Description: The Total Forest Carbon 2018 map was developed by Forest Service scientists using data from FIA plots measured between 2014-2018, in conjunction with remote sensing data. More info is available here - https://usfs.maps.arcgis.com/home/item.html?id=bd3c2c1ba3844ebabd8df6d1c4932387 . This image service was developed using data from over 213,000 national forest inventory plots measured during the period 2014-2018 from the USFS Forest Inventory and Analysis (FIA) program, in conjunction with other auxiliary information. Roughly 4,900 Landsat 8 OLI scenes, collected during the same time period, were processed to extract information about vegetation phenology. This information, along with climatic and topographic raster data, were used in an ecological ordination model of tree species. The model produced a feature space of ecological gradients that was then used to impute FIA plots to pixels. The plots imputed to each pixel were then used to assign values (tons per pixel) for total forest carbon. Carbon Pools can be found - https://usfs.maps.arcgis.com/home/item.html?id=4a604935bdce4a6eb77a967fab47ddff For more information about the methods used to produce this dataset please see the following references: • Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E. 2018. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS Journal of Photogrammetry and Remote Sensing. 137: 29-46. • Wilson, Barry Tyler; Woodall, Christopher W.; Griffith, Douglas M. 2013. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance and Management. 8:1. doi:10.1186/1750-0680-8-1 • Wilson, B. Tyler; Lister, Andrew J.; Riemann, Rachel I. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management. 271: 182-198. • Ohmann, Janet L.; Gregory, Matthew J. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A. Canadian Journal of Forest Research. 32: 725-741 Spatial Extent: CONUSUnits: Short tons per pixel
Name: Percent Change in Forest Carbon Stocks (2020-2070, RCP 8.5-SSP-2)
Display Field: NAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This data layer provides baseline estimates and future projections of carbon by county. Baseline carbon estimates for 2020 were derived by the FIA carbon team based on the most recent inventories for each state. Projections based on the baseline estimates were developed by the Resources Planning Act (https://www.fs.usda.gov/research/inventory/rpaa) team where forest area and total carbon estimates were provided for the year 2070, RCP 8.5, SSP2 (for information on RPA models and future scenarios see Langner et al. 2020: https://doi.org/10.2737/RMRS-GTR-412). Projections include the effects of land use change, climate, socioeconomics, timber harvest, fire, other disturbance, and forest growth. Counties were clipped to National Forest boundaries for display purposes. Scenario = RCP8.5-SSP2 combination (high warming, moderate growth), mean/min/max of 5 climate models used in RPA Assessment; another three RCP-SSP combinations are available from the information source. This uses the following five climate models: Least Warm-MRI-CGCM3; Hot-HadGEM2-ES; Dry-IPSL-CM5A-MR; Wet-CNRM-CM5; Middle-NorESM1-M (https://www.fs.usda.gov/research/treesearch/60113) Learn more at: U.S. Department of Agriculture, Forest Service. 2023. Future of America’s Forest and Rangelands: Forest Service 2020 Resources Planning Act Assessment. Gen. Tech. Rep. WO-102. Washington, DC. [in press] https://doi.org/10.2737/WO-GTR-102. Spatial Extent: CONUSUnits: Percent change in C stocks
Name: Percent Change in Forest Carbon Stocks (2020-2070, RCP 4.5-SSP-1)
Display Field: NAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This data layer provides baseline estimates and future projections of carbon by county. Baseline carbon estimates for 2020 were derived by the FIA carbon team based on the most recent inventories for each state. Projections based on the baseline estimates were developed by the Resources Planning Act (https://www.fs.usda.gov/research/inventory/rpaa) team where forest area and total carbon estimates were provided for the year 2070, RCP 4.5, SSP1 (for information on RPA models and future scenarios see Langner et al. 2020: https://doi.org/10.2737/RMRS-GTR-412). Projections include the effects of land use change, climate, socioeconomics, timber harvest, fire, other disturbance, and forest growth. Counties were clipped to National Forest boundaries for display purposes. Scenario = RCP4.5-SSP1 combination (low warming, moderate growth), mean/min/max of 5 climate models used in RPA Assessment; another three RCP-SSP combinations are available from the information source. This uses the following five climate models: Least Warm-MRI-CGCM3; Hot-HadGEM2-ES; Dry-IPSL-CM5A-MR; Wet-CNRM-CM5; Middle-NorESM1-M (https://www.fs.usda.gov/research/treesearch/60113) Learn more at: U.S. Department of Agriculture, Forest Service. 2023. Future of America’s Forest and Rangelands: Forest Service 2020 Resources Planning Act Assessment. Gen. Tech. Rep. WO-102. Washington, DC. [in press] https://doi.org/10.2737/WO-GTR-102. Spatial Extent: CONUSUnits: Percent change in C stocks
Name: Percent Change in Forest Carbon Stocks (2020-2070, RCP 8.5-SSP-2)
Display Field: FORESTNAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This data layer provides baseline estimates and future projections of carbon by forest. Baseline carbon estimates for 2020 were derived by the FIA carbon team based on the most recent inventories for each state. Projections based on the baseline estimates were developed by the Resources Planning Act (https://www.fs.usda.gov/research/inventory/rpaa) team where forest area and total carbon estimates were provided for the year 2070, RCP 8.5, SSP2 (for information on RPA models and future scenarios see Langner et al. 2020: https://doi.org/10.2737/RMRS-GTR-412). Projections include the effects of land use change, climate, socioeconomics, timber harvest, fire, other disturbance, and forest growth. Counties were clipped to National Forest boundaries for display purposes. Scenario = RCP8.5-SSP2 combination (high warming, moderate growth), mean/min/max of 5 climate models used in RPA Assessment; another three RCP-SSP combinations are available from the information source. This uses the following five climate models: Least Warm-MRI-CGCM3; Hot-HadGEM2-ES; Dry-IPSL-CM5A-MR; Wet-CNRM-CM5; Middle-NorESM1-M (https://www.fs.usda.gov/research/treesearch/60113) Learn more at: U.S. Department of Agriculture, Forest Service. 2023. Future of America’s Forest and Rangelands: Forest Service 2020 Resources Planning Act Assessment. Gen. Tech. Rep. WO-102. Washington, DC. [in press] https://doi.org/10.2737/WO-GTR-102. Spatial Extent: CONUSUnits: Percent change in C stocks
Name: Percent Change in Forest Carbon Stocks (2020-2070, RCP 4.5-SSP-1)
Display Field: FORESTNAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This data layer provides baseline estimates and future projections of carbon by forest. Baseline carbon estimates for 2020 were derived by the FIA carbon team based on the most recent inventories for each state. Projections based on the baseline estimates were developed by the Resources Planning Act (https://www.fs.usda.gov/research/inventory/rpaa) team where forest area and total carbon estimates were provided for the year 2070, RCP 4.5, SSP1 (for information on RPA models and future scenarios see Langner et al. 2020: https://doi.org/10.2737/RMRS-GTR-412). Projections include the effects of land use change, climate, socioeconomics, timber harvest, fire, other disturbance, and forest growth. Counties were clipped to National Forest boundaries for display purposes. Scenario = RCP4.5-SSP1 combination (low warming, moderate growth), mean/min/max of 5 climate models used in RPA Assessment; another three RCP-SSP combinations are available from the information source. This uses the following five climate models: Least Warm-MRI-CGCM3; Hot-HadGEM2-ES; Dry-IPSL-CM5A-MR; Wet-CNRM-CM5; Middle-NorESM1-M (https://www.fs.usda.gov/research/treesearch/60113) Learn more at: U.S. Department of Agriculture, Forest Service. 2023. Future of America’s Forest and Rangelands: Forest Service 2020 Resources Planning Act Assessment. Gen. Tech. Rep. WO-102. Washington, DC. [in press] https://doi.org/10.2737/WO-GTR-102. Spatial Extent: CONUSUnits: Percent change in C stocks
Name: Carbon Density (2023) and Percent Carbon Stock Change (2005-2023)
Display Field: FORESTNAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This data layer provides annualized estimates (2005-2023) for seven forest carbon pools (aboveground live, belowground live, soil organic carbon, forest floor, down dead wood, standing dead trees, understory vegetation) for each National Forest System unit based on information from the FIA program and the Carbon Calculation Tool (CCT) - https://www.nrs.fs.usda.gov/pubs/2394 . These estimates were used to make maps portraying trends in carbon stock change and carbon density by joining CCT estimates to the Forest Service administrative boundary layer. Spatial Extent: NFS system landsUnits: Total forest C stocks by year (Tg), C pools (%), C stock change (Tg/yr), average C stock density by year (Mg/ha), HWP C storage over time (Tg) [regional summary], % forest disturbance by year, C loss by disturbance type over time (Mg/ha), % reduction in non-soil C by disturbance, stand age distribution in 2011, NPP-stand age curves (Mg C/ha/yr), accumulated C over time (Tg).
Copyright Text: USDA Forest Service: Forest Inventory and Analysis (FIA)
Unique Value Renderer: Field 1: C_Desc Field 2: N/A Field 3: N/A Field Delimiter: ; Default Symbol:
N/A
Default Label: N/A UniqueValueInfos:
Value: High Carbon Density, Greater Than 10% Increase in Carbon Stock Label: High Carbon Density, Greater Than 10% Increase in Carbon Stocks Description: N/A Symbol:
Value: High Carbon Density, Less Than 10% Increase in Carbon Stock Label: High Carbon Density, Less Than 10% Increase in Carbon Stocks Description: N/A Symbol:
Value: High Carbon Density, Less Than 10% Decrease in Carbon Stock Label: High Carbon Density, Less Than 10% Decrease in Carbon Stocks Description: N/A Symbol:
Value: High Carbon Density, Greater Than 10% Decrease in Carbon Stock Label: High Carbon Density, Greater Than 10% Decrease in Carbon Stocks Description: N/A Symbol:
Value: Low Carbon Density, Less Than 10% Increase in Carbon Stock Label: Low Carbon Density, Less Than 10% Increase in Carbon Stocks Description: N/A Symbol:
Value: Low Carbon Density, Less Than 10% Decrease in Carbon Stock Label: Low Carbon Density, Less Than 10% Decrease in Carbon Stocks Description: N/A Symbol:
Name: Ratio of Live Aboveground Carbon to Dead (2023)
Display Field: FORESTNAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This data layer provides annualized estimates (1990-2023) for seven forest carbon pools (aboveground live, belowground live, soil organic carbon, forest floor, down dead wood, standing dead trees, understory vegetation) for each National Forest System unit based on information from the FIA program and the Carbon Calculation Tool (CCT) - https://www.nrs.fs.usda.gov/pubs/2394 . These estimates were used to make maps portraying trends in live to dead tree ratios by joining CCT estimates to the Forest Service administrative boundary layer. Spatial Extent: NFS system landsUnits: Total forest C stocks by year (Tg), C pools (%), C stock change (Tg/yr), average C stock density by year (Mg/ha), HWP C storage over time (Tg) [regional summary], % forest disturbance by year, C loss by disturbance type over time (Mg/ha), % reduction in non-soil C by disturbance, stand age distribution in 2011, NPP-stand age curves (Mg C/ha/yr), accumulated C over time (Tg).
Copyright Text: USDA Forest Service: Forest Inventory and Analysis (FIA)