LudVision -- Remote Detection of Exotic Invasive Aquatic Floral Species using Drone-Mounted Multispectral Data
This addresses the monitoring of invasive species for ecosystem preservation, though it is incremental as it modifies existing methods for a specific domain.
The study tackled the problem of detecting the invasive aquatic plant Ludwigia peploides using drone-mounted multispectral data, achieving a producer's accuracy of 79.9% and a user's accuracy of 95.5%.
Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. Ever-growing reports of invasive species have affected the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can negatively impact the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. We used images collected by a drone-mounted multispectral sensor to achieve this, creating our LudVision data set. To identify the targeted species on the collected images, we propose a new method for detecting Ludwigia p. in multispectral images. The method is based on existing state-of-the-art semantic segmentation methods modified to handle multispectral data. The proposed method achieved a producer's accuracy of 79.9% and a user's accuracy of 95.5%.