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Timely and effective post-disaster assessment is of significance for the design of rescue plan, taking disaster mitigation measures and disaster analysis. Field investigation and remote sensing methods are the common ways to perform post-disaster assessment, which are usually limited by dense cloud coverage, potential risk, and tough transportation etc. in the mountainous area. In this paper, we employ the 2017 catastrophic Xinmo landslide (Sichuan, China) to demonstrate the feasibility of using spaceborne synthetic aperture radar (SAR) data to perform timely and effective post-disaster assessment. With C-band Sentinel-1 data, we propose to combine interferometric coherence to recognize the stable area, which helps us successfully identify landslide source area and boundaries in a space-based remote sensing way. Complementarily, X-band TanDEM-X SAR data allow us to generate a precise pre-failure high-resolution digital elevation model (DEM), which provides us the ability to accurately estimate the depletion volume and accumulation volume of Xinmo landslide. The results prove that spaceborne SAR can provide a quick, valuable, and unique assistance for post-disaster assessment of landslides from a space remote sensing way. At some conditions (bad weather, clouds, etc.), it can provide reliable alternative. [ABSTRACT FROM AUTHOR]

This work describes a new procedure aimed to semi-automatically identify clusters of active persistent scatterers and preliminarily associate them with different potential types of deformational processes over wide areas. This procedure consists of three main modules: (i) ADAfinder, aimed at the detection of Active Deformation Areas (ADA) using Persistent Scatterer Interferometry (PSI) data; (ii) LOS2HV, focused on the decomposition of Line Of Sight (LOS) displacements from ascending and descending PSI datasets into vertical and east-west components; iii) ADAclassifier, that semi-automatically categorizes each ADA into potential deformational processes using the outputs derived from (i) and (ii), as well as ancillary external information. The proposed procedure enables infrastructures management authorities to identify, classify, monitor and categorize the most critical deformations measured by PSI techniques in order to provide the capacity for implementing prevention and mitigation actions over wide areas against geological threats. Zeri, Campiglia Marittima–Suvereto and Abbadia San Salvatore (Tuscany, central Italy) are used as case studies for illustrating the developed methodology. Three PSI datasets derived from the Sentinel-1 constellation have been used, jointly with the geological map of Italy (scale 1:50,000), the updated Italian landslide and land subsidence maps (scale 1:25,000), a 25 m grid Digital Elevation Model, and a cadastral vector map (scale 1:5000). The application to these cases of the proposed workflow demonstrates its capability to quickly process wide areas in very short times and a high compatibility with Geographical Information System (GIS) environments for data visualization and representation. The derived products are of key interest for infrastructures and land management as well as decision-making at a regional scale. [ABSTRACT FROM AUTHOR]

The mapping and classification of active landslides in high mountainous regions provide crucial information about the location and types of geohazards. Additionally, this process plays a vital role in ensuring the safety of the geological environments in mountainous towns. In this study, we presented a refined InSAR approach for mapping and classifying active landslide hazards in Deqin County, Tibetan Plateau, China. The study area is characterized by a high altitude and extremely rugged terrain. Consequently, conventional InSAR methods are limited in precisely estimating landslide deformation owing to severe atmospheric delays. To this end, we first propose a block-based linear model to correct tropospheric artifacts. This model considers the spatial variability of the atmosphere and provides an opportunity to accurately estimate heterogeneous atmospheric delays over high mountainous areas without any external data. Compared with the traditional global-window linear model and the GACOS approach, the new method demonstrated outstanding performance in reducing atmospheric artifacts. Second, based on the knowledge mapping of landslide types, we proposed a semi-automatic procedure to map and classify landslides using InSAR-derived displacements and auxiliary data (i.e., C-index and high-resolution optical images). Our results obtained from ascending and descending Sentinel-1 images revealed, for the first time, that there were 317 active landslides in Deqin County between May 2017 and June 2021. Among these, 10.7% were associated with slide activity, 7.9% with fall deformation, and the majority (81.4%) with flow movement. These results were cross-verified and evaluated using an a priori inventory map obtained from the visual interpretation of optical images and geological field surveys. This study demonstrates that InSAR can accurately map and classify active landslides over difficult mountainous terrains, provided the associated phase errors are effectively restrained. • A refined InSAR approach is developed to map active landslides in high mountain terrains. • A new strategy is proposed to mitigate the atmospheric artifacts in high mountain region. • A semi-automatic procedure is developed to identify and classify different types of landslides. • Three types of landslides are illustrated in terms of deformation spatiotemporal patterns. [ABSTRACT FROM AUTHOR]

Recently, a large number of synthetic aperture radar (SAR) images has been introduced into landslide investigations with the growing launch of new SAR satellites, such as ALOS/PALSAR-2 and Sentinel-1. Therefore, it is appropriate to develop new approaches to retrieve three-dimensional (3D) displacements and long-term (> 10 years) displacement time series to investigate the spatio-temporal evolution and creep behavior of landslides. In this study, a new approach for the estimation of 3D and long-term displacement time series of landslides, based on the fusion of C- and L-band SAR observations, is presented. This method is applied to map 3D and long-term displacements (nearly 12 years) of the landslides in Gongjue County, Tibet in China; four sets of SAR images from different platforms (i.e., L-band ascending ALOS/PALSAR-1, C-band descending ENVISAT, and C-band ascending and descending Sentinel-1 SAR datasets) covering the period of January 2007 to November 2018 were collected and exploited. First, the assumption that the landslide moves parallel to its ground surface is used to produce 3D displacement rates and time series by fusing ascending and descending Sentinel-1 SAR images, from which the optimal sliding direction for each pixel of the slope is well estimated. Then, the long-term displacement time-series of the landslide between January 2007 and October 2018 in the estimated sliding direction is recovered by fusing L-band ALOS/PALSAR-1 and C-band Sentinel-1 SAR images. In order to fill the time gap of nearly four years between ALOS/PALSAR-1 and Sentinel-1 SAR images, the Tikhonov regularization (TR) method is developed to establish the observational equation. Moreover, to solve the problem arising from ALOS/PALSAR-1 and Sentinel-1 images with different wavelengths, incidence angles and flight directions, the measurements from ALOS/PALSAR-1 and Sentinel-1 images are both projected to the estimated optimal sliding direction to achieve a unified displacement datum. Our results from ascending and descending Sentinel-1 images suggest that the maximum displacement rates of the study area in the vertical and east-west directions from December 2016 to October 2018 were greater than 70 and 80 mm/year, respectively, and 2D displacement results reveal that the displacement patterns and movement characteristics of all the detected landslides are not identical in the study area. Specifically, the 3D displacement results successfully revealed the spatiotemporal displacement patterns and movement direction of each block of the Shadong landslide, and long-term displacement time series showed for the first time that the maximum cumulative displacement exceeds 1.3 m from January 2007 to October 2018. Moreover, the kinematic evolution and possible driving factors of landslides were investigated using 2D and 3D and long-term displacement results, coupled with hydrological factors and unidimensional constitutive models of the rocks. • New approaches are presented to estimate 3D and long-term landslide displacements. • Optimal sliding direction of the landslide is retrieved using 3D displacements. • InSAR reveals 12-year's displacement time series and its creep behavior of a landslide. • InSAR measurements suggest that the landslide was in a stable deformation stage. • Non-linear landslide movement is likely caused by the Jinsha River water level changes. [ABSTRACT FROM AUTHOR]