Mission Mosquito

Sophia, SEES Earth System Explorer 2025

2025-09-12T20:18:05Z

Organizing your AOI and Land Cover Observations

Hello! My name is Sophia and I live in San Diego, California. I recently finished making all 37 observations for my AOI, which I did over the course of exactly one week. I took observations almost every day, spreading out my data-collecting, which I recommend if you start well before your data deadline so you don’t get too burnt out!

To start, I generated my AOI grid. Make sure to look at your grid in satellite view to ensure that the vast majority of the points are accessible. Zoom into each point to see if it is public property and accessible to take photos. I shifted the center point of my grid around my school and my home area in order to move a few points on the edge of my grid away from private property and onto roads/sidewalks. Shifting the center only a few meters can really help to put points on accessible trails and roads instead of in the middle of someone’s backyard or the middle of a wild space!

Next, I took a screenshot of my AOI grid in satellite view, and put it on a Google Slide. I labeled the rows and columns in “Battleship” style, with the rows as letters A-F, and the columns as numbers 1-6 (see Figure 1). This really helped me to identify which point I was going towards when I was walking between my points, as well as checking off the points and keeping track of the locations I needed to visit. For me, it was easier to look at the grid and say, “I’m at point E3,” compared to numbering the points as 1-37 and saying, “I’m at point 27” (what row is that? Where is that in my grid? I have no idea!).

Then, I labeled my points according to their accessibility and used this Google Slide to check off my points each day after I uploaded my data to GLOBE (see Figure 2). I only checked off points when I could see that my pictures had actually been uploaded to the GLOBE visualization system (some teammates had issues where data did not upload, so wait until you are sure you have success!). The labels were really helpful in my planning because I would do groups of similar points each day. For example, one day I did all of the easy-to-access neighborhood points in the NE corner, while another day I did the hiking points in the NW corner, and another day I did the hikes in the SE corner since they were mostly on the same trail. So, organizing the grid and labeling the points before even starting to go out and take observations was key in my data collection efficiency.

After making my grid, I made sure to save the CSV and GeoJSON files for the AOI points. Then, I wanted to upload my AOI grid into Google My Maps in order to use Google Maps on my phone to navigate to my points (credit Andrew Clark for strategy of connecting Google My Maps). However, Google My Maps will not accept GeoJSON files, and when I uploaded the CSV file to Google My Maps, only the points, not the 100 meter perimeter around each point, popped up on the map. This is an issue because I was planning on being within the 100 meter perimeter around some points because the actual point was inaccessible (in the middle of a house or private property). Being able to see the 100x100 meter square around my points was necessary. So, there are two ways to solve this problem. First, if you are confident in your CS skills, use a Javascript API to create an instance of Google Maps and import the GeoJSON file. If you have no idea what that means, use the second approach:

Download Google Earth Pro (free!)
Import the GeoJSON file into Google Earth Pro using "File" > "Open"
Export the Google Earth file as a KMZ file
Import the KMZ file into Google My Maps

Now that I had a Google My Map with the points and their associated squares, I was able to open the My Map on my Google Maps app on my phone and easily navigate to the points using Google Maps. I color coded the squares on the My Map in order to easily identify the rows, but this is optional (Figure 3).

Once I collected a point, I checked off the point on a list on my phone to keep track of which points I still needed to collect (A1, A2, A3, etc). You could also print out a physical checklist of the points or print out the map and check it off as well! One strategy that another teammate mentioned to me was to delete the points that had been observed from the My Map or other mapping software so that it is clear which points are still needed. I think this is a great way to clearly see needed data points, however the number of technical issues people have encountered with the GLOBE app and data loss would lead me to recommend that you don’t delete the points in case you need to return to them again (that way you don’t have to make the whole My Map again, although it doesn’t take that long).

A few notes about data collection and uploading:

Make sure to upload data on a strong Wifi connection
Upload in small batches or even one-by-one if you have a lot of data points
Avoid using manual mode to take the photos in the GLOBE app (it seems many people have data loss issues when taking the photos manually)
Avoid people and signs with words if possible in your photos
- This causes the photos to be flagged and it takes them much longer to eventually get to the GLOBE visualization system or could cause upload issues.
- For example, one day I took six data points. Two had no words/signs in them, and they popped into the GLOBE visualization system almost immediately after I uploaded them. The other four had street signs or other words in them, and they did not pop in until 2-3 days later, with the words blurred out.

I hope this helps with your AOI data collection!

Guest author, Sophia, is a rising senior from San Diego, California. 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 2025.

Jeannelyn, SEES Earth System Explorer 2025

2025-09-12T20:18:35Z

For my (Area of Interest) AOI project I collected my 37 points using GLOBE observer, google maps, and a grid creator. These tools made collecting my data points a lot easier.

To start I plotted where all my AOI points would be based on a central point. Using CVS and GeoJson files I was able to get all 37 AOI points in that area. When choosing a center point for your grid you should consider things that will affect your ability to take photos before placing your center point. Originally I had placed my AOI points in West Bloomfield but due to the large amount of private property and multiple of my points being in lakes I had to change my center point. I changed it to Detroit as there were less lakes and a lot of the spaces were public property.

After choosing your central point and seeing all the coordinates you can copy them into google maps and save them to a list. This will give you the directions to all of your points.

As google map guides you to your points there are some things I would recommend doing while collecting and uploading your points.

While collecting points you should keep a list of all the places you have visited. In the middle of collecting points I had to stop and go to camp and I couldn't remember where I had left off. Luckily my mom was writing each point I visited down, so I was able to continue where I had left off.
When you get to an AOI coordinate you should either take photos from your camera roll then upload them or if you are in an area with good internet press “send to GLOBE” on your globe observer app. I lost many of my photos, but the ones that were saved are because I did these two things.
Get as close as you can to the AOI coordinates. A lot of your future research will be based on these coordinates, so to be as accurate as possible get as close to the points as you can.

Overall this has been a wonderful experience and I hope my time with AOI is able to help others gather data more simply and efficiently.

This program has given me the experience of collecting, analyzing and using data on my own and with a group. I have really enjoyed it and want this blog to assist others in this program.

Guest author, Jeannelyn, is a rising senior from West Bloomfield Michigan. 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 2025.

Swarup, SEES Earth System Explorer 2025

2025-09-12T20:19:08Z

I did my AOI centered at a point near the entrance of my old elementary school, Wilshire Elementary, in Euless, which is where I live. I finished my 37 field observations in the span of exactly 7 days. This allowed me to have more breathing space when collecting my data. When strategizing a plan to get this done, I decided to keep my process as simple and efficient as possible while utilizing applications such as Google My Maps, EarthMap, and the link to generate the CSV and GeoJSON files for the AOI.

To start, I headed to the application that was provided by Andrew in Mighty Network to generate a 37-point grid including a center point, which was located at Wilshire for me. Since the school is going under renovations under the HEBISD project for renovating certain schools like my high school, I had to pick a centroid that was near the road (Kynette Dr).

Figure 1

Note: Unfortunately, when downloading my CSV and GeoJSON files, I didn’t notice that my computer glitched out and didn’t save them, so I had to “reverse engineer” the process and generate a grid that geographically matched most of the locations I went to in real time.

The next step in my process of obtaining data was uploading the downloaded GeoJSON file to EarthMap to see the coordinates and to visualize geographic land cover data that EarthMap had over my AOI. I didn’t actually use EarthMap for anything aside from just double checking that the GeoJSON data matched in both softwares (at least to a high extent). I toggled “Satellite” Mode on to see the actual locations as it is in real life to get an idea of where I was going to take these observations.

Figure 2

Luckily, none of my locations ended up in the middle of a highway or something ridiculous, but a couple of them were in completely restricted parking lots or garages which I wasn’t able to go to for obvious reasons, so I had to make do with the pictures that I took where I was as close to the actual location as possible.

After I had confirmation that my GeoJSON file was good to go and that the locations were also placed relatively decently, I went to Google My Maps to generate a customizable map of coordinates that were on my CSV file which matched my GeoJSON file. Initially, I tried to do this process with Apple Maps as that seemed more convenient in my head since I use that on a regular basis, but I realized it was going to be a complete nuisance trying to manually put in each individual coordinate and marking them as a pinned location. Doing this 37 times was definitely a no-go, so I went to Google My Maps and that was efficiency paradise compared to Apple Maps (not sponsored).

When opening a new My Maps, I added a layer and input my CSV file which is just a file with all the coordinates of the generated locations from the earlier link given in Mighty Network. I made sure the color of each point was dark so that I could see them well.

Figure 3

When I opened the map in the Google Maps app, I just hit the navigate button to my centroid point which was at Wilshire and went as close as I could to the exact location until the blue dot and the red marker were overlaying one another. I took the GLOBE Observer photos and drove to the next marker that I wanted to go to. Usually, the locations were about the same distance from the centroid point regardless if I went left or right, so I just picked one side of the map and went from location to location, marking them off as I finished taking pictures there.

Regarding the actual picture-taking process, I didn’t encounter many problems with the software aside from minor issues like my phone overheating in Texas heat and thus having to wait in my car until it cooled down til I could go back out. The pictures were all taken using the GLOBE observer app on my phone, and I held my phone out horizontally to take them. Sometimes my finger would get caught in the corners of the photos, especially in the first few pictures I took, but I realized I had to just hold my phone out further from my body and take the pictures like that. That method helped with getting my feet in the “Down” photos as well.

I took a screenshot of the entire map and edited it in my Photos app, using a black marker on the locations I had been to, indicating that I didn’t need to go there again on the other days that I went observing. I just thought this was the easiest way to do it (and most intuitive), and it worked for me pretty well as my photos were relatively close to the exact coordinates. I’m sure there were more interesting ways to go about the process of recording observations, but this seemed really efficient as I just went from one location to the next, marking them as I finished that area.

Figure 4

The only problem I encountered was after my observations with the aforementioned computer glitch, prompting me to redo my grid on the grid generator website. It was pretty annoying, considering I had around 10 iterations and had to keep overlaying them with the uploaded GLOBE observation layer that was provided on the arcGIS software by Andrew. Once I overlaid one that matched (the most it could) to the grid that I originally had, I called it a day.

As “non-complex” as this method was, I hope it was found helpful by someone.

Swarup is a junior from Hurst, Texas. 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 2025.

Saif, SEES Earth System Explorer 2025

2025-09-12T20:19:28Z

The Problem

You've got a list of AOI coordinates. Google Maps will show them to you, but it won't tell you the best order to visit them. If you’ve got more than a few points, this becomes a mess—guesswork, wasted time, and unnecessary backtracking.

We need a tool that will plan the most efficient route for us instead of figuring things out ourselves. That’s what this does, here’s how:

The Solution

This problem is a textbook example of the classic Traveling Salesman Problem (TSP):
How can we find the shortest route that visits each location (in our case, AOI coordinates) once and returns to the start?

Visualization of the TSP (left: map of points, right: solution)

But with 37 points, that’s 37! (or 1.38 × 10⁴³) possible routes—more combinations than seconds since the Big Bang. Even at a trillion routes per second, it would still take over 400 billion years to check them all.

So instead, I used a two-step approach that generates a near-optimal solution in seconds:

First, a “Greedy Nearest Neighbor” algorithm: Start at one point, then repeatedly travel to the closest unvisited one (we determine what’s closest by using real driving distances from external routing APIs). Simple, but gives a solid starting draft.

Second, 2-Opt Optimization: Go through that first draft and iteratively (one-by-one) swap route segments if doing so shortens the overall distance. These small tweaks add up to a much shorter path.

For this AOI, Greedy alone gave me a total drive of around 30.3 km. After running 2-Opt, that dropped to 26.7 km. That’s a 11.9% reduction!

While Greedy and 2-Opt don’t guarantee the perfect solution (nothing does at this scale), they’re fast, typically within 5–10% of optimal, and certainly better than the alternative of going through every point North-to-South East-to-West (which, in my case, would’ve been ~40% longer).

Bringing it to Life

To get started, I prompted different LLMs to build a working version of the tool. I outlined exactly what I needed: upload coordinates, calculate real driving distances, and optimize the stop order. I tried both Bolt.new and Lovable, then picked the one that got closest to what I had in mind.

For those interested in prompting, here’s the exact one I used to generate the MVP.

From there, I used GPT-4o to clean up the interface and add some useful features—like opening the route directly in Google Maps, removing individual stops, and including public WiFi locations (now optional).

Afterwards, I deployed the tool using Netlify. It connects directly to GitHub (which Bolt integrates with), auto-deploys changes, and runs a backend to fetch Google Places API data—all for free! You can try the app here: https://sees-route-planner.netlify.app

Final Thoughts

This wasn’t some genius-level project. It was just a clear problem, a few good tools, and the patience to figure things out.

That’s the real takeaway: if something’s broken or inefficient, you can fix it—fast—if you know how to work with what’s available to you.

Thanks to the SEES mentor team for giving this project a spotlight. If this comes out to anything, I hope it pushes someone else to stop waiting and just build.

About the author, Saif is a rising senior from the Cedar Park, TX area. 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 2025.

Community Chronicles

2025-02-12T16:31:34Z

This project is all about mixing the personal stories of citizen scientists with 30 years of the GLOBE (Global Learning and Observations to Benefit the Environment) program. It combines these two sources, as well as other data sources, to get a complete picture of how our environment change

This is the first year for the Climate Chronicle activity. Explore the collection of Climate Chronicles from the 2024 virtual SEES Earth System Explorers (ESE) 2024 interns.

The GLOBE program has been gathering climate data for over three decades. This considerable dataset includes temperature, rainfall, soil moisture, and more information. By analyzing this data, scientists can spot long-term trends and patterns in our climate.

NASA has a 50-year history of Landsat images. The Community Climate Chronicles activity uses this information to contextualize citizen scientists' observations. This mix of data sources lets you create a more detailed and nuanced understanding of climate change in your community.

Dae San, SEES Earth System Explorer, 2024

2024-12-06T18:55:51Z

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 project, we decided to look at elevation data, which is perhaps the most intuitive: water flows downhill and will accumulate in the lowest regions. To get our elevation data, we used opentopography.org, which has free global elevation data with a minimum of 10m accuracy. On the website, we used the "select a region" feature to isolate specific Houston AOIs and download the elevation data in a .TIF file.

The .TIF files contain what is called an "elevation raster." This is a two-dimensional grid made of longitude and latitude coordinates, and it has an elevation value at each point. Here is an example (AOI #3, Houston) where the elevations are represented by color:

We need to convert our raw elevation data into a flood map. To do this, we perform something called D8 flow analysis. This is an algorithm that takes an elevation raster (input) and converts it to a direction raster (output) through the following process:

At each point or "cell" within the elevation raster, find the adjacent cell (of 8) with the steepest descent from the original cell (the lowest neighbor).
Assign the original cell a new value from 1-255 based on the direction to the lowest neighbor, with 1 being east, 2 being southeast, and going counter-clockwise around doubling each time until we get 128 for northeast.

This new direction raster tells us how water flows at each point. Using the following code, we programmed a third raster that indicates, at each cell, how many other cells contribute flow into the cell:

Finally, we achieved this result for AOI #3:

We can see that water will pool in the streets, which makes sense due to their lower elevation.

For a more detailed explanation, check out ArcGIS's D8 flow accumulation tool: https://pro.arcgis.com/en/pro-app/latest/tool-reference/raster-analysis/flow-direction.htm

Also, here is our GitHub for the full implementation: https://github.com/dsk2025/SEES-2024-Flooding-Model

About the author, Dae San is a rising senior from the Cleveland, Ohio area. 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.

Kandyce, SEES Earth System Explorer, 2024

2024-12-06T16:32:20Z

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 to better understand the environment around us. During the last few weeks of the program, a team of seven other interns and I completed our own research project called "Harnessing Citizen Science to Enhance Land Surface Temperature Prediction." In this article, I will highlight the analysis and research done during my time as an intern, as well as my team's project.

GLOBE Observer App

To start off, our first task was to collect field data around our communities by applying the Adopt-a-Pixel research framework and creating a 3km x 3km Area of Interest (AOI) map. Once I made my AOI grid consisting of 37 points within a 9 km² square radius, it was time to take six directional photos at each spot - North, East, South, West, Up, and Down. In the end, I was able to visit and take pictures at 30 locations due to the other seven being inaccessible, either being on private property or in deep forest areas. An example of one of my GLOBE Observer directional photos is shown below!

I learned that taking these directional photos utilizing the GLOBE Observer app can help track land cover changes that occur overtime in my area and that information can be used to compare with satellite data.

ArcGIS Online

To further analyze our AOI locations, we developed maps with feature layers and dashboards on the ArcGIS Online platform. Each location had an associated smaller 100 m x 100 m box to define the boundaries of where the samples should be taken. The image shown below is my AOI sample grid map consisting of 37 points illustrated on ArcGIS.

The image shown right is a zoomed in version of one of my AOI points, displaying the aforementioned 100 m x 100 m sampling square. Ideally, the directional photos that I take on the GLOBE Observer app would be at the center point indicated by the orange dot. However, human error is inevitable in science so we declared that anything within the smaller sample square boundary is still considered acceptable.

The image shown left shows my attempt to be as accurate as possible and get as close as possible to the orange center point. Although I wasn't able to get exactly on it, my location was still within the 100 m x 100 m sampling square boundary.

ArcGIS was a helpful tool to perform analysis on our maps, specifically creating buffer feature layers that provided further analyzation.

Collect Earth Online (CEO)

After performing analysis on ArcGIS, we began to label specific land cover elements (building, impervious surface, tree, grass, etc.) for each of our 37 location within our AOI on the Collect Earth Online (CEO) platform. Within each location, 100 points were generated for us to manually label (total of 3700 labels) with the help of Sentinel-2 satellite sourced imagery. The image shown right is an example of what one completed CEO labeled area would look like.

The CEO tool was especially useful in quantifying land cover and observing how each element may contribute to bigger environmental issues. For example, more impervious surface cover and less vegetation could correlate with higher land surface temperatures.

Team Research Project: Harnessing Citizen Science to Enhance Land Surface Prediction

In the second half of our internship, we formed smaller working group teams to began the process of brainstorming and carrying out our research project. My team's project focused on creating machine learning models utilizing citizen science sourced data to predict land surface temperature in the context of the rising issue of surface urban heat islands around metropolitan areas. Surface urban heat islands are classified as developed areas indicated by higher land surface temperatures compared to surrounding areas. While exploring different online resources about our topic, our group started to notice the pattern of how more impervious surfaces (such as concrete, asphalt, and more) in an area resulted in higher land surface temperatures because of less vegetation being present. Later on, our team used this observation as one way to explain why some areas may potentially experience higher land surface temperatures than others. The image shown below displays the temperature distribution along various community types.

Source: World Meteorological Organization

After our preliminary research was completed, our team gained additional knowledge of the chosen topic and why it is important. The next step now was to extract, retrieve, and prepare our data from various sources to use as training data for our machine learning models. Training datasets used for models included downward GLOBE Observer land cover photos collected by the Earth System Explorers program (further labeled for land cover through Zooniverse with the help of citizen scientists), mean land surface temperature from the Landsat-8 satellite, and Collect Earth Online. Our team then developed Random Forest and XGBoost models trained with those numerous datasets and compared their results. I helped develop visualizations through Python for our Random Forest models to illustrate the process it goes through to develop a prediction. Creating these gave me a better understanding of how the model worked and revealed how one decision made in a tree based on feature importance could affect the whole prediction value in the end. Once we successfully achieved results from our model, our group started to develop our deliverables which included a collaborative research paper, poster, presentation, and archived codes. The products created for our project were then presented at the NASA SEES Symposium and submitted to the International Virtual Science Symposium (IVSS) and American Geophysical Union (AGU).

Reflection

In summary, going through this internship has taught me more about the environment around me and how remote sensing plays a huge part in unveiling climate issues that occur. I was exposed to a wide range of tools such as NASA Worldview, Landsat Time Series, ArcGIS, Collect Earth Online, and so much more that helped contribute to my team's overall research project. I particularly enjoyed getting the opportunity to combine both my interests of computer science and the environment in this program to then view how they can benefit each other. This experience has provided me with an immense amount of knowledge that I will cherish and apply throughout my educational and professional career, not only the data tools but also the mentors that helped me along the way.

About the author, Kandyce is a rising senior from Seattle, Washington. 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.

Sanan, SEES Earth System Explorer, 2024

2024-12-06T16:13:07Z

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.

Anna, SEES Earth System Explorer, 2024

2024-12-05T22:38:51Z

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 real-world scientific research. This internship allowed me to take on the roles of both a citizen scientist and a researcher, deeply engaging with my local community and environment while simultaneously considering issues related to climate change, social justice, and sustainable development on a large scale. I’ll take you through a few highlights of my incredible July and July, broken into chronological phases, although rest assured that some phases overlapped or are still ongoing!

Phase 1: Data collection through the GLOBE Observer App

After getting to know my fellow interns (shoutout to Working Group 2!), our amazing mentors, and our equally amazing peer mentors, Peder sent us out on a mission. I followed his enthusiastic instructions as I downloaded the GLOBE Observer app. Then, armed with a map of 37 distinct points in a 3 km x 3 km grid around my neighborhood, I set out walking and taking pictures. Yes, really! I went to each spot and stealthily snapped pictures in all four directions, plus up and down, before filling out the tedious land cover descriptions on GLOBE Observer. Of course, many locations fell on private property, so I had to strategically analyze Google Maps to get as close as possible without breaking any laws or seeming too suspicious.

I divided my research into several expeditions, each lasting an hour or two, as I had to do quite a bit of walking. Although data collection led to a few tired and busy afternoons, it allowed me to explore parts of my surrounding area I had never seen before or notice parts of the environment I had previously walked or driven past. For example, I never realized just how many evergreen trees there are surrounding every single house in my neighborhood. Although I still had no idea how to use this data, it was gratifying to know I was contributing to real-world land cover data as my photo count ticked up on the ArcGis website.

A few beautiful places I got to explore while hunting down points on my ArcGis map

(Oh yeah, and after realizing I chose the wrong setting on the app while taking the majority of my photos, I had to go back and redo them to get more accurate location data. That was fun.)

Everyone's amazingly ambitious ideas on Padlet

Role reversal: now I was the researcher! After a few weeks of data collection, plus learning about remote sensing and hearing from real scientists like Dr. Bri Lind and Dr. Di Yang, we were ready to explore our data and develop a research topic idea. Numerous past SEES interns also spoke to us about their projects, including Nikita Agrawal, who later furthered the skills she developed in SEES to develop a machine-learning model for wildfire prediction. My Working Group was all passionate about machine learning as well. After a tsunami of ideas dumped onto our Padlet, we settled on developing a machine learning model that would incorporate land cover data to predict surface temperatures in areas around the world.

Of course, I was stressed out! It was July 1st, and I had to complete an entire research project by July 19th along with a poster, video, and presentation. I had no prior experience with machine learning, and neither did the majority of my group mates. Nonetheless, we felt reassured that each of us had individual strengths we could bring to the team to make our project a reality. Having capable, experienced mentors was also a huge plus.

Phase 3: Land Surface Temperature Research

We had selected our idea of creating a machine-learning model to predict land surface temperatures, but how would we do it? When? Why? We didn’t know, but we quickly delved into research about urban heat islands–a phenomenon where urban areas become hotter than surrounding areas due to an abundance of impervious surfaces (concrete, etc) and a lack of cooling vegetation. Learning about the social implications of heat islands was intriguing. They were a topic I had never previously studied, but the more I investigated, the more it became clear that urban heat islands not only exacerbate public health risks but also contribute significantly to energy consumption and greenhouse gas emissions.

Researching heat islands for our project provided further motivation to create a predictive model for land surface temperature, as modeling temperatures is crucial for protecting the health of people and our environment.

Urban heat islands in a nutshell. Credit: NASA/JPL-Caltech

Examples of directional photos taken via GLOBE Observer. (Our group focused on the "down" photos, although we later realized adding in other directions would most likely have improved our model's accuracy.)

Of course, experts have been modeling surface temperatures for years, so I was initially unsure how our group of eight high school interns would contribute anything to the field. However, with Peder’s guidance, I realized that the dataset of GLOBE photos collected by SEES interns had yet to be explored in any scientific context. We set out to study how (and if!) citizen-collected data could increase the accuracies of predictive models for surface temperature. Specifically, we wanted to see if land cover data from GLOBE Observer “down” photos correlated to surface temperatures within a region. (Concrete surfaces would most likely be hotter, for instance, while areas with grass or water would be cooler.)

A flowchart showing our simplified process of data collection

Our research involved numerous tedious stages of data preparation: writing Python scripts to query the GLOBE API, learning to use Zooniverse so that citizen scientists could help us label photos based on land cover, and extracting labeled satellite data from Collect Earth Online to go along with our images. Then, of course, we had to learn about machine learning. We were fortunate to discover NASA ARSET training for machine learning models, including Random Forest and XGBoost, in the context of analyzing satellite data. I took on the role of training the Random Forest model, and there were many painful hours of dealing with error messages in Google Colab and waiting many minutes for small sections of code to run. However, nothing beat the gratification when I finally trained a model and saw it making realistic predictions for the first time. It took weeks of fine-tuning our models and datasets before we got to a place where we could begin working on our data, but I loved the process. I was delving into my passion for coding while seeing how our data could make a true difference in a real-world environment context.

The moment of glory: a few actual vs predicted values for land surface temperature generated by the Random Forest model

Smooth collaboration was also key to the success of our project. There were many hours spent over Zoom and Google Meets with each other brainstorming ideas, asking questions, going to office hours, and sharing what we had learned from various tutorials or conversations. We quickly learned to manage our time well–difficult in a project divided between eight different people living in four different time zones! To help with this, we separated into two groups and had each one focus on a different ML model. We also communicated as much as possible when we were struggling and tried to set clear goals before each weekly meeting. Although none of us had ever met before the internship, we quickly learned each other’s strengths and weaknesses, and I could not have completed any part of our project without the support of my amazing teammates and mentors.

Our model wasn’t perfect, but it provided us with confidence that more high-quality land cover data could be used to enhance future models for surface temperature prediction. We did our best to communicate our findings and their significance through our research paper. When we finally watched our recorded presentation at the SEES Symposium on July 23, I felt a ton of pride in what we had accomplished. I was also amazed by the in-depth, meaningful research projects that other working groups accomplished, which spanned from agrovoltaics to the socioeconomic correlations of heat islands. I was equally impressed with projects from the entire NASA SEES intern groups, which delved into many intriguing fields relating to engineering, space science, and sustainability.

If you're curious, you can check out our presentation at minute 43:15 of the SEES Symposium.

Phase 4: Reflection, Community Climate Chronicles, & More!

We were almost finished. One poster. One abstract. One presentation. A forty-page written research paper. However, even after the symposium, we had a lot to learn and finalize. One aspect of science we emphasized, with the help of Andrew Clark, was the importance of making our research accessible and reproducible through the principles of “open science.” To do so, we learned to use GitHub to store all our code products and datasets, as well as Zenodo to link our research paper to our unique ORCiD identifiers and data. Additionally, with Rusty and Cassie’s help, we worked on a plain language abstract for our research report. This is essentially a version of the abstract that eliminates scientific jargon to be easily understandable by everyone, not just specialized scientists.

Besides polishing our writing for submission to IVSS (the International Virtual Science Symposium) and AGU (the American Geophysical Union), we also finished up our Climate Chronicles blogs of our local areas! We used these blogs to document our communities’ histories while incorporating the scientific data we collected to tell a meaningful story about change over time. Delving into my local neighborhood, a small city famous for its wineries and nature outside a much larger urban metropolis, I learned so much about urbanization occurring in the region over time. This was even reflected in the data I collected for GLOBE, as well as satellite data for the AOI (Area of Interest) I studied over the last 40 years. It was empowering and surprising to see how my research reflected meaningful change in the place I have lived my whole life, and I feel empowered now that I have created a platform to share this story with others. You can check out my Climate Chronicles here:

Community Climate Chronicles: Woodinville, WA

Overall, I found this internship incredibly valuable, from the individual learning modules I did in May to the final paper I helped craft in July. I met so many amazing people and learned so much about machine learning, land cover research, open science, citizen science–and myself! Moving forward, I will take the principles of citizen science to heart as I attempt to be an active, engaged member of all my communities. I had always planned to pursue a career in a STEM field, mostly likely computer science; however, I now feel empowered to apply computer science skills to create real-world change in a field like environmental research or biomedical engineering. I feel so fortunate to have been a part of the 2024 Earth System Explorers, and I strongly encourage any eligible readers to pursue an internship with the NASA SEES program!

You can also check out this same blog via ArcGIS StoryMaps.

About the author, Anna is a rising senior from Seattle, Washington. 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

Akshada, SEES Earth System Explorer, 2024

2024-12-05T22:14:43Z

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 location. From this dataset, we categorized approximately 325 points as having a very low risk, 350 as moderate risk, and 578 as very high risk for wildfires. These categories served as the strata for our stratified random sampling method, ensuring a representative sample from each risk category.

GLOBE Observers Land Cover Map

Stratified Random Sampling

Stratified random sampling, unlike simple random sampling, involves dividing the population into homogenous subgroups (strata) and then selecting random samples from each subgroup. This approach captures key characteristics of the population, enhancing precision and reducing estimation errors. In our analysis, the entire dataset of 1253 observations was divided into three strata: very high, moderate, and very low wildfire risk categories. By employing disproportionate stratified sampling, we selected 33 random points from each stratum, enabling a focused analysis on land cover types across different risk categories. This method ensured a robust representation of the various strata in the dataset.

Spreadsheet illustrating strata organization: red indicates very high wildfire risk, orange represents moderate risk, and yellow denotes very low risk. Points from each section were analyzed for land cover type.

Land Cover Analysis

After selecting the sample points, the next step was to analyze the land cover at each location. Each row in the dataset contained the source code for an image, which we manually reviewed to identify the land cover type. The land cover categories included urban, forest, wetland, grassland, and shrubland. This detailed analysis allowed us to connect specific land cover types with corresponding wildfire risk levels, providing valuable insights into how different environments influence fire susceptibility. This process showed the importance of manual verification in ensuring data accuracy and reliability.

Random Forest Regressor and Model Training

The core of our analysis involved using a Random Forest Regressor to predict wildfire risk based on land cover data and historical fire incidents. The Random Forest algorithm constructs multiple decision trees from random subsets of the training data, reducing overfitting and variance. Each tree operates independently, and their aggregated predictions improve the model's accuracy. We trained the model on 80% of the data, reserving 20% for testing. This approach allowed for assessing the model's generalizability to unseen data. By leveraging the ensemble learning method, we enhanced predictive accuracy and identified key features influencing wildfire risk.

Evaluation Metrics and Model Performance

Evaluating the Random Forest model's performance involved several key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R²), and Mean Absolute Error (MAE). The model achieved decent values for each reflecting reasonable precision in estimating fire risk. The Random Forest Classifier's accuracy stood at 0.75, demonstrating strong predictive capabilities. These metrics highlighted the model's effectiveness while also indicating areas for improvement. By minimizing errors, the model ensured predictions closely aligned with actual values, enhancing its reliability in real-world applications.

Confidence Intervals and Implications

To quantify the certainty of the model's accuracy, I calculated the 95% confidence interval for the Random Forest Classifier's accuracy, which ranged from 60.22% to 89.78%. This interval, though broad due to the sample size of 33, provided valuable insights into the model's performance. It emphasized the need for more data to narrow the interval and improve precision. This analysis underscored the critical role of citizen science in collecting additional data points to enhance the reliability of wildfire risk predictions. The confidence interval highlighted the importance of ongoing data collection and model refinement in predicting wildfire risks accurately.

In the End

Through this process, I learned a great deal about wildfire risk assessment and the power of statistical analysis. Working with stratified random sampling and the Random Forest algorithm taught me how to handle large datasets, apply complex models, and understand the results. This experience showed me the importance of accuracy and detail in data analysis. It was exciting to see how our work could help identify areas at high risk for wildfires. Overall, this project made me appreciate data-driven methods in environmental science and showed me how important good statistical analysis is in solving real-world problems.

About the author, Akshada is a rising senior from Edison, New Jersey. 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.

Maako, SEES Earth System Explorer, 2024

2024-12-05T21:54:55Z

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 observations of their local community (these could be of cloud cover, land cover, or mosquito habitats) and these observations help scientists track changes in the environment. It’s part of a wider movement called “citizen science”, where everyday people (people who aren’t experts in Earth Science) can contribute to scientific research.

Probably my nicest GLOBE Observer location!

The goal was to help develop a better understanding of the impact that land cover has on the temperature of the environment. This is super important now more than ever because of climate change! Temperatures everywhere are increasing. The 18th and 19th of July both set records for the hottest days on record (since 1940). Although this summer’s extreme heat was partially caused by the El Niño event (a naturally occurring global weather phenomenon), average temperatures are continuing to increase globally due to climate change. One way to try and slightly reduce surface temperatures - apart from tackling the direct causes of climate change - is to alter the land cover of our towns and cities.

Major cities generally suffer from a phenomenon known as the Urban Heat Island (UHI). This is when cities experience temperatures that can be up to 10˚C higher than those of the surrounding environment, and can be visualized as an “island” of heat that is trapped within the city and even lasts during the nighttime! There are many reasons this might occur: tall buildings act as barriers to physically prevent heat from escaping the city (see image below); cities are full of low albedo materials such as concrete and asphalt, trapping heat at the surface of the city; and anthropogenic heat sources such as air conditioners and vehicles generate more heat to reinforce the severity of the UHI.

Diagram of the light/energy transfers that cause the urban heat island phenomenon. Light is converted to heat when it is absorbed, and this waste heat is trapped by the heat island. Additionally, light can be reflected several times between tall buildings, resulting in additional heat absorption. (Ren, 2015)

The UHI can have devastating impacts on the urban population. As of July 2024, urban heat islands are present in 65 American cities that are home to about 50 million people (15% of the US population) (Axios, 2024)! 68% of residents live in environments where the UHI can increase temperatures by up to 5˚C. Dozens of people have died as a result of the extreme heat in these urban areas this summer alone.

Illustration of causes of and solutions to the UHI. (Pavement Technology Inc, 2021)

There is a solution, however! And this is where our project comes in. I think our machine learning model, with additional training, could be a very useful tool for urban planners and city officials to gain a better understanding of how altering their land cover could help mitigate the intensity of the urban heat island in their cities. To train our model, we used:

Longitude and latitude of each location
Labels of GLOBE Observer “down” images (images of the ground)
Collect Earth Online labels for each area of interest in our investigation. Each location has 100 “dots” that indicate the land cover of the area directly underneath.

An example of Collect Earth Online labels I completed for one of my locations. Each dot in the grid can be assigned a label depending on what category of land cover is underneath it. The category options can be seen on the right.

The Collect Earth Online labels and GLOBE Observer images give an indication of the primary land cover of that specific location, which the model would then use to train on and better understand the relationship between the land cover of an area and the land surface temperature of that area. The land surface temperature data we used was sourced from Landsat 8 - a satellite launched in 2013 to track urban expansion, forest loss and regrowth, glacier melting, changes in land use, and water use by crops. Conveniently, it also collects data on the land surface temperature of the entire globe. We used this data to train our model. Unfortunately, we weren’t able to obtain this data for all 950 or so locations we were using from our SEES group; we could only get it for just less than half (~450).

I think our research could be a good first step towards developing a better understanding of how land cover can impact temperature. Although we were met with a lot of setbacks while collecting our data, I am happy with the accuracy we were able to reach with the limited data we had. I am confident that given more time, and if we were able to find the Landsat 8 data we were missing, we would definitely see much more accurate predictions in the models we developed. With additional training I think our models could demonstrate the strong link between vegetation and a reduced UHI intensity.

Overall, I feel I learned a lot through my experience with the program! I’ve gained a much better understanding of the crucial role vegetation can play in regulating surface temperatures - and this information is relevant now more than ever given recent events. I’d love to see how future interns could help take our project a step further - I think there’s a lot of potential for great improvement here!

Thanks for reading!

About the author, Maako is a rising senior from Preverenges, Vaud, Switzerland. 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.

Ashvin, SEES Earth System Explorer 2024

2024-12-05T21:36:49Z

Roboflow is an open-source dataset management platform, where users can import data, label it, and then have a model train on it. For my group's project, CS-FLARE, we used Roboflow to label brush, grass, trees, and leaf litter in our NESW Images as shown below.

When I initially began labeling images, I tried using bounding boxes for each picture like the leaf litter in the image above. This became a bit annoying as sometimes the bounding boxes would cut parts of objects like trees, but it was still faster than using the polygon tool to manually trace around objects. Eventually, I was introduced to the smart polygon tool which automatically created the polygons around objects and I could add or subtract to the initial polygon. At first, I thought this was a blessing as I could get the precision I wanted while still quickly labeling images. Unfortunately, because of some of my perfectionist tendencies, the smart polygon tool made labeling much more difficult for me. This was because sometimes the tool would select too little or too much of an object causing me to take time to have objects perfectly within the label. Take the image below as an example.

To me, what I have circled in red are areas that shouldn't be included in the labeling and annoy me a lot. To fix these areas though, it would maybe add on 1 or 2 minutes to my total labeling time as I would have to continuously add or subtract areas until the smart polygon tool understood how I wanted the shape of the label to look like. Even though that doesn't sound too bad, when you have 1000+ images to label, it adds up, especially when you could label an image like the one above in 20 seconds at the max. Because of small but noticeable areas that shouldn't be included in the labeling according to my standards, this process became incredibly tedious and time-consuming. Overall though, Roboflow is a great tool to use to annotate images and they even provide you with a model to train the images on if you aren't knowledgeable about AI. For these reasons, I recommend it to people who need to easily label their data, but personally, I would not want to use it again.

About the author, Ashvin is a rising senior from Frisco, Texas. 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.

Osemeren, SEES Earth System Explorer 2023

2023-11-29T20:15:40Z

Adopt-a-Pixel | Where does water meet land in my community?

Scanning the Area

As our first assignment as part of the Earth System Explorers team, we've been tasked with making our first observations using the GLOBE Observer App. The goal was to make 12 total observations (6 Potential Mosquito Habitat & 6 Land Cover).

To be more efficient, I decided to make all of my observations at 6 different still bodies of water. This way I could do a MHM and a LC observation at each location.

Before I began, I scouted potential bodies of water on Global Surface Water Explorer. Using the platform, I decided to focus on areas that showed gradation across both the 'Water Occurrence', and 'Water Occurrence Change Intensity' spectra.

I had hoped that these locations would make for interesting observations, as I wanted to view the difference in water occurrence from years prior, and contribute to documenting its increase/decrease.

However, a quick look on Observer showed that there weren't any prior observations made in those areas. After a little bit of investigating on Google Street View, I discovered that most of these locations were inaccessible to the public to begin with.

Ditch Diving

This was about when I remembered that there's a ditch near my neighborhood.

I filled my backpack with paper plates, gloves, and empty water bottles, and headed out.

Once I got close to the water, I thought I saw small little larvae swimming through the water. Feeling confident that I chose a good location to map, I set up base.

A Mosquito Masquerade

After I verified my location, classified the water body type, and took pictures of the surroundings, I reached the larva classification segment. To complete this, I had to collect, count, and identify a larvae sample.

Collecting the larvae took way longer than expected; over 15 minutes to catch one! After I drained the excess water from my paper plate, I took a zoomed picture of the larva for classification (I couldn't get my clip-on cellphone microscope to focus).

Now, time to classify the larva!

While swiping through the images of the larvae, something caught my eye... I saw a button with an illustration that closely resembled my sample. To my surprise, it was labeled, "No, my sample looks different". After clicking on the button, I was brought to a screen that read, "Based on your observation, this is not a mosquito larva". Shocked, I did Google Lens Search of the sample, and not many matches came up for mosquito larvae. It is likely that the 'larvae' in the ditch were actually just tadpoles!

Conclusion

Discouraged but not disheartened, I looked for other accessible bodies of water to document. Although the rest of my observations were relatively uneventful, I enjoyed exploring my community for potential mosquito habitats. I even got a couple of confused looks! Regardless, I'm happy I was able to contribute to a global observation effort, as a civilian.

Oseremen O. is an intern at NASA SEES, and part of the Earth Systems Explorers Team. He's excited to apply his skills, and pick up some new ones to help visualize Earth!

About the author, Oseremen is a rising senior from Frisco, Texas. 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 2023.

Ryan C, SEES Earth System Explorer 2023

2023-11-29T20:00:15Z

I built my mosquito traps on June 20th and ran the experiment until July 15th. I collected a total of 15 observations during this period. To construct the traps I used three identical pots to hold water. The goal of my experiment was to find which type of mosquito bait would be the most effective at attracting and sheltering mosquitos. For the three baits I used dried leaves, grass clippings, and a wooden plank. I chose these three baits because they represent possible real life environments that mosquitos may breed in. The dried leaves simulates a pool of water that does not evaporate due to the shade of a tree and has fallen leaves in it. The grass clippings represent a puddle of water in is a grass field that is a result of over-watering, and the wooden plank represents a fallen branch or a plant with water surrounding it.

I originally set the three pots out in my front lawn in the hope of attracting mosquitos but when it came for the first time to check for mosquito larvae, the water had dried out. As a result I had to move the pots in between a fence and my house so that it receives full shade and the water does not dry out. In addition to this, I decided to check at a 5 day interval to allow for the water to stay and mosquitos to still be present.

Pot with a wooden plank to represent a plant, tree, or fallen twig.

Pot with leaves to represent a pool of water with fallen leaves in it possibly due to the shade of a tree.

Pot with grass clippings to represent a puddle within a grass patch.

Unfortunately, throughout the entire runtime of the experiment, I did not detect any mosquito larvae. As a result I am unable to make any conclusions regarding the effectiveness of various mosquito baits. However, I know that there have been mosquitos around my house before in the past, which leads me to conclude that the environment at the time of the experiment was not suitable for mosquitos. During the experiment, the temperature regularly reached +90° F and, on some occasions, reached over 100° F. I believe that the temperature is the cause of the lack of mosquitoes. This result leads to the question of whether and how temperature affects mosquito reproduction. As a follow-up to this experiment, we can conduct a similar one where instead of different baits being the independent variable, we use temperature and place multiple mosquito traps within the same region in areas with varying temperatures, such as the beach or inner city.

About the author

Ryan is from Los Angeles, CA. 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 2023.

Manuel, SEES Earth System Explorer 2023

2023-11-28T20:54:33Z

My mosquito trap was definitely a rollercoaster, and with this being the last required update, I still plan on seeing how the traps do after the internship finishes since this has been really interesting. To give some background, I set out three traps around 30-40 yards away from each other around my AOI so I could get an idea of how different areas may yield different results. The first trap was in my backyard, the second trap was in a park close to my house, and the third was by a sidewalk (the pictures I use are from the third trap). The only trap I really had to refill with water was the one in my backyard, which is crazy with the Texas heat, but it is also due to the fact that it rained for almost 3-4 days during the experiment.

In the first week, I could already see a few larvae. The picture above was the location that I was most successful in, which is probably because this one was in more of an isolated area with less human disturbances. I did have to adjust to the crazy Texas weather throughout the whole experiment, with on-and-off rain and obvious Texas heat warnings each day.

In the second week (the picture), you can clearly see less water, which is due to the Texas heat. What you may not see is the fact that there were still larvae in the trap, which is good news that I was really content to see. The heat this week was the worse I've probably ever experienced, with it getting as high as 120 degrees, which may have affected the mosquito and larva population. Something I do think that I could have done better was use darker buckets since I was reading an article in my free time after I set the traps out that said this would yield better results.

This picture is the conclusion. There was very few water and no new larvae that I could spot. Just to conclude, I made the mosquito traps in different locations with different levels of human interference. The results weren't too surprising since I found that more human interference/disturbance means fewer mosquito larvae found. Something I did see was that with the extreme heat in Texas, there has actually been less mosquitos found, which is another factor as to why I didn't see any new mosquito larva this week. In conclusion, this was a really fun and challenging experiment that made me learn more about mosquitos and their behavior, but also the various factors that come into play when conducting an experiment of this magnitude.

About the author

Manuel, is from Katy, TX. 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 2023.

Abdullahi, SEES Earth System Explorer 2023

2023-11-28T19:07:20Z

Mosquito Trap
Week 1
For the mosquito traps, I decided quite early on that my independent variable would be the container. I got 3 different containers; an old tire, a black bucket, and a gallon bottle/container while keeping all other factors the same. I used the same bait and the same amount of water and placed all 3 traps in my backyard behind the shade of the tree. I decided on using a timeframe of about 5 days to take my samples.

Week 1:
Five days had passed, and it was time to sample the traps. Unfortunately, when I got outside, I noticed my tire trap, which I had the most hole for, had been basically destroyed by my neighbor’s dog so I had no values to report. Looking at the black bucket trap, there were 4 larvae in total surprisingly coming from 2 different species of larvae which I had not really thought about. There was the anopheles and the short-siphoned larvae. I hadn’t really expected much of the gallon trap, however, it did surprise me housing 3 larvae, all anopheles. After taking the mosquito habitat observation of each trap I emptied out the water and reﬁlled it, also replacing the tire trap in the process.

Next day:
I decided to check up on the traps the next day on impulse, and what I found surprised me. The tire trap had five larvae in one day as well as 2 different larvae similar to the black bucket the previous day. The black bucket trap also had 5 in a day, showing the same variation in larvae type it had previously. However what
surprised me the most was the fact that the gallon trap had the most larvae with 7 in one day. It also had the most variation with 3 different larvae species. This led me to question whether lighter traps are more appealing to mosquitoes than darker ones or if the elevation, in this case, might have affected the amount of larvae captured in the trap (the gallon was in a bit higher elevation than the rest of the traps).

Week 2:
Another week, another destroyed trap. This time both the old tire and black bucket traps were meddled with/emptied out, so I decided to ﬁx them both and change their location slightly in order to avoid future accidents. This would also make the results fairer/more equal as all the traps a re essentially in the same location receiving the same amount of shade. Next, I took the larvae samples of all the traps. The gallon trap had 13 larvae, the black bucket trap had a whopping 85 and the tire trap had 89. These results may validate the hypothesis I had at the beginning of the experiment, with the darker and older traps (speciﬁcally the tire) attracting more larvae than the lighter trap in form of the gallon. Placing them in the same vicinity had the intended effect of displaying this relation.

Week 3:
Alas, the ﬁnal week of the mosquito trap experiment had arrived. I didn’t really expect much larvae turnover as the weather has been consistently in the 100
degree range with no rain. I walk over to inspect my traps, and ﬁnd one of them, specifically the black bucket trap, has no water in it, it completely evaporated.

It also looks like some birds had meddled with that speciﬁc trap seeing as there was bird feces on it. This led to another question I had, why did only that speciﬁc trap completely evaporate even though they all had the same amount of water and similar levels of shade? The tire trap not completely evaporating is logical as it is rounded and hence has additional shade, however, the gallon trap has no such qualities and often retains the most amount of water. Perhaps the position the bucket is placed provides a more optimal angle to sun exposure compared to the other traps. Nonetheless, it was a 0 larvae count recording for the bucket trap.

Now it was time to analyze the results of both the gallon and tire trap, which had actually caught some larvae. After exceeding expectations in the ﬁrst week, the gallon trap has inevitably come down to Earth with a total of 8 mosquito larvae for this week. Additionally, as projected, the tire trap seems to be most effective, with a total number of 72 larvae for the week.

Conclusion:
This experiment was deﬁnitely a fun journey to embark on; although it was not as controlled or conclusive as I would want it to be (with the destroyed traps and all), I still learned a substantial amount about the mosquito population in my community (as there were mainly 2 types of mosquito larvae: short siphoned and anopheles I believe) and what traps worked most effectively to trap larvae, in this case being the old tire trap (darker colored traps were also more effective). I believe the skills I've learned in this experiment will help me produce more conclusive results in future experiments and hope to reproduce this experiment in the near future in a more controlled environment to solidify further the claims made in this blog.

About the author: Abdullahi A, is a junior at Harmony School of Innovation in Sugar Land, TX. 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 2023.

Hugo, SEES Earth System Explorer 2023

2023-11-28T17:07:06Z

AOI Fieldwork
At first, collecting land cover data for 37 points within my area of interest felt like a daunting task. However, after only a few "expeditions" in the past week, I was able to complete all 37 observations.

To make my work easier and more accurate, I intentionally choose an AOI that would contain as many points as possible within a nearby park. This was to ensure that I could access as many points as possible. Despite my best efforts, many of the points still landed in residential properties. This activity actually reminded me how many neighborhoods there are in my town. To begin the fieldwork, I collected observations from the origin of my AOI. These points were probably the easiest, as they landed in the middle of a park. This park contained a lot of forest, trails, and two bodies of freshwater. Although I wasn't able to access the points that landed on the water or in the deep woods, I still had a great time hiking around this park. Additionally, within these first few hours, I quickly found the best way to navigate the Globe Observer app and other tools on my phone. I set up a list of each coordinate pair and the point on my map that correlated. I used Google Maps to type in each coordinate pair to ensure that I was as close to my target as possible. This system allowed me to move between points as efficiently as possible.

After getting all of the points near the origin, I started to work in the corners and edges of my AOI. These points were much more difficult and took up the majority of my time. Since there are a few back country roads in my area, I was able to get really close to a few points that looked inaccessible on Google Maps. During this stage, I felt like I was more on an adventure to find the closest location that I could make an observation.

On the last day of making observations, I decided to switch tactics and went out on bike rather than foot. Although I couldn't start moving to the next point as soon as I was done taking pictures, the bike increased my speed tremendously. There was one section where 5 points lines up on one road, so using the bike allowed me to get all 5 points in under an hour! Overall, because of the duration and scale of this activity, I was really forced to use the tools available to me in as efficient a way as possible. In this manner, I learned what it was like to do fieldwork without someone "holding my hand." It was a great activity that I look forward to using the data collected in a meaningful way during week 4!

Mosquito Trap
For my trap project, I decided to change the amount of substrate (sticks) in each trap.
For each week I plan on using 3 traps (each with 0-2 sticks) near a stream's edge and 3 traps (identical to the other group) in a shaded forest. I gathered my supplies and began setting up the traps. This trap project became very difficult when I realized that my buckets had small holes on the bottom. I attempted to recover from this by using several layers of duct tape, plastic lining, and even flex seal to cover the holes. After this repair, I set up my traps and waited a day to see if there would be a loss of water due to these holes.

The next day, the water levels in 5 of 6 buckets were relatively normal, but the water level in one bucket was noticeable lower. To combat this, I simply added water to fill the bucket back to a normal level. Hopefully, this will not affect anything too dramatically, but I will need to find a better solution for week 2 and 3. Data from week 2 and 3 can be used to see whether losing and adding water midway through the experiment has an impact. Besides this problem, I am excited to see how these traps will turn out!

About the author: Hugo B. is a rising a rising high school student from New Jersey. His 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/). In this guest blog post, he shares the NASA SEES Earth System Explorers virtual internship in 2023.

Nikhil, SEES Earth System Explorer 2023

2023-11-27T16:58:37Z

My fieldwork to map land via the Globe app led me to understand where I live better. Texas is the second-largest U.S. state by both area and population. There are only four deserts in North America, and Texas is home to the Chihuahuan Desert. Texas Land Conservancy [TLC] works to conserve natural areas, protect the physical and ecological integrity of their wildlife habitat and native plant communities, and provide essential endangered species habitat. Texas boasts nearly 300 native tree species and is also home to 85 species of mosquitoes. My areas of interest (AOIs) spanned Williamson and Travis counties.

Austin (Travis County)

Previously known as Waterloo, Austin was incorporated in 1840 and named after Stephen F. Austin, the Founding Father of Texan Independence
The climate is classified as "humid subtropical" with hot summers and mild winters.
Austin’s preserve system began in 1935 with the creation of Zilker Nature Preserve.
The most common tree species are Ashe juniper, cedar elm, live oak, sugarberry, and Texas persimmon.

Round Rock (Williamson County)

The name of Round Rock was officially given to the community in 1854.
The city straddles the Balcones Escarpment, a fault zone that may be related to the Ouachita Mountains, formed 300 million years ago during a continental collision
Round rock has thick, black, calcerous soils.
The most common plant species include Texas redbuds, Mexican bush sage, marigolds, and more.

Cedar Park (Williamson County)

Climate: Cedar Park experiences long, muggy summers and short, windy winters. It has a humid subtropical climate.
There is plenty of limestone underneath the ground in Cedar Park.
The landscape of this area allows for optimal growth of Texas mountain laurels, holly, and wildflowers.

Leander (Williamson County)

Leander is home to many trees, including live oak, pistachio, Texas ash, and pecan.
This area’s source of water is Lake Travis, on the Colorado River in Travis County.
There are campgrounds, trails, and waterfalls open for public use here.
Turtles, bats, wild turkeys, and bluebonnets are all components of Leander’s ecosystem.

About the author: Nikhil P. is a rising senior at Round Rock High School in Austin, TX. His 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/). In this guest blog post, he shares the NASA SEES Earth System Explorers virtual internship in 2023.

Dr. Mullica Jaroensutasinee

2023-02-10T20:39:12Z

Concerns regarding the impact of global warming on vector-borne diseases have intensified interest in the relationship between atmospheric factors and dengue fever incidence. Global climate change poses the threat of serious social upheaval, population displacement, economic hardships, and environmental degradation. Changes in temperature, rainfall and relative humidity have potential to enhance vector development, reproductive and biting rates, shorten pathogen incubation period and encourage adult longevity. In addition, changes in wind direction, velocity and frequency will have an impact on adult mosquito populations, affecting dispersal, survival and aspects of the general behavior of many species.

Rainfall is one of the important elements for the breeding and development of mosquitoes. water not only provides the medium for the aquatic stages of the mosquito’s life cycle but also increases the relative humidity and hence longevity of adult mosquitoes. Rain may prove beneficial for mosquito breeding if moderate, but it may destroy or wash out existing breeding sites and interrupt the development of mosquito eggs when excessive. Increased rain may increase larval habitat and vector population size by creating a new habitat or increase adult survival. In tropical areas in particular, extensive and continuous rainfall can delay the build-up of some mosquito species until late in the season and thus delay transmission.

Temperature is an important environmental parameter with respect to enhancing vector development, gonotrophic cycle length, fecundity, time from emergence to first blood meal, biting rates, shortening pathogen incubation period and encouraging adult longevity. In addition, temperature is also a crucial factor in the dynamics of extrinsic incubation period (EIP) of the dengue virus in Ae. aegypti, transmission. It can promote infective potential and produce more effective and more frequent transmission.

GLOBE mosquito data would help us gain a better understanding on the range of vector-borne diseases, and how GLOBE atmospheric data might have a significant impact on the transmission of vector-borne disease in your area.

Dr. Mullica Jaroensutasinee

2023-02-13T17:18:20Z

When you start writing your GLOBE report for IVSS, it might be difficult because you might not sure where to start. We tend to start writing the Methods section first because it is something you did it yourselves, and it should be relatively easy and straightforward to write. Second, you should write the Results section, do graphs, tables and texts (think of a best way to present your cool data to the whole world). Third, you should start writing the Introduction stating your hypotheses and predictions. The next step would be the Discussion section. It is funny to say but as scientists, we consider this discussion part (how to reach your conclusion) would be the heart of the paper. You have to state your interpretations and opinions, explain the implications of your findings, and make suggestions for future researches. Its main function is to answer the questions posed in the Introduction, explain how the results support the answers and, how the answers fit in with existing knowledge on the topic.

Here are 7 easy steps for writing the discussion section.

1. Explain how your results relate to expectations and to the literature, clearly stating why they are acceptable and how they are consistent or fit in with previously published knowledge on the topic.

2. Address all the results relating to the questions, regardless of whether or not the findings were statistically significant. It might be a good idea to list all your findings, then discuss them point by point.

3. Defend your answers by explaining both why your answer is satisfactory and why others are not. Only by giving both sides to the argument can you make your explanation convincing.

4. Discuss any unexpected findings. This would be a fun part because a new discovery usually comes from unexpected findings.

5. Identify potential limitations and weaknesses. Sometimes we have to understanding that our results might be better if we have better equipment or measuring at different time of day or different seasons. This would be a good way to show the readers that you know your limitation and weakness.

6. Provide recommendations for further research. This would benefit to your friends who might be thinking of doing research projects.

7. Explain how the results and conclusions of this study are important and how they influence our knowledge or understanding of the problem being examined. If your results have some community impacts, please say so. It is a highlight part because it means what you do are relevant to the GLOBE community.

Remember, please write it short and sweet, concise and precise as much as possible. If you follow these suggestions, we are very sure that you are writing an awesome discussion already.