Maximum Likelihood Forest Canopy Profile Estimation

• Activity Number: 293
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Paul Van Deusen*+
• Companies: NCASI
• Keywords: forest sampling ; foliage estimator ; LiDAR

Abstract

A maximum likelihood estimator (mle) for the foliage density function (fdf) is developed that allows for a custom fdf to model the forest canopy profile. The estimator derived here depends on the user providing a functional form for the fdf. The unknown fdf parameters are estimated from a sample of heights-to-first-leaf taken at random locations from ground level under the canopy. The method presented here could be viewed as a discrete version of the well known MacArthur and Horn (1969) estimator (MHE). The mle has several advantages over the MHE. The mle allows the user to incorporate some prior knowledge of the canopy profile via the fdf and it provides a covariance matrix for the parameter estimates. The most important advantage is that sample data never result in division by zero for upper canopy heights as can happen with the MHE. Finally, a simulated comparison indicated that the mle has considerably less variance than the MHE.