This is the original extant Forest Ecosystem map for the South Coast sub-region, comprising 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been mapped in the extant map for this sub-region.; ; VIS_ID 3786; ; ANZLIC: ANZNS0208000141 Note that this map was superseded by VIS_IDs 3858 & 3859

The pre-1750 and extant Forest Ecosystem maps of the Western sub-region comprise a number of different Generalised Additive Models (GAMs) and interpreted API and soils data. Within the extant vegetation area, expert botanists developed the map by assigning API polygons to vegetation groups, determined by an ecological classification process using PATN software. On cleared land, a combination of soils, GAMs, and classified site data was used to assign vegetation groups to distinct topographic and soil patterns. This hybrid expert and modelling approach was approved and signed off by a review team of expert botanists including two independents, one NPWS representative and one SFNSW representative. Seventy-six distinct ecosystems have been mapped in the pre-1750 map for this sub-region. The extant map was derived from the pre-1750 map by cutting it to the extant vegetation. The extant vegetation layer incorporates the vegetated API polygons from the CRAFTI project, RN17 mapping, and remnants. Seventy-two distinct ecosystems have been mapped in the extant map for this sub-region. (VIS_ID 3792; ANZLIC: ANZNS0208000145)

The Southern Forests region covers an area of south-eastern New South Wales south of Oberon and Kiama and east of Albury and Boorowa (between longitude 146o 56 and 147o 6 E, and between latitude 33o 2 and 37 o 6 S). The total area mapped was 3 086 200 hectares. Terrestrial, wetland, and estuarine vegetation of the Southern Forests region were classified into 206 vegetation groups and mapped at a scale between 1:25 000 and 1:100 000. VIS ID 3858.

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.

This is a combined data set of canopy, subcanopy and understory composition by vegetation species and study site ID

The project objective was to prepare a map of forest ecosystems for the Southern CRA Region. Based on the JANIS criteria (JANIS 1997), forest ecosystems are intended to act as broad surrogates for biodiversity within the Southern CRA Region. Note this dataset is no longer available (superseded by VIS_ID 3858). ; VIS_ID 3789

The Southern Forests region covers an area of south-eastern New South Wales south of Oberon and Kiama and east of Albury and Boorowa (between longitude 146o 56 and 147o 6 E, and between latitude 33o 2 and 37 o 6 S). The total area mapped was 3 086 200 hectares. Terrestrial, wetland, and estuarine vegetation of the Southern Forests region were classified into 206 vegetation groups and mapped at a scale between 1:25 000 and 1:100 000. VIS ID 3859

Two spatial products generated for the Comprehensive Regional Assessment (CRA), namely floristic linework captured by aerial photographic interpretation (API), and a; forest ecosystem model, were integrated to provide an improved spatial map of forest ecosystem distribution on the Mid North Coast of NSW. The process involved intuitive assignment of dominant forest ecosystems to polygons captured by the API, and involved a number of iterative steps. The resultant layer is both polygon and grid cell based, and provides an interim spatial product upon which Government agencies will be able to identify high conservation value native vegetation across the Mid North Coast of NSW with reasonable reliability.; The original forest ecosystem classification included some 230 ecosystems, occurring from the Queensland Border to the Lower Hunter Valley. Of these, 124 were eliminated, either because they were not considered to occur in the Mid North; Coast, or they were replaced by new or existing ecosystems. A total of 40 ecosystems were introduced, including splits of existing ecosystems (eg. rainforest) or new ecosystems imported from more recent classifications. The final number of ecosystems mapped within the Mid North Coast for this project was 140. Derivation of current and former extent of each ecosystem was undertaken to provide an index of conservation status in the form of a %-cleared estimate. In; summary, 39 ecosystems possessed a %-cleared value of at least 50%, while 46 ecosystems were less than 20% cleared. Several of those with a high clearing rate are equivalent to endangered ecological communities listed under the Threatened Species Act 1995. In the longer term, an improved classification will be required to replace forest ecosystems, which is overly broad on coastal and private land given the influence of commercial forest types on the classification. Additional targeted API will also be; required in future, as the reliability of API undertaken for the CRA is questionable in many areas. Provision of an improved API coverage and a more appropriate classification will enable production of a new generation of vegetation mapping products for north-east NSW, providing the future basis for property vegetation planning, benchmarking, bio-certification and bio-banking.; VIS_ID 3886

SNF study location measurements of percent ground coverage provided by each understory species; percentages are averages of five 2-meter-diameter subsamples in each site (presented in table format)

Validating the extent of modelled "rare and vulnerable forest ecosystems" identified during the Southern CRA (1997-2000) was required for ecologically sustainable conservation management of reserves within the NPWS South Coast Region. The validation was for 47 forest ecosystems out of a total of 96 forest ecosystems that occur in the NPWS South Coast Region. Considerable improvements were made to the reliability and accuracy of the vegetation ecosystem map. Some 3000 edits were incorporated into the revised forest ecosystem map. Six new ecosystems were newly classified as part of this contract. VIS_ID 4155