# Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides

## TL;DR

This study uses satellite data to better understand how tree roots affect landslide risk in the Loess Plateau, improving predictions for disaster prevention.

## Contribution

A novel regional root cohesion inversion model using remote sensing data to capture spatial heterogeneity in root reinforcement.

## Key findings

- Incorporating root cohesion spatial heterogeneity significantly improves landslide susceptibility predictions.
- Farmland showed the highest landslide risk, followed by artificial and secondary forests.
- Post-rainfall susceptibility increased, highlighting the dynamic nature of landslide risk.

## Abstract

This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies spatial heterogeneity using tree height (derived from time series Landsat imagery) and above-ground biomass (from 30 m resolution satellite products). This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. High-resolution pre- and post-rainfall GaoFen satellite imagery validated landslide inventories, revealing dynamic susceptibility patterns: farmland exhibited the highest risk, followed by artificial and secondary forests, with susceptibility escalating post-rainfall. This study underscores the critical role of remote sensing-driven root cohesion mapping in landslide risk assessment, offering actionable insights for land use planning and disaster mitigation on the Loess Plateau.