WK 27 - Mapping Ecosystem Changes using NEON Airborne Remote Sensing Data in Google Earth Engine

Thursday, August 18, 2022

11:45 AM – 1:15 PM EDT

Location: 516B

Organizer(s)

BH

Bridget Hass

Environmental Remote Sensing Scientist
National Ecological Observatory Network
Boulder, CO, United States

Co-organizer(s)

JM

John Musinsky

Environmental Remote Sensing Scientist
National Ecological Observatory Network, United States
KM

Kate Murphy

Ecological Remote Sensing Scientist
National Ecological Observatory Network, United States

Session Description: The National Ecological Observatory Network (NEON) airborne datasets are optimally suited to analyze landscape-scale ecosystem change. Each year, NEON's Airborne Observation Platform (AOP) collects high-resolution hyperspectral imagery, discrete and waveform lidar, and photography at up to 81 terrestrial and aquatic sites across the United States. NEON now offers repeat data spanning 4-6 years at most sites. One of the major barriers to using AOP data has been its sheer volume, which requires intensive computing resources. In this workshop we present an example of ecological change analyses using AOP data in Google Earth Engine (GEE), a free, cloud-based platform that facilitates spatial analysis on large remote sensing datasets. 

This interactive workshop gives participants hands-on coding experience in GEE to analyze remote sensing data with reproducible science workflows. Using a combination of live-coding and demonstrations with time-series of AOP and satellite data, we illustrate an example of change detection of ecological disturbance and regeneration at a NEON site in the Western U.S. Participants will leave with knowledge of how to access and perform analyses on NEON data in GEE, and will generate a reusable GEE JavaScript workflow that can be expanded upon to further their own research interests.  

 

Prerequisites: Participants should have a basic knowledge of remote sensing. Some experience coding in a scientific programming language (e.g., Python, R, JavaScript) is recommended, but not required. Participants will need a google account and to sign up for Google Earth Engine to follow along with the live-coding exercises (https://earthengine.google.com/new_signup).