Landslide inventory mapping from landsat-8 ndvi time series using adaptive landslide interval detection

Abstract

A landslide inventory map is one of the essential sources of geospatial information for land resource management. This study proposes an interval-based landslide detection strategy using satellite images' time series vegetation index. Landslide trends to be abruptly changed with the landscape can be clearly detected using the time series vegetation index. The proposed adaptive landslide interval detection (LID) method is a two stage algorithm. The first stage extracts local extremes and divides the time series to obtain discriminative intervals. Then, a predefined threshold is applied to determine whether a landslide occurs in each interval. The experiment compares the proposed scheme and the traditional time series forest (TSF) algorithm on a pre-labeled dataset. In the comparison of the results obtained from TSF and LID, the validation results show that the proposed LID has better discriminative ability in real scenarios, and the landslide detection rate for large areas reaches 85%. But the TSF can only provide good results on well-defined and unmixing datasets. In addition, LID does not require a large number of training datasets and can be applied to irregular time series with various lengths. In summary, this study demonstrated that the LID global pattern concerned splitting strategy is more effective than TSF random interval segmentation.