Phytophthora cinnamomi - Likely Loss from Infection
dataset:
PC_RISK_0610
This dataset is a whole of landscape surface which shows the risk of vegetation loss from infection of Phytophthora cinnamomi. The values from 0 - 100 area a percentage of vegetation cover loss, which have been re-classed into a "Low", "Medium", and "High" cover loss.
This is a draft model and doesn't include all areas of known infection. Users should seek local advice about the impact of Phythophthora cinnamomi.
Also available as vic-risk-phytophthora-cinnamomi-1d_2010jul01_thm_bio_25m_vg94.tif
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Citation proposal Citation proposal
(2019) Phytophthora cinnamomi - Likely Loss from Infection Department of Energy, Environment and Climate Action https://uat-metashare.maps.vic.gov.au/geonetwork/srv/eng/catalog.search#/metadata/edc06378-7fba-5dc1-82e8-33af471d854a |
- Description
- Temporal
- Spatial
- Maintenance
- Format
- Contacts
- Keywords
- Resource Constraints
- Lineage
- Metadata Constraints
- Quality
- Acquisition Info
- Raster Data Details
- Raster Type Details
- Point Cloud Data Details
- Contour Data Details
- Survey Details
Simple
Description
- Title
- Phytophthora cinnamomi - Likely Loss from Infection
- Alternate title
- PC_RISK_0610
- Purpose
- This dataset is a whole of landscape surface which shows the risk of vegetation loss from infection of Phytophthora cinnamomi. This dataset is not designed for use at a site or property scale. The three classes of loss are for display purposes at broader landscape scales, and may not reflect the risk of vegetation loss due to infection at any particular site. The datasets accuracy is dependent on modelled data. As more data is acquired to improve the model, this dataset will be reviewed. The model should not be relied upon as a sole source of risk to an area i.e. other areas may be at risk that are not shown. Furthermore, the map does not show the extent of any infection. Local advice about the potential and known impact of Phythophthora cinnamomi should be sought before undertaking high risk activities.
- Supplemental Information
- Related Documents: None Refer to the metadata for the Habitat model of Phytophthora cinnamomi for how the potential distribution of Phytophthora cinnamomi was modelled. Refer to the metadata for the Vegetation Loss of Understorey Cover from Phytophthora cinnamomi to see how losses due to infection were modelled
- Status
- Completed
Temporal
Spatial
- Code
- 4283
Maintenance
- Maintenance and update frequency
- As needed
Format
- Title
- DIGITAL ESRI grid DIGITAL TIFF image 2
Contacts
Point of contact
Department of Energy, Environment and Climate Action
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VSDL Data Manager
PO Box 500
East Melbourne
Vic
3002
Australia
Cited responsible party
No information provided.
Cited responsible party
No information provided.
Cited responsible party
No information provided.
Cited responsible party
No information provided.
Cited responsible party
No information provided.
Cited responsible party
No information provided.
Keywords
- Topic category
-
- Biota
- Farming
- Health
Resource Constraints
- Use limitation
- General This dataset is not designed for use at a site or property scale. The three classes of loss are for display purposes at broader landscape scales, and may not reflect the risk of vegetation loss due to infection at any particular site. The datasets accuracy is dependent on modelled data. As such as more data is acquired to improve the model, this dataset will be reviewed.
- Classification
- Unclassified
Lineage
- Statement
- Dataset Source: The Pc Risk dataset was a derived using the following process: 1. Using Species Distribution Modelling approach, create habitat model of Phytophthora cinnamomi where habitat is defined as a probability > 0.384 (95% of records fall within this threshold). 2. Create Vegetation Loss of Understorey Cover from Phytophthora cinnamomi model. 3. Clip Pc habitat model to Pc veg loss model to create Pc Risk model. 4. Pc Risk was originally classed into Low, Medium and High through standard deviations from the mean loss. 5. The ranges for Medium and High were adjusted through expert consultation to produce the following classes: 1- 23: Low >23 - 41: Medium >41: High Dataset Originality: Primary & Derived
Metadata Constraints
- Classification
- Unclassified
Quality
Attribute Quality
- Comments
- Dataset accuracy is dependent of source data accuracy
Positional Accuracy
- Comments
- Dataset accuracy is dependent of source data accuracy
Conceptual Consistency
- Comments
- Good
Missing Data
- Comments
- Complete statewide coverage
Excess Data
- Comments
- Value: 1 - 100 Class: "Low", "Medium", "High"
Acquisition Info
Raster Data Details
Point Cloud Data Details
Contour Data Details
Survey Details
Overviews
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edc06378-7fba-5dc1-82e8-33af471d854a
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Associated resources
Not available