• PI: Benjamin Poulter, NASA Goddard
• Co-I: Andrew Chiodi, Univ. Washington
• Co-I: Doug Morton, NASA Goddard
• Co-I: J Morgan Varner, TallTimbers
• Co-I: Adam Watts, US Forest Service

Prescribed fires provide an opportunity to maintain and restore fire in wildland ecosystems as well as an opportunity to...

• PI: Sean Nolan, SAIC Gemini Inc.
• Co-I: Stephen Ambrose, SAIC Gemini Inc.

SAIC is pleased to partner with NASA on a wildfire applications project that utilizes Artificial Intuition, a Quantitative Complexity Management (QCM)-based computational engine, to pinpoint locations and areas of environmental stress that...

• PI: Tony Chang, Vibrant Planet, PBC
• Co-I: Bogdan State, Vibrant Planet, PBC
• Co-I: Leo Tsourides, Vibrant Planet, PBC

Climate change is expected to alter wildfire potential at a global scale with increased fire frequency and lengthened seasons. This change in wildfire potential has generated urgency...

• Principal Investigator: Miguel Villarreal, USGS Western Geographic Science Center (mvillarreal@usgs.gov)
• Co-Investigator: Brian Ebel, USGS Water Mission Area-Earth System Processes Division (bebel@usgs.gov)

Wildfires increase flood and debris flow risk by increasing the fraction of rainfall reaching the soil surface and reducing the...

• PI: Guy Schumann, ImageCat
• Co-I: Marina Mendoza, Krasniansky/ImageCat
• Co-I: Ron Eguchi, ImageCat
• Co-I: Ajay Gupta, HSR
• Co-I: Unmesh Kurup. Intuition Machines
• Co-I: Ron Hagensieker

Machine Learning (ML) brings significant advances in the area of disaster impact mapping and predictions. However, the biggest challenge facing ML and...

This Phase I study effort will begin the process of increasing exposure of NASA’s Wildland Fire Management Program and data with the Moraga-Orinda Fire District in California by using GeoCollaborate, a NASA SBIR Phase III innovation that enables access and sharing of disparate trusted data...

• Joel T. Johnson, The Ohio State University
• Dustin Horton, The Ohio State University

The incorporation of soil moisture information into wildfire prediction is becoming increasingly important, as has been shown in recent works relating pre-season soil moisture and seasonal anomalies to wildfire activity.  The use...

• PI: Shubharoop Ghosh, ImageCat
• Co-Is : Tirtha Banerjee, PhD, University of California
• Irvine and Ronald Eguchi, ImageCat

Catastrophic wildfire economic losses in recent years underscore the need to more accurately estimate vulnerability, loss potential, and future risk to support effective risk management decisions. This research...

• PI: Pete Robichaud, U.S. Forest Service
• Co-I: Mariana Dobre. University of Idaho
• Co-I: Ian Floyd. U.S. Army Engineer Research and Development Center
• Co-I: Mary Miller. Michigan Technological University
• Co-I: Guy Schumann, ImageCat, Inc.

We are collecting specific field and Earth Observation (EO) data after...

• PI: Thomas Buchholz, Spatial Informatics Group
• Co-I: Max Moritz, Spatial Informatics Group
• Co-I: Graham Wesolowski, Spatial Informatics Group Natural Assets Laboratory

Wildfire probability maps are a key data source for allocating resources to manage fire adapted forests and protect communities and infrastructure. This project will...