Using YouTube to Make Remote Sensing Data Accessible to Ecologists
Sep 5, 2025 • science-communication
How I used YouTube to bridge the gap between traditional ecology and landscape scale ecological research at NEON.
The Challenge
At the National Ecological Observatory Network (NEON) I built their NEON Data Skills program. Here, I faced a fundamental problem: ecologists weren’t ready for “big” data.
NEON, funded by the National Science Foundation is an effort to build and now operate over 50 research sites across the United States. The data collected by NEON are massive and complex. But they also empower ecologists to use long-term, standardized datasets to track changes in our environment over many decades. But for most ecologists—especially 10-15 years ago—working with large remote sensing datasets was completely foreign territory.
I knew this firsthand. During my PhD, I had to defend whether using LiDAR data to quantify streamside forest health even counted as science to the ecology department head at PSU. He studied mycorrhizal fungi at the plant scale. He and I operated in two completely different worlds.
A Solution: YouTube as a Bridge
I realized ecologists had unprecedented access to an incredible time series of data, but lacked the foundational understanding to use it. Before anyone could learn to process LiDAR or hyperspectral data, they needed to understand: How do these sensors actually work? What are their limitations? Why should I trust this data?
So I experimented with YouTube as a medium to demystify these highly technical topics. Working with Colin Williams, a talented graphics and animation artist on my team, I developed scripts for each video. For topics like LiDAR—the focus of my PhD—I could create accurate content directly. But for other topics like flux tower data, we brought in external scientific experts to review.
This review process was critical. Translating complex science into accessible content requires subject matter experts to ensure accuracy. It’s a model that makes science communication both rigorous and reachable.
And, it’s a model that we use at pyOpenSci to review technical Python content for accessibility and accuracy.
The Impact
Our Intro to LiDAR video quickly gained hundreds of thousands of views in just a few days (now close to or maybe even over 1 million views). These videos were one of the first of its kind associated with scientific sensor data. The format worked: make the scary, technical space accessible first, then teach people how to use the data for research.
Ultimately, I stepped away from video production—the resource investment was high relative to workshop-ready educational content. But these videos proved that creative science communication could reach audiences traditional academic approaches never would.
And even when I left NEON, I continued to use these videos in my courses when I taught remote sensing data science. The students always loved them.