Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data

Abstract

The effective management of landfills requires advancements in techniques for rapid data collection and analysis of gas emissions. This work aims to refine methane (CH₄) emission data acquired from landfills by applying a robust geostatistical method to drone-collected measurements. Specifically, we use UAV-mounted laser spectrophotometer technology (TDLAS-UAV) to gather rapid, high-resolution data, which can sometimes be noisy due to atmospheric variations and sensor drift. For data handling, the key innovation is the application of the local indicator of spatial association (LISA), a technique that typically provides p-values to assess the statistical significance of observed spatial clusters. This approach was applied both on an areal basis and on a linear basis, following the order of data acquisition, and it produced comparable results. Very low p-values are considered indicative of non-random clustering, suggesting the influence of an underlying spatial control factor. These results were subsequently validated through independent flux chamber surveys. This validation confirms the reliability and objectivity of our geostatistical method in improving drone-based methane emission assessments. The research highlights the need to optimize drone flight paths to ensure a uniform spatial distribution of data and reduce edge effects. It notes that many CH₄ flux measurements often yield non-detectable results, suggesting a review of detection limits. Future work should refine UAV flight patterns and data processing with semi-controlled experiments—using known methane sources—to determine optimal acquisition parameters, such as flight height, sampling frequency, grid resolution, and wind influence.