Chemical Ecology

Earth Observation (EO) technologies—including multi- and hyperspectral imagery—coupled with GIS analysis, offer unprecedented opportunities for ecological monitoring and geospatial intelligence. However, to generate the most accurate and meaningful EO products, robust ground truth data is essential for training, calibration, and validation of remote sensing outputs. In this context, chemical ecology and spectroscopy play a critical role, as detailed spectral and chemical signatures of plants, soils, fungi, and other organisms provide indispensable benchmarks for interpreting remotely sensed data. Proximal sensing techniques, such as field- and lab-based spectroscopy, capture the fine-scale chemical and physiological variation that underpins ecological dynamics, serving as a vital bridge between raw EO data and biological meaning.

This research explores the integration of EO data, GIS analysis, chemical ecology, and cutting-edge approaches such as computer vision and machine learning for detecting environmental signatures and even human presence across diverse ecosystems. By harnessing multi-scale data—including spectral and chemical ground truth collected through field sampling and laboratory analyses—end-users can enhance environmental monitoring, quantify human impact, and support informed decision-making in resource management and conservation. This interdisciplinary approach not only facilitates comprehensive analysis of ecological and biogeochemical processes but also paves the way for innovative solutions to address contemporary environmental challenges.