These datasets consist of soil maps generated to assess baselines, drivers and trends for soil health and stability within the NSW Regional Forest Agreement (RFA) regions. The maps are organised into empirical soil maps, digital soil maps, and data cube maps. Empirical soil maps consists of four products. Maps include topsoil pH, carbon, Emerson Aggregate Stability and Soil Profile Quality Confidence. Each map consists of 2,162 units. Maps were generated using the most representative soil profile for each unit available within the Soil and Land Information System (SALIS). The 2008 woody vegetation coverage was used as baseline. Maps reflect values when the sampling occurred with temporal changes not being accounted for. Locations with missing or of poor quality data are identified, providing a confidence rating map as part of the evaluation process. Digital soil maps include map products of key soil condition indicators covering the Regional Forest Agreement regions of eastern NSW. Raster maps of key soil indicators, such as soil carbon, pH, bulk density, hillslope erosion and others, were created at 100 m resolution. For each key soil indicator, maps include baseline (approximately 2008) levels as well as trends of change resulting from different human and natural disturbances such as forest harvesting, uncontrolled stock grazing, climate change and bush fire. Data cube maps include time series of soil organic carbon (SOC) between January 1990 and December 2020 for the Regional Forest Agreement regions of eastern NSW. Products provide estimates of SOC concentrations and associated trends through time. Modelling was carried out using a data cube platform incorporating machine learning space-time framework and geospatial technologies. Important covariates required to drive this spatio-temporal modelling were identified using the Recursive Feature Elimination algorithm (RFE). A web mapping application on the NSW Spatial Collaboration Portal depicts these datasets. Access the webapp through the link below: https://portal.spatial.nsw.gov.au/portal/home/item.html?id=af9c71935f024f4a8f64cb39f5eba007

This publication is the final report of a project that assessed the baselines, drivers and trends for soil health and stability within the NSW Regional Forest Agreement (RFA) regions. The NSW Department of Planning, Industry and Environment (DPIE) and the University of Sydney with collaboration from the University of New England, were engaged by the NSW Natural Resource Commission (NRC) to prepare this report as part of the NSW Forest Monitoring and Improvement Program. Full descriptions of the soil map products, methods and results are presented in this technical report: 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. Version 1.1 Supporting data packages of deliverables can be sourced from the following three datasets below: NSW eastern forest soil condition: digital soil maps NSW eastern forest soil condition: empirical soil maps NSW eastern forest soil condition: Spatio-temporal data cube maps

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,

This dataset contains spatial layers describing Forest Canopy Loss and Recovery from 1998-2019 in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. For this dataset product and the processing of metrics, aspects of canopy loss and disturbances in the forest estate were investigated. Measures of canopy loss and recovery are seen as one of the multiple indicators of forest health. This is related to agents or pressures that affect the capacity of native forests and commercial operations to maintain normal ecosystem functions and sustainably provide productive capacity. To attribute disturbances, as a driver of change, a Multiple Lines of Evidence (MLE) approach was used that leveraged available spatial datasets. This allowed for a project-wide disturbance and disturbance context layer to be generated. This information can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest loss can be potentially assigned or at the very least investigated. The time taken, in terms of years, for areas to recover from losses in forest canopy cover extent can has also been determined. This process identifies the time taken for a patch of forest to return to a 20% canopy cover threshold, and other characteristics such as the forest type and likely disturbance or loss event. Forest Canopy Loss and Recovery uses measures of canopy loss and disturbances which can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest canopy loss can be potentially assigned or at the very least investigated. Time taken in years for areas to recover for losses has also been determined, as-well as other characteristics such as forest type and likely disturbance/loss event. Base cover extent grids used are from the NSW RFA Historic Forest Canopy Cover Extent – 1995 to 2019 product. Read more about the project on the Natural Resources Commission website: https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1 This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Canopy Loss and Recovery - 1998 to 2020'

This dataset contains spatial layers describing Forest Canopy Extent from 1995-2019 in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. Forest Canopy Extent is the likelihood that a certain area has forest at any given time. Forest Canopy is defined in accordance with the National State of the Forests Report which defines forests as containing as a minimum, a mature or potentially mature stand height exceeding 2 metres, stands dominated by trees usually having a single stem, where the mature or potentially mature stand component comprises 20% canopy coverage using a Crown Projective Cover (CPC) measure. These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. To calculate forest canopy extent, these base grids have been processed through a series of land use and vegetation type exclusion masking and a through a fuzzy-logic based certainty analysis to reflect a forest cover extent coverage for NSW that is reflective of past and current coverage. Read more about the project on the Natural Resources Commission website: https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1 This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Canopy Cover Extent - 1995 to 2020'

The spatial layers in this dataset detail forest cover extent over NSW. They have been created for the NSW Natural Resources Commission to detail historic baseline and trends of forest cover extent coverage for NSW for all land tenures, including all RFAs and IFOAs. These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2021). These base grids are Landsat in origin and have a resolution of 25m. These base grids have been processed through a series of land use and vegetation type exclusion masking and a through a fuzzy-logic based certainty analysis to reflect a forest cover extent coverage for NSW that is reflective of past and current coverage. These grids cover the years from 1995 to 2020. The year gaps are triennial or biennial data layers from 1995 to 2004. 1996,1997,1999,2001,2003 years missing as these were not assessed in original applied database. From 2004 to 2020 data layers become annualised. Read more about the project on the Natural Resources Commission website: https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1 This dataset supersedes "NSW Forest Monitoring and Improvement Program RFA Historic Forest Cover Extent – 1995 to 2019". https://portal.tern.org.au/metadata/TERN/fef2d61b-7c5e-42be-88c1-849a3fc6a70a.

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

This dataset contains spatial layers describing Forest Loss and Recovery from 1998-2020 in NSW. For this dataset product and the processing of metrics, aspects of canopy loss and disturbances in the forest estate were investigated. Measures of canopy loss and recovery are seen as one of the multiple indicators of forest health. This is related to agents or pressures that affect the capacity of native forests and commercial operations to maintain normal ecosystem functions and sustainably provide productive capacity. To attribute disturbances, as a driver of change, a Multiple Lines of Evidence (MLE) approach was used that leveraged available spatial datasets. This allowed for a project-wide disturbance and disturbance context layer to be generated. This information can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest loss can be potentially assigned or at the very least investigated. The time taken, in terms of years, for areas to recover from losses in forest cover extent can has also been determined. This process identifies the time taken for a patch of forest to return to a 20% canopy cover threshold, and other characteristics such as the forest type and likely disturbance or loss event. Base cover extent grids used are from the NSW State-wide Historic Forest Cover Extent – 1995 to 2020 product. These have been processed through a series of land use and vegetation type exclusion masking and a through a fuzzy-logic based certainty analysis to reflect a forest cover extent coverage for NSW that is reflective of past and current coverage. Read more about the project on the Natural Resources Commission website: https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1 This dataset supersedes "NSW Forest Monitoring and Improvement Program RFA Historic Forest Loss and Recovery – 1998 to 2019".

Export DataAccess APIThe Forest Monitoring Steering Committee commissioned a consortium between the NSW Department of Planning, Industry and Environment and the University of Sydney to deliver Baselines, Drivers and Trends for soil stability and health in forest catchments across the NSW Regional Forest Agreement areas.Find out more about the project here.Baseline Soil Maps: Digital Soil Modelling (DSM) outputs describing the baseline conditions of key indicators of soil condition, based on quantitative modelling techniques that relate known soil qualities with known environmental qualities and extrapolates using continuous environmental data.Metadata Portal Metadata InformationContent TitleBaselines for Soil Health and Stability in NSW RFA Regions: Baseline Soil MapsContent TypeScene Layer/Scene Layer PackageDescriptionDigital Soil Modelling (DSM) outputs describing the baseline conditions of key indicators of soil condition, based on quantitative modelling techniques that relate known soil qualities with known environmental qualities and extrapolates using continuous environmental data.Initial Publication Date14/06/2022Data Currency14/06/2022Data Update FrequencyOtherContent SourceOtherFile TypeMap Feature ServiceAttributionData Theme, Classification or Relationship to other DatasetsAccuracySpatial Reference System (dataset)OtherSpatial Reference System (web service)OtherWGS84 Equivalent ToOtherSpatial ExtentContent LineageData ClassificationUnclassifiedData Access PolicyOpenData QualityTerms and ConditionsCreative CommonsStandard and SpecificationData CustodianNSW Natural Resources CommissionPoint of ContactEmma Pearce (Emma.Pearce@nrc.nsw.gov.au)Data AggregatorData DistributorSpatial VisionAdditional Supporting InformationTRIM Number

This dataset contains spatial layers describing Forest Connectivity from 1995-2019, in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. Forest Connectivity accounts for the general quality of terrestrial habitats supporting biodiversity at each location, the fragmentation of habitat within its neighbourhood and how its position in the landscape contributes to connectivity among the habitats across a region. These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. Forest Connectivity, including canopy cover connectivity and fragmentation is concerned and linked to forest condition. Concepts applied are to be aligned with definitions as found in the NSW Biodiversity Indicator Program (BIP) and the Spatial Links methodology for calculating connectivity. Base cover extent grids used are from the NSW RFA Historic Forest Canopy Cover Extent – 1995 to 2019 product. Read more about the project on the Natural Resources Commission website: https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1 This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Connectivity - 1995 to 2020'