Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding
-
Published:2022-08-17
Issue:8
Volume:22
Page:2637-2653
-
ISSN:1684-9981
-
Container-title:Natural Hazards and Earth System Sciences
-
language:en
-
Short-container-title:Nat. Hazards Earth Syst. Sci.
Author:
Burrows KatyORCID, Marc OdinORCID, Remy Dominique
Abstract
Abstract. Heavy-rainfall events in mountainous areas trigger destructive landslides, which pose a risk to people and infrastructure and significantly affect the landscape.
Landslide locations are commonly mapped using optical satellite imagery, but in some regions their timings are often poorly constrained due to persistent cloud cover. Physical and empirical models that provide insights into the processes behind the triggered landsliding require information on both the spatial extent and the timing of landslides. Here we demonstrate that Sentinel-1 synthetic aperture radar amplitude time series can be used to constrain landslide timing to within a few days and present four techniques to accomplish this based on time series of (i) the difference in amplitude between the landslide and its surroundings, (ii) the spatial variability in amplitude between pixels within the landslide, and (iii) geometric shadows and (iv) geometric bright spots cast within the landslide. We test these techniques on three inventories of landslides of known timing, covering various settings and triggers, and demonstrate that a method combining them allows 20 %–30 % of landslides to be timed with an accuracy of 80 %. Application of this method could provide an insight into landslide timings throughout
events such as the Indian summer monsoon, which triggers large numbers of landslides every year and has until now been limited to annual-scale analysis.
Funder
Centre National d’Etudes Spatiales
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference76 articles.
1. Aimaiti, Y., Liu, W., Yamazaki, F., and Maruyama, Y.: Earthquake-Induced
Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using
PALSAR-2 Data, Remote Sensing, 11, 2351, https://doi.org/10.3390/rs11202351, 2019. a 2. Ao, M., Zhang, L., Dong, Y., Su, L., Shi, X., Balz, T., and Liao, M.:
Characterizing the evolution life cycle of the Sunkoshi landslide in Nepal
with multi-source SAR data, Sci. Rep., 10, 1–12, 2020. a, b 3. Baghdadi, N., Choker, M., Zribi, M., Hajj, M. E., Paloscia, S., Verhoest,
N. E., Lievens, H., Baup, F., and Mattia, F.: A new empirical model for radar
scattering from bare soil surfaces, Remote Sensing, 8, 920, https://doi.org/10.3390/rs8110920, 2016. a, b 4. Ban, Y., Zhang, P., Nascetti, A., Bevington, A. R., and Wulder, M. A.: Near
real-time wildfire progression monitoring with Sentinel-1 SAR time series and
deep learning, Sci. Rep., 10, 1–15, 2020. a, b 5. Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and location
of shallow rainfall-induced landslides using a model for transient,
unsaturated infiltration, J. Geophys. Res.-Ea. Surf.,
115, F03013, https://doi.org/10.1029/2009JF001321, 2010. a
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|