This data consists of observations of individual trees that were subjected to prescribed fire in western US national parks. Information on individual trees include measurements of tree live/dead status, growth, size, competition, and fire-caused damage. The data also includes estimates of plot-level vapor pressure deficit anomaly before fire. First posted - August 28, 2018 (available from author). Revised - February 10, 2020.

This data consists of observations of individual trees that were subjected to prescribed fire in western US national parks. Information on individual trees include measurements of tree live/dead status, growth, size, competition, and fire-caused damage. The data also includes estimates of plot-level vapor pressure deficit anomaly before fire. First posted - August 28, 2018 (available from author). Revised - February 10, 2020.

This dataset provides annual estimates of tree mortality due to fires and bark beetles from 2003 to 2012 on forestland in the continental western United States. Tree mortality was estimated at 1-km spatial resolution by combining tree aboveground carbon (AGC) and disturbance datasets derived largely from remote sensing. Tree mortality is expressed as the amount of AGC stored in trees killed by disturbance (Mg carbon per km2). The dataset also includes annual uncertainty maps that were generated using a Monte Carlo approach in which tree biomass, biomass carbon content, and disturbance severity were iteratively varied by their uncertainty.

This dataset was used to summarize and analyze the mortality factors recorderd on dead trees in the Sierra Nevada Forest Dynamics Plot Network, which is managed by the Sequoia and Kings Canyon Field station of the U.S. Geological Survey's Western Ecological Research Center. Each row of the dataset represents an individual dead tree. These are dead trees that were recorded in the network from 1998 to 2010 for the subset of plots as described in the associated manuscript.

This dataset provides annual estimates of tree mortality due to fires and bark beetles from 2003 to 2012 on forestland in the continental western United States. Tree mortality was estimated at 1-km spatial resolution by combining tree aboveground carbon (AGC) and disturbance datasets derived largely from remote sensing. Tree mortality is expressed as the amount of AGC stored in trees killed by disturbance (Mg carbon per km2). The dataset also includes annual uncertainty maps that were generated using a Monte Carlo approach in which tree biomass, biomass carbon content, and disturbance severity were iteratively varied by their uncertainty.

Data are derived from 2010-2014 (6-10 years) aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the years recorded in the five-year dataset are thought to have significant impacts and likely mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as very light .

These data consist of environmental covariates and estimated plot-level mortality of ponderosa pine trees. Environmental covariates include growing season temperature and soil moisture, and values are summarized into long-term mean conditions, and anomalies observed between forest inventory sampling events for each plot. Data also include plot locations (with uncertainty introduced by the US Forest Service to maintain private property rights), plot basal area, and several variables related to estimated mortality rate of ponderosa pine trees under various assumptions about basal area conditions.

These data consist of environmental covariates and estimated plot-level mortality of ponderosa pine trees. Environmental covariates include growing season temperature and soil moisture, and values are summarized into long-term mean conditions, and anomalies observed between forest inventory sampling events for each plot. Data also include plot locations (with uncertainty introduced by the US Forest Service to maintain private property rights), plot basal area, and several variables related to estimated mortality rate of ponderosa pine trees under various assumptions about basal area conditions.

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