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Keyword: forest

First report of the Armillaria root-disease pathogen, Armillaria gallica, associated with several woody hosts in three states of Central Mexico (Guanajuato, Jalisco, and Michoacan)

Publications Posted on: March 19, 2021
In July-August 2019, seven Armillaria isolates (derived from rhizomorphs and mycelial fans of infected roots) were collected in association with woody hosts in the central Mexico: states of Guanajuato (MEX204), Jalisco (MEX206, MEX208, MEX209), and Michoac´an (MEX211, MEX214, MEX216). All seven isolates were identified as Armillaria gallica based on translation elongation factor 1a (tef1) gene sequences (GenBank accession nos.

Decadal changes in fire frequencies shift tree communities and functional traits

Publications Posted on: March 15, 2021
Global change has resulted in chronic shifts in fire regimes. Variability in the sensitivity of tree communities to multi-decadal changes in fire regimes is critical to anticipating shifts in ecosystem structure and function, yet remains poorly understood.

Digital surface, terrain, and canopy height models for Moscow Mountain in 2009

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Field observations for "A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA"

Datasets Posted on: December 30, 2020
These data represent a portion of the forest inventory data used in Hudak et al. (in review) "A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA".

Digital surface, terrain, and canopy height models for Boise Basin Experimental Forest in 2018

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Digital surface, terrain, and canopy height models for Priest River Experimental Forest in 2011

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Digital surface, terrain, and canopy height models for Deception Creek Experimental Forest in 2011

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Measured and modelled above- and below-canopy turbulent fluxes for a snow-dominated mountain forest using GEOtop

Publications Posted on: October 02, 2020
The prediction of snowmelt in mountainous forests strongly depends on the accurate description of sensible and latent heat turbulent fluxes. Uncertainty about the within‐ canopy wind conditions especially poses a challenge, with relatively few studies examining both above‐ and below‐canopy turbulent fluxes.

Mapping multiple insect outbreaks across large regions annually using Landsat time series data

Publications Posted on: June 17, 2020
Forest insect outbreaks have caused and will continue to cause extensive tree mortality worldwide, affecting ecosystem services provided by forests. Remote sensing is an effective tool for detecting and mapping tree mortality caused by forest insect outbreaks. In this study, we map insect-caused tree mortality across three coniferous forests in the Western United States for the years 1984 to 2018.

Modelling the effect of accelerated forest management on long-term wildfire activity

Publications Posted on: May 04, 2020
We integrated a widely used forest growth and management model, the Forest Vegetation Simulator, with the FSim large wildfire simulator to study how management policies affected future wildfire over 50 years on a 1.3 million ha study area comprised of a US national forest and adjacent lands.

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