A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data

The 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. A suite of assessment procedures is applied to each of the modeled dataset presented. These indicate high accuracies, at the scales of assessments used, for total live-tree basal area per hectare and for many of the most common tree species found in the study area. The end result is an approach that enables the mapping of individual tree species distributions, while retaining much of the species covariance found on the forest inventory plots, at a level of spatial detail approaching that required for many regional management and planning applications. The proposed approach has the potential for operational application for simultaneously mapping the distribution and abundance of numerous common tree species across large spatial domains. Published by Elsevier B.V.