Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data
The effective management of landfills requires advancements in techniques for rapid data collection and analysis of gas emissions. This work aims to refine methane (CH<sub>4</sub>) emission data acquired from landfills by applying a robust geostatistical method to drone-collected measure...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1890 |
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| author | Maurizio Guerra Maurizio De Molfetta Antonio Diligenti Marco Falconi Vincenzo Fiano Chiara Fiori Donatello Fosco Lucina Luchetti Bruno Notarnicola Pietro Alexander Renzulli Enrico Sacchi Nino Tarantino Marcello Tognacci Antonella Vecchio |
| author_facet | Maurizio Guerra Maurizio De Molfetta Antonio Diligenti Marco Falconi Vincenzo Fiano Chiara Fiori Donatello Fosco Lucina Luchetti Bruno Notarnicola Pietro Alexander Renzulli Enrico Sacchi Nino Tarantino Marcello Tognacci Antonella Vecchio |
| author_sort | Maurizio Guerra |
| collection | DOAJ |
| description | The effective management of landfills requires advancements in techniques for rapid data collection and analysis of gas emissions. This work aims to refine methane (CH<sub>4</sub>) 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 <i>p</i>-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 <i>p</i>-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 CH<sub>4</sub> 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3472e35cb2d44ab6a4e626e11f8f36f22025-08-20T02:33:08ZengMDPI AGRemote Sensing2072-42922025-05-011711189010.3390/rs17111890Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV DataMaurizio Guerra0Maurizio De Molfetta1Antonio Diligenti2Marco Falconi3Vincenzo Fiano4Chiara Fiori5Donatello Fosco6Lucina Luchetti7Bruno Notarnicola8Pietro Alexander Renzulli9Enrico Sacchi10Nino Tarantino11Marcello Tognacci12Antonella Vecchio13Department for the Geological Survey of Italy, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale), Italian Institute for Environmental Protection and Research, 00144 Rome, ItalyDipartimento Jonico, Università degli Studi di Bari Aldo Moro, 70121 Taranto, ItalyARPA Abruzzo, Distretto di Chieti, 65100 Chieti, ItalyDepartment for the Geological Survey of Italy, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale), Italian Institute for Environmental Protection and Research, 00144 Rome, ItalyDepartment for the Geological Survey of Italy, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale), Italian Institute for Environmental Protection and Research, 00144 Rome, ItalyDepartment for the Geological Survey of Italy, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale), Italian Institute for Environmental Protection and Research, 00144 Rome, ItalyDipartimento Jonico, Università degli Studi di Bari Aldo Moro, 70121 Taranto, ItalyRegione Abruzzo—PNRR Department, 65100 Pescara, ItalyDipartimento Jonico, Università degli Studi di Bari Aldo Moro, 70121 Taranto, ItalyDipartimento Jonico, Università degli Studi di Bari Aldo Moro, 70121 Taranto, ItalyL.A.V. Srl., 47924 Rimini, ItalyCommissario Unico per la Bonifica delle Discariche e dei Siti Contaminati, 00187 Rome, ItalyWhitelab Srl-LAV, 47924 Rimini, ItalyDepartment for the Geological Survey of Italy, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale), Italian Institute for Environmental Protection and Research, 00144 Rome, ItalyThe effective management of landfills requires advancements in techniques for rapid data collection and analysis of gas emissions. This work aims to refine methane (CH<sub>4</sub>) 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 <i>p</i>-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 <i>p</i>-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 CH<sub>4</sub> 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.https://www.mdpi.com/2072-4292/17/11/1890methane detectionUAV acquisitiongeostatistical analysisflux chamberlandfill |
| spellingShingle | Maurizio Guerra Maurizio De Molfetta Antonio Diligenti Marco Falconi Vincenzo Fiano Chiara Fiori Donatello Fosco Lucina Luchetti Bruno Notarnicola Pietro Alexander Renzulli Enrico Sacchi Nino Tarantino Marcello Tognacci Antonella Vecchio Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data Remote Sensing methane detection UAV acquisition geostatistical analysis flux chamber landfill |
| title | Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data |
| title_full | Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data |
| title_fullStr | Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data |
| title_full_unstemmed | Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data |
| title_short | Detection of Methane Emissive “Hot Spots” in Landfills: An Advanced Statistical Method for Processing UAV Data |
| title_sort | detection of methane emissive hot spots in landfills an advanced statistical method for processing uav data |
| topic | methane detection UAV acquisition geostatistical analysis flux chamber landfill |
| url | https://www.mdpi.com/2072-4292/17/11/1890 |
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