163 results — topic: Remote Sensing & Imagery

Article

Impact of Crosstalk on Reflectivity and Doppler Measurements for the WIVERN Polarization Diversity Doppler Radar

The WIVERN (Wind VElocity Radar Nephoscope) mission, one of the four ESA Earth Explorer 11 candidate missions, aims at globally observing, for the first time, simultaneously vertical profiles of reflectivities and line of sight winds in cloudy and precipitating regions. WIVERN adopts a dual-polariza

Rizik A., Battaglia A., Tridon F.2023IEEE Transactions on Geoscience and Remote SensingDOI: 10.1109/tgrs.2023.3320287Cited 10 times
Dataset

WRF Large-Eddy Simulation Data from Realtime Runs Used to Support UAS Operations during LAPSE-RATE

Realtime micro-scale weather simulations were performed to support UAV (Uncrewed Aerial Vehicle) flights during the ISARRA Lower Atmospheric Process Studies at Elevation a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field deployment. These simulations were performed by driving a nested gr

Pinto, James, Jimenez, Pedro, Hertneky, Tracey2021DOI: 10.5065/83r2-0579
Dataset

MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V061

Schaaf, Crystal, Wang, Zhuosen2021DOI: 10.5067/modis/mcd43a3.061Cited 22 times
Dataset

MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61

Hall, Dorothy K., Riggs, George A.2021DOI: 10.5067/modis/mod10a1.061Cited 26 times
Dataset

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly avail

Heidi Steltzer, Amanda Henderson, Chelsea Wilmer2021DOI: 10.15485/1842910
Dataset

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly avail

Heidi Steltzer, Amanda Henderson, Chelsea Wilmer2021DOI: 10.15485/1842910
Dataset

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly avail

Heidi Steltzer, Amanda Henderson, Chelsea Wilmer2021DOI: 10.15485/1842910
Dataset

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly avail

Heidi Steltzer, Amanda Henderson, Chelsea Wilmer2021DOI: 10.15485/1842910
Dataset

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly avail

Heidi Steltzer, Amanda Henderson, Chelsea Wilmer2021DOI: 10.15485/1842910
Dataset

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly avail

Heidi Steltzer, Amanda Henderson, Chelsea Wilmer2021DOI: 10.15485/1842910
Dataset

POLLEN, STARCH, AND ORGANIC RESIDUE (FTIR) ANALYSIS OF SAMPLES FROM SITE 5GN2404, GUNNISON COUNTY, COLORADO.

Site 5GN2404, situated on a south-facing slope overlooking the Gunnison River Valley, was examined as part of work on the Blue Mesa-Skito Transmission Line. This large scatter of flaked lithic and ground stone artifacts also includes several thermal pits (Barb Lockwood, personal communication, Augus

Linda Cummings2021
Dataset

Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and LiDAR airborne data.

This package is part of the Watershed Function SFA project and contains a remote sensing dataset acquired at the East River, Colorado. The remote sensing dataset is composed of vegetation maps computed from hyperspectral and LiDAR airborne data acquired by the NEON team in June 2018. The maps show t

Falco N, Balde A, Breckheimer I2021DOI: 10.15485/1602034Cited 3 times
Dataset

Site-level Foliar C, N, delta13C data from samples collected during field survey associated with NEON AOP survey, East River, CO 2018.

Carbon (C) and nitrogen (N) weight percent concentrations were obtained from the bulk foliar samples collected across East River, Washington Gulch, Slate River, and Coal Creek watersheds in Gunnison, Colorado during the summer of 2018. These samples were collected from sun-lit leaves of meadow, shru

Chadwick K D, Grant K, Bill M2021DOI: 10.15485/1631278
Dataset

NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset.

Lawrence Berkeley National Laboratory (LBNL) contracted the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) to observe watersheds of interest near Crested Butte, CO with remotely sensed data including imaging spectroscopy. The flight box design encompassed the waters

Goulden T, Hulslander D, Hass B2021DOI: 10.15485/1617204
Dataset

Geophysical borehole logging data of wells ER-GLS1, ER-GUM1, ER-PLM7, and ER-PLM8 at the East River Watershed, Colorado.

The purpose of this data set is to provide a means of characterizing the bedrock variability both within a single borehole location and across the East River Watershed. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United St

Sebastian Uhlemann2021DOI: 10.15485/1650355Cited 1 times
Dataset

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018.

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, t

Chadwick K D, Brodrick P, Grant K2021DOI: 10.15485/1618133
Dataset

1 m Resolution NDVI for the Upper Gunnison Basin derived from September 2019 NAIP Imagery

This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (N

Ian Breckheimer2021
Dataset

1 m Resolution 4-band orthoimagery for the Upper Gunnison Basin derived from October 2017 NAIP imagery

This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (N

Ian Breckheimer2021
Dataset

1m Resolution NDVI for the Upper Gunnison Basin derived from October 2017 NAIP Imagery

This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (N

Ian Breckheimer2021
Dataset

Drone ortho basemap of the Gothic Townsite, May 25th 2019

This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in sunny conditions on May 25th 2019, collecting RGB images using the built-in camera on a DJI Mavic 2 Pro. The raw imagery had an approximately 3cm ground sample distance (G

Ian Breckheimer2021