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.

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

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.

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.

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

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.

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

A vegetation map was produced for the Northern Rivers Catchment Management Authority (NRCMA) by integrating two products undertaken for the northern Comprehensive Regional Assessment (CRA), namely the forest ecosystem model and API coverage. The work was undertaken by Eco Logical Australia Pty Ltd in 2005. The new product represents a substantial improvement over the original forest ecosystem model as it provides nomenclatural consistency between forest ecosystem types and API polygon labels, includes remnant vegetation not originally captured by the CRAFTI API project, improves the spatial rigour of forest ecosystem distribution, and introduces a number of new ecosystems based on fine scale API and expert advice. The map incorporates 167 ecosystems covering a combined area of 3,332,900 ha, about 67% of the NRCMA region. It includes dry and moisttableland types, rainforest and wet escarpment brushbox and eucalypt forests, dry foothills eucalypt forest, rugged gorges woodlands, and non-eucalypt coastal types. The map includes a table which lists areal estimates for each ecosystem, including area outside the NRCMA region and pre-1750 area.; Albeit an improved product, the final map was assembled from two existing layers which have major limitations on private land, including an unsuitable vegetation; classification and a broad and often unreliable API layer. It is thus recommended that the product be used only in the interim by the NRCMA to support identification; and prioritisation of high conservation value vegetation, and that any attempt to reconcile the map with local landscapes be done with due caution.; ; The original NRCMA Veg layer is in grid format but was converted to polygons. See data history section.; ; Data custodian - Northern Rivers CMA; VIS_ID 524

This dataset created by the University of Sydney, includes time series digital soil map products of soil organic carbon (SOC) between January 1990 and December 2020 for the Regional Forest Agreement regions of eastern NSW. Modelling was completed using a data cube platform incorporating machine learning space-time framework and geospatial technologies. Products provide estimates of SOC concentrations and associated trends through time. Also important covariates required to drive this spatio-temporal modelling are identified using the Recursive Feature Elimination algorithm (RFE), which including a range of predictors that vary in space, time and space and time. Full description of the digital soil maps and methods are presented in: Moyce MC, Gray JM, Wilson BR, Jenkins BR, Young MA, Ugbaje SU, Bishop TFA, Yang X, Henderson LE, Milford HB, Tulau MJ, 2021. Determining baselines, drivers and trends of soil health and stability in New South Wales forests: NSW Forest Monitoring & Improvement Program, Final report v1.1 for NSW Natural Resources Commission by NSW Department of Planning, Industry and Environment and University of Sydney. The metadata's data packages section includes project scripts and code, final project report and an external Cloudstor link to download the predicted SOC map products,

The extant Forest Ecosystem map for the South Coast sub-region comprises a number of different models and API data. Expert botanists developed the map within extant vegetation, by assigning API polygons to vegetation groups, determined by an ecological classification process using PATN software. The processes used were approved and signed off by a review team of expert botanists including two independents, one NPWS representative and one SFNSW representative. On cleared land, a combination of soils, GAMs modelling, and classified site data was used to assign vegetation groups to distinct topographic and soil patterns. The extant map was derived from masking the pre-1750 map to the extant vegetation. 101 distinct ecosystems have been mapped in the extant map for this sub-region. (VIS_ID 3786; ANZLIC: ANZNS0208000141)