Chemical Ecology

Earth Observation (EO) technologies including multi and hyperspectral imagery, coupled with GIS analysis, offer unprecedented opportunities for research in ecological monitoring and geospatial intelligence. But for the most useful and accurate EO products, ground truth data is crucial for training and validating remote sensing technologies. This research explores the integration of EO data, GIS analysis, and cutting-edge techniques such as computer vision and machine learning for the detection of environmental signatures and human location in diverse ecosystems. By harnessing these technologies, coupled with field and lab collected ground truth data, end-user can leverage diverse data to enhance environmental monitoring, identify human impact, and support informed decision-making for resource management and conservation efforts. This interdisciplinary approach not only facilitates the comprehensive analysis of ecological dynamics but also paves the way for innovative solutions to address contemporary challenges.