Our project for SEES 2024 was "Creating a Model to Identify
High-Risk Areas for Flash Flooding in Houston, Texas." In this
blog, I will discuss how we calculated which areas are "high-risk."
There are many ways to identify the most at-risk points of flooding
in a given region. Such points are commonly identified in a flood map,
such as the one here:
For our...
SEES 2024 Experience
Getting the opportunity to participate in the NASA SEES Earth System
Explorers program has positively impacted my educational and career
journey in more ways than I could've imagined. Throughout this
internship, I gained an immense amount of knowledge in Earth science,
utilizing the GLOBE Observer app, ArcGIS, Collect Earth Online,
Python, and many other tools...
If you had asked me what “citizen science” was before this internship
with the NASA SEES (STEM Enhancement in Earth Science) Earth System
Explorers, I most likely would have conjured a description that
involved studying the human body. Now, however, I feel confident in
saying that citizen science is a dynamic, extraordinary meaningful
process by which everyday citizens contribute to...
The wildfire data analysis journey began with a dataset comprising
over 1200 points located in the California region. To uncover the
correlation between land cover types and wildfire risk, we first
needed to determine the wildfire risk for each point using the FEMA
Wildfire Risk Map. This step involved entering coordinates into the
software to obtain the wildfire risk index for each...
My group’s project focused on using land cover data and GLOBE
Observer images to create a machine learning model that can predict
land surface temperature - a measure of how hot the surface of the
Earth feels in a given location. For context, GLOBE is short for
“Global Learning and Observations to Benefit the Environment”. It’s an
app that lets volunteers across the world take...
