Thesis

Analysing mangrove spectral signatures in French Guiana from satellite image time series

Mangroves are coastal ecosystems consisting of assemblages of salt-tolerant trees and shrubs that grow in the intertidal regions of the tropical and subtropical coastlines. They represent a distinctive vegetation type, which plays a crucial role in coastal protection, water purification, carbon sequestration, or biodiversity preservation by providing habitat for various species. Despite their key role, mangroves are threatened by various factors including human activity, climate change, and disease proliferation. Mapping mangroves and understanding their dynamics is therefore of utmost interest for ecosystem preservation.

Objectives

In this master thesis, we are interested in studying the suitability of Sentinel-2 time series to analyse mangrove spectral signatures in the French Guyana coastline.

References

[1] Sainte Fare Garnot, V., & Landrieu, L. (2020). Lightweight temporal self-attention for classifying satellite images time series. In Advanced Analytics and Learning on Temporal Data (AALTD): 5th ECML PKDD Workshop, (pp. 171-181). Springer International Publishing.

[2] Lafabregue, B., Weber, J., Gan¸carski, P., & Forestier, G. (2022). End-to-end deep representation learning for time series clustering: a comparative study. Data Mining and Knowledge Discovery, 36(1), 29-81.

[3] Maurya, K., Mahajan, S., & Chaube, N. (2021). Remote sensing techniques: Mapping and monitoring of mangrove ecosystem—A review. Complex & Intelligent Systems, 7, 2797-2818.

[4] Viennois, G., Proisy, C., Feret, J. B., Prosperi, J., Sidik, F., Rahmania, R., … & Gaspar, P. (2016). Multitemporal analysis of high-spatial-resolution optical satellite imagery for mangrove species mapping in Bali, Indonesia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3680-3686.