A matching procedure to improve k-NN estimation of forest attribute maps
Author(s)
Baffetta, Federica
Corona, Piermaria
Fattorini, Lorenzo
Date Issued
2012
Type
article
Abstract
The integration of forest inventory and mapping has emerged as a major issue for assessing forest attributes
and multiple environmental functions. Associations between remotely sensed data and the biophysical
attributes of forest vegetation (standing wood volume, biomass increment, etc.) can be
exploited to estimate the attribute values for sampled and non-sampled pixels, thus producing maps
for the entire region of interest. Among the available procedures, the k-nearest neighbours (k-NN) technique
is becoming popular, even for practical applications. However, the k-NN estimates at the pixel level
tend to average towards the population mean and to have suppressed variance, since large values are usually
underestimated and small values overestimated. This tendency may be detrimental for k-NN applications
in forest resource management planning and scenario analysis where the representation of the
spatial variability of each attribute of interest across the surveyed territory is fundamental. The present
paper proposes a procedure to tackle such an issue by modifying k-NN estimates via a post-processing
procedure of distribution matching. The empirical distribution function of the population values is estimated
from the sample of ground data by using the 0-inflated beta distribution as the assisting model
and the k-NN estimates are subsequently modified in such a way as to match the estimated distribution.
The statistical properties of the distribution matching estimators for totals and averages are theoretically
derived, while the performance of the distribution matching estimator at the pixel level are empirically
evaluated by a simulation study.
Additional information
L'articolo è disponibile sul sito dell'editore www.Journals.elsevier.com
Citation
Baffetta, F. et al. 2012. A matching procedure to improve k-NN estimation of forest attribute maps. "Forest Ecology and Management" 272: 35–50
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