Spatial prediction of forest stand variables

By Arne Nothdurft, Joachim Saborowski, Johannes Breidenbach. European Journal of Forest Research, 2009

48 citations recorded by the source corpus.

This study aims at the development of a model to predict forest stand variables in management units (stands) from sample plot inventory data. For this purpose we apply a non-parametric most similar neighbour (MSN) approach. The study area is the municipal forest of Waldkirch, 13 km north-east of Freiburg, Germany, which comprises 328 forest stands and 834 sample plots. Low-resolution laser scanning data, classification variables as well rough estimations from the forest management planning serve as auxiliary variables. In order to avoid common problems of k-NN-approaches caused by asymmetry at the boundaries of the regression spaces and distorted distributions, forest stands are tessellated into subunits with an area approximately equivalent to an inventory sample plot. For each subunit only the one nearest neighbour is consulted. Predictions for target variables in stands are obtained by averaging the predictions for all subunits. After formulating a random parameter model with variance components, we calibrate the prior predictions by means of sample plot data within the forest stands via BLUPs (best linear unbiased predictors). Based on bootstrap simulations, prediction errors for most management units finally prove to be smaller than the design-based sampling error of the mean. The calibration approach shows superiority compared with pure non-parametric MSN predictions.

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

View Paper: https://www.semanticscholar.org/paper/8785686d7d81b20b866f0f2fcff7fa1b798aaee3

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In Environmental Engineering

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands (2012, methodology): “Nothdurft et al. (2009b) addressed issues such as difficulties in fitting empirical variograms (in situations where there is no evidence of strong correlations between nearby and distant locations) and using external trend functions (in situations where multiple responses are being predicted from a…”
Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors (2010, methodology): “Whereas some studies show promising results of using traditional NN methods like MSN when estimating plot-level stand volume, basal area and tree height (Nieschulze, 2003; Packalén and Maltamo, 2006; Packalén and Maltamo, 2007; Nothdurft et al., 2009), recent studies, e.”
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, methodology): “Nothdurft et al. (2009) estimated various tree species-specific variables (timber volume, diameter distributions) by combining ALS metrics, sample plot data and auxiliary information from an inventory by compartments.”
Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011, methodology): “Nothdurft et al. (2009) reported using bootstrap procedures to estimate variances of estimates of forest variables obtained using nearest neighbors techniques but did not elaborate on implementation of the resampling procedures.”