Displaying 111 - 120 of 305

Mapeo y Monitoreo de los Bosques con Datos SAR

Podest, E.; McCullum, A.; Torres-Pérez, J.; McCartney, S.; Siqueira, P.; Lei, Y.; Whelen, T.; Kraatz, S. (2020).

Forest Mapping and Monitoring with SAR Data

Podest, E.; McCullum, A.; Torres-Pérez, J.; McCartney, S.; Siqueira, P.; Lei, Y.; Whelen, T.; Kraatz, S. (2020).

Forest cover and land surface temperature (LST) around Maine's Penobscot River during Summer 2021. The shades of green distinguish between evergreen (dark) and deciduous (light) forest which were classified using Landsat 8 OLI imagery. The purple gradient corresponds to LST derived from Terra MODIS where the warmest areas, including Bangor, are white. Changing land use and warming temperatures along Maine’s rivers are associated with reducing juvenile Atlantic salmon (Salmo salar) survivorship.

Utilizing Earth Observations to Monitor Federally Endangered Atlantic Salmon (Salmo Salar) Habitat in Maine: An Interactive Workshop 

Topic
DEVELOP
Capacity Building
Ecological Conservation
Maine Ecological Forecasting III (Summer 2022) Team: Jonathan Falciani (Project Lead), Colin Hogan, Linda Mitchell, Makario Sarsozo Summary: Shifting patterns in land use and land...
Satellite image of the US with ARSET logo
Health & Air Quality

ARSET - NASA Air Quality-Focused Remote Sensing for EPA Applications

Training Type
In-Person Training
Level
Advanced
Date
March 21, 2023 - March 23, 2023
Annual max biomass calculated from 2021 daily Harmonized LandSat Sentinel-2 (HLS) dataset. Dark purple colors show low biomass while the bright orange show high biomass. Regions with high vegetation and tree canopy cover are shown in orange on the left of the image. High biomass values are of an interest to rangeland managers to help inform their grazing decisions based on which areas of the ranch produce the most biomass/vegetation.

Rangeland Monitoring to Inform Grazing Management in Eastern Colorado

Topic
DEVELOP
Capacity Building
Agriculture
Eastern Plains Agriculture (Fall 2022) Team: Jillian Joubert, Sarah Hettema, Deni Ranguelova, TR Ingram, Max Stewart Summary: Adaptive management on cattle ranches requires rangeland managers...
Potential for land cover to change from trees to grass, forb, and herb between 1987 and 2020 in the Teton Mountain Range. Image created from LCMS land cover data which are derived from Landsat 4, 5, 7, and 8 imagery (1987 - 2020). High transition potential is depicted in yellow and low transition potential is indigo. Transition potentials help to forecast land cover and inform land managers on the locations of suitable bighorn sheep habitat.

Assessing Forage Change and Winter Habitat Availability for Bighorn Sheep that Employ a High-Elevation Overwintering Strategy to Identify Areas for Intervention

Topic
DEVELOP
Capacity Building
Ecological Conservation
Grand Teton Ecological Forecasting (Fall 2021) Team: Alex Posen (Project Lead), Allyson Earl, Sarah Hettema, and Michael Hitchner Summary: Grand Teton National Park provides habitat...

SAR for Landcover Applications

Podest, E.; McCartney, S.; McNair, H.; Jiao, X.; Banks, S.; Behnamian, A. (2019).

SAR para Desastres y Aplicaciones Hidrológicas

Podest, E.; McCartney, S.; Fielding, E.J.; Handwerger, A.L.; Grunfeld Brook, N.A. (2019).

Median daily evapotranspiration (ET) from September 2021 to May 2022 calculated from ISS ECOSTRESS data. The image covers the agricultural fields adjacent to the Maipo River, Chile and depicts water stress. The color scale ranges from red (low ET) to blue (high ET), with higher ET observed over irrigated agricultural fields. ET is used for estimating crop irrigation requirements. This data will inform irrigation management practices and help alleviate water scarcity within the area. ​

Determining Crop Coefficients Using Remote Sensing for the Maipo River Valley Basin in Chile 

Topic
DEVELOP
Capacity Building
Agriculture
Maipo River Valley Agriculture (Summer 2022) Team: Benjamin Goffin (Project Lead), Duncan Srsic, Rishudh, Sarah Carlos Summary: Agriculture is the primary use of water in...
Disasters

Observed Flooding Areas Using USGS / NASA Landsat 8 and Copernicus / ESA Sentinel SAR data

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