Head of the Scientific Chair "Use of Satellite Data and Biodiversity" AIBSI — Université de la Guyane

1) Research activities

This project relies on an integrated, multidisciplinary approach combining remote sensing, in situ measurements, and environmental DNA (eDNA) analysis to study the coastal and estuarine systems of French Guiana, which are characterized by high turbidity and cloud cover. The goal is to develop or improve models to provide robust analyses of physical and biological processes. The research project is structured around two main axes.

(1) Structuring multi-source observational data. The first task involves centralizing and ensuring the interoperability of in situ physicochemical and hydrodynamic data obtained from the deployment of instrumented buoys, low-cost sensors, and eDNA sampling systems. The second task aims to map turbidity, vegetation, biofilms, and other indicators using satellite imagery (e.g., Sentinel-2, Landsat, Pléiades) as well as LiDAR and hyperspectral imagery to complement the in situ dataset.

The third work package focuses on improving or developing new environmental models—based on statistical methods, AI, and data fusion—that will be calibrated and validated using in situ data, while accounting for scale transitions. (2) Analyzing hydro-morpho-sedimentary dynamics and biodiversity. Within this axis, the first task involves running hydrodynamic simulations using the previously developed models to analyze system evolution under the pressures of climate change.

The second task focuses on studying fishery biodiversity and coastal habitats by integrating existing models with satellite data and eDNA analyses. A third task is dedicated to modeling the dynamics of mangrove and palm forests using imagery combined with field data to enable spatiotemporal monitoring.

The successful candidate is expected to develop and lead major projects that contribute to advancing the research areas associated with this position. Two doctoral students and approximately six interns enrolled in master's programs will join the person recruited for the chair to form a solid project team over a three-year period.

2) Teaching Activities

The training project aims to build and structure a scientific community focused on remote sensing and image processing through three distinct training approaches.

The first approach involves delivering training modules to UG students enrolled in BUT or Master’s programs, helping them master the physical principles of remote sensing, identify various onboard sensors, handle multi-source imagery, perform pre-processing, and apply thematic analysis methods.

The second approach focuses on a thematic school dedicated to data fusion. The goal is to enable Master’s and PhD students to understand the specific characteristics of data sources and the challenges of data fusion, as well as to implement advanced methods such as machine learning and deep learning.