Reducing anthropogenic methane (CH₄) emissions is recognised as one of the most effective strategies for mitigating global warming in the near term. Among anthropogenic sources, landfills represent a major and persistent contributor to CH₄ emissions. Current monitoring techniques—such as flux chambers in Europe and Surface Emission Monitoring (SEM) in the United States—suffer from critical limitations, including low spatial resolution, limited coverage, and inaccessibility to key emission zones such as steep slopes and infrastructure components. To address these limitations, this study presents an operational workflow based on high-resolution unmanned aerial vehicle (UAV) imaging and Object-Based Image Analysis (OBIA). The method integrates RGB and multispectral data acquired via UAVs within the QGIS environment to enable semi-automatic mapping of biogas extraction wells and the identification of areas with a higher probability of methane emissions. This approach enhances spatial and temporal coverage, enabling improved detection of anomalies such as vegetation stress, surface fissures, and depressions. By overcoming the limitations of traditional ground-based monitoring, the proposed UAV-OBIA framework provides a robust, non-invasive, and scalable tool for modern landfill management. The integration of remote sensing and image analysis supports more targeted methane mitigation strategies, contributes to improved operational safety, and aligns with broader climate objectives related to greenhouse gas emission reduction.