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Description

April 18, 2017 - April 20, 2017

Remote sensing data can help professionals manage and monitor extreme weather conditions. In this training, participants will learn how to access and analyze available NASA data relevant to flood management. This includes data on precipitation, soil moisture, inundation mapping, and elevation modeling. Participants will learn to apply data for assessing flood risk, monitoring conditions, and planning relief. Each session will include a presentation followed by an opportunity to use the data and tools covered in the presentation.

Agenda Cite This Training

Citation
(2017). ARSET - NASA Remote Sensing for Flood Monitoring and Management. NASA Applied Remote Sensing Training Program (ARSET). http://disasters.nasa.gov/get-involved/training/english/arset-nasa-remote-sensing-flood-monitoring-and-management
Objective

At the end of this course, participants will be able to:

  • Apply open-source data and tools to flood monitoring and management
  • Utilize satellite observations to evaluate extreme weather conditions, prepare for impending flooding, and plan relief activities
Audience

Local, regional, state, federal, and non-governmental organizations involved in flood monitoring and management.

Sessions
Session 1A: Precipitation from Remote Sensing Measurements
  • Introduction
  • NASA Applied Sciences Program on Disaster Management
  • About ARSET and Training Outline
  • Overview of TRMM and GPM Precipitation Data
  • QGIS and Precipitation Data Access and Analysis
Session 1B: Digital Elevation Data from Remote Sensing
Session 2A: Overview of Synthetic Aperture Radar (SAR) Data for Flood Detection
Session 2B: Soil Moisture Active Passive (SMAP) Mission
Session 3A: Flood Management and Monitoring Tools
  • Overview of Flood Monitoring and Mapping Tools
  • Overview and Access of Socioeconomic Data
  • Assessment for a Flood Case Study (Pre-flood monitoring, flood monitoring, and identifying impacts)
Session 3B: Flood Monitoring and Management Case Study

All data for this training is available at the Dewberry Github

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