Himalayan Disasters Script Amanda Rumsey: Nepal is highly susceptible to landslides due to its mountainous topography, monsoon rain, seismic activity, and underdeveloped infrastructure. In April 2015, the magnitude 7.8 Gorkha earthquake struck Nepal, causing over 9,000 casualties and created over $1 billion in damages. Because of this earthquake, rainfall-triggered landslides are likely to emerge as a significant induced hazard during the monsoon season. Previous research efforts have utilized landslide event data for hazard assessment and landslide modeling, but landslide event databases are limited in scope and size due to non-reporting biases associated with the lack of knowledge of events in under-populated areas and with the grouping of events with their primary triggering hazards. With current underestimation of landslide impacts, it is critical to develop a landslide event inventory that has the ability to accurately quantify landslide event information. Conventional methods for landslide event detection rely on visual interpretation of high-resolution imagery with manual digitization by analysts. These methods are resource intensive and are plagued by spatial and temporal constraints. Jessica Fayne: The DEVELOP team at Goddard Space Flight Center has developed an automated landslide detection tool called the Sudden Landslide Identification Product (SLIP). SLIP helps researchers identify landslides at a 60 meter scale or larger and helps them recognize changes in bare earth such as increased soil moisture and snow melt. SLIP also creates a catalog detailing where and approximately when the land changes occur. The SLIP program is simple, can run on any operating system, and is completely free, which eliminates the need for complex and expensive software. The program first searches the United States Geological Survey database to download a collection of 11 Landsat 8 images. The red bands from the 10 oldest images are composited to produce a cloud-free red band baseline image. The red band from the newest image is then compared to the red band baseline image using the percent change formula. Pixels with a red increase greater than 40% represent a significant change in vegetation. Next, the near infrared bands and the short wave infrared bands from the 10 oldest images are composited separately to produce cloud-free baseline images. The near infrared and short wave bands from the newest image are then subtracted from the infrared baseline images to show increases or decreases in moisture. Areas with significant change in these band combinations represent changes in soil moisture. Finally, a slope layer derived from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation models is used to identify areas that are more likely to experience mass wasting due to high slopes. The slope layer is also used to filter out areas of flat terrain such as river bottoms and farms, which regularly experience reflectance changes and moisture increases. Pixels that show a percent red increase greater than 40%, a significant soil moisture increase, and a slope of greater than 15% are flagged as landslide events and changes in bare earth to produce the final SLIP product. Jordan Scheffler: Currently, the Goddard DEVELOP team is validating the SLIP algorithm. To do this, first the raster that is output from the SLIP product is converted into a polygon feature class using ArcMap. Next, the polygons are given a 60 meter buffer that clusters the individual polygons into larger landslide features. After the buffers are created, 50 or more of the largest features are extracted and validated by visual inspection with pan sharpened Landsat 8 true color imagery. Future work on SLIP will include the creation of an online web platform that will feature the calibrated outputs of the SLIP product. Additionally, the algorithm is currently being tested with runoff and soil moisture data from the Global Land Data Assimilation System (GLDAS) to calibrate the SLIP product.