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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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 12; číslo 6; s. 1000
Hlavní autoři:	Moran, Christopher J., Kane, Van R., Seielstad, Carl A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 20.03.2020
Témata:	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
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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.
AbstractList	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.
Author	Kane, Van R. Moran, Christopher J. Seielstad, Carl A.
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SubjectTerms	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
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Title	Mapping Forest Canopy Fuels in the Western United States with LiDAR–Landsat Covariance
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