Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data

By Matti Maltamo, J. Malinen, Petteri Packalen, Aki Suvanto, Jyrki Kangas. 2006

185 citations recorded by the source corpus.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d3d6957a1a62e9bcf20ba034becf6b8908ce6e1d

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data (2008, background): “Published by Elsevier Inc. Keywords: Forestry; k-NN imputation; LiDAR remote sensing; Mapping; Random forest”
Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, methodology): “Maltamo et al. (2006) and Hudak et al. (2008) outlined a number of advantages in using non-parametric procedures, which can make them preferable depending on each application.”
Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data (2009, methodology): “When the present results are compared with those of earlier crown height studies based on laser scanning (Næsset and Økland 2002, Holmgren and Persson 2004, Maltamo et al. 2006a, Popescu and Zhao 2008), the results of the current study seem to be more accurate.”
Spatial prediction of forest stand variables (2009, methodology): “Maltamo et al. (2006) detected that the application of laser scanning data in the k-nearest-neighbour (k-NN) approach is superior to aerial photographs or the combination of class variables and old inventory data.”
Variable selection strategies for nearest neighbor imputation methods used in remote sensing based forest inventory (2012, methodology): “Maltamo et al. (2006) used an algorithm that first inserts transformations from continuous variables and then deletes variables that do not significantly contribute via stepwise optimization of the relative root mean square error (RMSE).”
Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland (2009, methodology): “Corresponding results have also been obtained by Holmgren (2004) and Maltamo et al. (2006a), but there are some restrictions on the generalization of these findings.”
Non-parametric prediction of diameter distributions using airborne laser scanner data (2009, methodology): “…methods (Maltamo et al., 2007; Mehtätalo et al., 2007; Breidenbach et al., 2008), (3) applications in boreal forest reserves, i.e. in areas with a great variety of distribution forms (Maltamo et al., 2006a; Bollandsås & Næsset, 2007), and (4) preharvest measurements (Peuhkurinen et al., 2008).”
Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors (2010, methodology): “Different numbers of k have been applied in several studies (e.g. Franco-Lopez et al., 2001; Holmström and Fransson, 2003; Haapanen et al., 2004; Maltamo et al., 2006; Packalén and Maltamo, 2006; Packalén and Maltamo, 2007; Finley and McRoberts, 2008; Vauhkonen et al., 2010), in some of which the…”
The use of airborne laser scanning to estimate sawlog volumes (2008, background): “Maltamo et al. (2006) , for example, obtained a percent root mean squared error (RMSE”