Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories

By Steen Magnussen, Ronald E. McRoberts, Erkki Tomppo. 2009

76 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0934d57dee0ea52235dad4635a97ede4dbad6a29

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In Ecology

In Environmental Engineering

In Nature and Landscape Conservation

Citations in the corpus

Adapting National Forest Inventories to changing requirements - the case of the Swedish National Forest Inventory at the turn of the 20th century (2014, background): “Along with the Nordic NFIs, the American NFI was also started in the 1920s, whereas a sample-based NFI was not initiated in Canada until 1981 (Magnussen et al. 2009; Gillis et al. p. 97–111, in Tomppo et al. 2010).”
Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data (2010, background): “The model-based approach can also be extended to allow spatially correlated references that were excluded in this study (McRoberts et al. 2007; Magnussen et al. 2009).”
Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data (2015, background): “For k ≥ 25, McRoberts et al. (2011) and McRoberts (2012) reported similar values for parametric and bootstrap estimates. However, Magnussen, McRoberts and Tomppo (2009) reported accurate parametric estimates for k N 25, but less accurate estimates for k b 7; the results of this study confirm the fin”
Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery (2009, background): “…satellite imagery (Franco-Lopez et al., 2001; McRoberts et al., 2007), increasing the precision of probabilitybased forest inventory estimates (Baffetta et al., 2007; McRoberts et al., 2002; Tomppo, 1991, 1996), and model-based inference (Magnussen et al., 2009-this issue; McRoberts et al., 2007).”
Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011, methodology): “For model-based inference, Magnussen et al. (2009) developed an estimator for mean square error, and Magnussen et al. (2010a) reported a balanced repeated replications (BRR) estimator of variance.”