National Aeronautics and Space Administration Jet Propulsion Laboratory
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology In. SAR-based detection method for mapping and monitoring slowmoving landslides in remote regions with steep and mountainous terrain: An application to Nepal D. Bekaert, A. Handwerger, P. Agram, D. Kirschbaum Science Question: Every year landslides cause disruption to day life, frequently inhibit the transport of goods and services, and kill thousands of people. Yet, in most regions of the world, the characterization of landslide locations and impacts remain largely unknown due to the complex morphologies and geographic settings in which they typically occur and the difficulty of collating and updating inventories. Data & Results: We used freely available In. SAR data from the Copernicus Sentinel-1 satellites between 2014 and 2017 to identify and monitor slow-moving landslides in the Trishuli River catchment, Western Nepal. We presented a novel method for the detection of landslides (and other localized deformation) over a large region. We identified a minimum of 6 large, slow-moving landslides where continuous deformation is likely driven by monsoonal precipitation. Most of these landslides are proximal to roads and infrastructure and thus will likely cause damage and disruption that will impact the local communities. Figure: Landslide inventory map and In. SAR line-of-sight (LOS) velocity for the pre-Gorkha period (October 2014–April 2015) draped over a hillshade of the topography. Regional scale inventory shown in panel. Black polygons show the landslides identified using our In. SAR methodology. Gray polygons show landslides mapped by Tsou et al. (2018). Black arrows show the satellite LOS and flight direction (Vsat). Blue line corresponds to the Trishuli river and white lines show the road network. Bekaert, D. P. , Handwerger, A. L. , Agram, P. , & Kirschbaum, D. B. (2020). In. SARbased detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal. Remote Sensing of Environment, 249, 111983. https: //doi. org/10. 1016/j. rse. 2020. 111983 This work was partially supported by a 2018 ROSES ESI grant awarded to AH. Significance: Mapping and monitoring landslides in remote areas with steep and mountainous terrain is logistically challenging, expensive, and time consuming. We developed a novel In. SAR localized deformation detection approach to identify slow-moving landslides without making a priori assumptions of their location. Our findings highlight the potential for region-wide mapping of slow-moving landslides using freely available remote sensing data in remote areas such as Nepal and future work will benefit from expanding our methodology to other regions around the world.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Contact: David Bekaert, M/S 300 -243 , Jet Propulsion Laboratory, Pasadena, CA 91109 david. bekaert@jpl. nasa. gov Alexander Handwerger, M/S 300 -323, Jet Propulsion Laboratory, Pasadena, CA 91109 alexander. handwerger@jpl. nasa. gov alhandwerger@g. ucla. edu Citation: Bekaert, D. P. , Handwerger, A. L. , Agram, P. , & Kirschbaum, D. B. (2020). In. SAR-based detection method for mapping and monitoring slowmoving landslides in remote regions with steep and mountainous terrain: An application to Nepal. Remote Sensing of Environment, 249, 111983. https: //doi. org/10. 1016/j. rse. 2020. 111983 Data Sources: The Copernicus Sentinel-1 SAR data were processed up to SLC level by ESA, and downloaded from the ASF DAAC (https: //earthdata. nasa. gov/eosdis/daacs/asf). The daily accumulated precipitation (final run) data were provided by the NASA/Goddard Space Flight Center's and PPS, which develop and compute the GPM_3 IMERGDF. 05 dataset as a contribution to GPM, and archived at the NASA GES DISC. Shakemap and earthquake information was provided by the USGS (https: //earthquake. usgs. gov/earthquakes/). DEMs from the NASA High Mountain Asia project and the SRTM topographic data. Technical Description of Figure: Landslide inventory map and In. SAR line-of-sight (LOS) velocity for the pre-Gorkha period (October 2014–April 2015) draped over a hillshade of the topography. A velocity value of 0 corresponds to pixels that have been masked out but is set to yellow color for viewing purposes. Black polygons show the landslides identified using our In. SAR methodology. Gray polygons show potentially slow or “coherent” landslides mapped by Tsou et al. (2018). Black dashed line shows the Main Central Thrust. Black arrows show the satellite LOS and flight direction (Vsat). Blue line corresponds to the Trishuli river and white lines show the road network. Scientific significance, societal relevance, and relationships to future missions: Our results highlight how state-of-the-art remote sensing data can be used to better understand landslide processes. Future missions, such as the NASA-ISRO Synthetic Aperture Radar (NISAR) will provide key data that can be used to improve our understanding of the mechanisms that control landslides.
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