Project discussion notes
Virtual meeting #3
Full team did not meet/attend.
Team theme, tentative area of interest, or question:
Our team's interested in GEDI data, assessing canopy heights and how they are changing due to disturbance events - how effective GEDI is at identifying disturbance.
Day 1: March 12, 2024 - CU Boulder
Brainstormed scientific questions to tackle: - How effective is GEDI data and GEDI + imagery-based ML models? - Can we visually detect forest disturbances with GLAD ARD data? - Can we detect insect outbreaks using GEDI or satellite imagery? - Can we predict the spread of insect outbreaks in forests, and predict the amount of damage? - Can we use ML to assess fire risk for a given area, given fuel availability, and other environmental covariate factors? - Is this type of detection easier with Landsat or GEDI data?
Selected scientific question:
Can we visually detect forest disturbances with GLAD ARD data?
Day 2: March 13, 2024 - CU Boulder
- Started working with GLAD (using Landsat dataset, harmonized from frame-to-frame), downloaded data and started creating GIFs for visualizing using Landsat band combinations such as RGB, and NIR (Len)
- Exploring GEDI csv data through example (Mihir and Mike)
- Exploring TreeMap data, disturbance stack, GEDI height products from GEE (Sarah)
- Exploring Landfire disturbance data (Mike): trying to export a GIF of Landfire disturbance data.