Staff Scientist National Ecological Observatory Network, United States
Session Description: The National Ecological Observatory Network (NEON) provides the research community with a broad suite of ecological data across temporal and spatial scales. Each year, NEON's Airborne Observation Platform (AOP) collects high-resolution hyperspectral imagery, discrete and waveform lidar, and RGB photography at up to 81 terrestrial and aquatic sites across the United States. NEON also collects and provides co-located observational and instrumented data at each site. Following the 2022 sampling season, AOP now offers repeat data spanning 4-7 years of data at most NEON sites. One of the major challenges with using AOP data has been its volume; AOP data has traditionally required computationally intensive and expensive computing resources. Google Earth Engine (GEE) is a free and powerful cloud-computing platform for conducting remote sensing geospatial analysis, with easily accessible satellite data and built-in machine-learning routines optimized for geospatial datasets.
In this short course, participants will work through reproducible live-coding exercises in Earth Engine to explore research applications across spatial scales. The course covers an introduction to finding and visualizing NEON remote sensing data in Earth Engine. Participants will then follow along with two examples of ecological research applications: 1) scaling observational data to NEON remote sensing data to model ecosystem properties, using an Earth Engine machine learning routine, and 2) scaling NEON hyperspectral data collected coincidentally with LandSat data. Time permitting, participants can start exploring a research question of their own in Earth Engine, using NEON, satellite, or other datasets.