Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery

By Erkki Tomppo, Caterina Gagliano, F. D. Natale, Matti Katila, Ronald E. McRoberts. 2009

84 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/47e9fd8404d543cb2e5fedf983595a9ad6e43ff5

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In Environmental Engineering

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “Each location’s estimate is predicted as a weighted average value with k spectrally nearest neighbors using a weighting method (Tomppo et al. 2009).”
Integration of forest mapping and inventory to support forest management (2010, methodology): “Examples of NN applications are provided by Franco-Lopez et al. (2001), Tomppo et al. (2002a, 2009), Chirici et al. (2008), McRoberts (2009a).”
Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data (2009, methodology): “With kNN, forest parameter predictions, for pixels without ground reference data, are calculated as linear combinations of reference pixel values that are nearest in feature space according to some distance metric (Tomppo et al., 2009).”
Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors (2010, methodology): “…estimation, the NN methods have been first introduced from the late 1980s (Kilkki and Päivinen, 1987), applied later for the estimation of standing timber volume by, e.g., Tomppo (1993), and have recently been examined to predict forest type as a categorical attribute (Tomppo et al., 2009).”
Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011, methodology): “Tomppo and Halme (2004), Tomppo et al. (2009), and McRoberts (2009b) used a genetic algorithm approach to optimize the weights for ancillary variables in the distance metric.”