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Title: A new method to detect changes in displacement rates of slow-moving landslides using InSAR time series
Abstract

Slow-moving landslides move downslope at velocities that range from mm year−1to m year−1. Such deformations can be measured using satellite-based synthetic aperture radar interferometry (InSAR). We developed a new method to systematically detect and quantify accelerations and decelerations of slowly deforming areas using InSAR displacement time series. The displacement time series are filtered using an outlier detector and subsequently piecewise linear functions are fitted to identify changes in the displacement rate (i.e., accelerations or decelerations). Grouped accelerations and decelerations are inventoried as indicators of potential unstable areas. We tested and refined our new method using a high-quality dataset from the Mud Creek landslide, CA, USA. Our method detects accelerations and decelerations that coincide with those previously detected by manual examination. Second, we tested our method in the region around the Mazar dam and reservoir in Southeast Ecuador, where the time series data were of considerably lower quality. We detected accelerations and decelerations occurring during the entire study period near and upslope of the reservoir. Application of our method results in a wealth of information on the dynamics of the surface displacement of hillslopes and provides an objective way to identify changes in displacement rates. The displacement rates, their spatial variation, and the timing of accelerations and decelerations can be used to study the physical behavior of a slow-moving slope or for regional hazard assessment by linking the timing of changes in displacement rates to landslide causal and triggering factors.

 
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Award ID(s):
2023112
NSF-PAR ID:
10369335
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
Landslides
Volume:
19
Issue:
9
ISSN:
1612-510X
Page Range / eLocation ID:
p. 2233-2247
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Please refer to the manuscript and its supplemental materials for full details. (A link will be appended following publication)

    File formatting information is listed below, followed by a sub-section of the text describing the Geodetic Drought Index Calculation.



    The longitude, latitude, and label for grid points are provided in the file "loading_grid_lon_lat".




    Time series for each Geodetic Drought Index (GDI) time scale are provided within "GDI_time_series.zip".

    The included time scales are for 00- (daily), 1-, 3-, 6-, 12- 18- 24-, 36-, and 48-month GDI solutions.

    Files are formatted following...

    Title: "grid point label L****"_"time scale"_month

    File Format: ["decimal date" "GDI value"]




    Gridded, epoch-by-epoch, solutions for each time scale are provided within "GDI_grids.zip".

    Files are formatted following...

    Title: GDI_"decimal date"_"time scale"_month

    File Format: ["longitude" "latitude" "GDI value" "grid point label L****"]


    2.2 GEODETIC DROUGHT INDEX CALCULATION

    We develop the GDI following Vicente-Serrano et al. (2010) and Tang et al. (2023), such that the GDI mimics the derivation of the SPEI, and utilize the log-logistic distribution (further details below). While we apply hydrologic load estimates derived from GPS displacements as the input for this GDI (Figure 1a-d), we note that alternate geodetic drought indices could be derived using other types of geodetic observations, such as InSAR, gravity, strain, or a combination thereof. Therefore, the GDI is a generalizable drought index framework.

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