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This USGS Data Release section presents tipping-bucket rain gage data collected following the 2000 Cerro Grande Fire near Los Alamos, New Mexico. Further details are provided in https://onlinelibrary.wiley.com/doi/10.1002/hyp.6806.
Data accompanying the manuscript 'Patterns and drivers of early conifer regeneration following stand-replacing wildfire in Pacific Northwest (USA) temperate maritime forests' by Laughlin, Rangel-Parra, Morris, Donato, Halofsky and Harvey published in Forest Ecology and Management. Data include field measurements of post-fire seedling abundance and additional information about the forest stands where data were collected. See the main text of the manuscript for complete descriptions of how data were collected, and greater specifics on values and classifications.
The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm uses predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Scene-level products include pixel-level burn probability (BP) and burn classification (BC) images corresponding to each Landsat image in the ARD time series. Annual composite products are also available by summarizing the scene-level products. Prior to generating annual composites, individual scenes that had > 0.010 burned proportion...
Categories: Data,
Data Release - Revised;
Tags: Burned area,
Landsat,
Prescribed fire,
USGS Science Data Catalog (SDC),
United States,
This dataset includes spatial projections of the post-fire recruitment index for ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) using climate data from different time periods (1980-1989, 1990-1999, 2000-2009, 2010-2014) and a future climate scenario of a global mean increase in temperature of two degrees Celsius. The post-fire recruitment index varies from 0 to 1 and represents the proportion of the first five years following wildfire that had climate suitable for regeneration of the given species. We chose a five-year window because the majority (69%) of recruitment across all sites in the dataset used to build our recruitment models occurred within the first five post-fire years. In the...
Categories: Data;
Tags: Pinus ponderosa,
Pseudotsuga menziesii,
US Intermountain West,
biota,
environment,
Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire...
Rain-gages are critical instrumentation for documenting the rainfall forcing of post-wildfire hydrologic, erosional, and water-quality response. This USGS Data Release presents tipping-bucket rain gage data following two wildfires: the 2000 Cerro Grande Fire near Los Alamos, New Mexico and the 2010 Fourmile Canyon Fire near Boulder, Colorado. The data presented in this USGS Data Release are used for analyses that demonstrate important concepts in precipitation characteristics that relate to temporal and spatial scales. Further information regarding the location and data processing are available in the metadata.
Data accompanying the manuscript 'Patterns and drivers of early conifer regeneration following stand-replacing wildfire in Pacific Northwest (USA) temperate maritime forests' by Laughlin, Rangel-Parra, Morris, Donato, Halofsky and Harvey published in Forest Ecology and Management. Data include field measurements of post-fire seedling abundance and additional information about the forest stands where data were collected. See the main text of the manuscript for complete descriptions of how data were collected, and greater specifics on values and classifications.
We developed a screening system to identify introduced plant species that are likely to increase wildfire risk, using the Hawaiian Islands to test the system and illustrate how the system can be applied to inform management decisions. Expert-based fire risk scores derived from field experiences with 49 invasive species in Hawai′i were used to train a machine learning model that predicts expert fire risk scores from among 21 plant traits obtained from literature and databases. The model revealed that just four variables can identify species categorized as higher fire risk by experts with 90% accuracy, while low risk species were identified with 79% accuracy. We then used the predictive model to screen 365 naturalized...
Categories: Data,
Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Hawaii,
farming,
fire,
fire risk,
flammability,
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