Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection

Abstract

Despite landslides impact the society worldwide every day, landslide information is inhomogeneous and lacking. When landslides occur in remote areas or where the availability of optical images is rare due to cloud persistence, they might remain unknown, or unnoticed for long time, preventing studies and hampering civil protection operations. The unprecedented availability of SAR C-band images provided by the Sentinel-1 constellation offers the opportunity to propose new solutions to detect landslides events. In this work, we perform a systematic assessment of Sentinel-1 SAR C-band images acquired before and after known events. We present the results of a pilot study on 32 worldwide cases of rapid landslides entailing different types, sizes, slope expositions, as well as pre-existing land cover, triggering factors and climatic regimes. Results show that in about eighty-four percent of the cases, changes caused by landslides on SAR amplitudes are unambiguous, whereas only in about thirteen percent of the cases there is no evidence. On the other hand, the signal does not allow for a systematic use to produce inventories because only in 8 cases, a delineation of the landslide borders (i.e., mapping) can be manually attempted. In a few cases, cascade multi-hazard (e.g., floods caused by landslides) and evidences of extreme triggering factors (e.g., strong earthquakes or very rapid snow melting) were detected. The method promises to increase the availability of information on landslides at different spatial and temporal scales with benefits for event magnitude assessment during weather-related emergencies, model tuning, and landslide forecast model validation, in particular when accurate mapping is not required.

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

“…However, there are certain obstacles exist in the pre-processing. The SAR acquisition geometry affects the quality of images over mountainous areas where landslides are likely to occur, and hence the use of SAR signals in a traditional statistical classification approach becomes difficult mainly due to speckling [16]. Moreover, both the SAR intensity-based and coherence-based methods used in this study are partially/highly dependent on the satellite geometry, wavelengths, and temporal baseline.…”

“…The detection of landslides by using measures of the changes between pre-and post-event SAR amplitude images is based on the assumption that landslide occurrences change the local land cover and some of its properties (e.g., roughness and moisture content) [16,45]. However, such land cover changes can be triggered not only by landslides but also by human activities, such as the cultivation of flatlands.…”

“…Goorabi [15] detected the Maleh-Kabood landslide caused by the Azgleh, Iran, earthquake comparing the pre-and post-earthquake coherence histograms using Sentinel-1A/B data. Mondini et al [16] detected 32-case landslides worldwide (e.g., rainfall, earthquake and rapid snow melting-induced landslides), using photo-interpretive methods as measures of changes in amplitude in pre-and post-event Sentinel-1 images. Uemoto et al [17] proposed a landslide detection method utilizing height differences derived from pre-and post-event airborne Pi-SAR2 digital elevation models (DEMs) combined with amplitude differences and applied this method to landslides triggered by the 2016 Kumamoto earthquake.…”

“…Shao et al [25] produced a landslide susceptibility map of the area by applying logistic regression (LR) and support vector machine (SVM) to the high-resolution Planet (optical) images. Mondini et al [16] applied photo-interpretive methods to detect the Hokkaido earthquake-induced landslides as measures of changes in amplitude in pre-and post-event Sentinel-1 images. However, a detailed quantitative analysis of the landslides has not yet been reported.…”

A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

“…They verified the performance of the method in finding PPA with the case of the 2017 Iran-Iraq earthquake and a landslide-prone region of NW Argentina. Mondini et al [14] proposed that using Sentinel-1 SAR C-band images could well solve the problem of lack of pre and postlandslide optical images due to cloud persistence. They analyzed 32 global landslide cases, and results showed that changes caused by landslides on SAR amplitudes were unambiguous in about 84% of cases.…”