Filters: Tags: {"type":"USGS Scientific Topic Keyword"} (X) > partyWithName: Todd J Hawbaker (X)
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Geospatial data were developed to characterize pre-fire biomass, burn severity, and biomass consumed for the Black Dragon Fire that burned in northern China in 1987. Pre-fire aboveground tree biomass (Mh/ha) raster data were derived by relating plot-level forest inventory data with pre-fire Landsat imagery from 1986 and 1987. Biomass data were generated for individual species: Dahurian larch (Larix gmelinii Rupr. Kuzen), white birch (Betula platyphylla Suk), aspen (Populus davidiana Dode and Populus suaveolens Fischer), and Mongolian Scots pine (Pinus sylvestris var. mongolica Litvinov). A raster layer of total aboveground tree biomass was also generated. Burned area was manually delineated using the normalized...
Plot-level field data were collected in the summer of 2014 to estimate aboveground and belowground biomass in the Great Dismal Swamp National Wildlife Refuge and Dismal Swamp State Park in North Carolina and Virginia. Data were collected at 85 plots. The location of the center of each plot was recorded with a Trimble ProXH global positioning system (GPS) and differentially corrected. Data files included 1: GDS_plots.csv, 2. GDS_FWD.csv, 3. GDS_LWD.csv, 4. GDS_Shrubs.csv, 5. GDS_Trees.csv, and 6. GDS_plot_summaries.csv. The data contained in GDS_plot_summaries.csv were calculated from the GDS_plots.csv, GDS_FWD.csv, GDS_LWD.csv, GDS_Shrubs.csv, GDS_Trees.csv files using the R statistical software environment (R Core...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Biomass,
Botany,
Cover,
Duff,
Ecology,
Interpretations of post-fire condition and rates of vegetation recovery can influence management priorities, actions, and perception of latent risks from landslides and floods. In this study, we used the Waldo Canyon fire (2012, Colorado Springs, Colorado, USA) as a case study to explore how a time series (2011-2016) of high-resolution images can be used to delineate burn extent and severity, as well as quantify post-fire vegetation recovery. We applied an object-based approach to map burn severity and vegetation recovery using Worldview-2, 3, and QuickBird-2 imagery. The burned area was classified as 51% high, 20% moderate and 29% low burn-severity. Across the burn extent, the shrub cover class showed a rapid recovery,...
This data release provides output produced by a statistical, aridity threshold fire model for 11 extensively forested ecoregions in the western United States. We identified thresholds in fire-season climate water deficit (FSCWD) that distinguish years with limited, moderate, and extensive area burned for each ecoregion. We developed a new area burned model using these relationships and used it to simulate annual area burned using historical climate from 1980 - 2020 and output from global climate models (GCMs) from 1980 - 2099. The data release includes a comparison of mean annual FSCWD for 13 GCMs that we used to select five GCMs that bracket the range of conditions projected for the RCP 8.5 emissions scenario....
Disturbance disrupts the balance between gross primary productivity and respiration, resulting in a net C loss for some time after a stand-replacing fire. However, our understanding of this process is based on a limited number of studies. Ecosystem C recovery post-fire must be explicitly and carefully examined in order to generate accurate predictions of C cycle impacts of future wildfires and change in fire regimes. Montane ponderosa and lodgepole pine forests, either single-species stands or mixed, dominate surface area in the Southern Rockies. These species have drastically different relationships with wildfire; the current narrative portrays ponderosa pine as accustomed to low-severity surface fires with low...
This data release provides inputs needed to run the LANDIS-II landscape change model, NECN and Base Fire extensions for the Greater Yellowstone Ecosystem (GYE), USA, and simulation results that underlie figures and analysis in the accompanying publication. We ran LANDIS-II simulations for 112 years, from 1988-2100, using interpolated weather station data for 1988-2015 and downscaled output from 5 general circulation models (GCMs) for 2016-2100. We also included a control future scenario with years drawn from interpolated weather station data from 1980-2015. Model inputs include raster maps (250 × 250 m grid cells) of climate regions and tables of monthly temperature and precipitation for each climate region. We...
Landscape carbon (C) flux estimates are necessary for assessing the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have allowed for coarse-scale estimates of gross primary productivity (GPP) (e.g., MODIS 17), yet efforts to assess spatial patterns in respiration lag behind those of GPP. Here, we demonstrate a method to predict growing season soil respiration at a regional scale in a forested ecosystem. We related field measurements (n=144) of growing season soil respiration across subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors with a Random Forest model (30 m pixel size). We...
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...
Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex making it challenging for end-users, such as those who use or create WUI maps, to apply. We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs, and Woolsey. We used a CNN model from Esri to detect buildings from high-resolution imagery. This dataset represents the state-of-the-art of what is readily available...
Categories: Data;
Tags: Building footprint,
California,
Convolutional neural network,
Ecology,
Geography,
Stakeholder science needs were determined by reviewing more than 200 recently published literature items and web pages from Colorado River Basin (CRB) stakeholders. These stakeholder communications were used to characterize over 400 stakeholder science needs by reviewing their priorities, strategies, issues, missions, and concerns related to drought in the CRB. Members of the CRB Integrated Science Pilot Project team identified each of the stakeholder’s science needs and categorized the needs based on science themes and science topics that the needs address in the landscape. The terms used as science topics were initially created by Pilot Project team members but then later were cross-walked to match terms in the...
Wildfires and prescribed fires are frequent but under-mapped across wetlands of the southeastern United States . High annual precipitation supports rapid post-fire recovery of wetland vegetation, while associated cloud cover limits clear-sky observations. In addition, the low burn severity of prescribed fires and spectral confusion between fluctuating water levels and burned areas have resulted in wetland burned area being chronically under-estimated across the region. In this analysis, we first quantify the increase in clear-sky observations by using Sentinel-2 in addition to Landsat 8. We then present an approach using the Sentinel-2 archive (2016-2019) to train a wetland burned area algorithm at 20 m resolution....
Landsat Normalised Difference Vegetation Index (NDVI) is commonly used to monitor post-fire green-up; however, most studies do not distinguish new growth of conifer from deciduous or herbaceous species, despite potential consequences for local climate, carbon and wildlife. We found that dual season (growing and snow cover) NDVI improved our ability to distinguish conifer tree presence and density. We then examined the post-fire pattern (1984–2017) in Landsat NDVI for fires that occurred a minimum of 20 years ago (1986–1997). Points were classified into four categories depending on whether NDVI, 20 years post-fire, had returned to pre-fire values in only the growing season, only under snow cover, in both seasons...
These data were compiled for/to provide an example and assess methods and results of pre-fire estimation of predicted differenced normalized burn ration (dNBR) for predicting post-fire debris flow hazard classification. Objective(s) of our study were to develop predictive models for burn severity, using variables of pre-fire conditions, for two large wildfires from 2020 in Colorado, USA. These data represent pre-fire predictions of post-fire differenced normalized burn ratio (dNBR) as a proxy of burn severity and further understand pre-fire modeling of burn severity. These data were collected/created in the fire perimeters the East Troublesome Fire (10/14/2020 – 11/30/2020) and the Grizzly Creek Fire (8/10/2020...
The Department of the Interior (DOI) Office of Wildland Fire and USGS created the The Wildfire Hazard and Risk Assessment Inventory to meet the Monitoring, Maintenance, and Treatment Plan requirements under the Bipartisan Infrastructure Law (BIL). It provides an inventory of key national, regional, and state wildfire risk and fire hazard assessments useful for understanding different characterizations of fire risk. Some of the assessments may be useful for communicating contributions toward risk reduction of treatments funded by DOI, including investments under BIL. For each assessment, the inventory provides a description and information about the spatial extent, resolution, fire modeling approach, values considered...
Plot-level field data were collected in the summer of 2014 to estimate aboveground and belowground biomass in the Great Dismal Swamp National Wildlife Refuge and Dismal Swamp State Park in North Carolina and Virginia. Data were collected at 85 plots. The location of the center of each plot was recorded with a Trimble ProXH global positioning system (GPS) and differentially corrected. Data files included 1: GDS_plots.csv, 2. GDS_FWD.csv, 3. GDS_LWD.csv, 4. GDS_Shrubs.csv, 5. GDS_Trees.csv, and 6. GDS_plot_summaries.csv. The data contained in GDS_plot_summaries.csv were calculated from the GDS_plots.csv, GDS_FWD.csv, GDS_LWD.csv, GDS_Shrubs.csv, GDS_Trees.csv files using the R statistical software environment (R Core...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Biomass,
Botany,
Cover,
Duff,
Ecology,
This data release provides inputs needed to run the LANDIS PRO forest landscape model and the LINKAGES 3.0 ecosystem process model for the area burned by the Black Dragon Fire in northeast China in 1987, and simulation results that underlie figures and analysis in the accompanying publication. The data release includes the fire perimeter of Great Dragon Fire; input data for LINKAGES including soils, landtype, and climate data; initial conditions of stands in the study area before the Great Dragon Fire; and maps of LANDIS PRO output for each model grid cell including total trees, total biomass (Mg/ha), and tree density (trees/ha) in two-year timesteps.
Nature-based solutions is a leading policy option for mitigating climate change. We mapped areas of potential restoration and conservation opportunities in the conterminous U.S. (CONUS). The potential for five scenarios were examined: increasing forest cover in urban centers, restoring historically forested areas that have been converted to grasslands, conserving pristine grasslands, rewetting peatlands, and conserving vulnerable tidal wetlands.
Wildfires and housing development have increased since the 1990s, presenting unique challenges for fire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have contributed to risk to homes. We fit a random forest using weather, land cover, topography, and past fire history to predict burn probabilities and uncertainty intervals. Then, we estimated risk at 1-km resolution and monthly intervals from 1990 through 2019 by combining predicted burn probabilities with housing density across the Southern Rocky Mountains. We used 3 scenarios to evaluate how housing growth and changes in burn probability influenced risk individually and combined (observed, 1990...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Climatology,
Colorado,
Ecology,
Geography,
Land Use Change,
Burn probability (BP) models the likelihood that a location could burn. However, predicting BP is extremely challenging, because fire behavior varies strongly among landscapes and with changing weather conditions and wildfire spread simulations are computationally intensive and require integration of data with large spatial and temporal variability. In this data release we include the monthly BP estimation for the state of California, USA for the 2015-2019 period produced using a machine learning model and two different sets of input features. For the first case, the baseline, the model used all available input features to predict BP. The second output set corresponds to the BP predictions when the model used only...
Categories: Data;
Tags: California,
Climatology,
Ecology,
Land Use Change,
USGS Science Data Catalog (SDC),
This data release provides inputs needed to run the LANDIS PRO forest landscape model and the LINKAGES 3.0 ecosystem process model for the temperate-boreal ecotone Great Xing’an Mountains of northeastern China, and simulation results that underlie figures and analysis in the accompanying publication. The study compared the impacts of small and large fires on vegetation dynamics. The data release includes input data for LINKAGES including soils, landtype, and climate data; initial conditions of stands in the study area for LANDIS PRO; and maps of LANDIS PRO output for each model grid cell including total trees, total biomass (Mg/ha), and tree density (trees/ha) in ten-year timesteps. Output for four climate and fire...
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