This data release provides inputs needed to run the LANDIS PRO forest landscape model and the LINKAGES 3.0 ecosystem process model for the area burned by the Black Dragon Fire in northeast China in 1987, and simulation results that underlie figures and analysis in the accompanying publication. The data release includes the fire perimeter of Great Dragon Fire; input data for LINKAGES including soils, landtype, and climate data; initial conditions of stands in the study area before the Great Dragon Fire; and maps of LANDIS PRO output for each model grid cell including total trees, total biomass (Mg/ha), and tree density (trees/ha) in two-year timesteps.

Export DataAccess APIThese datasets were produced as part of a study undertaken by the University of Newcastle, commissioned by the NSW Natural Resources Commission. The study produced a report, titled ‘Retrospective Analysis of Forest Structure Change: ALS Data Comparison and Interpretation’.These datasets are part of a web application on the Spatial Collaboration Portal, accessible through the below URL:https://portal.spatial.nsw.gov.au/portal/apps/experiencebuilder/experience/?id=7ab99290b6514fed880df16af1fcc7e6 Metadata Portal Metadata InformationContent TitleALS Analysis into Forest Structure Change - Coffs HarbourContent TypeOtherDescriptionALS derived canopy height & coverage models and associated factors.Initial Publication Date24/05/2024Data Currency24/05/2024Data Update FrequencyOtherContent SourceOtherFile TypeImagery LayerAttributionData produced by University of Newcastle for the Natural Resources CommissionData 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 DataThese datasets were produced as part of a study undertaken by the University of Newcastle, commissioned by the NSW Natural Resources Commission. The study produced a report, titled ‘Retrospective Analysis of Forest Structure Change: ALS Data Comparison and Interpretation’.These datasets are part of a web application on the Spatial Collaboration Portal, accessible through the below URL:https://portal.spatial.nsw.gov.au/portal/apps/experiencebuilder/experience/?id=7ab99290b6514fed880df16af1fcc7e6 Metadata Portal Metadata InformationContent TitleALS Analysis into Forest Structure Change - Styx RiverContent TypeOtherDescriptionALS derived canopy height & coverage models and associated factors.Initial Publication Date24/05/2024Data Currency24/05/2024Data Update FrequencyOtherContent SourceOtherFile TypeImagery LayerAttributionData produced by University of Newcastle for the Natural Resources CommissionData 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 provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.

Data points intensively sampling 46 North American biomes were used to predict the geographic distribution of biomes from climate variables using the Random Forests classification tree. Techniques were incorporated to accommodate a large number of classes and to predict the future occurrence of climates beyond the contemporary climatic range of the biomes. Errors of prediction from the statistical model averaged 3.7%, but for individual biomes, ranged from 0% to 21.5%. In validating the ability of the model to identify climates without analogs, 78% of 1528 locations outside North America and 81% of land area of the Caribbean Islands were predicted to have no analogs among the 46 biomes. Biome climates were projected into the future according to low and high greenhouse gas emission scenarios of three General Circulation Models for three periods, the decades surrounding 2030, 2060, and 2090. Prominent in the projections were (1) expansion of climates suitable for the tropical dry deciduous forests of Mexico, (2) expansion of climates typifying desertscrub biomes of western USA and northern Mexico, (3) stability of climates typifying the evergreen–deciduous forests of eastern USA, and (4) northward expansion of climates suited to temperate forests, Great Plains grasslands, and montane forests to the detriment of taiga and tundra climates. Maps indicating either poor agreement among projections or climates without contemporary analogs identify geographic areas where land management programs would be most equivocal. Concentrating efforts and resources where projections are more certain can assure land managers a greater likelihood of success.