Main Logo of Southern Research Station, Stating: Southern Research Station - Asheville, NC, with a saying of 'Science you can use!'
[Images] Five photos of different landscape

Publication Information

Mail this page   Give us your feedback on this publication

Title: Mapping U.S. forest biomass using national forest inventory data and moderate resolution information
Author(s): Blackard, J.A.; Finco, M.V.; Helmer, E.H.; Holden, G.R.; Hoppus, M.L.; Jacobs, D.M.; Lister, A.J.; Moisen, G.G.; Nelson, M.D.; Riemann, R.; Ruefenacht, B.; Salajanu, D.; Weyermann, D.L.; Winterberger, K.C.; Brandeis, T.J.; Czaplewski, R.L.; McRoberts, R.E.; Patterson, P.L.; Tycio, R.P.
Date: 2008
Source: Science Direct, Remost Sensing of Environment, Vol. 112: 1658-1677
Description: A spatially explicit dataset of aboveground live forest biomass was made from ground measured inventory plots for the contenninous U.S., Alaska and Puerto Rico. The plot data are from the USDA Forest Service Forest Inventory and Analysis (PIA) program. To scale these plot data to maps, we developed models relating field-measured response variables to plot attributes serving as the predictor variables. The plot attributes came from intersecting plot coordinates with geospatial datasets. Consequently, these models serve as mapping models. The geospatial predictor variables included Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; land cover proportions and other data from the National Land Cover Dataset (NLCD); topographic variables; monthly and annual climate parameters; and other ancillary variables. We segmented the mapping models for the U.S. into 65 ecologically similar mapping zones, plus Alaska and Puerto Rico. First, we developed a forest mask by modeling the forest vs. nonforest assignment of field plots as functions of the predictor layers using classification trees in See. Secondly, forest biomass models were built within the predicted forest areas using tree-based algorithms in Cubist©. To validate the models, we compared field-measured with model predicted forest/nonforest classification and biomass from an independent test set, randomly selected from available plot data for each mapping zone. The estimated proportion of correctly classified pixels for the forest mask ranged from 0.79 in Puerto Rico to 0.94 in Alaska. For biomass, model correlation coefficients ranged from a high of 0.73 in the Pacific Northwest, to a low of 0.31 in the Southern region. There was a tendency in all regions for these models to over-predict areas of small biomass and under-predict areas oflarge biomass, not capturing the full range in variability. Map-based estimates of forest area and forest biomass compared well with traditional plot-based estimates for individual states and for four scales of spatial aggregation. Variable importance analyses revealed that MODIS-derived infromation could contribute more predictive power than other classes of information when used in isolation. However, the true contribution of each variable is confounded by high correlations. Consequently, excluding anyoneclass of variables resulted in only small effects on overall map accuracy. An estimate oftotal C pools in live forest biomass of U.S. forests, derived from the nationwide biomass map, also compared well with previously published estimates.
View and Print this Publication (2.83 MB)
Pristine Version: An uncaptured or "pristine" version of this publication is available. It has not been subjected to OCR (Optical Character Recognition) and therefore does not have any errors in the text. However it is a larger file size and some people may experience long download times. The "pristine" version of this publication is available here:

View and Print the PRISTINE copy of this Publication (3.83 MB)
Publication Notes:
  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
  • Our on-line publications are scanned and captured using Adobe Acrobat. During the capture process some typographical errors may occur. Please contact the SRS Webmaster, srswebmaster@fs.fed.us if you notice any errors which make this publication unuseable.
[ Get Acrobat ] Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility




Publication Links:

FIA Resource Bulletins

Publications Search


Search for on-line publications
containing the following:

 


(Uncheck this box to search all R&D Publications.)

Small logo of the USDASmall logo of the Forest Service