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LANDFIRE's (LF) 2022 update (LF 2022) Existing Vegetation Cover (EVC) represents the vertically projected percent cover of the live canopy for a 30-m cell. EVC is produced separately for tree, shrub, and herbaceous lifeforms. Training data depicting percentages of canopy cover are obtained from plot-level ground-based visual assessments and lidar observations. These are combined with Landsat imagery (from multiple seasons), to inform models built independently for each lifeform. Tree, shrub, and herbaceous lifeforms each have a potential range from 10% to 100% (cover values less than 10% are binned into the 10% value). The three independent lifeform datasets are merged into a single product based on the dominant...
LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. Historical Disturbance (HDist) is developed from the base annual LF disturbance products, and attribute code system, to represent the history of disturbance for a 10-year span. Each year's disturbance scenarios are checked against time relevant LF vegetation products to check for logical inconsistencies. Errant codes are flagged and updated to a discard code with the remaining disturbance types cross-walked/aggregated to Fuel Disturbance (FDist) types. HDist includes the year of disturbance that is recorded for that pixel. In LF 2022, the time since disturbance code is the same for both HDist...
LANDFIRE (LF) 2022 Fuel Vegetation Type (FVT) represents the LF Existing Vegetation Type Ecological Systems (EVT) product, modified to represent pre-disturbance EVT in areas where disturbances have occurred over the past 10 years. Due to shifting EVT codes and labels throughout the years, the FVT codes are based on an early version of EVT codes translated from the current version. FVT 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 FVT is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances...
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...
In May 2021, the Grand Canyon Monitoring and Research Center (GCMRC) of the U.S. Geological Survey’s (USGS), Southwest Biological Science Center (SBSC) acquired airborne multispectral high resolution data for the Colorado River in Grand Canyon in Arizona, USA. The imagery data consist of four bands (Band 1 – red, Band 2 – green, Band 3 – blue, and Band 4 – near infrared) with a ground resolution of 20 centimeters (cm). These image data are available to the public as 16-bit GeoTIFF files, which can be read and used by most geographic information system (GIS) and image-processing software. The spatial reference of the image data are in the State Plane (SP) map projection using the central Arizona zone (FIPS 0202)...
Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. The blue water evapotranspiration (BWET) raster dataset at 30-meter resolution is created to estimate agricultural irrigation water consumption. The dataset contains seasonal total (1 May to 30 September) BWET time series (1986 – 2020) for the croplands across the U.S. High Plains aquifer region. The BWET estimates are generated by integrating an energy-balance ET model (Operational Simplified Surface Energy Balance model) and a water-balance ET model (Vegetation ET model). BWET in croplands reflects crop consumptive use of irrigation water extracted from surface water and groundwater...
This data release includes 2022 data for the Louisiana Outer Coast Restoration Project for Whiskey Island. Specifically, this data release includes a detailed habitat map, general habitat map, and georeferenced imagery. These habitat maps are developed using the methods and classification scheme from Louisiana Coastal Protection and Restoration Authority’s (CPRA) Barrier Island Comprehensive Monitoring (BICM) program. For more details on BICM habitat classes, see the Entity and Attribute Information section of the metadata. Please consult the accompanying readME.txt file for information and recommendations on the contents of this dataset (that is, dataset and recommended symbology). For more information about BICM...
An unmanned aerial system (UAS) was used to acquire high-resolution imagery of the intertidal zone at West Whidbey Island, Washington on June 4, 2019. This imagery was processed using structure-from-motion (SfM) photogrammetric techniques to derive a high-resolution digital surface model (DSM), orthomosaic imagery, and topographic point clouds. In order to maximize the extent of the subaerially exposed area, the survey was timed to coincide with a spring low tide occurring at approximately 18:02 Universal Coordinated Time (UTC) (11:02 Pacific Daylight Time (PDT)), with a predicted water level of -0.74 meters below mean lower-low water (MLLW) at the Sunset Beach NOAA subordinate tide station (station ID 9447951)....
Tags: Geomorphology,
Remote Sensing
We characterized seafloor sediment conditions near the mouth of the Elwha River from underwater photographs taken every four hours from September 2011 to December 2013. A digital camera was affixed to a tripod that was deployed in approximately 10 meters of water (Tripod location from September 2011 to April 2013: 48.15333, -123.55931; tripod location from April 2013 to December 2013: 48.15407, -123.55444). Each photograph was qualitatively characterized as one of six categories: (1) base, or no sediment; (2) low sediment; (3) medium sediment; (4) high sediment; (5) turbid; or (6) kelp. For base conditions, no sediment was present on the seafloor. Low sediment conditions were characterized by a light dusting of...
Categories: Data;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Clallam County,
Elwha River,
Olympic Peninsula,
Remote Sensing,
State of Washington,
An unmanned aerial system (UAS) was used to acquire high-resolution imagery of the intertidal zone at Puget Creek and Dickman Mill Park in Tacoma, Washington on June 3, 2019. This imagery was processed using structure-from-motion (SfM) photogrammetric techniques to derive high-resolution digital surface models (DSM), orthomosaic imagery, and topographic point clouds. In order to maximize the extent of the subaerially exposed area, the survey was timed to coincide with a spring low tide occurring at approximately 18:36 Universal Coordinated Time (UTC) (11:36 Pacific Daylight Time (PDT)), with an observed water level of -1.47 meters relative to the NAVD88 vertical datum at the Tacoma NOAA tide station (station ID...
Tags: Geomorphology,
Remote Sensing
This portion of the data release presents a digital surface model (DSM) and hillshade image of the intertidal zone at Lone Tree Point, Kiket Bay, WA. The DSM has a resolution of 4 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-05. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The raw imagery used to create the DSM was acquired using a UAS fitted with a Ricoh...
These data were compiled for evaluating plant water use, or river-reach level evapotranspiration (ET) data, in the riparian corridor of the Colorado River delta as specified under Minute 319 of the 1944 Water Treaty. Additionally, these data were compiled for evaluating restoration-level data in Reach 2 and Reach 4, as specified under Minute 323 of the 1944 Water Treaty. Objectives of our study were to measure the peak growing season evapotranspiration (ET) for the average of months in summer-fall (May to October) for the seven reaches, for the full riparian corridor, and for four restoration sites, from 2013 through 2022. The seven reach areas from the Northerly International Boundary (NIB) to the end of the delta...
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...
In January 2020, the North Carolina Department of Transportation (NCDOT) began work on the Interstate 26 (I 26) highway widening project that involves a bridge crossing over the French Broad River (FBR) near Asheville, North Carolina. The U.S Geological Survey (USGS) in cooperation with the NCDOT conducted a pre-construction light detection and ranging (lidar) survey of the streambanks within a one-kilometer reach of the FBR at the bridge construction site in November 2019 (Whaling and others, 2023). In December 2021, a canoe-based repeat streambank lidar survey was collected approximately 23 months after construction began, with the purpose to monitor geomorphological changes to the streambank and inform the NCDOT...
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