Intro
Recycle2Trade conducted a comparative analysis between a UAV-based methane emissions survey and Sentinel-2 Earth Observation data at a Landfill Site in North England. Sentinel-2 multispectral imagery was accessed and processed via the EOPro application on the Earth Observation DataHub (EODH) platform, from which NDVI and NDWI index datasets were extracted and downloaded as GeoJSON metadata for subsequent analysis. Orange triangular markers on the spatial overlay represent methane emission points detected during the drone survey, while the polygon clusters, derived from forest anomaly detection and DBSCAN analysis applied to the EO-derived indices, represent spatially validated emission zones identified through anomaly scoring.
The Challenge
Landfill sites are among the largest anthropogenic sources of methane, a greenhouse gas more than 80 times more potent than CO₂ over a 20-year period and responsible for approximately 11% of global greenhouse gas emissions. Despite the scale of the problem, monitoring remains largely manual, relying on infrequent walkovers with handheld devices that leave significant temporal and spatial gaps in coverage. Drone surveys provide a solution to gapfilling methane data coverage, where commercial tasking of methane Earth Observation missions is inaccessible due to cost barriers for SMEs. Proxies such as soil indices, waste water, and the presence of lichens can be used by correlation to detect the presence of methane gas emissions. As such, investigating the integration of drone survey methane datasets with EO-derived soil indices presented an opportunity to provide a scalable solution to methane gas monitoring at landfill sites. This project set out to answer a practical question: can open-source satellite data from the Earth Observation DataHub (EODH), combined with machine learning-based anomaly detection, complement the results of a targeted drone methane survey at a regulated UK landfill site?
Solution
Our satellite analysis reveals anomalies distributed across multiple zones with no corresponding drone detections. This is a key finding: rather than detecting surface point emissions, satellite indices detect the downstream surface expression of subsurface gas migration, manifesting as vegetation stress (reduced NDVI) moisture anomalies (elevated NDWI) and Land Surface Temperature at locations distant from primary emission sources.
