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A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster dataAuthor(s): B. Tyler Wilson; Andrew J. Lister; Rachel I. Riemann
Source: Forest Ecology and Management. 271: 182-198.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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Related Research Highlights Nationwide Datasets of Tree Species Distributions Created
DescriptionThe paper describes an efficient approach for mapping multiple individual tree species over large spatial domains. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-m pixel size for the entire eastern contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer. Data pre-processing is also described, which includes the use of Fourier series transformation for data reduction and characterizing seasonal vegetation phenology patterns that are apparent in the MODIS imagery.
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CitationWilson, 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.
Keywordsnearest-neighbor imputation, canonical correspondence analysis, MODIS, vegetation phenology, forest inventory, species distribution
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