# Enhanced Landslide Monitoring in Complex Mountain Terrain Using Distributed Scatterer InSAR and Phase Optimization: A Case Study in Zhenxiong, China

## TL;DR

A new InSAR method called SETP-EMI improves landslide monitoring in complex mountainous areas with dense vegetation, offering better accuracy and detection rates than traditional techniques.

## Contribution

The SETP-EMI method is first applied in plateau mountainous regions with dense vegetation, overcoming coherence loss and improving deformation monitoring.

## Key findings

- SETP-EMI achieved a 94.1% landslide detection rate in Zhenxiong, China.
- The method generated 2.49 million measurement points, outperforming PS-InSAR and SBAS-InSAR by factors of 22.5 and 3.2.
- Validation showed a standard deviation of 5.21 mm/year, confirming high accuracy and robustness.

## Abstract

What are the main findings?
This study applies the SETP-EMI method for the first time to plateau mountainous regions with dense vegetation, demonstrating its ability to overcome severe coherence loss.The integrated DS-InSAR framework significantly improves distributed scatterer density, phase stability, and deformation continuity compared with PS-InSAR and SBAS-InSAR.

This study applies the SETP-EMI method for the first time to plateau mountainous regions with dense vegetation, demonstrating its ability to overcome severe coherence loss.

The integrated DS-InSAR framework significantly improves distributed scatterer density, phase stability, and deformation continuity compared with PS-InSAR and SBAS-InSAR.

What are the implications of the main findings?
The demonstrated performance of SETP-EMI in challenging high-altitude, vegetation-covered terrain indicates its strong potential for large-scale geohazard monitoring in complex mountainous environments.The method provides an effective technical route for enhancing early-warning capability of landslides where conventional InSAR approaches typically fail due to low coherence.

The demonstrated performance of SETP-EMI in challenging high-altitude, vegetation-covered terrain indicates its strong potential for large-scale geohazard monitoring in complex mountainous environments.

The method provides an effective technical route for enhancing early-warning capability of landslides where conventional InSAR approaches typically fail due to low coherence.

Landslide deformation monitoring plays a critical role in geohazard prevention and risk mitigation in mountainous regions, where timely and reliable deformation information is essential for early warning and disaster management. Monitoring landslide deformation in mountainous areas remains a persistent challenge, largely due to rugged topography, dense vegetation cover, and low interferometric coherence—factors that substantially limit the effectiveness of conventional InSAR methods. To address these issues, this study aims to develop a robust time-series InSAR framework for enhancing deformation detection and measurement density under low-coherence conditions in complex mountainous terrain, and accordingly introduces the Sequential Estimation and Total Power-Enhanced Expectation–Maximization Inversion (SETP-EMI) approach, which integrates dual-polarization Sentinel-1 SAR time series within a recursive estimation framework, augmented by polarimetric coherence optimization. This methodology allows for dynamic assimilation of SAR data, improves phase quality under low-coherence conditions, and enhances the extraction of distributed scatterers (DS). When applied to Zhenxiong County, Yunnan Province—a region prone to geohazards with complex terrain—the SETP-EMI method achieved a landslide detection rate of 94.1%. It also generated approximately 2.49 million measurement points, surpassing PS-InSAR and SBAS-InSAR results by factors of 22.5 and 3.2, respectively. Validation against ground-based leveling data confirmed the method’s high accuracy and robustness, yielding a standard deviation of 5.21 mm/year. This study demonstrates that the SETP-EMI method, integrated within a DS-InSAR framework, effectively overcomes coherence loss in densely vegetated plateau regions, improving landslide monitoring and early-warning capabilities in complex mountainous terrain.