- PI: Christopher Uejio, Florida State University
- Co-I: Christopher Holmes, Florida State University
- Co-I: Holly Nowell, Florida State University
Prescribed fire is a critical tool for mitigating wildfire risk, promoting agriculture, and managing ecosystem services. Even though fires in the Southeast United States burn a significant...
- 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...
SAIC Advanced Wildfire Planning Introduces Quantitative Complexity Management (QCM)/Artificial Intuition
- 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...
Using GEDI data to calibrate fine-grained Canopy Height Models for monitoring wildfire risk and behavior
- 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 (email@example.com)
- Co-Investigator: Brian Ebel, USGS Water Mission Area-Earth System Processes Division (firstname.lastname@example.org)
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...
Engaging the Wildfire Community and Decision Makers with Improved Trusted Data Integration and Interoperability through Real-Time Synchronous Cross-Platform Sharing using GeoCollaborate
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
- 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...
Community Wildfire Vulnerability Index for Risk Assessment and Response Planning using Earth Observation (EO) Data and Modeling
- 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...