Abstract
Remnant explosive devices are a deadly nuisance to both military personnel and civilians. Traditional mine detection and clearing is dangerous, time-consuming, and expensive. And routine production and testing of explosives can create groundwater contamination issues. Remote detection methods could be rapidly deployed in vegetated areas containing explosives as they are known to cause stress in vegetation that is detectable with hyperspectral sensors. Hyperspectral imagery was employed in a mesocosm study comparing stress from a natural source (drought) to that of plants exposed to two different concentrations of Royal Demolition Explosive (RDX; 250 mg kg−1, 500 mg kg−1). Classification was accomplished with the machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), and Least Discriminant Analysis (LDA). Leaf-level plant data assisted in validating plant stress induced by the presence of explosives and was detectable. Vegetation indices (VIs) have historically been used for dimension reduction due to computational limitations; however, we measured improvements in model precision, recall, and accuracy when using the complete range of available wavelengths. In fact, almost all models applied to spectral data outperformed their index counterparts. While challenges exist in scaling research efforts from the greenhouse to the field (i.e., weather, solar lighting conditions, altitude when imaging from a UAV, runoff containment, etc.), this experiment is promising for subsequent research efforts at greater scale and complexity aimed at detecting emerging contaminants.
Recommended Citation
P. V. Manley et al., "UAV-Based Phytoforensics: Hyperspectral Image Analysis to Remotely Detect Explosives Using Maize (Zea Mays)," Remote Sensing, vol. 17, no. 3, article no. 385, MDPI, Feb 2025.
The definitive version is available at https://doi.org/10.3390/rs17030385
Department(s)
Civil, Architectural and Environmental Engineering
Publication Status
Open Access
Keywords and Phrases
emerging contaminants; explosives; hyperspectral imagery; machine learning; phytoforensics; RDX; unmanned aerial vehicle
International Standard Serial Number (ISSN)
2072-4292
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Feb 2025
Comments
National Science Foundation, Grant IIA-1355406