Export DataThe 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.Empirical Soil Maps: Soil data collected using an empirical approach, presented on 2162 units, based on the unit's most representative soil profile available within the Soil and Land Information System (SALIS). Maps reflect values when the sampling occurred with temporal changes not being accounted for.Metadata Portal Metadata InformationContent TitleBaselines for Soil Health and Stability in NSW RFA Regions: Empirical Soil MapsContent TypeScene Layer/Scene Layer PackageDescriptionSoil data collected using an empirical approach, presented on 2162 units, based on the unit's most representative soil profile available within the Soil and Land Information System (SALIS). Maps reflect values when the sampling occurred with temporal changes not being accounted for.Initial Publication Date30/05/2022Data Currency30/05/2022Data 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

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.Drivers & Trends: Maps describing trends of soil quality indicators and the impact of key drivers of soil condition change, such as Climate Change, Land Disturbance, Bushfires etc. This includes DSM models and Data Cube models using machine learning techniques.More information and the underlying datasets can be found here.Metadata Portal Metadata InformationContent TitleBaselines for Soil Health and Stability in NSW RFA Regions: Drivers and TrendsContent TypeScene Layer/Scene Layer PackageDescriptionMaps describing trends of soil quality indicators and the impact of key drivers of soil condition change, such as Climate Change, Land Disturbance, Bushfires etc. This includes DSM models and Data Cube models using machine learning techniques.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

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

The Forest Monitoring Steering Committee commissioned the University of New England and the NSW Departments of Primary Industries and Planning, Industry and Environment to deliver baselines, drivers and trends for species occupancy and distribution in NSW forests across tenures. Over 15 leading scientists formed a project team lead by Nick Reid.This work delivered a comprehensive suite of models to baseline species occupancy and distribution and help explore drivers in change.Find out more about the project here.This webmap contains those models of a selected group of vulnerable flora & fauna.Model Information:Maximum Entropy (Maxent) & Species Occupancy Model (SOM) outputs are combination outputs dependent on known species occurrence in the landscape, the species relationship with environmental variables (covariates) such as temperature, rainfall and topography; and its predicted occurrence based on covariate analysis. Maxent models do not predict actual occupancy, but rather habitat suitability, while SOMs predict actual occupancy. confounding factors such as inter-species competition, geographical barriers and disturbance events play a significant role in species occurrence, and are not considered in Maxent or SOM.To find more information about the models and processes involved, or to access the underlying data, click here.________For a User Guide for this Webapp, follow this link:User GuideTo leave feedback on your experience with this web site or its data, follow this link:NRC Contact PageTo leave feedback on your experience with the Spatial Collaboration Portal, follow this link:Spatial Collaboration Portal Feedback________This webmap and associated webapps are part of the Forest Monitoring & Improvement Program. Metadata Portal Metadata Information Content TitleSpecies Occupancy and Distribution Baselines in NSW RFA Regions WebmapContent TypeWeb MapDescriptionThis Webmap contains a series of spatial outputs describing probabilistic species predictive occupancy (Species Occupancy Models, or SOM) & habitat suitability (Maximum Entropy, or Maxent) surfaces.Initial Publication Date06/06/2022Data Currency01/01/2000Data Update FrequencyOtherContent SourceFile TypeMap Feature ServiceAttributionData Theme, Classification or Relationship to other DatasetsAccuracySpatial Reference System (dataset)WGS84Spatial Reference System (web service)EPSG:4326WGS84 Equivalent ToGDA94Spatial ExtentContent LineageData ClassificationUnclassifiedData Access PolicyOpenData QualityTerms and ConditionsCreative CommonsStandard and SpecificationData CustodianNatural Resources CommissionPoint of Contactnrc@nrc.nsw.gov.auData AggregatorData DistributorAdditional Supporting InformationTRIM Number

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 was derived from the primary ‘SLATS Landsat woody change data (25m) for 1988 – 2010’ raster (grid) layers used to generate the annualised woody vegetation change rates for the 2010 NSW Annual Report of Native Vegetation. This data describes the areas and type of woody vegetation change (loss) based on the analysis of multi-date Landsat imagery covering NSW. This data is based on a biennial LANDSAT coverage between 1988-2006 and annual coverage 2006-2010. LANDSAT Imagery 1988-2008 was processed by Geoscience Australia at 25m resolution. 2008 onwards is based on USGS processed LANDSAT at 30m resolution. Note, this vector data may generate slightly different aerial statistics to those generated from the source raster data. This is due to variation caused by the data transformation and vector cleaning processes applied in generating the vector data.

This dataset includes digital soil map products of key soil condition indicators covering the Regional Forest Agreement regions of eastern NSW. Raster maps at 100 m resolution reveal baseline (approximately 2008) levels of the soil indicators soil carbon, pH, bulk density, hillslope erosion and others. Maps are presented on trends of change resulting from different human and natural disturbances such as forest harvesting, uncontrolled stock grazing, climate change and bush fire. 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 Soil Regolith Stability classification is used in the assessment of soil water erosion and water pollution hazards. It is currently in use as part of the EPA 1998/99 Pollution Control Licences within state forests. This map identifies soil regolith stability classes using linework and soil information from published soil landscape mapping and reconnaissance soil landscapes of Northern and Southern Comprehensive Regional Assessment (CRA) mapping projects. Each soil landscape unit on the coverage has been allocated a regolith stability classification (soil erodibility), comprising of four classes: class R1 – High coherence soils with low sediment delivery potential class R2 – Low coherence soils (when wet) with low sediment delivery potential class R3 – High coherence soils with high sediment delivery potential class R4 – Low coherence soils (when wet) A regolith stability classification consists of a dominant class and up to three sub-dominant classes (if necessary), for example, R1(R3,R4), where R1 is the dominant regolith stability class and (R3 & R4) are the sub-dominant regolith stability classes. Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area. Reference: Murphy, C.L., Fogarty, P.J. and Ryan P.J. 1998, Soil Regolith Stability Classification for State Forests in Eastern New South Wales, Technical Report No. 41, Department of Land and Water Conservation.