Investigating the Landslide Susceptibility Assessment Methods for Multi-Scale Slope Units Based on SDGSAT-1 and Graph Neural Networks.

Landslide susceptibility assessment is crucial for preventing landslide risks. However, existing methods only consider local environmental features related to landslides, neglecting remote yet interconnected geographical features, leading to unreliable landslide susceptibility maps. This study fully considers the complex terrain and landform features of mountainous areas where landslides occur. From the perspectives of mapping units and susceptibility assessment models, it introduces geographical environmental correlations to achieve a comprehensive association between landslides and affected environments, thereby improving the accuracy of landslide susceptibility assessments. At the same time, since the world's first scientific satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development, the Sustainable Development Goals Scientific Satellite 1 (SDGSAT-1), was launched in 2021, its potential in monitoring and assessing landslide disasters remains to be developed. Therefore, this study innovatively applies SDGSAT-1 data in the field of landslide research and conducts landslide susceptibility assessment in Jiulong County, Ganzi, based on the optimal scale slope units and Graph Neural Networks (GNN).

We propose the following method: First, establish appropriately sized slope units using R.Slopeunits to simulate complex mountainous terrain. Second, extract various landslide influencing factors using SDGSAT-1 satellite imagery data. Then, select the most representative graph nodes by constraining environmental similarity and influencing factor feature similarity, constructing a graph structure. Finally, perform landslide susceptibility assessment in the study area using the GraphSage model, which includes environmental information aggregation.

This study's distinctive feature lies in fully considering the complex terrain and landform characteristics of mountainous areas where landslides occur. From the perspectives of mapping units and evaluation models, it introduces geographical environmental correlations to achieve a comprehensive association between landslides and affected environments. Furthermore, to validate the effectiveness of the proposed method, we selected raster units and the classic Artificial Neural Network (ANN) model as control experiments. Simultaneously, we conducted comparative experiments using Landsat and SDGSAT-1 satellite imagery, analyzing differences from two aspects: landslide influencing factors and landslide susceptibility evaluation results.

The results indicate that: (1) Compared to the commonly used Landsat series satellite data in previous studies, SDGSAT-1 satellite imagery offers higher spatial resolution, capturing more spectral information with richer hue and detail. Additionally, it can generate more angles of landslide influencing factors compared to Landsat satellite data. (2) Employing global heterogeneity evaluation metrics allows for reasonable determination of slope unit scales, thereby maximizing internal consistency and external heterogeneity control within slope units. (3) By utilizing the Graph Neural Network (GNN) model that incorporates environmental information aggregation for landslide susceptibility assessment in the study area, it can, to some extent, overcome spatial limitations and integrate complex mountainous environmental information, facilitating the induction of reliable landslide characteristics.