Ecologist
Email:
agwells@usgs.gov
Office Phone:
928-556-7344
Location
|
These data were compiled for the Altar Valley Conservation Alliance and the U. S. Fish and Wildlife Service to identify and quantify the spatial distribution of fine fuels in relation to wildfire management across jurisdictional boundaries. Objective(s) of our study were to map the 2021 annual distribution of the biomass (kg/ha) of fine fuels (grasses, shrubs, and forbs) for the whole of the Altar Valley, AZ, including the Buenos Aires National Wildlife Refuge. These data represent estimated biomass of fine fuels (kg/ha) at a 10-m resolution. These data were collected/created in September through October 2021 for the Altar Valley, located in Pima County, AZ, USA. These data were collected/created by the U.S. Geological...
Tags: 10-m resolution,
Altar Valley,
Arizona,
Botany,
Buenos Aires National Wildlife Refuge, All tags...
Ecology,
Geography,
Information Sciences,
Pima County,
Sentinel-2A MSI data,
USGS Science Data Catalog (SDC),
abundance,
biomass,
biota,
data release,
distribution,
environment,
environmental management,
field sampling,
fine-fuels,
fire ecology,
fire management,
fires,
fuel loading,
fuel loads,
grasslike plants,
ground cover,
herbaceous vegetation,
image collections,
in-situ,
leaf-off,
leaf-on,
mixed-modeling,
natural resource management,
plant biomass,
pre-fire conditions,
remote sensing,
shrublands,
south central Arizona,
southern Arizona,
temporal change,
vegetation,
wildfire risk,
wildfires, Fewer tags
|
Note: this data release has been deprecated. Please see new data release here: https://doi.org/10.5066/P9347I2H. These data were compiled to estimate fine-fuel abundance and distribution across the Buenos Aires National Wildlife Refuge in southern Arizona, USA. The objectives of the study were to develop and validate annual estimates of fine fuel (biomass of herbaceous vegetation) for the refuge and to assess changes in fine fuels through time in response to wildfire and prescribed fire. These data represent the amount of herbaceous biomass or fine fuels (kg/ha) present at a 10-m resolution derived from in-situ estimates of ground cover modeled with satellite imagery. These satellite image data were collected over...
Categories: Data;
Tags: 10-m resolution,
Arizona,
Botany,
Buenos Aires National Wildlife Refuge,
Ecology, All tags...
Geography,
Information Sciences,
Pima County,
Remote Sensing,
Sentinel-2A MSI data,
abundance,
biomass,
biota,
distribution,
environment,
environmental management,
field sampling,
fine-fuels,
fire management,
fires,
fuel loading,
fuel loads,
grasslike plants,
ground cover,
herbaceous vegetation,
image collections,
in-situ,
leaf-off,
leaf-on,
mixed-modeling,
natural resource management,
plant biomass,
pre-fire conditions,
prescribed fire,
remote sensing,
southern Arizona,
temporal change,
vegetation,
wildfire risk,
wildfires, Fewer tags
|
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...
Tags: Arapaho National Forest,
Botany,
Colorado,
East Fork Troublesome Creek,
East Troublesome Fire, All tags...
Ecology,
Estes Park,
Forestry,
Garfield County,
Geography,
Glenwood Canyon,
Glenwood Springs,
Granby,
Grand County,
Grand Lake,
Grizzly Creek,
Grizzly Creek Fire,
Information Sciences,
Kremmling,
Remote Sensing,
Rocky Mountain National Park,
Soil Sciences,
USGS Science Data Catalog (SDC),
Water Resources,
White River National Forest,
biota,
burn severity,
dNBR,
data release,
debris flow,
differenced normalized burn ratio,
environment,
fire damage,
fire perimeters,
fires,
fuel,
hazards,
landslides,
machine learning,
modeling,
post-fire differenced normalized burn ratio,
post-fire hazard,
pre-fire conditions,
pre-fire estimation,
pre-fire modeling,
pre-fire predictions,
predicted differenced normalized burn ration,
predictive models,
random forest modeling,
remote sensing,
volume of debris flow,
water quality,
wildfire,
wildland fire, Fewer tags
|
|
