Harnessing Robotics for EU Forest Habitats Monitoring
Simone Tolomei, Giovanni Di Lorenzo, Franco Angelini, Leopoldo de Simone, Emanuele Fanfarillo, Tiberio Fiaschi, Silvia Cannucci, Simona Maccherini, Paolo Remagnino, Claudia Angiolini, Manolo Garabini
AI summary
Problem
Traditional EU forest habitat monitoring is labor-intensive, time-consuming, and difficult to execute in rugged, unstructured environments, while existing robotic tools lack the capability to perform close-up structural and floristic surveys required by conservation protocols.
Approach
We deploy a quadrupedal robot to autonomously map forest plots and capture imagery, then process the data with geometric algorithms and a CNN to extract tree measurements and identify indicator plant species in compliance with EU monitoring standards.
Key results
- Successfully navigated steep slopes and cluttered forest terrain
- Accurately estimated tree counts and Diameter at Breast Height comparable to expert measurements
- Automated detection of key indicator plant species using a trained CNN model
- Reduced total monitoring time significantly compared to traditional expert-led surveys
Why it matters
Offers a scalable, standardized robotic workflow that can assist conservationists and policymakers in efficiently tracking biodiversity and habitat conservation status across the EU.
Abstract
This paper presents a novel approach to forest habitat monitoring using robotics and advanced data analysis techniques. We introduce a quadrupedal robot with LiDAR and onboard cameras to collect detailed data about forest structure and composition. The data is then processed using a combination of data analysis techniques and machine learning algorithms to perform a comprehensive dendrometric and floristic survey. Our approach provides an efficient and accurate method for assessing the ecological health of forest ecosystems. This work contributes to the ongoing efforts in habitat conservation and offers a promising tool for future environmental monitoring tasks.