Displaying 141 - 150 of 305

Mapping post-wildfire hydrologic risk using satellite-based soil moisture observations

Topic
Wildland Fires
Principal Investigator: Miguel Villarreal, USGS Western Geographic Science Center (mvillarreal@usgs.gov) Co-Investigator: Brian Ebel, USGS Water Mission Area-Earth System Processes Division (bebel@usgs.gov) Wildfires increase flood and...

Hurricane Florence 2018

Disaster Type
Hurricanes & Cyclones
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

Topic
DEVELOP
Capacity Building
Disasters
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...
2022 Spring PUP Mexico Disasters Website Image

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

Topic
DEVELOP
Capacity Building
Disasters
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...
An NDWI waterbody mask derived from Landsat-8 OLI cloud-masked imagery from March-May 2019-2021 is shown in blue at 10-meter resolution over Mexico, Belize, and Guatemala. The ALOS-PALSAR-2 fine-beam L-band false color composite includes Refined Lee speckle filtered HV, HH, HH/HV bands. Darker and brighter green hues represent to low and high backscatter, respectively. Differentiating between open water, like the Hondo River, from vegetated inundation is important when distinguishing inundation by land cove

Mapping Inundation Below the Forest Canopy in the Maya Tri-National Forest

Topic
DEVELOP
Capacity Building
Water Resources
Maya Forest Water Resources II (Fall 2021) Team: Stephanie Jiménez (Project Lead), Karen Alvarez, Rene Castillo, Daniel Nohren, and Stephanie Lawlor Summary: To monitor seasonal...

Assessing Estuarine Ecosystems in California for Improved Wetland Monitoring and Management

Topic
DEVELOP
Estuaries are vital ecosystems that serve important ecological functions. The Marine Life Protection Act aims to protect these ecosystems by establishing a network of marine...

Evaluating Urban Heat Islands and Flooding to Enhance Green Infrastructure Initiatives in Coastal Communities in Mobile, Alabama

Topic
DEVELOP
This project utilized satellite Earth observations to assess locations in Mobile County, Alabama, that are vulnerable to the urban heat island effect (UHI) and flood...
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

Topic
DEVELOP
Capacity Building
Disasters
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...
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  

Topic
DEVELOP
Capacity Building
Agriculture
Mato Grosso Agriculture (Summer 2022) Team: Max Rock (Project Lead), Elijah Dalton, Aidan Harvey, Kate Reynolds Summary: Ranked as the fourth largest food producer in...

Employing NASA Earth Observations to Map Riparian Flooding in Guatemala and El Salvador

Topic
DEVELOP
Central America is one of the world’s most vulnerable regions to natural disasters, including landslides and precipitation-driven flooding. In an effort to support disaster management...

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