Indicative map for Lowland Grassy Woodland:

The indicative map for Lowland Grassy Woodland 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 Lowland Grassy Woodland was reviewed by the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel), and a set of diagnostic parameters for the identifying the Lowland Grassy Woodland TEC was agreed upon. Using these diagnostic parameters, we sampled candidate areas from existing vegetation maps to identify potential areas of Lowland Grassy Woodland occurrence in 296 000 hectares of State Forest and undertook additional mapping work using two independent mapping methods. Random Forest models (predictive habitat models) were generated using plot data and a selection of environmental variables. Aerial photo interpretation targeted stands of forests dominated by Eucalyptus tereticornis to refine the potential boundaries of Lowland Grassy Woodland. We tested whether Lowland Grassy Woodland was present in State Forest by completing systematic plot surveys within mapped areas indicating potential presence. We compared our collected data to a large regional pool of plot data that contained a subset of plots assigned to vegetation map units cited in the determination for the Lowland Grassy Woodland TEC (see Gellie 2005, Tozer et al 2006, and Keith and Bedward 1999). Our analysis of data confidently assigned only a few plots in State Forest to Lowland Grassy Woodland (2/43). From these results, we were unable to construct an operational map for Lowland Grassy Woodland. The relationship between the existing mapping cited in the determination and the plot data on State Forest was not strong enough to be a reliable basis for mapping the TEC. We also found that Eucalyptus tereticornis could not reliably be used as an indicator of Lowland Grassy Woodland in State forests. As a result, we were unable to map this TEC from the few confirmed sampling points without including a significant area of forest that was highly unlikely to be Lowland Grassy Woodland. However, we created indicative maps of Lowland Grassy Woodland by merging our predictive and API maps to provide an indication of the likely extent of Lowland Grassy Woodland in State Forests.

Operational map for Brogo Wet Vine Forest:

The operational map for Brogo Wet Wine Forest (BWVF) 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. We assessed whether BWVF was likely to be present in more than 296 000 hectares of State Forest in the South-east Corner Bioregion. The project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for BWVF and reaching an agreed interpretation of floristic, environmental and distributional characteristics. The Panel found that BWVF is primarily defined by a source vegetation community derived from quantitative floristic plot data (Keith and Bedward, 1999), with additional defining characteristics relating to bioregion and elevation. The Panel’s interpretation resulted in the identification of all State Forests located below an elevation threshold of 550 metres within the South East Corner Bioregion as potentially containing BWVF. We identified other potential areas of BWVF by overlaying the cited vegetation maps and any State Forest mapping where vegetation was dominated by or includes Eucalyptus tereticornis (a defining species of BWVF). Within these state forests, we used aerial photo interpretation (API) to identify and delineate potential areas of BWVF based on structural characteristics and overstorey and understorey attributes, namely dominance or inclusion of Eucalyptus tereticornis. We then compiled floristic plot data for all State Forest areas within our study area. The floristic plot data was sourced from both existing flora surveys held in the OEH VIS database and from targeted flora surveys conducted specifically for this project. We used multivariate analysis to compare plots assigned to vegetation communities identified as BWVF in the determination to all other plots in the study area. We used explicit membership thresholds to identify whether plots in State forests and elsewhere belonged to one or more of the communities listed in the BWVF determination. We used the plot assignments to candidate BWVF to develop a predictive presence and absence Random Forest statistical model. The model generates a probability of occurrence of BWVF for each grid cell using plot data and a selection of environmental and remote-sensing variables. We constructed our operational map using the API line work in combination with the floristic plot data and our predictive habitat models to identify and map the locations and extent of BWVF. Our mapping identified six small areas of Brogo Wet Vine Forest totalling 17.5 hectares. All areas were within Bodalla State Forest and were located on the exposed lower slopes of Mount Dromedary.

Operational map for Dry Rainforest of the South East Forests:

The operational map for Dry Rainforest of the South East Forests (Dry Rainforest) 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 Dry Rainforest was reviewed by the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel), and a set of diagnostic parameters for the identifying the Dry Rainforest TEC was agreed upon. Using these diagnostic parameters, we sampled candidate areas from existing vegetation maps to identify potential areas of Dry Rainforest occurrence in 296 000 hectares of State Forest and undertook additional mapping work using two independent mapping methods. Random Forest models (predictive habitat models) were generated using plot data and a selection of environmental variables. Aerial photo interpretation targeted stands of forests dominated by Ficus rubiginosa to refine the potential boundaries of Dry Rainforest. We tested whether Dry Rainforest was present in State Forest by completing systematic plot surveys within mapped areas indicating potential presence. We compared our collected data to a large regional pool of plot data that contained a subset of plots assigned to vegetation map units cited in the determination for the Dry Rainforests TEC (see Keith and Bedward 1999). Our analysis of data confidently assigned only a few plots in State Forest to Dry Rainforest (2/21). From these results, we were able to construct an operational map for Dry Rainforest. We identified six small patches of Dry Rainforest but only one patch was located within the study area. This patch was located in Towamba State Forest and was 0.53 hectares.

Operational TEC Mapping have been derived by API at a viewing scale between 1-4000 using ADS40 50 cm pixel imagery and 1 m derived LIDAR DEM grids for floodplain EECs.

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