Unmanned Aerial Vehicle (UAV)-Based Mapping of Iris Pseudacorus L. Invasion in Laguna del Sauce (Uruguay) Coast

2025-02-28

AI Summary

This study addresses the threat posed by invasive yellow flag iris (Iris pseudacorus L.) to water resource sustainability along the Sauce Lagoon in Uruguay. To map its distribution with high spatial fidelity, we propose a drone-based multispectral remote sensing framework integrating spectral feature enhancement with semi-supervised classification—specifically, self-training SVM and deep clustering. This approach is the first to be applied in South American wetland ecosystems and effectively mitigates challenges of limited labeled samples and spectral confusion inherent in small-scale, highly heterogeneous habitats. The resulting invasion distribution map achieves an overall accuracy of 92.3%, substantially outperforming conventional satellite-based methods. Our framework enhances the robustness and transferability of remote sensing–based invasive plant detection, delivering high spatiotemporal resolution mapping essential for targeted ecological monitoring and management interventions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Biological invasions pose a significant threat to the sustainability of water sources. Efforts are increasingly being made to prevent invasions, eradicate established invaders, or control them. Remote sensing (RS) has long been recognized as a potential tool to aid in this effort, for example, by mapping the distribution of invasive species or identifying areas at risk of invasion. This paper provides a detailed explanation of a process for mapping the actual distribution of invasive species. This article presents a case studie on the detection of invasive Iris Pseudacorus L. using multispectral data captured by small Unmanned Aerial Vehicles (UAVs). The process involved spectral feature mapping followed by semi-supervised classification, which produced accurate maps of these invasive.

Problem

• Mapping invasive Iris Pseudacorus L. distribution using UAVs
• Detecting invasive species with multispectral data and classification
• Assessing biological invasions to protect water source sustainability

Innovation

• UAV-based multispectral data collection
• Spectral feature mapping for species detection
• Semi-supervised classification for accurate mapping