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Boulder County Disasters (Spring 2022) Team: Sarah Hettema (Project Lead), Jennifer Rogers, Ibuki Sugiura, Erin Twaddell Summary: In recent years, record-breaking wildfire activities in the...

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

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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...

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

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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...

2021 Earth Science Applications Week

August 9, 2021

August 12, 2021

Normalized Difference Moisture Index (NDMI) processed 2021 Landsat 8 OLI imagery over land and Sentinel-2 MSI over ocean, ranging from yellow (low) to blue (high), for Hilo, Hawaii. Overlayed with four-day rainfall accumulation cells in blue acquired during Hurricane Lane (Aug 22-25, 2018) from CHIRPS Daily. NDMI monitors water content of leaves and is useful for classifying land cover. Impervious surfaces and areas that have received excessive historical rainfall are susceptible to future flood events.

Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island

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Hawai‘i Island Disasters (Fall 2021) Team: Garren Kalter (Project Lead), Rose Eichelmann, Sanketa Kadam, Erin Azuma, and Anna Mikkelsen Summary: As the County of Hawai‘i...

Comparing Feasibility of Flood Detection Methods Using Google Earth Engine and Open Data Cube for Flood Mitigation in Mexico

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Mexico Disasters (Spring 2022) Team: Philip Casey (Project Lead), John Willis, Sean Nelsen, Zachary Silberman Summary: In this project, NASA DEVELOP partnered with Mexico’s National...

NDWI-processed imagery from Landsat 8 Operational Land Imager data. This mosaicked image was taken from January to December 2021 of Jekyll Island, Georgia. NDWI contrasts the water features in a satellite image to its surroundings by measuring the moisture content. Low moisture content is shown in white, and high moisture content is shown in blue.

Evaluating the Impacts of Hurricane Irma on Georgia Heirs Property Owners Using Earth Observations

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Georgia Disasters (Fall 2022) Team: Isabella Chittumuri (Project Lead), Nancee Uniyal, Nathan Tesfayi, Shakirah Rogers Summary: In September 2017, Hurricane Irma made landfall in southern...

NDVI over Mato Grosso, Brazil processed from March 2021 Landsat-8 OLI data. Calculated NDVI values correspond with colors shown to indicate plant health. High NDVI values (green to blue) indicate dense vegetation found in tropical and temperate forests or crops at their peak growth. Low values (yellow to red) depict barren land, water, and moisture-stressed vegetation. These data can support timely and efficient agricultural responses to ensure productive yields and food security.

Enhancing Crop Classification Mapping Using Optical and Radar Satellite Sensors to Enhance Agricultural Management and Policymaking in Mato Grosso, Brazil  

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