Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors

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Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass (2014, background): “…in form of descriptive statistical attributes such as mean height, height percentiles and comparable derivatives (Tsui et al., 2012) which have been successfully applied to estimate forest biomass (e.g., Clark et al., 2011; Latifi et al., 2010; Næsset et al., 2011; Tian et al., 2012).”
Variable selection strategies for nearest neighbor imputation methods used in remote sensing based forest inventory (2012, methodology): “Numerous distance metrics are used in NN imputation and many comparisons of them have been published recently (e.g., LeMay and Temesgen, 2005; Chirici et al., 2008; Hudak et al., 2008; Breidenbach et al., 2010; Latifi et al., 2010).”
Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands (2012, methodology): “…heuristics (Barros and Rutledge 1998, Cadima and Joliffe 2001), the latter methods such as GA have been successfully applied in selecting approximately uncorrelated predictors and alternatives for k-NN models, for example by Bakken and Jurs (2000), Tomppo and Halme (2004) and Latifi et al. (2010a).”