Abstract

Abstract The challenge to retrieve canopy height from large-footprint satellite lidar waveforms over mountainous areas is formidable given the complex interaction of terrain and vegetation. This study explores the potential of GLAS (Geoscience Laser Altimeter System) for retrieving maximum canopy height over mountainous areas in the Pacific Coast region, including two conifers sites of tall and closed canopy and one broadleaf woodland site of shorter and sparse canopy. Both direct methods and statistical models are developed and tested using spatially extensive coincident airborne lidar data. The major findings include: 1) the direct methods tend to overestimate the canopy height and are complicated by the identification of waveform signal start and terrain ground elevation, 2) the exploratory data analysis indicates that the edge-extent linear regression models have better generalizability than the edge-extent nonlinear models at the inter-site level, 3) the inter-site level test with mixed-effects models reveals that the edge-extent linear models have statistically-justified generalizability between the two conifer sites but not between the conifer and woodland sites, 4) the intra-site level test indicates that the edge-extent linear models have statistically-justified generalizability across different vegetation community types within any given site; this, combined with 3), unveils that the statistical modeling of maximum canopy height over large areas with edge-extent linear models only need to consider broad vegetation differences (such as woodlands versus conifer forests instead of different vegetation communities within woodlands or conifer forests), and 5) the simulations indicate that the errors and uncertainty in canopy height estimation can be significantly reduced by decreasing the footprint size. It is recommended that the footprint size of the next-generation satellite lidar systems be at least 10 m or so if we want to achieve meter-level accuracy of maximum canopy height estimation using direct and statistical methods.

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