Assessment of White Gum Moist Forest on NSW Crown Forest Estate

An indicative map for White Gum Moist Forest (WGMF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the eastern Regional Forest Agreements. The determination of WGMF was reviewed by the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel), and a set of diagnostic parameters for identifying the WGMF TEC was agreed. Our mapping process relied upon the occurrence of E.dunnii to diagnose the presence of WGMF.

We reviewed existing vegetation maps, predictive models and observation records of E.dunnii to identify State Forests that are known or likely to include stands of the species. We then attempted several different approaches to sampling and mapping E.dunnii using ground based surveys, predictive modelling and aerial photograph interpretation (API). We used API assessment of known E.dunnii stands to identify image patterns and signatures that indicated the presence of E.dunnii. We used our findings to examine un-surveyed areas of relevant state forests via API, and then we mapped any areas which appeared to be dominated or co-dominated by E.dunnii. We also developed a Random Forest presence-absence model and used it to predict the distribution of WGMF across its range. We constructed an indicative map of WGMF using the combined results of our API mapping and our predictive model. In total, we mapped approximately 980 hectares of forest likely to be dominated or co-dominated by E.dunnii across 16 State Forests. Two thirds of the mapped area is associated with the northern populations of E.dunnii – the largest areas were in Beaury, Donaldson and Yabbra State Forests. In the southern area, Kangaroo River State Forest includes the largest representation of E.dunnii in State Forest. Our conclusions from this exercise is that our API interpretation is capable of separating E.dunnii from other related eucalypts but only where it is supported by field reconnaissance. Therefore, further work is required to increase API confidence throughout its range before our maps are suitable for operational applications. Nonetheless, our indicative map is still useful for providing a list of State Forests that include mapped areas of E.dunnii and identifying the areas that have corroborating field based evidence of E.dunnii. As our indicative map stands at present, we consider that it overestimates the extent of E.dunnii and its dominance, however, it is unlikely that extensive stands exist outside our mapped areas. We also conclude that existing mapping (including both forest type mapping and OEH (2012) mapping) significantly underestimates the likely true extent).

Indicative TEC Mapping have been generated from best available composite environmental data layers - standardised to 30 m pixels.

Data and Resources

Metadata Summary What is metadata?

Field Value
Alternative Title Indicative White Gum Moist Forest: Survey, Classification and Mapping Completed for the NSW Environment Protection Authority
Metadata Date 02/11/2016
Date Created 01/10/2016
Edition Version 1
Purpose Native Forestry Regulation on State Forests
License Creative Commons Attribution 4.0
Update Frequency Data is updated irregulary
Keywords Threatened Ecological Community,Endangered Ecological Community,Vegetation,State Forest,Indicative White Gum Moist Forest,EEC,TEC,Environment Protection Authority,EPA
Field of Research Environmental Science and Management not elsewhere classified
Spatial

Dataset extent

Map data © OpenStreetMap contributors
Geospatial Topic Environment,Biota
Language English
Temporal Coverage From 01/10/2016
Datum GDA94 Geographic (Lat\Long)
Landing page https://datasets.seed.nsw.gov.au/dataset/assessment-of-white-gum-moist-forest-tec
Legal Disclaimer Read
Attribution Environment Protection Authority (EPA) asserts the right to be attributed as author of the original material in the following manner: "© State Government of NSW and Environment Protection Authority (EPA) 2016"

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