Displaying 111 - 120 of 161
This image of Land Surface Temperature (LST) illustrates the urban heat island (UHI) effect that Milwaukee County experiences. Light orange areas represent hotter temperatures, which are concentrated in the high impervious areas of the city. Darker pinks represent cooler rural areas and underscore the temperature contrast across the landscape. The hotter areas of the city are historically redlined communities of color that disproportionately feel the effects of the UHI, including higher energy bills heat re

Assessing Climate Vulnerability through the InVEST Model on Urban Cooling in Milwaukee Using NASA Earth Observations

Topic
Urban Development
DEVELOP
Capacity Building
Milwaukee Urban Development II (Fall 2022) Team: Nash Keyes (Project Lead), caleigh McLaren, Nati Phan, Dalia Vazques Summary: Milwaukee’s neighborhoods experience increased social, health, and...
Daytime land surface temperature for Wichita, Kansas summer 2017-2022, retrieved through Landsat 8 TIRS and Landsat 9 TIRS-2 surface temperature. Located in the northwest corner of the city, this image depicts higher temperatures through dark orange hues in developed areas, and lighter orange hues to blue colors where the temperature is lower. The land surface temperature was utilized to construct a heat vulnerability index for the city, looking into disproportionate impacts on underserved communities.

Quantifying and Mapping Urban Heat to Inform Equitable and Sustainable Urban Planning Initiatives in Wichita, Kansas

Topic
DEVELOP
Climate
Capacity Building
Wichita Climate II (Fall 2022) Team: Ritisha Ghosh (Project Lead), Richard Kirschner, Ria Mukherjee, Raina Monaghan Summary: Wichita, Kansas is experiencing a host of climate...
four images showing different satellite data collections of same fire
Wildfires

Using Infrared Imaging Used to Track Wildfires

By
Katrina Sharonin, Wildland Fire program area
Ecological Conservation

How City Lights Alter Wildlife

By
Megan Gannon, Applied Sciences Program
Wildfires
Disasters

RECOVERing after the Woolsey Fire

By
Marissa Kunerth, Earth Applied Sciences Writer
Evaporative Stress Index (ESI) processed imagery from ECOSTRESS data, captured on October 4, 2021, for Marin County in the San Francisco Bay Area. Darker regions have a higher ESI value, signifying moister conditions, while lighter regions are drier. Vegetative moisture has a significant impact on fire severity and understanding localized moisture conditions can inform the county’s fire suppression efforts. Land outside of Marin County shows NDVI processed imagery derived from Sentinal-2A data.

Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Topic
DEVELOP
Wildfires
Marin County Wildland Fires (2023 Spring) Team: Suhani Dalal (Project Lead), Katera Lee, Chandler Ross, Gabriel Rosenstein Summary: Heightened occurrence of severe wildfires in the...
Canopy cover in the greater Ben Delatour Scout Ranch region from a 2013 NAIP Image with a slope and aspect layer derived from a 10-meter digital elevation model (DEM). This image illustrates how elevation-derived variables are used to inform classification models to map canopy cover. Canopy is displayed as red, and the map illustrates that there are higher concentrations of canopy on northern aspects- illustrating the nature of tree density in our study region. ​

Evaluating the Efficacy of Remote Sensing Imagery in Monitoring Forest Fuels Treatment Methods

Topic
DEVELOP
Wildfires
Front Range Wildland Fires (2023 Spring) Team: Nora Carmody (Project Lead), Lillian Gordon, Nathan Teich, Josh Virene Summary: Over the last several decades, wildfire frequency...
Snow persistence data were processed into snow zones from Terra MODIS 500m resolution data from 2001-2020. 2021 30m resolution rangeland biomass data from the RPMS website. RPMS data (brown and black) are derived from Landsat 5 TM, 7 ETM+, 8 OLI, and 9 OLI-2. The image displays snow persistence zones, with intermittent, transitional, and persistent snow zones in orange, light blue, and dark blue respectively. The image includes parts of the Sawatch and Elk Mountains.

Identifying Drivers of Rangeland Production for Drought Planning on the Western Slope of the Southern Rockies

Topic
DEVELOP
Agriculture
Southern Rockies Western Slope Agriculture (2023 Spring) Team: Adelaide Gonzalez (Project Lead), Rachel Buchler, Max VanArnam, Stephanie Willsey Summary: Over the last decade, the southern...
Land cover classification of the Arizona – Mexico border region from April 26, 2022 Landsat 8 OLI imagery. Colors represent land cover types determined by a machine learning clustering algorithm. Classifications help natural resource specialists assess vegetation cover and invasive spread, informing land management practices. The landscape impact of the border is visible as the horizontal break roughly mid-image––Coronado National Memorial and other conserved lands abut the border and experience landscape i

Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails 

Topic
DEVELOP
Ecological Conservation
Coronado Ecological Conservation (2023 Spring) Team: Carson Schuetze (Project Lead), Tyler Guigneaux, Charles Robinette, Josie Bourne Summary: Species monitoring is essential for mitigating the impacts...
The image was collected by Landsat 9 on January 26 of 2023 showing Skidaway Island and waterways on the Georgia coast. The data shows areas of healthy vegetation and urban areas using a Normalized Difference Vegetation Index (NDVI). The darker orange represents grass and urban areas. Yellow and purple represents forested and densely vegetated areas. The black represents open water.

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

Topic
DEVELOP
Disasters
Georgia Disasters II (2023 Spring) Team: Shakirah Rogers (Project Lead), Nathan Tesfayi, Matthew Murray, Clarence Jackson Summary: Heirs property owners are especially vulnerable to natural...

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