Abstract
A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the time slider in Google Earth. We observed 77 deformed slopes, which were similar in shape and texture, to the inventoried landslides. We then sought to label the observed formations based on their spatial relationship with surrounding conditions. A three-phase methodology was developed. First, an inventory of landslide, no landslide, vulnerable slopes, and unlabeled features was analyzed based on spatial cluster patterns, and then the dimension was reduced using the t-distributed stochastic neighbor embedding (t-SNE). Second, the Apriori algorithm, based on association rule mining, was used to identify common relations in the inventory using landslide antecedent factors (derived from topographic and landcover maps) that are linked to areas of unlabeled features. Third, the findings were validated using Landsat TM (Thematic mapper) and ETM+(Enhanced thematic mapper) images acquired before and after the original inventory. Current research offers practical and economical solutions (reduced reliance on paid remote sensing sensors and field survey) to labeling and classification of missing or outdated spatial attributed information.
Highlights
Complete records of previous slope failures, their distribution, and slope condition, are among the best data for predicting the location of future failures
To run the association analysis, the antecedent data of the eight independent conditioning factors, each classified into five index values (Figure 7), and the consequent slope formation inventory data in four classes were stacked into one data frame
The selected conditioning factors had been previously shown to be effective in susceptibility analysis with landslide class alone [28]
Summary
Complete records of previous slope failures, their distribution, and slope condition, are among the best data for predicting the location of future failures. The inventory record needs, to the extent possible, to include complete and specific information on triggering factors, amount, type, area, volume, and date of incidents [1]. The distribution of shallow landslides, a typical land degradation, tends to be spatially clustered and to reflect the distribution and structure of the slope material [2,3,4]. Topographic factors, such as slope condition, vegetation cover, soil type, and other land covers, tend to have classes or values common to each specific landslide type.
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