Semi-autonomous systems enable advances in Earth Observation

Overview

Abstract

The description and characterization of the Earth system is a field that routinely leverages technological advances to improve our understanding of diverse ecosystems. One of the most promising areas of application is the use of autonomous and semi-autonomous platforms in precision agriculture. Such systems are capable of performing data collection, analysis, and decision-making tasks with minimal human intervention. The Hydrology, Agriculture, and Land Observation (HALO) laboratory at KAUST is at the forefront of this technology, employing a combination of satellites, UAVs and most recently - an agile robot from Boston Dynamics - in a semi-autonomous way to analyze and explore a range of ecosystems. A focus of current research is the collection of data for crop phenotyping, carbon accounting, and water-energy-carbon nexus studies. To this end, UAVs are employed to collect high-resolution images of crops from multi-spectral, hyperspectral, thermal, and LiDAR sensors, with the ground-based robot used to complement and augment these datasets. The combination of these platforms provides a more comprehensive and multi-view characterization of the studied system. Future goals aim to integrate collected information into a more intelligent and autonomous framework, allowing individual platforms to perform tasks such as collective exploration, perception, and localization.

Biography

Prof. McCabe's research focuses on issues related to water and food security, climate change impacts, precision agriculture, water resources monitoring and modeling, and the novel use of technologies for enhanced Earth system observation. The research undertaken in his group combines models and observations to answer questions on the distribution, variability and exchanges of water at local, regional and global scales, as well as the interactions with vegetation. CubeSats, unmanned aerial vehicles (UAVs) and in-situ monitoring techniques are all employed to monitor terrestrial processes, while a range of modeling and statistical approaches are used to understand and predict system behavior. Improved description and understanding of the water-food nexus is a key objective of his research.