Reverse-time migration for evaluating the internal structure of tree-trunks using ground-penetrating radar. (October 2020)

• Record Type: Journal Article
• Authors: Alani, Amir M.; Giannakis, Iraklis; Zou, Lilong; Lantini, Livia; Tosti, Fabio
• Is Part Of: NDT & E international. Volume 115(2020)
• Publication Date: 2020-10
• Subjects: Ground penetrating radar (GPR) -- Tree health monitoring -- Migration -- Tree -- Decay -- Emerging infectious diseases (EIDs); Nondestructive testing -- Periodicals; Contrôle non destructif -- Périodiques; Electronic journals; 620.1127
• DOI: 10.1016/j.ndteint.2020.102294
• Languages: English
• ISSNs: 0963-8695

Abstract

Abstract: Modern socioeconomic factors such as global timber trade and international travelling have contributed to the rapid increase of Emerging Infectious Diseases (EIDs) of trees with devastating effects to the European forests and woodlands. To that extent, numerous non-destructive methodologies have been suggested as diagnostic tools in order to effectively monitor and maintain potential outbreaks. Ground-penetrating radar (GPR) is an appealing method for tree monitoring as it provides with a trivially deployable and efficient detection tool, suitable for large-scale forestry applications. Nonetheless, traditional GPR approaches are tuned for surface measurements and they are not compatible with the unique measurement configurations associated with forestry applications. Within that context, we present a novel-processing framework, which is capable of addressing features with irregular measurements on closed surfaces. A positioning method is described that exploits a wheel-measuring device in order to accurately associate each A-Scan with its corresponding coordinates. In addition, a processing pipeline is presented that aims at eliminating the ringing noise due to the layered nature of the trees. Lastly, a reverse-time migration is applied to the processed B-Scan in order to effectively map the reflectors present within the trunk. The suggested scheme is successfully tested in both numerical and real-field experiments, indicating the validity of the current approach. Highlights: A processing framework for detecting tree decays in diseased trees. Numerical case studies that support the suggested scheme. A real-field case study that demonstrates the capabilities of hte suggested method.