Filters: Tags: Remote sensing (X)
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Vegetation classification model (Veg) and associated metadata for basin A1.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Colorado,
Rio Blanco,
UAS,
Unmanned Aerial Systems,
biota,
These digital images were taken at select locations over the Potomac River using 3DR Solo unmanned aircraft systems (UAS) in October 2019. These images were collected for the purpose of evaluating UAS assessment of river habitat data such as water depth, substrate type, and water clarity. Each UAS was equipped with a Ricoh GRII digital camera for natural color photos, used to produce digital elevation models and ortho images, a MicaSense RedEdge multi-spectral camera that captures five specific bands of the visible spectrum (blue, green, red, rededge, and near-infrared), which can be used to classify vegetation, or FLIR Vue Pro R 640 13mm radiometric thermal camera that provides temperature data embedded in every...
These data were compiled for investigating the relationship between acoustic backscattering by riverbeds composed of various riverbed substrates (bed sediment), and for developing and testing a probabilistic model for substrate classification based on high-frequency multibeam acoustic backscatter. The model is described in Buscombe et al. (2017). The data consist of various quantities on coincident grids, from various sites along the Colorado River in Grand Canyon, including water depth, bed roughness, the area (or footprint) of the acoustic beam, unfiltered and filtered backscatter magnitude, sediment classification (for each location, 1 of 5 sediment classes in a categorical scheme), and the probabilities for...
Digital terrain model (DTM) and associated metadata for basin B1.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Colorado,
Rio Blanco,
UAS,
Unmanned Aerial Systems,
biota,
Final surface model (SRF) and associated metadata for basin B1.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Colorado,
Rio Blanco,
UAS,
Unmanned Aerial Systems,
biota,
The dataset consists of a shapefile of measurements of surface velocity magnitude and direction at the Colorado River at Compact Point near Lees Ferry, AZ, on March 18, 2021. The dataset contains approximately 1.2 km of river length. The surface velocity measurements were made by applying Large-Scale Particle Image Velocimetry (LSPIV) techniques, using overlapping videos collected by small Unmanned Aircraft Systems (sUAS). Total time to capture all videos was less than one hour, and all frames (except frame 1, see Process Steps below) from all videos were used. Additional attributes, including divergence, curl, shear, and strain, were calculated from the surface velocity measurements and are included in the dataset.
The USGS Land Remote Sensing Program has established a long-term study to better understand the users, uses, and value of Landsat satellite imagery. The current Landsat satellites provide high-quality, multi-spectral, moderate-resolution imagery of all areas of the world. This imagery is applied in a variety of applications, such as global climate change, environmental management, and planning and development. Landsat imagery is unique among current satellite imagery due to an archive of free global imagery collected continuously since 1972. More than 20 million Landsat scenes have been downloaded, the vast majority since a no-cost data policy was put into place in 2008. The Fort Collins Science Center’s Social...
Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2018
This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2018), with the data processed through September 30, 2018. The primary data for water year 2018 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2018) and processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) is computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation, and disaggregated to hourly PET by using the...
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...
Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystems condition in arid and semiarid lands. We developed an innovative approach by integrating multiple information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of five major parts: field sample collection, high-resolution mapping of shrubland components using WorldView-3 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, coarse resolution estimate of shrubland components across a large geographic extent using Landsat 8 phenological mosaics and regression tree models, and...
Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystems condition in arid and semiarid lands. We developed an innovative approach by integrating multiple information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of five major parts: field sample collection, high-resolution mapping of shrubland components using WorldView-3 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, coarse resolution estimate of shrubland components across a large geographic extent using Landsat 8 phenological mosaics and regression tree models, and...
The geographic information system (GIS) format spatial data set of vegetation for Apostle Islands National Lakeshore (APIS) was created for the National Park Service (NPS) Vegetation Inventory Program (VIP). The APIS covers an area of approximately 28,972 ha (71,591 acres). The map classification scheme used to create the vegetation data set is designed to represent local vegetation types at the finest level possible using the National Vegetation Classification (NVC) Standard (Vr 2). Physiognomic information was also recorded, including height (woody vegetation), canopy density, and coverage patterns. The vegetation data set was developed by interpreting aerial photographs collected in 2004 and extensive field surveys....
This dataset provides early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on May 3rd. We develop and release EAG fractional cover map with an emphasis on cheatgrass (Bromus tectrorum) but it also includes number of other species, i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and medusahead (Taeniatherum caput-medusae. The dataset was generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; Harmonized...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
These BioLake raster data provide global estimates (~10.0 x 12.4 km resolution) of twelve bioclimatic variables based on estimated lake temperature. Eleven of these twelve variables (BioLake01 - BioLake11) are estimated for each of three lake strata: lake mix (surface) layer, lake bottom, and total lake water column. These eleven variables correspond to CHELSA (Climatologies at high resolution for the earth's land surface areas) bioclimatic variables BIO1 - BIO11, except that these BioLake variables are based on lake water temperature and CHELSA BIO1 - BIO11 variables are based on air temperature. CHELSA BIO is also calculated a finer spatial resolution (~1 x 1 km). The twelfth variable (BioLake20; months with non-zero...
Globally, groundwater dependent ecosystems (GDEs) are increasingly vulnerable to groundwater extraction and land use practices. Groundwater supports these ecosystems by providing inflow, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity that they support. Many aquatic systems receive groundwater as a portion of base flow, and in some systems (e.g., springs, seeps, fens) the connection with groundwater is significant and important to the system’s integrity and persistence. Groundwater management decisions for human use may not consider ecological effects of those actions on GDEs, which rely on groundwater to maintain ecological function. This disconnect between...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Ecology,
Hydrology,
Land Use Change,
New England,
Remote Sensing,
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