Loess Landslide Inventory Map Based on GF-1 Satellite Imagery

Abstract

Rainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented method (OOA) has been developed to recognize loess landslides by combining spectral, textural, and morphometric information with auxiliary topographic parameters based on high-resolution multispectral satellite data (GF-1, 2 m) and a high-precision DEM (5 m). The quality percentage (QP) values were all greater than 0.80, and the kappa indices were all higher than 0.85, indicating good landslide detection with the proposed approach. We quantitatively analyze the spectral, textural, morphometric, and topographic properties of loess landslides. The normalized difference vegetation index (NDVI) is useful for discriminating landslides from vegetation cover and water areas. Morphometric parameters, such as elongation and roundness, can potentially improve the recognition capacity and facilitate the identification of roads. The combination of spectral properties in near-infrared regions, the textural variance from a grey level co-occurrence matrix (GLCM), and topographic elevation data can be used to effectively discriminate terraces and buildings. Furthermore, loess flows are separated from landslides based on topographic position data. This approach shows great potential for quickly producing accurate results for loess landslides that are induced by extreme rainfall events in the hilly and gully regions of the Loess Plateau, which will help decision makers improve landslide risk assessment, reduce the risk from landslide hazards and facilitate the application of more reliable disaster management strategies.

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

“…Sediment-storage landscapes such as loess terrain and sensitive-clay basins are particularly susceptible. Many large landslides have been triggered in the Loess Plateau of China(Sun et al, 2017;Zhuang et al,…”

Remote Sensing of Landslides—A Review

“…Optical sensors involved in this special issue include long time-series of Landsat TM/ETM, SPOT 1-5, ASTER, IRS-1C LISS III, and RapidEye between 1986 and 2016 [10], ZY-3 satellite high spatial resolution satellite images [11], and GF-1 [12]. In addition, an image correlation algorithm was applied to SPOT-5 images to investigate the landslide cinematics [13].…”

“…An automated detection algorithm was developed to recognize loess landslides by combining spectral, textural, and morphometric information with auxiliary topographic parameters based on high-resolution multispectral satellite data and high-precision DEM [12]. In addition, spatial autocorrelation changes induced by event landslides were measured in a set of multi-temporal Sentinel-1 intensity images [7].…”

Multi-Temporal X-Band Radar Interferometry Using Corner Reflectors: Application and Validation at the Corvara Landslide (Dolomites, Italy)

“…The launches of new generations of optical (e.g., Geoeye-1, WorldView-2, Pleiades 1A and 1B, SPOT5, 6 and 7, Formosat-2 and Kompsat-2, Sentinel-2) and radar (e.g., TerraSAR-X, COSMO-SkyMed, Sentinel-1) satellites with short repeat-pass cycles and high spatial resolutions resulted in better capabilities to acquire data over wide areas shortly after major landslide triggering events [25][26][27][28] and to monitor them at regular intervals [29,30]. In particular, radar interferometry is today frequently applied to landslide detection and monitoring of ground-deformations and landslide processes [31][32][33][34][35][36][37][38].…”