Canadian Forest Service Publications

A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds. 2016. Magnussen, S.; Næsset, E.; Kändler, G.; Adler, P.; Renaud, J.P.; Gobakken, T. Remote Sensing of Environment 184, 496–505.

Year: 2016

Available from: Pacific Forestry Centre

Catalog ID: 37662

Language: English

CFS Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1016/j.rse.2016.07.035

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Abstract

Forest inventories, with a probability sampling of a target variable Y and a potentially very large number of auxiliary variables (X) obtained from an aerial laser scanner or photogrammetry, are faced with the issue of model and variable selection when a model for linking Y to X is formulated. To bypass this step we propose a generic functional regression model (FRM) for use in both a design- and a model-based framework of inference. Wed emonstrate applications of FRM with inventory data from France, Germany, and Norway. The generic FRM achieved results that were comparable to those obtained with more traditional approaches based on model and variable selections. The proposed FRM generates interpretable regression coefficients and enables testing of practically relevant hypotheses regarding estimated models.

Plain Language Summary

The large number of metrics that can be derived from LiDAR data and photogrammetric point clouds tempt an over elaboration of the model used for estimation of key inventory attributes. We propose to use a generic functional regression model with interpretable results that is resistant to the curse of dimensionality and qualifies as an external working model within a design based framework of inference. The proposed generic model performed well in a test with inventory data from France, Norway, and Germany. We recommend the proposed model for use in Enhanced Forest Inventories and for inventories supported by hyperspectral data.

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