skip to main content

Title: Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine
Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Natural Hazards and Earth System Sciences
Page Range / eLocation ID:
753 to 773
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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.

    more » « less
  2. Abstract. We developed a new approach for mapping landslide hazards by combiningprobabilities of landslide impacts derived from a data-driven statisticalapproach and a physically based model of shallow landsliding. Ourstatistical approach integrates the influence of seven site attributes (SAs) onobserved landslides using a frequency ratio (FR) method. Influential attributesand resulting susceptibility maps depend on the observations of landslidesconsidered: all types of landslides, debris avalanches only, or source areasof debris avalanches. These observational datasets reflect the detection ofdifferent landslide processes or components, which relate to differentlandslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical andphysically based probabilities as indices and calculates a joint probabilityof landsliding at the intersections of probability bins. A ratio of thejoint probability and the physically based model bin probability is used asa weight to adjust the original physically based probability at each gridcell given empirical evidence. The resulting integrated probability oflandslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentiallyunstable areas with the proposed integrated model are statisticallyquantified. We provide multiple landslide hazard maps that land managers canuse for planning and decision-making, as well as for educating the publicabout hazards from landslides in this remote high-relief terrain. 
    more » « less
  3. Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information, we aim to demonstrate the advantages and limitations of these algorithms through a case study of the 2022 Madagascar flooding event. The HYDRAFloods toolbox was used to perform preprocessing, image correction, and automated flood water detection based on the state-of-the-art Edge Otsu, Bmax Otsu, and Fuzzy Otsu algorithms for the satellite images; the FwDET tool was deployed upon the cloud computing platform (Google Earth Engine) for rapid estimation of flood area/depth using the digital elevation model (DEM) data. Generated surface water maps from the respective techniques were evaluated qualitatively against each other and compared with a reference map produced by the European Union Copernicus Emergency Management Service (CEMS). The DEM-based maps show generally overestimated flood extents. The satellite-based maps show that Edge Otsu and Bmax Otsu methods are more likely to generate consistent results than those from Fuzzy Otsu. While the synthetic-aperture radar (SAR) data are typically favorable over the optical image under undesired weather conditions, maps generated based on SAR data tend to underestimate the flood extent as compared with reference maps. This study also suggests the newly launched Landsat-9 serves as an essential supplement to the rapid delineation of flood extents. 
    more » « less
  4. null (Ed.)
    ABSTRACT The Mw 7.5 earthquake that struck central Papua New Guinea in 2018 was the largest event ever recorded in the region with modern seismic instruments. The ground motions associated with this event also triggered widespread landslides and affected more than 500,000 people. However, due to the absence of a local seismic and Global Positioning System network in the vicinity, the fault location, system, and slip distribution of this earthquake are not well documented. In this study, we use the subpixel offset method on the Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) images to calculate the 3D coseismic displacement of the 2018 Papua New Guinea earthquake. The results show clear fault traces that suggest coseismic slip on the Mubi fault and the Mananda fault that triggered landslides that spread out in a more than 260  km2 region. Finite-source inversions based on the subpixel offset measurements show up to 4.1 and 6.5 m coseismic slip on the Mubi and Mananda faults, respectively. Despite higher data uncertainty (∼0.4–0.8  m) of the subpixel offset data, synthetic resolution tests show resolvable slip above 8 km in depth. The lack of shallower slip on the west side of the Mananda fault could be due to an inflated geothermal gradient near the dormant volcano, Mount Sisa, as a slip barrier. The result of the coulomb stress change suggests possible southeastward slip propagation from the Mananda fault to the Mubi fault. Our work successfully resolves 3D coseismic displacement in highly vegetated terrains and demonstrates the feasibility of using the subpixel offset on SAR images to help our understanding of regional active tectonic systems. 
    more » « less
  5. Abstract

    Landslides commonly occur in areas with steep topography and abundant precipitation and pose a significant hazard to local communities. Some of the largest known landslides occur in Alaska, including several that caused local tsunamis. Many landslides may have gone undetected in remote areas due to lack of observations. Here, we develop a semiautomated workflow using both seismic and geodetic observations to detect, locate, validate, and characterize landslides in Alaska. Seismic observations have shown promise in continuously monitoring landslide occurrence, while remote sensing techniques are well suited for verification and high‐resolution imaging of landslides. We validate our procedure using the 28 June 2016, Lamplugh Glacier landslide. We also present observations of a previously unknown landslide occurred on 22 September 2017 in the Wrangell Mountains region. The Wrangell Mountains landslide generated a coherent surface wavefield recorded across Alaska and the contiguous United States. We used Sentinel‐1 Synthetic Aperture Radar and Sentinel‐2 optical imagery to map the respective mass deposit. To investigate the landslide dynamics, we inverted regional seismic surface wave data for a centroid single force failure model. Our model suggests that the Wrangell Mountains landslide lasted for about 140 s and had two subevents involving at least five distinct stages. We estimate that the landslide had displaced 3.1–13.4 million tons of rocks over a distance of ∼2 km. Our results suggest that combining seismic and geodetic observations can vastly improve the detection and characterization of landslides in remote areas in Alaska and elsewhere, providing new insights into the landslide dynamics.

    more » « less