Purpose of this project is to evaluate the effectiveness of Synthetic Aperture Radar in mapping surface damage caused by landslides in reported locations.

• General Info
• Data Sources
• Example Data
• Workflow
• Installation
• Project Status
• Acknowledgements
• Contact
• License

• This project is built upon a previously developed low-accuracy large spatial scale landslide database. Space-based SAR imagery will be utilized in order to improve the accuracy of landslide detection. This is a capstone project in the University of Colorado-Boulder Earth Analytics certifcation program.
• Predicting landslides is challenging due to the many variables that should be considered when trying to identify what triggered a landslide. There is a need to better identify landslide locations across a large spatial scale. Can combining a low-accuracy large spatial scale landslide database with SAR imagery improve the accuracy of landslide detection across a global scale?
• utilize human-assisted, programmatic, and possibly machine learning methods to identify landslides from satellite imagery. We hope that this will bridge the gap with landslide event detection and future event prediction.

• [NASA Global Landslide Catalog (GLC)] Link: https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4 Info: The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. Type: Geospatial data can be downloaded as: KLM, KMZ, Shapefile or GeoJSON

• [Sentinel-1 SAR] Link: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar Info: SENTINEL-1 is an imaging radar mission providing continuous all-weather, day-and-night imagery at C-band. Type: The SENTINEL-SAFE format wraps a folder containing image data in a binary data format and product metadata in XML.

• [Capella Space Open Data Program] Link: https://www.capellaspace.com/ Info: Capella aims to provide the most frequent, timely and high-quality SAR imagery products available, accessible through an intuitive self-serve online platform. Type: Capella Space uses Synthetic Aperture Radar (SAR) to capture and track small-scale movements on the surface of the Earth

• [ICEYE Open Data Program] Link: https://www.iceye.com/ Info: ICEYE provides easy and flexible access to Earth observation data. Type: SAR data will be utilized.

Installation and usage of this repository requires an account on Google Earth Engine in order to access the Sentinel-1 data. As of 25 April, area of interests (AOIs) are ingested into the workflow through a json.load() function. The json files are located within the ./inputs path. The virtual environment is best constructed within an Anaconda Powershell prompt. To initiate the envrionment after forking the repository, open an Anaconda Powershell prompt, change the local directory path to the ./env path, then execute the command: "conda env create -f environment.yml"

• Project requirements/dependencies:
• Google Earth Engine account access

Project is: in progress

• Each week we meet on Tuesday to discuss progress on the project on zoom.
• We have currently worked on a Google Colab Document but are moving our code to Jupyter notebook to have access Version Control on Git Hub.
• This document will be our workspace as we continue to make edits and progress on our code.
• Data is uploaded by .csv file through the verified landslide database that was provided to us.
• We are detecting changes in Sentinel-1 Imagery.
• We will note changes, especially those due to land-change. The changes will show up as colored pixels.
• We will be utilizing the a sequential omnibus change detection algorithm initially provided by Google Earth Engine to detect change.
• The results we find from this sequential change detection using SAR imagery using GEE can then be interpreted, and future work will be determined when we get to them
• We will be continuing a weekly-meeting setup throughout the sememster and into the summer.

• We can be better about communicating where we are at with our project with eachother, but version control will help with this.

• Get our code to work for major verified landslide sites.
• Continue to clean our code up to make it as reproducible as possible.
• Discuss summer session plans

• This project was inspired by Dr. Elsa Culler, CU Boulder Earth Lab
• This project was based on:
  • A multi-sensor evaluation of precipitation uncertainty for landslide-triggering storm events
  • Assessment of Sentinel-1 and Sentinel-2 Data for Landslides Identification using Google Earth Engine
  • Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection
  • Alaska Satellite Facility SAR Basics Tutorial
  • This tutorial on detecting changes in Sentinel-1 imagery

• @Leah Manak
• @Mitch Thompson
• @Elsa Culler

This project is open source and available under the Apache License 2.0.