Ant Nest Detection Using Underground P-Band TomoSAR

TL;DR

This paper demonstrates a novel approach combining P-band SAR and CNNs to accurately detect and size underground leaf-cutting ant nests in forests, offering a powerful tool for forest management.

Contribution

It introduces an integrated method using electromagnetic simulations, drone-borne SAR, and deep learning for precise underground nest detection in large forest areas.

Findings

100% detection accuracy of ant nests

0% false alarm rate

21% average size estimation error

Abstract

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry.