A Comparison of Statistical Methods for Estimating Forest Biomass from Light Detection and Ranging Data

By Yuzhen Li, Hans-Erik Andersen, R. McGaughey. 2008

120 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/1a8f2d82b2177d69653f2b7339759f9c9847742f

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In Ecology

In Environmental Engineering

In Nature and Landscape Conservation

Citations in the corpus

Inference for lidar-assisted estimation of forest growing stock volume (2013, result): “Similar results have also been reported for multiple other studies including Frazer et al. (2011), Li et al. (2008), and Zhao et al. (2009).”
Manual for building tree volume and biomass allometric equations: from field measurement to prediction (2012, unknown): “, 2009) it is little used for forestry models (Li et al., 2008; Picard et al., 2012).”
Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar (2015, methodology): “Model (8) was tested over three sites in Washington and Alaska by Li et al. (2008), who argued that Hmc and CVc, carry information about tree height and crown depth along the vertical direction and CCf represents canopy cover along the horizontal direction so that they can explain the majority of…”
A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (2013, unknown): “…that many of the ALS metrics are known to be correlated, we recommend that users select appropriate metrics for model building based on their information need or by using a method such as principal component analysis, or some other feature selection approach (Li et al. 2008; Stephens et al. 2012).”
Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest (2013, result): “The literature has been consistent with these conclusions [4,30,34,35].”
Using Bayesian Model Averaging to Predict Tree Aboveground Biomass in Tropical Moist Forests (2012, unknown): “2009), and forest science (Li et al. 2008).”
Reducing the error in biomass estimates strongly depends on model selection (2015, methodology): “Selecting a single model does not seem to be the usual outcome of BMA (Li et al. 2008; Picard et al. 2012).”