Filters: Tags: vegetation height (X)
20 results (87ms)
Filters
Date Range
Types Contacts
Categories Tag Types
|
Low-altitude (70 m above ground level) aerial images were collected over five areas of interest within the northern portion of Assateague Island National Seashore (ASIS) in mid-October, 2022. These images were collected to generate photogrammetric products (e.g. digital surface model (DSM), reflectance orthomosaic) to support science and data needs of National Park managers attempting to locate invasive Phragmites australis expansion. A DJI Matrice 600 uncrewed aircraft system (UAS) was equipped with a MicaSense Altum-PT multispectral camera and a Ricoh GRII true-color RGB camera to collect images simultaneously with sufficient overlap for photogrammetric processing. Ground control points (GCPs), black and white...
Vegetation cover was extracted from the Landfire Existing Vegetation Height dataset.> 100cm vegetation height = good60 - 100cm vegetation height = moderate
Yukon Flats National Wildlife Refuge (YKF NWR) and Koyukuk NWR (KUK NWR), U.S. Fish and Wildlife Service (USFWS), initiated a project with the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center to acquire map products needed for moose habitat assessment. The objective of this work was to create a suite of products which included: Estimated Vegetation Heights, probability of Willow Estimates, and Vegetation Type Maps. These products are based on spectral characteristics found in bands 2 through 7 of Landsat 8 OLI scenes processed to surface reflectance, acquired in summer of 2013, and late winter of 2014. Training data was collected by fixed wing aircraft and helicopter by USFWS refuge staff,...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
Rangeland ecosystems provide critical wildlife habitat (e.g., greater sage grouse, pronghorn, black-footed ferret), forage for livestock, carbon sequestration, provision of water resources, and recreational opportunities. At the same time, rangelands are vulnerable to climate change, fire, and anthropogenic disturbances. The arid-semiarid climate in most rangelands fluctuates widely, impacting livestock forage availability, wildlife habitat, and water resources. Many of these changes can be subtle or evolve over long time periods, responding to climate, anthropogenic, and disturbance driving forces. To understand vegetation change, scientists from the USGS and Bureau of Land Management (BLM) developed the Rangeland...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
These data map in high detail surficial cross-sections of North Core Banks, a barrier island in Cape Lookout National Seashore, NC, in October 2022. U.S. Geological Survey field efforts are part of an interagency agreement with the National Park Service to monitor the recovery of the island from Hurricanes Florence (2018) and Dorian (2019). Three sites of outwash, overwash, and pond formation were targeted for extensive vegetation ground-truthing, sediment samples, bathymetric mapping with a remote-controlled surface vehicle, and uncrewed aerial systems (UAS) flights to collect multispectral imagery. Five semi-permanent ground control points were also installed and surveyed to act as control for additional aerial...
Categories: Data;
Tags: Atlantic Ocean,
Bathymetry and Elevation,
CMHRP,
Cape Lookout,
Coastal and Marine Hazards and Resources Program,
Two DJI M600 small Uncrewed Aircraft Systems (sUAS) were used to collect aerial remote sensing data over various locations within the Lower Darby Creek Superfund Site and the adjacent John Heinz National Wildlife Refuge (NWR). Datasets included natural (RGB) color images, multispectral images, and LiDAR Point Clouds (LPCs). These data were processed to produce high resolution digital elevation models (DEM) and natural-color and multispectral reflectance image mosaics. Black and white cross-coded ground control points (GCPs) were sureveyed using RTK-GPS and RTK-GPS enabled Aeropoints to georeference the model and orthomosaics during post-processing. The elevation and imagery products were produced to help partners...
Categories: Data,
Image;
Tags: CMHRP,
CSV,
Carlson SurvCE,
Clearview Landfill,
Coastal and Marine Hazards and Resources Program,
Shrublands have seen large changes over time due to factors such as fire and drought. As the climate continues to change, vegetation monitoring at the county scale is essential to identify large-scale changes and to develop sampling designs for field-based vegetation studies. This dataset contains two raster files that each depict the height of vegetation. The first layer is restricted to actively growing vegetation and the second is restricted to dormant/dead vegetation. Both layers cover open space areas in San Diego County, California. Height calculations were derived from Lidar data collected in 2014 and 2015 for the western two-thirds of San Diego County. Lidar point clouds were pre-classified into ground and...
The purpose of this field data collection was to test and compare the OceanInsight HDX Mini Spectrometer as an accessible alternative against the more expensive ASD Fieldspec for collecting ground-based hyperspectral reflectance profiles for landcover analysis. The data collection took place in Dog Head Marsh and South Cape Beach within the Waquoit Bay National Estuarine Research Reserve (WBNERR). The hyperspectral profiles were collected side-by-side with both field-spectrometers using comparable sensor collection settings for various ground cover samples. The terrain and vegetation type of these sample were described as well as surveyed using Real Time Kinematic Global Positioning System (RTK-GPS). This data...
Categories: Data;
Tags: CMHRP,
CSV,
Carlson SurvCE,
Coastal and Marine Hazards and Resources Program,
Dog Head Marsh,
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
|
![]() |