Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information
Introduction
The Forest Inventory and Analysis (FIA) program of the USDA Forest Service collects data annually on the status and trends in forested ecosystems nationwide. These inventory data support estimates of forest population totals over large geographic areas, (Scott et al., 2005). Regional maps of forest characteristics would make these extensive forest resource data more accessible and useful to a larger and more diverse audience. Important applications of such maps include broad-scale mapping and assessment of wildlife habitat; documenting forest resources affected by fire, fragmentation, and urbanization; identifying land suitable for timber production; and locating areas at high risk for plant invasions, or insect or disease outbreaks. Thus, there is a need to produce and distribute geospatial data of forest attributes, complementing FIA inventory data.
Total aboveground live biomass is a forest characteristic of particular interest. Forest soils and woody biomass hold most of the carbon in Earth's terrestrial biomes (Houghton, 1999). Land-use change, mainly forest burning, harvest, or clearing for agriculture, may compose 15 to 40% of annual human-caused emissions of carbon to the atmosphere, and terrestrial ecosystems, mainly through forest growth and expansion, absorb nearly as much carbon annually. However, estimates of land-atmosphere carbon fluxes, and the net of expected future ones, have the largest uncertainties in the global atmospheric carbon budget, which adds to uncertainties about future levels and impacts of greenhouse gasses (GHGs) in the atmosphere (Houghton, 2003, Prentice et al., 2001).
Consequently, the levels, mechanisms and spatial distribution of forest land-atmosphere C fluxes are an important focus for reducing uncertainties in the global C budget (Fan et al., 1998, Holland et al., 1999, Pacala et al., 2001, Schimel et al., 2001). Ecosystem process models that are physiologically-based, and that use satellite image-derived indices of photosynthesis, have permitted unprecedented global assessments of ecosystem productivity and carbon sinks at a spatial resolution of 0.5° (Nemani et al., 2003, Potter et al., 2003). The mechanistic nature of these models identifies how observed patterns in ecosystem productivity may relate to climate and atmospheric changes (Nemani et al., 2003). However, validating atmospheric and ecosystem model estimates of net forest C fluxes, and quantifying the C fluxes associated with changes in land use, which dominate these fluxes over longer time periods, requires spatially extensive data on forest C pools and net fluxes. Maps of forest biomass permit spatially explicit estimates of forest carbon storage and net fluxes from land-use change.
Our objectives here are to 1) produce a spatially explicit dataset of aboveground live forest biomass from ground measured inventory plots, at a 250-m cell size, for the conterminous U.S., Alaska and Puerto Rico; 2) evaluate model performance and spatially depict uncertainty in the dataset; 3) explore the relative contribution of the many predictor layers to the biomass models; and 4) use the resulting dataset to estimate aboveground live forest biomass and implied carbon storage for this area. We also describe a national geospatial predictor database that supported the mapping and how we standardized national FIA data, developed predictive models, and assessed model error.
Section snippets
Response variables
The US Forest Service FIA program inventories the Nation's forests via a network of ground-based inventory plots in which forest structure and tree species composition are measured to produce estimates of forest attributes like basal area by species, total volume, and total biomass. Plots are located with an intensity of about one plot per 2400 ha. Although the program historically collected data periodically (every 5 to 20 years) for each state in the country, it recently shifted to an annual
Results
All maps produced in this study, including the forest/nonforest mask, forest probability, forest biomass, and biomass percent error, are available for download via http://svinetfc4.fs.fed.us/rastergateway/biomass/.
Discussion
Image products from MODIS were useful for this study not only because they were practical, but also because they were preferable for scaling reasons. From a practical standpoint, the coarser spatial resolution of MODIS imagery makes applications at sub-continental scales computationally less intensive compared with finer resolution data. Moreover, MODIS image products, like tree cover data and preprocessed image composites that minimize cloud cover, along with the larger scene and tile sizes,
Conclusions
Spatially explicit forest biomass information at the scale of the US provides an unprecedented picture of how forest biomass is distributed spatially across US landscapes and permits visual assessment of forest biomass distribution. It synthesizes point data from tens of thousands of ground plots into one spatial dataset that can easily feed into those ecosystem and atmospheric models that do not assimilate the point-based data. The accuracy assessments reflect the understanding that the data
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