Thursday, 1 February 2024: 2:15 PM
309 (The Baltimore Convention Center)
Observing Green House Gases from space can be performed in many ways. With a heightened need for legal enforcement of large emitting sources of methane and monitoring other emission sources, developing tools to combine multiple datasets to increase spatial and temporal resolution are needed to maximize space assets. NASA, DOI, and ESA satellites—EMIT, Landsat 8 and 9, Sentinel 2 A & B, respectfully, all offer freely available high spatial resolution data that can detect Methane emissions on land and sea. By combining available data in the 30m-60m spatial resolution range, the ability to see detections emissions more frequently becomes possible. In this R&D effort, we developed tools to ingest a list or emission sources or enter a special area of interest, retrieve available datasets, and rapidly enhance the data to highlight methane plumes. These tools reduce noise, give users control of different visualization techniques and provide output options to combine with other datasets over sites. In collecting plume shapes, sizes, and spectral signatures, machine learning training datasets can be developed in a movement toward more automated plume extraction in regions of oil and gas production, agriculture, landfills, and other methane source regions. These tools can be used with commercial sensors with user accounts and API tools to retrieve imagery. With clouds obscuring areas such as coastal drilling, being able to access, analyze and visualize multisource/modal data to support carbon budget activities can aid in efficiency in controlling methane emissions.

