Advancing wildland fire risk assessment using multi-modal remote sensing
| Interdisciplinary Areas: | Data and Engineering Applications, Innovation and Making, Power, Energy, and the Environment |
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Project Description
Longer fire seasons and the expanding wildland-urban interface are increasing the frequency and severity of wildland fires. Monitoring of pre-fire forested areas is important to assess the fire risk for both insurance purposes and operational use.
The likelihood, speed, and intensify of a fire is strongly dependent upon fuel structure, fuel moisture, and the underlying soil moisture. In practice, it is difficult to obtain measurements of relevant parameters, such as live fuel moisture content, with sufficient accuracy, resolution, and spatial extent to be effectively used in predicting fire risk. Fire risk remains an important gap in present-day digital forestry capabilities.
Airborne and satellite remote sensing can play an important role in producing these measurements over extended areas. Full-waveform lidar can extract vegetation structure and hyperspectral imaging can provide vegetation water content. Although microwave remote sensing has been successful at sensing soil moisture on larger scales, its current resolution from satellite is not sufficient for fire risk mapping.
Purdue is supporting a collaborative research initiative on active and passive microwave remote sensing with a specific interest in their application to fire risk assessment. Developing forward models and retrieval methods will be a significant part of this research.
Start Date
May 2025
Post Doc Qualifications
Doctorate in Electrical Engineering, physics, geophysics, civil engineering, geomatics, or related field. Strong background in signal processing and electromagnetics. Thesis research related to remote sensing is desirable. Field experience in forestry, agriculture, earth, soil and/or plant science would also be desirable.
Co-Advisors
Prof. James L. Garrison, jgarriso@purdue.edu, Aeronautics and Astronautics
Collaborators
Prof. Michael Saunders, msaunder@purdue.edu, Forestry & Natural Resources
Qianlai Zhuang, qzhuang@purdue.edu, Earth Atmospheric and Planetary Sciences
Bibliography
Laneve, Giovanni, et al. "Preventing Forest Fires Through Remote Sensing: Achievements of the Prevention and Recovery of Forest Fires Emergency in the Mediterranean Area project." IEEE Geoscience and Remote Sensing Magazine 8.3 (2020): 37-49.
Fernández-Álvarez, Marta, Julia Armesto, and Juan Picos. "LiDAR-based wildfire prevention in WUI: The automatic detection, measurement and evaluation of forest fuels." Forests 10.2 (2019): 148.
Chaparro, David, et al. "Predicting the extent of wildfires using remotely sensed soil moisture and temperature trends." IEEE journal of selected topics in applied earth observations and remote sensing 9.6 (2016): 2818-2829.