Sanan, SEES Earth System Explorer, 2024 - Mission Mosquito

In this blog post, I will go through every step of the internship and outline my experience!

Before I do that, first of all, I would like to say that I am so happy that I got to spend my summer with SEES! This program was so amazing and helped guide me a bit more on what I want to do in my future career and in college! I am very grateful for all of our mentors who guided us through the program and provided great knowledge and support to all of us! Thank you for this opportunity.

Modules: Climate Science Background and Exploring the Earth

To prepare for the internship, we were assigned two modules to do along with python lessons. In these modules, we learned about remote sensing, the spheres of the Earth, different satellites, how satellites are used in natural disasters, and my favorite activity of the bunch, learning about the Earth Now website. I love this website so much because it actually makes learning about different satellites more memorable and interesting! It is so cool to see all of them simultaneously orbiting around the Earth and recording different measurements, and I like how the website has a description for each satellite and how we can view the actual satellite and all of its parts! I also like how the website has animations for different measurements, such as CO2 or surface air temperature.

AOI Data Collection

Before I started the “virtual” internship, I didn’t realize how much time I would spend outdoors! Although I really liked this part of the program since it meant spending a little time away from a computer, I also enjoyed it because I got to find places in my town that I hadn’t seen before.

1. Globe Observer App

• First, I downloaded the GLOBE Observer app and connected it to SciStarter. During my first time using the app, I was a bit confused on how it worked, but on my second try, I realized how simple it was to take the photos (simply turning your phone horizontally and moving it to the direction you are taking). I liked how the app shows the directions for North, East, South, and West because it made it extremely easier to take photos in those directions. Overall, the app was very easy to use, and thankfully I did not run into many issues.

2. Sampling Grid

• This was probably the most difficult part for me. My town is very small and a good bit of it is private property or farmland/undeveloped, and even though I would put it in the center of town in a college campus, there would still be a good chunk of points difficult to reach (farmland, in private property). Finally, I got my grid to where most of the points were in easy to access areas, and most of the points located in neighborhoods/housing areas were in parking lots or roads.

3. Data Collection Timeline

• I was able to get my points completed by week 4. Some of my points that I had done were not within the 100 meter box. I realized this after we made the field map in ArcGIS. For my second time around when redoing some of the points and taking the last half of my points, I downloaded the ArcGIS Field Map app (which was very useful!) because it would give me notifications when I was within the 100 meter box, and would show where I was at/moving within each 100 meter box.

Creating Field Maps

During our Wednesday sessions, we were introduced to ArcGIS Online. This was one of my favorite tools that we learned about during the internship.

In our map, we added several layers. One of the layers included all the land cover points taken in GLOBE Observers. I thought it was so cool how I could see every point ever taken on the map and click on the points for a description of when they were taken, longitude/latitude, etc! Another layer we added was the sentinel-2 10m land use/land cover time series. This layer would color code the map and show areas developed/built on, farmland, water, trees, etc.

What I loved the most about ArcGIS was how it was so useful for collecting my AOI data! We added another layer with our sampling grid to our maps, a 3 km filter to define our sampling grid, and a filter to define the 100 meter areas. From this, we could see each of the 100 meter boxes and it was easier to define what areas were within that box looking at the satellite image. When I actually went to take my points, I knew what buildings or areas to look for to be within the box. I downloaded the ArcGIS Field Map app so I could see if I was within my 3 km area and within each 100 meter box. By adding a geofence to read our layers, we could get notifications when we were in each area. This was very useful for seeing if I was at a centerpoint or within a 100 meter area. Apart from this, we also made a dashboard, which was very useful to have all our information in one spot.

Collect Earth Online and Community Climate Chronicles

We used Collect Earth Online to classify our land cover images and label 100 points within each 100 meter box. We labeled the point as impervious surface, tree, grass, unvegetated land, etc. This further analyzed our AOI and showed the distribution of impervious/pervious surfaces in each of the 100 meter boxes.

I really enjoyed doing the Community Climate Chronicles story map because it opened my eyes to certain aspects of my town that I hadn’t noticed before, such as its ecology and wide variety of outdoor leisure activities. For instance, I had never realized the amount of diversity of the species of plants and animals in my town, and it is something I admire now.

Project Time!!

From weeks 6 to 9, we did our team project! At first we were a bit unsure of what topic to settle on, but we decided on flooding. However, our project scope was very broad so we had to narrow it down to pluvial flooding. We decided on this type of flash flooding because current FEMA flood maps do not take into account pluvial flooding to predict flood risk of an area. Pluvial floods mostly occur in urban areas due to the prominence of impervious surfaces, which prevents water from absorbing into the ground. Our research focus was to see where water will accumulate during pluvial flood events in Houston, Texas. We chose this urban city because it had an abundance of AOI data and also deals with flash flooding frequently. Our team collected data from several sources including the GLOBE Observer land covers, OpenTopography elevation data, and QGIS analysis features, and used it to develop a Python program that predicts areas at high risk for severe flash flooding. Our model is a valuable tool for predicting and mitigating the impacts of urban pluvial flash flooding. Each of us in our team had a good skill set for the project and we each were a bit more specialized in a certain skill, so it was easy to split the project while also being able to help each other out. Throughout the project we had very good communication by using Whatsapp and Mighty Networks to consistently communicate and update each other on progress.

Reflection

This experience has solidified my passion for environmental science and has shown me the potential career paths in this field. Before this summer, I was a bit unsure of what career to pursue. I am very interested in satellites and remote sensing and how we can view the Earth changing from space day-to-day and during/after certain weather events. I was already very interested in geosciences, but after this internship I’m more interested in hydrology and climatology. I hope I can intersect my interest in these topics with sustainability and environmental justice in the future! I am very grateful to have been able to have this exposure in high school, with the team science sessions, team project, and modules that we completed prior.

About the author, Sanan is a rising senior from Statesboro, Georgia This virtual internship is part of a collaboration between the Institute for Global Environmental Strategies (IGES) and the NASA Texas Space Grant Consortium (TSGC) to extend the TSGC Summer Enhancement in Earth Science (SEES) internship for U.S. high school (http://www.tsgc.utexas.edu/sees-internship/). This guest blog shares the NASA SEES Earth System Explorers virtual internship in 2024.