An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon

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Capturing diversity and interspecific variability in allometries: A hierarchical approach (2008, methodology): “More recently the remote sensing community has used estimates of canopy height derived from lidar or radar to calculate forest stand biomass from allometric relations (Hurtt et al., 2004; Drake et al., 2003, 2002; Lefsky et al., 2001, 1999; Kasischke et al., 1994).”
An improved strategy for regression of biophysical variables and Landsat ETM+ data. (2003, background): “The spectral depth of ETM+ is essentially three-dimensional (Cohen et al., 1995; Crist & Cicone, 1984) and we have increasing numbers of temporal image series available for greater predictive power (Lefsky et al., 2001).”
A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland (2006, background): “Moreover, Lefsky et al. (2001) evaluated the performances of five sensors (Landsat TM, Landsat TM multitemporal, AVIRIS, ADAR, and a LiDAR sensor) for the estimation of eight forest stand attributes in a Douglas-fir (Pseudotsuga menziesii) forest.”
Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data (2008, background): “Active LiDAR remote sensing is now widely regarded as having greater potential for characterizing highly variable forest canopy structure than passive optical imagery, including aerial photos (Lefsky et al., 2001; Koukoulas & Blackburn, 2005).”