Multi-scaled drivers of severity patterns vary across land ownerships for the 2013 Rim Fire, California

Context As the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by weather, fuels, topography, and management. Objectives Identify the biophysical and management drivers of severity patterns and their spatial v...

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Vydané v:	Landscape ecology Ročník 35; číslo 2; s. 293 - 318
Hlavní autori:	Povak, Nicholas A., Kane, Van R., Collins, Brandon M., Lydersen, Jamie M., Kane, Jonathan T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.02.2020 Springer Nature B.V
Predmet:	algorithms autocorrelation Biomedical and Life Sciences California Ecology environmental factors Environmental Management Extreme weather fire severity fire spread fire weather Forest management Fuels fuels (fire ecology) Heterogeneity land ownership Landscape Ecology Landscape/Regional and Urban Planning landscapes Life Sciences mountains National forests National parks Nature Conservation prediction Predictive control Research Article Restoration Spatial analysis Spatial dependencies Stanislaus National Forest Sustainable Development Topography Variability Variance Weather Wildfires United States > US California Stanislaus National Forest Rim fire Fire severity Spatial autocorrelation Variable importance Variance partitioning Machine learning
ISSN:	0921-2973, 1572-9761
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