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Forest carbon stocks per 900 m2 pixel area, modeled from FIA data and satellite imagery, representing the time period 2014–2018 |
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Forest carbon stocks per 900 m2 pixel area, modeled from FIA data and satellite imagery, representing the time period 2014–2018 |
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USDA Forest Service: Forest Inventory and Analysis (FIA); BIGMAP |
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5000 |
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<DIV STYLE="text-align:Left;font-size:12pt"><DIV><DIV><P><SPAN>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 </SPAN></P><P><SPAN /></P><P><SPAN>Spatial Extent: CONUS</SPAN></P><P><SPAN>Units: Short tons per pixel</SPAN></P></DIV></DIV></DIV> |
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<DIV STYLE="text-align:Left;font-size:12pt"><DIV><DIV><P><SPAN>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.</SPAN></P><P><SPAN /></P></DIV></DIV></DIV> |
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| title:
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Total Forest Carbon (2018) |
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| tags:
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["Climate Risk Viewer","US Forest Service","USFS","Office of Sustainability and Climate","OSC","CRV","Climate Change","Forest"] |
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en-US |
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150000000 |
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