Landslides near critical infrastructure, such as power transmission lines, represent significant safety and economic risks, especially in regions prone to geohazards. Early detection and monitoring are essential to mitigate potential damage. Interferometric Synthetic Aperture Radar (InSAR) technology has become a powerful tool for detecting slow-moving landslides and monitoring millimetre-scale ground displacements over time. Among the various satellite data sources, Sentinel-1 provides consistent and high-resolution data, advancing research in landslide kinematics and instability prediction. However, accurate delineation of landslide-affected areas remains particularly challenging in densely vegetated regions, where signal decorrelation limits traditional methods. To address these limitations, this study introduces a modified Distributed Scatterer InSAR (DSI) method designed to assess landslide velocity more effectively. The proposed approach incorporates a regularization technique into the covariance matrix estimation process, reducing phase estimation bias and improving the signal-to-noise ratio of displacement time series. The modified DSI method was applied to the Faer Town landslide in Guizhou Province, Southwest China. Results from synthetic and real-data experiments demonstrate significant improvements in the accuracy and reliability of landslide velocity detection, with a higher density of reliable measurement points compared to traditional approaches. These findings highlight the method's potential for enhancing landslide monitoring and risk mitigation in challenging environments.