Filters: Tags: Wildfire (X) > Date Range: {"choice":"month"} (X)
12 results (40ms)|
Filters
Date Types (for Date Range)
Types Contacts
Categories Tag Types Tag Schemes
|
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,
Wildfires are one of the greatest threats to human infrastructure and the ecosystem services humans value in the western US, but are also necessary in fire-adapted ecosystems. Wildfire activity is widely projected to increase in response to climate change in the Northwest, but we currently lack a comprehensive understanding of what this increase will look like or what its impacts will be on a variety of ecological and hydrologic systems. This project addressed one critical part of those impacts: the islands of unburned vegetation within wildfires. Unburned islands occur naturally as wildfires burn across landscapes, and are important habitat refuges for species -- places where plants and animals survive the fire...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2014,
CASC,
Completed,
Data Visualization & Tools,
Data Visualization & Tools,
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...
Fire in the western U.S. poses one of the greatest threats to human and ecological communities alike. In fact, fire management is the largest single expenditure of land management funds on federal lands. Now, climate change is altering wildfire patterns. Climate change in the West is creating warmer and drier conditions, resulting in an increase in the amount of dead vegetation available to fuel fires. This project sought to assess the vulnerability of forests in the southwestern U.S. to climate change and wildfire, in order to understand how these ecosystems might become altered as a result. Researchers (a) examined how climate change impacts wildfires in the region, to better understand fire risk; (b) identified...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2012,
CA,
CASC,
CO,
Completed,
Aspen is a highly valued tree species in western forests, but it has been declining due to drought, herbivory, and fire suppression. Fire suppression has contributed to aspen’s decline because post-fire environments provide more opportunities for aspen to regenerate, both by resprouting and by seed. Regeneration from seed in these post-fire settings is particularly valuable because it increases aspen population genetic diversity and resilience. However, natural seeding events are not common, and mortality rates for seedlings are quite high. Nursery-grown seedlings provide a promising alternative and an opportunity for assisted migration, but planting protocols have not been well developed and mortality rates for...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2023,
Aspen,
CASC,
Forests,
Landscapes,
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.
In California, increased wildfire activity has been linked to decreasing snowpack and earlier snowmelt. Not only has this translated into a longer fire season, but reduced snowpack has cascading effects that impact streamflow, water supplies, agricultural productivity, and ecosystems. California receives 80% of its precipitation during the winter, so mountain snowpack plays a critical role in replenishing the state’s water supply. One factor that affects the amount of winter precipitation (and therefore snowpack) in California is the North Pacific Jet (NPJ)—a current of strong, high altitude winds that occur over the northern Pacific Ocean. Winters when the NPJ is located further north than normal are drier than...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service,
Report;
Tags: 2013,
CA,
CA-wide,
CASC,
Completed,
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,
|
|
