Dune Vegetation Identification from Satellite high-resolution images


Recent improvements in spectral and spatial resolution of satellite imagery open new and exciting prospects for large-scale environmental monitoring. Still this potential is largely unused in dune ecogeomorphology, due to the challenges related with the small size and density of dune plants and the complexity and heterogeneity of the existing species. Machine learning techniques and subpixel classification methodologies, like the Random Forest Soft Classification (RFSC), have shown promising results in similarly challenging environments in terms of plant size and heterogeneity, with high accuracies in subpixel fractional abundance of marsh-vegetation species. Even though subpixel classification could improve monitoring biodiversity from satellite imagery, similar approaches have never been tested for dune environments.

These challenges and gaps inspired the DEVISE exploratory project, built around the idea of testing subpixel classification methods for dune plant species identification using high-resolution satellite imagery. Building on the demonstrated capacity of RFSC to identify plant species distribution in marsh environments, we plan to transfer and adapt the methodology to the more highly mixed and challenging environment of Mediterranean coastal dunes, and more specifically to the barrier system of Ria Formosa, a wetland in South Portugal with high ecological and socio-economic significance (Natural Park (1987), Ramsar and Natura 2000 site). Two data-collection campaigns will be performed, corresponding to low and high plant growth phases (autumn and late-spring), including: a) on-demand acquisition of high-resolution WorldView 2 imagery and b) extensive fieldwork on plant reflectance measurement and species mapping.

Project Main Goals:

Test initial scientific hypothesis that RFSC methods can be successfully used to identify dune plant species from high-resolution satellite imagery.

Optimise the subpixel classification methodology, assess its predictive capacity and identify potential limitations.

Improve current capacity in monitoring coastal dune vegetation through satellite data.


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University of Seville: Juan Bautista Gallego-Fernandez 

Project Consultants: Sonia Silvesti (University of Bologna) | Zhincheng Yang (University of Georgia)