Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

Abstract

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

Characterization of Susceptible Landslide Zones by an Accumulated Index

“…An overview of the process to characterize, through an accumulated index, the areas susceptible to landslides is shown in Figure 4. Geological map of the study area [53]. As can be seen, the proposal is made up of five stages:…”

“…A landslide inventory map ( Figure 5B) was used to assess the resulting susceptibility landslide model. This inventory was automatically produced through unsupervised detection methods applied to ASTER-derived products [53].…”

“…(A) Landslide polygons digitized in Google earth validated. (B) landslide inventory map resulting from unsupervised detection methods[53].…”

Application of an Incomplete Landslide Inventory and One Class Classifier to Earthquake-Induced Landslide Susceptibility Mapping

“…A vegetation index for a quantitative analysis of vegetation coverage can be constructed through the spectral differences between visible and NIR regions of vegetation. NDVI is sensitive to the change of surface vegetation and has been widely used in landslide extraction [36], [37]. Therefore, in this study, the change detection based on NDVI was used to product landslide inventory.…”

Landslide Scars Detection using Remote Sensing and Pattern Recognition Techniques: Comparison Among Artificial Neural Networks, Gaussian Maximum Likelihood, Random Forest, and Support Vector Machine Classifiers

“…These phenomena cause economic damages and casualties when occurring in occupied areas (HAQUE et al, 2019). For monitoring, landslide inventory maps are used, identifying mass movement scars, providing information about past events, such as location, types and patterns, and assisting to build landslide susceptibility models (RAMOS-BERNAL et al, 2018). Thus, landslide inventory maps are crucial to support urban planning and disaster risk reduction (LUPIANO et al, 2019).…”