Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar

Andrew W. Whelan, Jeffery B. Cannon, Seth W. Bigelow, Brandon T. Rutledge, Andrew J. Sánchez Meador

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Modeling forest attributes using lidar data has been a useful tool for forest management but the need to correlate lidar to ground-based measurements creates challenges to modeling in diverse forest landscapes. Many lidar models have been based on metrics derived from summarizations of individual lidar returns over sample plot areas, but more recently, metrics based on summarization by volumetric pixel (voxel) have shown promise to better characterize forest structure and distinguish between diverse forest types. Voxel-based metrics may improve characterization of leaf area distribution and horizontal forest structure, which could help create general models of forest attributes applicable in complex landscapes composed of many distinct forest types. We modeled wood volume in longleaf pine woodlands and associated forests to compare how area- and voxel- based lidar metrics predicted wood volume in forest type specific and general predictive models. We created four area-based and six voxel-based metrics to fit models of wood volume using a multiplicative power function. We selected models and compared metric importance using AIC and evaluated model performance using cross-validated mean prediction error. We found that one area-based metric and four voxel-based metrics consistently improved model predictions We suggest that area-based metrics alone may have limitations for characterizing complex forest structure. Area-based summarizes of lidar returns are more heavily influenced by upper canopy returns because lidar returns attenuate below the canopy. By contrast, summarizing lidar returns into a single value per voxel prior to summarization over plots homogenizes point density, giving added weight to sub-canopy returns. Thus voxel-based metrics may be more sensitive to structural variation that may not be adequately captured by area-based metrics alone. This study highlights the potential of voxel-based metrics for characterizing complex forest structure and model generalization capable of accurate forest attribute prediction across diverse forest types.

Original languageEnglish (US)
Article number113362
JournalRemote Sensing of Environment
Volume284
DOIs
StatePublished - Jan 2023

Keywords

  • Airborne lidar
  • Diversity
  • Longleaf pine
  • Maximum likelihood
  • Voxel-based
  • Wood volume

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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