Contacts

Identifiers and Keywords

ANZLIC Id

ANZVI0803005667 

Jurisdiction

Victoria 

Topic category

• Inland waters

Resource Constraints

Use limitation

. 

Classification

Unclassified  

Creative Commons Attribution 4.0 (CC-BY)

• 
• License Text

Other Dataset Details

Date ( Revision )

2021-07-14

Status

Completed  

Purpose

The model outputs will be useful tools for any person or agency requiring an understanding of where seasonal herbaceous wetlands are likely to occur in the landscape. Users include the Commonwealth Government, natural resource management agencies, non-government organisations, state and local government, land developers and landholders. Uses of the model outputs will include: raising awareness of the potential occurrence of seasonal herbaceous wetlands, assisting with urban planning, raising awareness of responsibilities under the EPBC Act, identifying areas to target for landholder incentive programs and assisting with provision of guidance to landholders on protection or restoration of seasonal herbaceous wetlands. A report that describes the model and dataset development can be downloaded from: www.delwp.vic.gov.au/ari/reports (on this page search for seasonal herbaceous wetlands) 

Format

DIGITAL - TIFF FORMAT 2 

Supplemental Information

Related Documents: None 

Maintenance and update frequency

As needed  

Spatial

Spatial representation type

Grid  

Description

General - Victoria 

N

S

E

W

Temporal

Begin date

2015-03-14 

End date

2016-08-03 

Quality

Missing Data

Full Extent of the model - State of Victoria and the following catchment basins: Millicent Coast, Lower Mallee, Lower Murray River Completeness Verification: Not Applicable 

Excess Data

Modelled spatial extent of the target wetland type using Bagged Random Forests. The resultant model fits the field observations robustly and model validation suggests that the model extrapolates successfully even when presented with novel field observations. Model outputs include both likelihood and uncertainty surfaces. The likelihood surface depicts the mean likelihood (of 30 independent models) of seasonal herbaceous wetland occurrence at each 25 x 25 m pixel and the uncertainty surface is the standard deviation derived from the set of 30 likelihood of occurrence predictions at each pixel. The model found that seasonal herbaceous wetlands are extremely rare even within the bioregions in which they are known to occur. We estimate that they comprise less than 0.001% of the surface area of the relevant lowland bioregions in south-eastern Australia. 

Attribute Quality

Modelled likelihood of occurrence of seasonal herbaceous wetlands in south-east Australia using field observations and a suite of temporally informed statistics for indices derived from the Landsat and ALOS satellite platforms. Model outputs include both an uncertainty surface and a likelihood surface. The resultant model fits the field observations robustly and model validation suggests that the model extrapolates successfully even when presented with novel field observations.The likelihood surface depicts the mean likelihood (of 30 independent models) of seasonal herbaceous wetland occurrence at each 25 x 25 m pixel and the uncertainty surface is the standard deviation derived from the set of 30 likelihood of occurrence predictions at each pixel. These two surfaces can be combined and/or thresholded for decision making contexts that may be more or less risk averse. Note that the "probability" (i.e. values between 0 and 1) have been changed to integer values from 1-100. In the mean prediction application of the model, probabilities of less than 0.05 have been put to "NoData" 

Lineage

Statement

Dataset Source: Seasonal Herbaceous Wetland likelihood model by ARI, DELWP Dataset Originality: Derived 

About the Metadata Record

Metadata identifier

abe99eb6-399c-595d-a41a-b077c1003afc

Resource Type

Dataset  

Date info ( Revision )

2021-07-14

Standard Name

ISO 19115-3:2018 

Profile Name

DELWP Profile 

Profile Version

Version 1 

Profile Date

2019-05-24  

Metadata Constraints

Classification

Unclassified