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FOSTER-An R package for forest structure extrapolation.

Martin Queinnec1, Piotr Tompalski1, Douglas K Bolton2

  • 1Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada.

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This summary is machine-generated.

A new R package, FOSTER, models 3D forest structure using airborne laser scanning and satellite data. This framework enables efficient extrapolation of forest attributes for improved forest management decisions.

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Area of Science:

  • Forestry
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) provide 3D forest structure data.
  • Forest structural attributes are crucial for enhanced forest inventories.
  • Regional mapping of forest attributes can be achieved by linking ALS/DAP data with satellite imagery.

Purpose of the Study:

  • To introduce FOSTER (Forest Structure Extrapolation in R), an open, efficient framework for modeling and imputing 3D forest attributes.
  • To provide a versatile and computationally efficient software solution for forest inventory and management.

Main Methods:

  • FOSTER derives spectral trends from remote sensing time series.
  • It employs a structurally guided sampling approach for spatially auto-correlated data.
  • k-NN imputation is used to extrapolate 3D forest structure measures, offering advantages over traditional regression.

Main Results:

  • FOSTER successfully imputed two ALS-derived variables (elev_p95 and cover) in British Columbia, Canada.
  • Relative RMSEs were 18.5% for elev_p95 and 11.4% for cover.
  • Relative biases were low, at -0.6% for elev_p95 and 1.4% for cover.

Conclusions:

  • FOSTER offers an innovative and versatile framework for 3D forest attribute mapping.
  • The software is computationally efficient and suitable for researchers and forest managers.
  • It supports informed forest management decisions across entire forest estates.