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Low-altitude aerial imagery from unmanned aerial systems (UAS) at select locations over the Potomac River, October 2019 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... Tags: 3dr solo quadcopter, Brunswick, Ecology, Geomorphology, Hydrology, All tags... Maryland, Point of Rocks, Potomac River, Remote Sensing, Shepherdstown, USA, USGS Science Data Catalog (SDC), Water Resources, White's Ferry, aerial images, aerial photography, airborne imagery, drone, elevation, flir, flir vue pro r 640 13mm, gcp, geomorphology, geoscientificInformation, geospatial datasets, ground control point, image collections, images, jpeg images, location, micasense, micasense rededge, nir, rededge, remote sensing, ricoh grii, targets, tiff images, u.s. geological survey, uas, unmanned aerial system, usgs, Fewer tags LANDFIRE 2022 Existing Vegetation Cover (EVC) AK 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... Tags: AK, Alaska, EVC, Existing Vegetation Cover, LANDFIRE 2022, All tags... LF 2022, OCONUS, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, United States, biota, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags LANDFIRE 2022 Historical Disturbance (HDist) HI 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... Tags: HDist, HI, Hawaii, Historical Disturbance, LANDFIRE 2022, All tags... LF 2022, OCONUS, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, United States, biota, disturbance, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags LANDFIRE 2022 Fuel Vegetation Type (FVT) Puerto Rico US Virgin Islands 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... Tags: EVT, Existing Vegetation Type, FVT, Fuel Vegetation Type, IA, All tags... Insular Areas, LANDFIRE 2022, LF 2022, OCONUS, Puerto Rico, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, US Virgin Islands, United States, biota, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags Vegetation and water classifications for a segment of the Paria River upstream of the Colorado River Confluence, Arizona, USA 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... Tags: Arizona, Botany, Cloud Optimized GeoTIFF data, Colorado River, Ecology, All tags... Geography, Glen Canyon, Glen Canyon Dam, Grand Canyon, Green Normalized Difference Vegetation Index, Hydrology, Information Sciences, Lees Ferry, Lonely Dell, Marble Canyon, Northern Arizona, Paria River, Remote Sensing, USGS Science Data Catalog (SDC), United States, Water Resources, airborne imagery, biota, change detection, classification maps, data release, ecosystem monitoring, flooding, gNDVI, image classification, image collections, imageryBaseMapsEarthCover, inlandWaters, maps and atlases, multispectral high resolution data, multispectral imaging, remote sensing, riparian vegetation, river ecosystems, river reaches, river systems, river tributaries, supervised classification, surface water (non-marine), total vegetation, vegetation, vegetation classification maps, water maps, wetland ecosystems, Fewer tags LANDFIRE 2022 Forest Canopy Cover (CC) CONUS 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... Tags: CC, CONUS, Canopy Cover, Conterminous United States, Continental U.S., All tags... LANDFIRE 2022, LF 2022, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, United States, biota, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags LANDFIRE 2022 Fuel Vegetation Cover (FVC) CONUS 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... Tags: CONUS, Conterminous United States, Continental U.S., EVC, Existing Vegetation Cover, All tags... FVC, Fuel Vegetation Cover, LANDFIRE 2022, LF 2022, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, United States, biota, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags Aerial imagery data of the Colorado River Corridor, Arizona - 2021 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)... Tags: Arizona, Bright Angel Creek, Colorado River, Diamond Creek, GeoTIFF, All tags... Glen Canyon Dam, Grand Canyon, Havasu Creek, Kanab Creek, Lake Mead, Lake Powell, Lees Ferry, Leica ADS80/82, Little Colorado River, Marble Canyon, Page, Paria River, Peach Springs, Pearce Ferry, Shinumo Creek, Tapeats Creek, United States, aerial photography, airborne imagery, airborne multispectral high resolution data, data release, digital aerial imagery, four band image mosaic, image collections, image mosaics, imageryBaseMapsEarthCover, infrared imaging, multispectral imaging, natural color, near infrared, orthoimagery, remote sensing, Fewer tags Freshwater Introduction South of Highway 82 (ME-16): 2018 land-water classification The Louisiana State Legislature created the Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed pursuant to these acts specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the Freshwater Introduction South of Highway 82 (ME-16) project for 2018. This data is used as a basemap land-water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss... Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: CWPPRA, Ecology, Geography, Geography, Gulf of Mexico, All tags... Land Use Change, Land Use Change, Louisiana, Louisiana Coastal, ME-16, Remote Sensing, Remote Sensing, USGS Science Data Catalog (SDC), aerial photography, conservation, digital elevation models, ecosystem monitoring, geographic information systems, image collections, image mosaics, remote sensing, wetland ecosystems, wetland restoration, Fewer tags Oyster Bayou Marsh Creation and Terracing (CS-59): 2018 land-water classification The Louisiana State Legislature created the Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed pursuant to these acts specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the Oyster Bayou Marsh Creation and Terracing (CS-59) project for 2018. This data is used as a basemap land-water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss within... Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: CS-59, CWPPRA, Geography, Gulf of Mexico, Land Use Change, All tags... Louisiana, Louisiana Coastal, Remote Sensing, USGS Science Data Catalog (SDC), aerial photography, conservation, digital elevation models, ecosystem monitoring, geographic information systems, image collections, image mosaics, remote sensing, wetland ecosystems, wetland restoration, Fewer tags Highway 384 Hydrologic Restoration (CS-21): 2015 land-water classification The Louisiana State Legislature created Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed pursuant to these acts specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the Highway 384 Hydrologic Restoration (CS-21) project for 2015. This data is used as a basemap land-water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss within their... Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: CS-21, CWPPRA, Ecology, Geography, Gulf of Mexico, All tags... Land Use Change, Louisiana, Louisiana Coastal, Remote Sensing, USGS Science Data Catalog (SDC), aerial photography, conservation, digital elevation models, ecosystem monitoring, geographic information systems, image collections, image mosaics, remote sensing, wetland ecosystems, wetland restoration, Fewer tags East Sabine Lake Hydrologic Restoration (CS-32): 2015 land-water classification The Louisiana State Legislature created Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed pursuant to these acts specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the East Sabine Lake Hydrologic Restoration (CS-32) project for 2015. This data is used as a basemap land-water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss within their... Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: CS-32, CWPPRA, Geography, Gulf of Mexico, Land Use Change, All tags... Louisiana, Louisiana Coastal, Remote Sensing, USGS Science Data Catalog (SDC), aerial photography, conservation, digital elevation models, ecosystem monitoring, geographic information systems, image collections, image mosaics, remote sensing, wetland ecosystems, wetland restoration, Fewer tags Bayou Dupont Marsh and Ridge Creation (BA-48): 2016 land-water classification The Louisiana State Legislature created the Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed pursuant to these acts specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the Bayou Dupont Marsh and Ridge Creation (BA-48) project for 2016. This data is used as a basemap land-water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss within... Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: BA-48, CWPPRA, Ecology, Geography, Gulf of Mexico, All tags... Land Use Change, Louisiana, Louisiana Coastal, Remote Sensing, USGS Science Data Catalog (SDC), aerial photography, conservation, digital elevation models, ecosystem monitoring, geographic information systems, image collections, image mosaics, remote sensing, wetland ecosystems, wetland restoration, Fewer tags Orthomosaic images of the middle and lower Elwha River, Washington, 2012 to 2017 This dataset presents 28 georeferenced orthomosaic images of the middle and lower reaches of the Elwha River. Each mosaic image was created by stitching together thousands of individual photographs that were matched based on numerous unique tie points shared by the photographs. The individual photographs were taken by a plane-mounted camera during multiple flights over the study area spanning 2012 to 2017. Because each mosaic is orthogonal to the earth's surface and is georeferenced to real-world coordinates, changes to the river channel and surrounding morphology can be seen and measured, including channel width, river braiding, bar formation, and other metrics to assess responses of the river to the removal of... Tags: CMGP, Coastal and Marine Geology Program, Elwha Dam (historical), Elwha River, Glines Canyon Dam (historical), All tags... Lake Aldwell (historical), Lake Mills (historical), NPS, National Park Service, Olympic National Park, PCMSC, Pacific Coastal and Marine Science Center, State of Washington, Substrate, U.S. Bureau of Reclamation, U.S. Geological Survey, USBR, USGS, aerial photography, digital elevation models, field inventory and monitoring, image collections, image mosaics, inlandWaters, lidar, remote sensing, river bank, river channel, river reaches, river systems, sediment deposition, time series datasets, topographic maps, Fewer tags Digital elevation models (DEMs) of the lower Elwha River, Washington, water year 2013 to 2016 Digital elevation models (DEMs) of the lower Elwha River, Washington, were created by synthesizing lidar and PlaneCam Structure-from-Motion (SfM) data. Lidar and still digital photographs were collected by airplane during surveys from 2012 to 2016. The digital photographs were used to create a SfM digital surface model. Each DEM represents the ending conditions for that water year (for example, the 2013 DEM represents conditions at approximately September 30, 2013). The final DEMs, presented here, were created from the most recent lidar before September 30 of a given year, supplemented with an error-corrected SfM model from a low-flow summer Elwha PlaneCam flight as close to 30 September as possible. This synthetic... Tags: CMGP, Coastal and Marine Geology Program, Elwha Dam (historical), Elwha River, Glines Canyon Dam (historical), All tags... NPS, National Park Service, PCMSC, Pacific Coastal and Marine Science Center, State of Washington, Substrate, U.S. Bureau of Reclamation, U.S. Geological Survey, USBR, USGS, digital elevation models, field inventory and monitoring, image collections, image mosaics, inlandWaters, lidar, river bank, river channel, river reaches, river systems, sediment deposition, time series datasets, topographic maps, Fewer tags Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-27 Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5. Tags: Bathymetry and Elevation, Big Sur, CMHRP, California, Cape San Martin, All tags... Central California Coastal, Coastal and Marine Hazards and Resources Program, Monterey, PCMSC, Pacific Coastal and Marine Science Center, U.S. Geological Survey, USGS, United States, Villa Creek, aerial and satellite photography, aerial photography, agents of coastal change, anthropogenic agents of coastal change, coastal change hazards topics, coastal landslides, coastal landsliding, coastal processes, coastal processes, coastal sediment budget, coastal zone management, effects of coastal change, elevation, geomorphology, geoscientificInformation, hazards, hazards and disasters, human responses to coastal change, image analysis, image collections, imageryBaseMapsEarthCover, landslides, remote sensing, topography, transportation, Fewer tags Remotely-sensed observations of restoration sites of the riparian corridor of the Colorado River Delta in Mexico, 2013-2022 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... Tags: 1944 Water Treaty, Arizona, Botany, Colorado River, Colorado River delta, All tags... Ecology, Geography, Gulf of California, Information Sciences, International Boundary, Land Use Change, Landsat images, Landsat time series, Lower Colorado River delta, Mexico, Mexico, Minute 319, Minute 323, Morelos Dam, Remote Sensing, San Luis, USGS Science Data Catalog (SDC), Yuma, actual ET (ETa) in mm/day, average growing season daily ET, biogeography, biota, climatologyMeteorologyAtmosphere, data release, ecosystem monitoring, enhanced vegetation index 2 (EVI2), environment, evapotranspiration, evapotranspiration (ET) data, geoscientificInformation, geospatial datasets, greenness (EVI2), image collections, imageryBaseMapsEarthCover, inlandWaters, land use and land cover, land use change, meteorological station data, peak growing season evapotranspiration, peak-season daily water use, plant water use, precipitation (atmospheric), remediation, remote sensing, restoration, riparian community, riparian corridor, riparian ecosystem, riparian vegetation, riparian woodland ecosystem, riparian zones, river ecosystems, seaonal mean difference, time series datasets, vegetation, vegetation greeness index (EVI2), water use, water use, wetland ecosystems, Fewer tags LANDFIRE 2022 Canadian Forest Fire Danger Rating System (CFFDRS) AK 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... Tags: AK, Alaska, CFFDRS, Canadian Forest Fire Danger Rating System, LANDFIRE 2022, All tags... LF 2022, OCONUS, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, United States, biota, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags LANDFIRE 2022 Forest Canopy Height (CH) AK 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... Tags: AK, Alaska, CH, Canopy Height, LANDFIRE 2022, All tags... LF 2022, OCONUS, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, United States, biota, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags LANDFIRE 2022 Fuel Disturbance (FDist) AK 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),... Tags: AK, Alaska, FDist, Fuel Disturbance, LANDFIRE 2022, All tags... LF 2022, OCONUS, U.S. Forest Service (USFS), U.S. Geological Survey (USGS), US, United States, biota, disturbance, fires, geographic information systems, geospatial datasets, hazard preparedness, image collections, imageryBaseMapsEarthCover, raster digital data, remote sensing, Fewer tags 1 2 3 4 5 ... 43 »