Estimation of forest stand characteristics using individual tree detection, stochastic geometry and a sequential spatial point process model

Airborne Laser Scanning (ALS) results in point-wise measurements of canopy height, which can further be used for Individual Tree Detection (ITD). However, ITD cannot find all trees because small hide below larger tree crowns. Here we discuss methods where the plot totals and means tree-level characteristics are estimated such context. The starting point is a previously presented Horvitz–Thompson-like (HT-like) estimator, detectability based on crowns tuning parameter α that models detection condition. We propose new method modeling spatial pattern hidden locations using sequential process model, with θ. also explore whether variability parameters θ predicted ALS features to improve predictions. accuracy stand density, dominant height mean as comparison criteria cross-validation procedure. HT-like estimator empirically performed best. overall performance was comparable. computationally less demanding, makes it attractive practical use.