Record name and number of State Forest, address, sheet number, subject and date of map.

(3/3093). 1 box.

Note:

This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

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.

The operational map for Montane Peatlands and Swamps 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 project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) reviewed the determination for Montane Peatlands and Swamps and agreed upon a set of diagnostic parameters for its identification through aerial photograph interpretation (API). These parameters included an elevation of greater than 400m and, broadly, the presence of treeless native vegetation on poorly drained soils. Using API, we then assessed whether Montane Peatlands and Swamps is present within more than 828,000 hectares of state forests within the coastal, tableland and montane regions of eastern NSW. A number of State Forests were excluded from the assessment because they fell below the elevation threshold or were underlain by Triassic sandstone sediments, which are explicitly excluded in the determination for Montane Peatlands and Swamps. In total we identified 1729.5 hectares of candidate Montane Peatlands and Swamps across State Forests in eastern NSW. From this we constructed several operational maps showing the extent of the Montane Peatlands and Swamps TEC within the relevant State Forests. More than 60% of the total mapped areas were located in the southern tablelands. The largest areas of the candidate TEC were mapped in Bago, Glenbog and Badja State Forests in the south, and in Boonoo and Girard State Forests in the north. Patch size varied, with more than 200 patches being smaller than 0.1 hectare and around 50 patches being larger than 30 hectares. It is noted that the broad mapping criteria will have captured a wide range of floristic assemblages including swamps, bogs, marshes, fens, meadows, grasslands and herb fields. Not all of these assemblage will be Montane Peatlands and Swamps, and it is highly likely that the mapping has captured two related TECs due to their overlapping environmental gradients and similar vegetation structure. These two TECs (Upland Wetlands of the Drainage Divide of the New England Bioregion and Carex Sedgeland of the New England Tableland, Nandewar, Brigalow Belt South and NSW North Coast Bioregions) are both candidate TECs within State Forests in their own right.

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.

Operational map for River-flat Eucalypt Forest:

The operational map for River-flat Eucalypt Forest (RFEF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. In both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for RFEF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that RFEF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms. Following on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of RFEF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. We used aerial photograph interpretation (API) to assess the floristic and structural attributes of the vegetation cover found on our modelled alluvial environments, and thus delineated polygons likely to contain RFEF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of eucalypt, casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments. We then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast 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 compared these plots with those previously assigned to flora communities listed in the determination of RFEF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the RFEF determination. Following this, we developed a predictive statistical model of the probability of occurrence of RFEF using plot data and a selection of environmental and remote-sensing variables. For the north coast, we used a Random Forest model, while for the south coast we used a Boosted Regression Tree model. To create the operational map, we assigned every mapped API polygon to RFEF if appropriate based on the plot data, over-storey and understorey attributes, landform features and modelled probabilities underlying each API polygon. We mapped 3819 hectares of RFEF on the south coast and 198 hectares of RFEF on the north coast.

Operational map for Swamp Oak Floodplain Forest:

The operational map for Swamp Oak Floodplain Forest (SOFF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. In both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for SOFF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that SOFF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms. Following on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of SOFF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. We used aerial photograph interpretation (API) to assess floristic and structural attributes of the vegetation cover on our modelled alluvial environments, and thus delineated polygons likely to contain SOFF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments. We then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast 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 compared these plots with those previously assigned to flora communities listed in the determination of SOFF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the SOFF determination. To create the operational map, we assigned every mapped API polygon to SOFF based on the plot data, over-storey and understorey attributes, landform features and model output underlying each API polygon. In total, we mapped approximately 272 hectares of SOFF across our full study area.

Operational map for Swamp Sclerophyll Forest:

The operational map for Swamp Sclerophyll Forest (SSF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. In both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for SSF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that SSF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms. Following on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of SSF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. We used aerial photograph interpretation (API) to assess the floristic and structural attributes of the vegetation cover on our modelled alluvial environments, and thus delineated polygons likely to contain SSF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of eucalypt, casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments. We then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast 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 compared these plots with those previously assigned to flora communities listed in the determination of SSF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the SSF

The operational map for Swamp Oak Floodplain Forest (SOFF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. In both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for SOFF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that SOFF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms. Following on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of SOFF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. We used aerial photograph interpretation (API) to assess floristic and structural attributes of the vegetation cover on our modelled alluvial environments, and thus delineated polygons likely to contain SOFF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments. We then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast 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 compared these plots with those previously assigned to flora communities listed in the determination of SOFF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the SOFF determination. To create the operational map, we assigned every mapped API polygon to SOFF based on the plot data, over-storey and understorey attributes, landform features and model output underlying each API polygon. In total, we mapped approximately 272 hectares of SOFF across our full study area.

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.

The operational map for Swamp Sclerophyll Forest (SSF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. In both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for SSF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that SSF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms. Following on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of SSF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. We used aerial photograph interpretation (API) to assess the floristic and structural attributes of the vegetation cover on our modelled alluvial environments, and thus delineated polygons likely to contain SSF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of eucalypt, casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments. We then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast 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 compared these plots with those previously assigned to flora communities listed in the determination of SSF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the SSF determination. Following this, we developed a predictive statistical model of the probability of occurrence of SSF using plot data and a selection of environmental and remote-sensing variables. For the north coast, we used a Random Forest model, while for the south coast we used a Boosted Regression Tree model. To create the operational map, we assigned every mapped API polygon to SSF if appropriate based on the plot data, over-storey and understorey attributes, landform features and modelled probabilities underlying each API polygon. In total, we mapped approximately 1131 hectares of SSF across out study area.

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.

The operational map for Coastal Saltmarsh 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 project’s Threatened Ecological Community Reference Panel (the Panel) reviewed the determination for Coastal Saltmarsh and agreed upon a set of diagnostic parameters for its identification. We identified that any treeless saline and sub saline native vegetation found in the intertidal zone had the potential to be Coastal Saltmarsh. We estimated the extent of the intertidal zone by using a fine scale digital elevation model to determine the highest astronomical tideline (HAT). We then mapped potential Coastal Saltmarsh by analysing recent fine scale three dimensional aerial imagery to identify any native vegetation that comprised of low-growing treeless communities and was located within the HAT and on the landward side of mangroves. Mapping criteria used a tree cover tolerance of up to 30% to include areas that had a mixed cover of mangrove, paperbark or casuarina species with a saltmarsh understorey. Exposed mudflats and banks were also mapped when they were visible. Our mapping covered 1.4 million hectares of State Forest within the south, central and north coast regions of NSW. We identified a total of 111.9 hectares of Coastal Saltmarsh within 14 State Forests along the east coast. The most extensive areas are located in Bermagui and Mogo State Forests on the south coast and in Wallaroo State Forests on the north coast. We validated our map of coastal saltmarsh using an existing independent map of estuarine habitats (Creese et al 2009). Our mapping consistently identified almost twice as much coastal saltmarsh as Creese et al (2009), but this was attributable to differences in the mapping criteria rather than any error.

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.

Operational map for Lowland Rainforest:

The operational map for Lowland Rainforest (LORF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for LORF. The Panel found that the determination for LORF relies almost exclusively on a rainforest classification system described by Floyd (1990) where several rainforest ‘suballiances’ make up the LORF assemblage. Floyd’s suballiance classifications presented a challenge to our project as they were largely subjective and were not compatible with quantitative analysis, meaning that it was difficult to distinguish between the LORF TEC and other rainforest vegetation using statistically sound methods. To overcome some of these problems we revisited a set of reference sites that were assigned by Floyd to the suballiances cited in the LORF determination and in other rainforest TEC determinations, and collected new floristic data using standard flora survey methods. We also targeted a range of localities on State Forest that we considered likely to include LORF and other rainforest TECs based on the suballiance descriptions, cited localities in Floyd (1990), and preliminary distribution models. Over 300 new rainforest plots were combined with a large pool of existing data covering eastern NSW to construct a provisional revised rainforest classification. We used the rainforest groups derived from this analysis to compare the species composition of Floyd’s suballiances, determination assemblage lists and recent rainforest classifications included in regional classifications. Rainforest groups (and the plots that defined them) were assigned to the Floyd suballiance with the highest degree of floristic similarity. We conferred with the Panel to resolve any inconsistencies between the results of our analyses and statements relating to the distribution and composition of individual suballiances in Floyd (1990) and the determinations. We then used plot data and a selection of environmental and remote-sensing variables to develop a Random Forest (RF) model of the probability of occurrence of LORF. We assessed the location of plots assigned to LORF against the distribution of the RF model on and adjoining State Forests. We then completed detailed aerial photograph interpretation (API) using a prescribed set of mapping classes to delineate rainforest areas for a range of canopy cover thresholds. We constructed an operational map of LORF by assigning our API polygons as being LORF based on the modelled probabilities and plot data underlying the polygon. Our mapping identified a total of approximately 14,036 hectares of LORF, the vast majority of which was located in the north coast region. We mapped 13,209 hectares of LORF on the north coast, with the largest areas found in Ewingar and Unumgar State Forests. Only 827 hectares of LORF were mapped on the south coast, with the largest areas found in Yadboro and Currowan State Forests.

Operational map for Lowland Rainforest on Floodplains:

The operational map for Lowland Rainforest on Floodplains (LRFP) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for LRFP. The Panel found that the determination for LRFP relies mainly on a rainforest classification system described by Floyd (1990) where several rainforest ‘suballiances’ make up the LRFP assemblage. The determination also identifies a range of floodplain and alluvial descriptors. Floyd’s suballiance classifications presented a challenge to our project as they were largely subjective and were not compatible with quantitative analysis, meaning that it was difficult to distinguish between the LRFP TEC and other rainforest vegetation using statistically sound methods. To overcome some of these problems we revisited a set of reference sites that were assigned by Floyd to the suballiances cited in the LRFP determination and other rainforest TEC determinations, and collected new floristic data using standard flora survey methods. We also targeted a range of localities on State Forest that we considered likely to include LRFP and other rainforest TECs based on the suballiance descriptions, cited localities in Floyd (1990), and preliminary distribution models. Over 300 new rainforest plots were combined with a large pool of existing data covering eastern NSW to construct a provisional revised rainforest classification. We used the rainforest groups derived from this analysis to compare the species composition of Floyd suballiances, determination assemblage lists and recent rainforest classifications included in regional classifications. Rainforest groups, (and the plots that defined them), were assigned to the Floyd suballiance with the highest degree of floristic similarity. We conferred with the TEC Project Reference Panel (the Panel) to resolve inconsistencies between the results of our analyses and statements relating to the distribution and composition of individual suballiances in Floyd (1990), and the determinations. We attempted to use plot data and a selection of environmental and remote-sensing variable to develop Random Forest models of the probability of occurrence of LRFP, but we were unable to assign any of our rainforest groups to the assemblage lists or the primary suballiances cited in the LRFP determination. We overcame this problem by constructing a fine scale digital elevation model (DEM) of landscape elements that we considered were likely to be associated with the range of floodplain and alluvial descriptors identified in the determination for LRFP. We then mapped our rainforest groups onto the DEM and assigned any rainforest assemblage that overlapped with our alluvial and floodplain DEM map as LRFP TEC. Using this method we constructed an operational map of LRFP in State Forests on the NSW coast. Our mapped identified a total of 680 hectares of LRFP, all of which was located in the north coast region.

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 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.

There are two maps in this dataset, solid geology map of the Greenhills-Bago-Maragle State Forests in NSW and regolith landforms map covering the same area. The regolith map shows the types of soil or regolith that are found on different geology types.

Flora Reserves within State Forests in NSW are gazetted areas set aside by FCNSW to provide examples of the original vegetation of the surrounding area. They are generally areas of undisturbed or minimal disturbance forest.

These maps were prepared and published by the Mapping Branch of the Forestry Commission of New South Wales. They contain information relating to State forests and timber reserves.


(SR Map Nos.52563-70). 8 maps.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

The operational map for River-flat Eucalypt Forest (RFEF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. In both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for RFEF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that RFEF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms. Following on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of RFEF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. We used aerial photograph interpretation (API) to assess the floristic and structural attributes of the vegetation cover found on our modelled alluvial environments, and thus delineated polygons likely to contain RFEF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of eucalypt, casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments. We then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast 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 compared these plots with those previously assigned to flora communities listed in the determination of RFEF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the RFEF determination. Following this, we developed a predictive statistical model of the probability of occurrence of RFEF using plot data and a selection of environmental and remote-sensing variables. For the north coast, we used a Random Forest model, while for the south coast we used a Boosted Regression Tree model. To create the operational map, we assigned every mapped API polygon to RFEF if appropriate based on the plot data, over-storey and understorey attributes, landform features and modelled probabilities underlying each API polygon. We mapped 3819 hectares of RFEF on the south coast and 198 hectares of RFEF on the north coast.

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.

Bangalay Sand Forest is a threatened ecological community (TEC) associated with coastal sand plains found in the Sydney Basin and South East Corner bioregions. The most common tree species are Eucalyptus botryoides (bangalay) and Banksia integrifolia (coast banksia). The understorey is characterised by a mix of sclerophyll and mesophyll species. In this report, we focus on the distribution of this TEC in the NSW South Coast region, an area that extends from Sydney to the Victorian border. This study assesses whether Bangalay Sand Forest is located within the 350,000 hectares of state forest found in our southern study area. Our interpretation of Bangalay Sand Forest (BASF) was informed by the six previously described vegetation communities cited in the final determination that were relevant to the South Coast region. Four are eucalypt-dominated forests and one a coastal scrub dominated by Banksia integrifolia and Leptospermum species. An additional community has a mixed canopy composition for which the final determination includes a qualifying statement to exclude stands dominated by Casuarina glauca. Initially we examined existing maps of coastal sand landforms and geology along with available vegetation maps to determine the likely extent of habitats suitable to support the presence of the TEC within state forest. We reviewed candidate areas that were within or proximate to state forests using interpretation of high-resolution digital aerial imagery as a basis for planning field surveys. We identified a small number of areas in Termeil and East Boyd State Forests that were plausible locations for BASF and an additional two areas in Nullica and Mogo State Forests identified from existing vegetation mapping. Sites that had not already been subject to field survey were visited and were either systematically sampled or were rejected on site where the species composition and landform were clearly mapping inaccuracies (e.g. estuarine mudflat) Our analyses of plot data assigned 66 plots (out of 8452) to Bangalay Sand Forest, based on allocation to a previously defined community cited in the final determination and agreed substrate qualifiers. We used plot data and a selection of environmental and remote-sensing variables to develop a Random Forest (RF) presence-absence model of the probability of occurrence of Bangalay Sand Forest across the study area. We used the RF model and the locations of plot data to further assess whether Bangalay Sand Forest occurred on state forest.

We found no evidence of Bangalay Sand Forest occurring on any state forest within our study area based on the results of our field surveys, analysis of plot data, review of existing map data and predictive models.

Operational map:

The operational map for Grey Box Grey Gum Wet Sclerophyll Forest (GBWS) 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 project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) interpreted the determination for GBWS and agreed that GBWS TEC is defined from quantitative floristic analyses of systematic plot data. Based on a strong association with the determination assemblage list and documented occurrences referenced in the determination, we have interpreted GBWS to be equivalent to a community described in a recent classification study in the Northern Rivers (OEH, 2012); 1000-1665: (Grey Gum - Grey Box - Hoop Pine shrubby open forest on hinterland hills of the Richmond and Clarence catchments, South Eastern Queensland Bioregion and NSW North Coast Bioregion). We conducted plot-based floristic comparison to assess whether GBWS or the equivalent Community 1000-1665 was present within 800 000 hectares of State Forest in the North Coast area. A map was developed based on plot assignments, aerial photography interpreted map polygons delineated from overstorey and understorey patterns, and results of predictive modelling. In total, we identified approximately 2936 hectares of GBWS TEC in State forests north from Cherry Tree State Forest. Another state forest area has been identified as potentially supporting GBWS forest and is presented in a separate Indicative map.

Indicative map:

The indicative map for Grey Box Grey Gum Wet Sclerophyll Forest (GBWS) 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 project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) interpreted the determination for GBWS and agreed that GBWS TEC is defined from quantitative floristic analyses of systematic plot data. Based on a strong association with the determination assemblage list and documented occurrences referenced in the determination, we have interpreted GBWS to be equivalent to a community described in a recent classification study in the Northern Rivers (OEH, 2012); 1000-1665: (Grey Gum - Grey Box - Hoop Pine shrubby open forest on hinterland hills of the Richmond and Clarence catchments, South Eastern Queensland Bioregion and NSW North Coast Bioregion). We conducted plot-based floristic comparison to assess whether GBWS or the equivalent Community 1000-1665 was present within 800 000 hectares of State Forest in the North Coast area. A map was developed based on plot assignments, aerial photography interpreted map polygons delineated from overstorey and understorey patterns, and results of predictive modelling. In total, we identified approximately 2936 hectares of GBWS TEC in State forests north from Cherry Tree State Forest. However, we also assigned three plots to GBWS, which are disjunct from and well outside the previously known distribution, to the south. Of the three disjunct plots, only one is in our state forest study area, in Nymboida state forest. We have no evidence that GBWS occurs south of Nymboida state forest. We identify Nymboida and Kangaroo River state forests in this Indicative Map, as plausible locations for the GBWS TEC. We recommend the GBWS TEC in these areas be diagnosed on a site-by-site basis using our field key until further survey and mapping can be completed in these forests.

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.

Abstract

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Abstract: Old growth forest mapping from aerial photograph interpretation of canopy species regeneration and senescent growth stages. Scale 1:25,000. Bounded by NSW Morriset Forestry District. Boundaries include the New England Highway and Hunter River in the North,the Blue mountains and Wollemi National Park in the west and the Illawarra highway in the south. VIS_ID 4122

Purpose

To map old growth forest in the Morriset area.

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Digitised on screen over 1:250,000 scale topographic maps. Attributes were verified in the field and by NSW state forests. Stereoscopic interpretation using a range of stereoscopes with a variety of magnifications (eg. Topcon and Abrams stereoscopes with x10 magnification). Attributes table & codes - Information contained in the attribute table is: regrowth-juvenile and sapling, regrowth-pole, Mature-early mature & mature, senescent-late mature and overmature, disturbance code. The project was only concerned with pyrophytic vegetation consequently, vegetation that was <10% pyrophytic was coded with an O and rainforest was coded with an R. Mapping pathway - An api pathway was developed specifically for the BOGMP. O =0%.if vegetation obviously rainforest= R 1 =0-10%,if vegetetation obviously rainforest=R 2 =11-20% and 3 = 21-30% If understorey rainforest rather than grass or heath. On ecological grounds this should be called rainforest but there might be some debate over the upper part of class 3.= R 4 = 31-50% difficult to see the understorey, would require ground truthing. Could have rainforest in which case there would be an argument about whether to be treated as a separate vegetation type or as a serial stage of rainforest Discretionary(presence/absence of rainforest understorey) 5 =51-60% Could have rainforest elements but difficult to determine as rainforest. Eucalypt = 81-100% unlikely to be rainforest in understorey. Eucalypt The mapping pathway specified that in eucalypt forest, primary polygon primary polygon delineation was based on floristics then split firstly on structural differences, structure (% regrowth and senescence), secondly on height classes then regrowth size class, and tagged for relative stand density and disturbance indicators. No senescence was recorded for polygons outside of State Forest with >30% regrowth. Eucalyptus-dominated vegetation with <20% ccp was not delineated. Data recording. The aerial photographs were pre-prepared and supplied to interpreters with Effective areas and land tenure boundaries marked directly on to the photographs. The effective area of a photograph included all images closer to the centre of the photograph than to the centre of any other. The central old growth study area used recent logging disturbance maps provided by state forests. Land Cover - Vegetation Cover greater than 20% canopy cover Floristics - Classification into pyrophytic vegetation, <10% pyrophytic vegetation, and rainforest Strata - Mapping pathway delineates a code of rainforest or eucalypt according to understorey type in areas with discrepencies Growth Stage - Regeneration and senescence Multi-attribute Mapping - Native vegetation greater than 20% ccp delineated. Relative stand density for the regrowth component of the vegetation also identified. Special features identified (eg. exotic pine plantation). Land use / cover not identified. Survey Type point to plant transects. Inaccessible areas were assessed using aircraft. Information Collected Growth stage, disturbance and vegetation assessment Date of surveys -1996? Minimum Polygon Size -25 hectares Edge Matching - Not assessed Polygon Attribution - Comparison of the growth stage polygon codes and linework against a hard copy map and against the original linework on the aerial photographs.. Both a 10% random sample of the photographs,and all the photographs in a specific area were checked for coding and linework errors. Custodian - NPWS Date of map product -1996 Strengths - A validation process was implemented. Detailed growth stage information and disturbance information. Field checking was undertaken. Multi-attribute mapping with broad geographic coverage, relatively high quality data capture techniques, Weaknesses - The difference in ability of the interpreters (eg moist, high site quality forest types were more reliably mapped than other forest types).Field work was insufficient, confined to state forest tenure. High possibility of post mapping logging and disturbance.

Dataset Citation

NSW Office of Environment and Heritage (2015) Old Growth Forest Mapping Broad, Central, 1996. VIS_ID 4122 2015 20150116. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/85a296b9-0c03-4dec-a0c1-cb22debbdbd1.

SFNSW Eden survey of Tallanganda and Badja State Forests for Vegetation Map for Tallanganda EIS. The SF_QFS(SFNSW Eden survey of Tallanganda and Badja State Forests for Vegetation Map for Tallanganda EIS) Survey is part of the Vegetation Information System Survey Program of New South Wales which is a series of systematic vegetation surveys conducted across the state between 1970 and the present. Please use the following URL to access the dataset: http://aekos.org.au/collection/nsw.gov.au/nsw_atlas/vis_flora_module/SF_QFS

The operational map for Tablelands Snow Gum, Black Sallee, Candlebark and Ribbon Gum Grassy Woodland (Tableland Snow Gum or TSG) was constructed to resolve long-standing issues surrounding its …Show full descriptionThe operational map for Tablelands Snow Gum, Black Sallee, Candlebark and Ribbon Gum Grassy Woodland (Tableland Snow Gum or TSG) 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 TSG was reviewed by the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel), and a set of diagnostic parameters for identifying the TSG TEC was agreed upon. These parameters included the bioregions in which TSG is likely to be located in, landscape features such as elevation and geology, and quantitative floristic attributes from vegetation communities explicitly listed in the determination. Using these diagnostic parameters, we defined the study area as being all IBRA subregions that cover the 600-1400m elevation range within the South Eastern Highlands, Sydney Basin, South East Corner and Australian Alps bioregions. We then compiled floristic plot data for all State Forest areas within our study area. 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 compared these plots with those previously assigned to flora communities listed in the determination of TSG. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the TSG determination. We also conducted presence-absence predictive distribution modelling to identify potential distributions of each of the primary vegetation communities cited in the determination for TSG. The modelling predicted the likelihood of occurrence for each community across State Forests in the study area based on a modelled relationship with environmental and remotely sensed variables. As such, the modelling assisted in identifying un-surveyed areas of potential TSG habitat and guiding follow-up survey efforts and aerial photography interpretation (API) work. API assessment was carried out for all State Forests where the predictive modelling identified areas with high probability-of-occurrence values. We used recent high resolution stereo digital imagery in a digital 3D GIS environment to delineate areas of potential TSG based on observable patterns in canopy species dominance, understorey characteristics and landform elements. We constructed the operational map by assigning our API polygons as being TSG based on the extent to which the floristic plots within or near to each API polygon belonged to TSG. We used a precautionary approach and assessed a mapped polygon as TSG if the map unit to which it belonged contained any TSG plot. 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.

The operational map for Swamp Oak Floodplain Forest (SOFF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. \r In both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for SOFF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that SOFF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms.\r Following on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of SOFF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas.\r We used aerial photograph interpretation (API) to assess floristic and structural attributes of the vegetation cover on our modelled alluvial environments, and thus delineated polygons likely to contain SOFF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments.\r We then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast 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 compared these plots with those previously assigned to flora communities listed in the determination of SOFF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the SOFF determination.\r To create the operational map, we assigned every mapped API polygon to SOFF based on the plot data, over-storey and understorey attributes, landform features and model output underlying each API polygon. \r In total, we mapped approximately 272 hectares of SOFF across our full study area.\r \r 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.

No abstract available