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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.
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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...
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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...
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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...
    map background search result map search result map Maps of post-fire conifer recruitment from: Fire-catalyzed vegetation shifts in ponderosa pine and Douglas-fir forests of the western United States The Landsat Collection 2 Burned Area Products for the conterminous United States (ver. 2.0, April 2024) Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 Post-wildfire rain gage data for Rendija Canyon, New Mexico Post-wildfire rain gage data for Rendija Canyon, New Mexico Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 Maps of post-fire conifer recruitment from: Fire-catalyzed vegetation shifts in ponderosa pine and Douglas-fir forests of the western United States The Landsat Collection 2 Burned Area Products for the conterminous United States (ver. 2.0, April 2024)