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

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Publicado en:Remote Sensing vol. 17, no. 11 (2025), p. 1890
Autor principal: Guerra, Maurizio
Otros Autores: De Molfetta Maurizio, Diligenti, Antonio, Falconi, Marco, Fiano Vincenzo, Fiori Chiara, Donatello, Fosco, Luchetti Lucina, Notarnicola Bruno, Renzulli, Pietro Alexander, Sacchi Enrico, Tarantino Nino, Tognacci Marcello, Vecchio Antonella
Publicado:
MDPI AG
Materias:
Statistics
Data acquisition
Data processing
Methane
Flight characteristics
Measurement techniques
Spatial distribution
Flight
Landfill gas
Statistical methods
Landfill
Data collection
Edge effect
Statistical analysis
Geostatistics
Landfills
Waste disposal sites
Detection limits
Spatial data
Gases
Spectrum analysis
Unmanned aerial vehicles
Lasers
Clustering
Sensors
Optimization
Natural gas distribution
Emissions
Drones
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2072-4292
DOI:10.3390/rs17111890
Fuente:Advanced Technologies & Aerospace Database