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This data release contains time-lapse imagery taken at U.S. Geological Survey (USGS) stream gaging stations with associated hydrologic and meteorological data related to each image. These data are to help improve the development of models in detecting water elevation at a given stream gaging station. Images of the water surface and surroundings at USGS stream gaging stations were taken at varying time intervals ranging between every five minutes to an hour. Cameras used include trail cameras, web cameras, and the custom river imagery sensing (RISE) camera. Time-lapse images for each USGS stream gaging station are provided in compressed files (file extension .7z). These files are named in a format to identify the...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Remote Sensing,
USGS Science Data Catalog (SDC),
Water Resources,
camera,
image,
These data represent total vegetation and surface water along approximately 12 kilometers of the Paria River upstream from the confluence of the Colorado River at Lees Ferry, Arizona. They are derived from airborne, multispectral imagery obtained in late May 2009, 2013, and 2021, collected with a push-broom sensor with 4 spectral bands depicting Blue, Green, Red and Near-Infrared wavelengths at a spatial resolution of 20 centimeters. The vegetation classification data were created using a supervised classification algorithm provided by Harris Geospatial in ENVI version 5.6.3 (Exelis Visual Information Solutions, Boulder, Colorado). The water data were created using a Green Normalized Difference Vegetation Index...
LANDFIRE's (LF) 2022 Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. CC supplies information for fire behavior models to determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. To create this product, plot level CC values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance CC and Canopy Height (CH) are used as predictors of disturbed CC using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and...
LANDFIRE (LF) 2022 Fuel Vegetation Cover (FVC) represents the LF Existing Vegetation Cover (EVC) product, modified to represent pre-disturbance EVC in areas where disturbances have occurred over the past 10 years. EVC is mapped as continuous estimates of canopy cover for tree, shrub, and herbaceous lifeforms with a potential range from 10% to 100%. Continuous EVC values are binned to align with fuel model assignments when creating FVC. FVC is an input for fuel transitions related to disturbance. Fuel products in LF 2022 were created with LF 2016 Remap vegetation in non-disturbed areas. To designate disturbed areas where FVC is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance...
The LANDFIRE (LF) Canadian Forest Fire Danger Rating System (CFFDRS) product depicts fuel types as an identifiable association of fuel elements of distinctive species, form, size, arrangement, and continuity. CFFDRS exhibits characteristic fire behavior under the specified burn conditions. In LF 2022 Canadian fuel models are derived from the Fuel Model Guide to Alaska Vegetation (Alaska Fuel Model Guide Task Group, 2018) and subsequent updates. The LF CFFDRS product contains the fuel models used for the Fire Behavior Prediction (FBP) system fuel type inputs. Default values assigned to the Canadian Fuel Models required to run the Prometheus fire behavior software (Prometheus, 2021) are added as attributes to the...
LANDFIRE's (LF) 2022 Forest Canopy Height (CH) describes the average height of the top of the canopy for a stand. CH is used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CH supplies information for fire behavior models, such as FARSITE (Finney 1998), that can determine the starting point of embers in the spotting model, wind reductions, and the volume of crown fuels. To create this product, plot level CH values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance Canopy Cover and CH are used as predictors of disturbed CH using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and time since...
LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. LF 2022 Fuel Disturbance (FDist) uses the latest Annual Disturbance products from the effective disturbance years of 2013 to 2022. FDist is created from LF 2022 Historical Disturbance (HDist) which in turn aggregates the Annual Disturbance products. FDist groups similar disturbance types, severities and time since disturbance categories which represent disturbance scenarios within the fuel environment. FDist is used in conjunction with Fuel Vegetation Type (FVT), Cover (FVC), and Height (FVH) to calculate Canopy Cover (CC), Canopy Height (CH), Canopy Bulk Density (CBD), Canopy Base Height (CBH),...
These data were compiled for Cabeza Prieta National Wildlife Refuge (CPNWR) in southern Arizona, to support managment efforts of water resources and wildlife conservation. Objective(s) of our study were to 1) measure water storage capacity at select stage heights in three tanks (also termed tinajas), 2) build a stage storage model to help CPNWR staff accurately estimate water volumes throughout the year, and 3) collect topographic data adjacent to the tanks as a means to help connect these survey data to past or future work. These data represent high-resolution (sub-meter) ground based lidar measurements used to meet these objectives and are provided as: processed lidar files (point clouds), rasters (digital elevation...
These data were collected to support the development of detection and classification algorithms to support Bureau of Ocean Energy Management (BOEM) studies and assessments associated with offshore wind energy production. There are 3 child zip files included in this data release. 01_Codebase.zip contains a codebase for using deep learning to filter images based on the probability of any bird occurrence. It includes instructions and files necessary for training, validating, and testing a machine learning detection algorithm. 02_Imagery.zip contains imagery that were collected using a Partenavia P68 fixed-wing airplane using a PhaseOne iXU-R 180 forward motion compensating 80-megapixel digital frame camera with...
These datasets provide early estimates of 2024 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from April to late June. Typically, the EAG estimates are publicly released within 7-13 days of the latest satellite observation used for that version. Each weekly release contains five fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) Field Brome (Bromus arvensis); 4) medusahead (Taeniatherum caput-medusae); and 5) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory,...
These data were compiled for Cabeza Prieta National Wildlife Refuge (CPNWR) in southern Arizona, to support managment efforts of water resources and wildlife conservation. Objective(s) of our study were to 1) measure water storage capacity at select stage heights in three tanks (also termed tinajas), 2) build a stage storage model to help CPNWR staff accurately estimate water volumes throughout the year, and 3) collect topographic data adjacent to the tanks as a means to help connect these survey data to past or future work. These data represent high-resolution (sub-meter) ground based lidar measurements used to meet these objectives and are provided as: processed lidar files (point clouds), rasters (digital elevation...
In December 2022, the U.S. Geological Survey (USGS) flew uncrewed aerial systems (UAS) to collect very high-resolution imagery and lidar data of Vicksburg National Military Park to address areas of substantial landslide hazard within the park. The fine spatial resolution and high accuracy of this data are needed to fully characterize and quantify landslides and to understand the potential for continued landslide activity in other areas of the park. UAS flights were conducted by the USGS National Uncrewed Systems Office (NUSO). This data release publishes the 0.5m DTM created from the UAS-borne lidar data collected in December 2022.
This data release includes multispectral images and field measurements of water depth from the Sacramento River near Glenn, California, used to evaluate the potential for efficient reach-scale mapping of river bathymetry using Uncrewed Aircraft Systems (UAS). The images were acquired by a MicaSense RedEdge-MX Dual Camera deployed from a Trinity F90 vertical take-off and landing (VTOL) UAS. The 4 km long study area along the Sacramento River was subdivided into three distinct but adjacent areas of interest (AOIs) and image data were collected from one AOI each day between September 14 and 16, 2021. The image data were ortho-rectified using Quantum-Systems QBase 3D and Agisoft Metashape software and saved as GeoTIFF...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Aquatic Biology,
California,
Geomorphology,
Glenn,
Hydrology,
LANDFIRE's (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance...
LANDFIRE's (LF) 2022 Canopy Base Height (CBH) supplies information used in fire behavior models to determine the critical point at which a surface fire will transition to a crown fire in conjunction with other environmental factors, such as wind speed and moisture content. CBH data are continuous from 0 to 9.9 meters (to the nearest 0.1m) and describe the lowest point in a stand where there is enough available fuel (0.25in diameter) to propagate fire vertically through the canopy. Critical CBH is defined as the lowest point at which the Canopy Bulk Density (CBD) is .012kg m-3. Under different scenarios of disturbance and based on previous research incorporating plot-level CBH calculations, CBH for disturbed areas...
LANDFIRE (LF) 2022 Fuel Vegetation Height (FVH) represents the LF Existing Vegetation Height (EVH) product, modified to represent pre-disturbance EVH in areas where disturbances have occurred over the past 10 years. EVH is mapped as continuous estimates of canopy height for tree, shrub, and herbaceous lifeforms with a potential range of 0-100m. Continuous EVH values are binned to align with fuel model assignments when creating FVH. FVH is an input for fuel transitions related to disturbance. Fuel products in LF 2022 were created with LF 2016 Remap vegetation in non-disturbed areas. To designate disturbed areas where FVH is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance...
LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. LF 2022 Fuel Disturbance (FDist) uses the latest Annual Disturbance products from the effective disturbance years of 2013 to 2022. FDist is created from LF 2022 Historical Disturbance (HDist) which in turn aggregates the Annual Disturbance products. FDist groups similar disturbance types, severities and time since disturbance categories which represent disturbance scenarios within the fuel environment. FDist is used in conjunction with Fuel Vegetation Type (FVT), Cover (FVC), and Height (FVH) to calculate Canopy Cover (CC), Canopy Height (CH), Canopy Bulk Density (CBD), Canopy Base Height (CBH),...
LANDFIRE's (LF) 2022 update (LF 2022) Existing Vegetation Height (EVH) represents the average height of the dominant vegetation for a 30-m cell. EVH is produced separately for tree, shrub, and herbaceous lifeforms using training data depicting the weighted average height by species cover and Existing Vegetation Type (EVT) lifeform. Decision tree models using field reference data, lidar, and Landsat are developed separately for each lifeform, then lifeform specific height class layers are merged along with land cover into a single EVH product based on the dominant lifeform of each pixel. EVH ranges are continuous for the herbaceous lifeform category ranging from 0.1 to 1 meter with decimeter increments, 0.1 to 3...
The Skykomish, Snoqualmie, and Middle Fork Snoqualmie River Basins have historically provided critical spawning, rearing, and core habitat for several salmonid species. These salmonid species include natural populations of Chinook salmon (O. tshawytscha), steelhead trout (O. mykiss), and bull trout (Salvelinus confluentus)—listed as “Threatened” under the Endangered Species Act—as well as coho salmon (O. kisutch)—listed as a ”Species of concern”—pink salmon (O. gorbuscha), chum salmon (O. keta), and native char (S. malma) (Solomon and Boles, 2002; Stohr and others, 2011; Svrjcek and others, 2013; Snohomish County Surface Water Management and the Sustainable Lands Strategy Executive Committee [SWM], 2017; U.S. Fish...
We developed habitat suitability models for three invasive plant species: stiltgrass (Microstegium vimineum), sericea lespedeza (Lespedeza cuneata), and privet (Ligustrum sinense). We applied the modeling workflow developed in Young et al. 2020, developing similar models for occurrence data, but also models trained using species locations with percent cover ≥10%, ≥25%, and ≥50%. We chose predictors from a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using...
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