ecosystem types from 1988 to 2020 across the Murray-Darling Basin. The annual layers are in raster format (GeoTiffs) at a spatial resolution of 100 m × 100 m pixel using the GDA2020 Australian Albers coordinate projection system (EPSG:9473). These ecosystem extent data have been derived using expert-based decision tree rules that are built upon two time-series products: (i) the dynamic remotely sensed Land Cover Classification System (LCCS), including Digital Earth Australia (DEA) land cover and fractional cover datasets (Geoscience Australia 2021, https://www.dea.ga.gov.au/) (Owers et al. 2022, https://doi.org/10.1080/17538947.2022.2130461), and (ii) the National Carbon Accounting System (NCAS) woody, sparse woody and non woody mapping (DISER 2021). The decision rules also included three static datasets: (i) the Australian National Aquatic Ecosystems (ANAE) classification for the MDB (Brooks 2021, https://brooks.eco/projects/the-australian-national-aquatic-ecosystems-anae-classification-of-the-murray-darling-basin_117s31), (ii) the Australian Hydrological Geospatial Fabric (Geofabric v3.0; Bureau of Meteorology 2015, http://www.bom.gov.au/water/geofabric/documents/v3_0/ahgf_productguide_V3_0_release.pdf) and (iii) flood inundation probability (Chen et al. 2012, https://publications.csiro.au/rpr/download?pid=csiro:EP124995&dsid=DS2). The decision tree rules were implemented in the Python programming language through a Jupyter Notebook interface (https://jupyter.org/). Read-only versions of the Jupyter Notebook code are included in this collection.
 
Publisher CSIRO
 
Contributor Glenn Newnham
Anna Richards
Steph Johnson
Sally Tetreault Campbell
Becky Schmidt
 
Date 2024-02-27
 
Type —
 
Format —
 
Identifier csiro:56546
 
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