Estimating net primary productivity (Npp) and debris-fall in forests using lidar time series

Abstract

Temporal series of lidar, properly field-validated, can provide critical information allowing inferences about the dynamics of biomass and carbon in forest canopies. Forest canopies gain carbon through net primary production (NPP) and lose carbon through canopy component damage and death, such as fine and coarse woody debris and litterfall (collectively, debris-fall). We describe a statistical method to extract gamma distributions of NPP and debris-fall rates in forest canopies from lidar missions repeated through time and we show that the means of these distributions covary with ecologically meaningful variables: topography, canopy structure, and taxonomic composition. The method employed is the generalized method of moments that applies the R package gmm to uncover the distribution of latent variables. We present an example with ecological interpretations that support the method’s application to change in biomass estimated for a boreal forest in south-central Alaska. The deconvolution of net change from remote sensing products as distributions of NPP and debris-fall rates can inform carbon cycling models of canopy-level NPP and debris-fall rates.