Mapping Forest Canopy Fuels in the Western United States with LiDAR–Landsat Covariance

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 12; no. 6; p. 1000
Main Authors: Moran, Christopher J., Kane, Van R., Seielstad, Carl A.
Format: Journal Article
Language:English
Published: Basel MDPI AG 20.03.2020
Subjects:
Accuracy
Algorithms
als
Bulk density
Canopies
canopy base height
canopy bulk density
canopy cover
canopy fuel mapping
canopy height
Covariance
data collection
Datasets
fire behavior
fire severity
Forest & brush fires
forest canopy
Forest management
Forests
Fuels
fuels (fire ecology)
gradient boosting machine
Landsat
Landsat satellites
landscapes
Lidar
Machine learning
Mapping
model validation
Performance enhancement
prediction
Prediction models
Prescribed fire
Remote sensing
Risk assessment
Risk taking
Spatial data
Variables
Western United States
Wildfires
United States > US
Western United States
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary: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.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs12061000