Toward forest dynamics’ systematic knowledge: concept study of a multi-sensor visually tracked rover including a new insect radar for high-accuracy robotic monitoring

Abstract

Forest dynamics research is crucial in understanding the global carbon cycle and supporting various scales of forest decision-making, management, and conservation. Recent advancements in robotics and computing can be leveraged to address the need for systematic forest monitoring. We propose a common autonomous sensor box platform that enables seamless data integration from multiple sensors synchronized using a time stamp–based mechanism. The platform is designed to be open-source–oriented, ensuring interoperability and interchangeability of components. The sensor box, designed for stationary measurements, and the rover, designed for mobile mapping, are two applications of the proposed platform. The compact autonomous sensor box has a low-range radar that enables high-detail surveillance of nocturnal insects and small species. It can be extended to monitor other aspects, such as vegetation, tree phenology, and forest floor conditions. The multi-sensor visually tracked rover concept also enhances forest monitoring capabilities by enabling complex phenology monitoring. The rover has multiple sensors, including cameras, lidar, radar, and thermal sensors. These sensors operate autonomously and collect data using time stamps, ensuring synchronized data acquisition. The rover concept introduces a novel approach for achieving centimeter-accuracy data management in undercanopy forest conditions. It utilizes a prism attached to the rover, which an oriented robotic total station automatically tracks. This enables precise positioning of the rover and accurate data collection. A dense control network is deployed to ensure an accurate coordinate transfer from reference points to the rover. The demonstrated sample data highlight the effectiveness and high potential of the proposed solutions for systematic forest dynamics monitoring. These solutions offer a comprehensive approach to capturing and analyzing forest data, supporting research and management efforts in understanding and conserving forest ecosystems.