Estimation of standing dead tree class distributions in northwest coastal forests using lidar remote sensing

The amount and variability of living and dead wood in a forest stand are important indicators of forest biodiversity, as it relates to structural heterogeneity and habitat availability. In this study, we investigate whether light detection and ranging (lidar) can be used to estimate the distribution of standing dead tree classes within forests. Twenty-two field plots were established in which each stem (DBH >10 cm) was assigned to a wildlife tree (WT) class. For each plot, a suite of lidar-derived predictor variables were extracted. Ordinal regression using a negative log-log link function was then employed to predict the cumulative proportions of stems within the WT classes. Results indicated that the coefficient of variation of the lidar height data was the best predictor variable (chi(2) = 106.11, p < 0.00; Wald = 4.83, p = 0.028). The derived relationships allowed for the prediction of the cumulative proportion of stems within WT classes (r = 0.90, RMSE = 6.0%) and the proportion of dead stems within forest plots (r = 0.61, RMSE = 16.8%). Our research demonstrates the capacity of lidar remote sensing to estimate the relative abundances of standing living and dead trees in forest stands and its ability to characterize vegetation structure across large spatial extents.