Mapping forest canopy fuels in thewestern united states with LiDAR-Landsat covariance

Christopher J. Moran, Van R. Kane, Carl A. Seielstad

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Comprehensive spatial coverage of forest canopy fuels is relied upon by fire management in the US to predict fire behavior, assess risk, and plan forest treatments. Here, a collection of light detection and ranging (LiDAR) datasets from the western US are fused with Landsat-derived spectral indices to map the canopy fuel attributes needed for wildfire predictions: canopy cover (CC), canopy height (CH), canopy base height (CBH), and canopy bulk density (CBD). A single, gradient boosting machine (GBM) model using data from all landscapes is able to characterize these relationships with only small reductions in model performance (mean 0.04 reduction in R2) compared to local GBM models trained on individual landscapes. Model evaluations on independent LiDAR datasets show the single global model outperforming local models (mean 0.24 increase in R2), indicating improved model generality. The global GBM model significantly improves performance over existing LANDFIRE canopy fuels data products (R2 ranging from 0.15 to 0.61 vs.-3.94 to-0.374). The ability to automatically update canopy fuels following wildfire disturbance is also evaluated, and results show intuitive reductions in canopy fuels for high and moderate fire severity classes and little to no change for unburned to low fire severity classes. Improved canopy fuel mapping and the ability to apply the same predictive model on an annual basis enhances forest, fuel, and fire management.

Original languageEnglish
Article number1000
JournalRemote Sensing
Volume12
Issue number6
DOIs
StatePublished - Mar 1 2020

Keywords

  • ALS
  • Canopy base height
  • Canopy bulk density
  • Canopy cover
  • Canopy fuel mapping
  • Canopy height
  • Gradient boosting machine
  • Landsat
  • LiDAR

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