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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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 12; H. 6; S. 1000
Hauptverfasser:	Moran, Christopher J., Kane, Van R., Seielstad, Carl A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 20.03.2020
Schlagworte:	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-Zugang:	Volltext
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