Spectroscopic Analysis for Mapping Wildland Fire Effects from Remotely Sensed Imagery
Dale Hamilton1, Mikhail Bowerman1, Jason Colwell1
1Department of Math and Computer Science, Northwest Nazarene University, 623 S University Blvd, Nampa, ID 83686.
Journal of Unmanned Vehicle Systems
|August 13, 2019
Summary
Wildfire monitoring is enhanced by Unmanned Aircraft Systems (UAS) and spectroscopic analysis. This technology enables high-resolution mapping of wildland fire severity and extent, improving ecological assessments.
Area of Science:
- Environmental Science
- Remote Sensing
- Ecology
Background:
- Wildfires impact millions of hectares annually in the US, causing erosion and habitat loss.
- Current monitoring methods lack the fine spatial resolution needed for detailed post-fire analysis.
- Unmanned Aircraft Systems (UAS) offer a promising solution for on-demand, high-resolution environmental monitoring.
Purpose of the Study:
- To develop a high-resolution mapping technique for wildland fire severity and extent.
- To leverage spectroscopic analysis and machine learning for improved fire effects assessment.
- To demonstrate the capabilities of UAS in post-fire monitoring.
Main Methods:
- Spectroscopic analysis of live and combusted vegetation samples.
- Identification of spectral separability between vegetation classes.
- Selection of optimal spectra for machine learning classifiers.
- Utilizing UAS for high-resolution data acquisition.
Main Results:
- Demonstrated spectral separability of vegetation classes.
- Developed an optimal spectral set for machine learning.
- Enabled high-resolution mapping of fire severity and extent.
- Showcased the effectiveness of UAS in post-fire analysis.
Conclusions:
- UAS and spectroscopic analysis provide an affordable and effective method for detailed wildland fire monitoring.
- This approach significantly enhances the spatial resolution of fire effects assessment.
- The technology supports improved ecosystem management and wildlife habitat restoration strategies.
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