A Post-Doc position under the supervision of Dr. Duccio Rocchini is available in the GIS and Remote Sensing Platform (Platform chief: Dr. Markus Neteler), Department of Biodiversity and Molecular Ecology of the Research and Innovation Centre. The position is related to the FP/ European Project EU BON Building the European Biodiversity Observation Network (http://www.eubon.eu/).
Focus 1 – Advanced tools for interpreting satellite or aerial imagery using environmental datasets and machine learning methods Description: Remotely sensed images are typically classified on the basis of spectral reflectance data alone; however other environmental datasets (e.g. DEM, soils) are widely available and have the potential to inform and refine such classifications. Using such data, Random Forest (RF) or other machine learning methods can produce much finer vegetation differentiation and higher accuracy than would otherwise be possible, allowing ecologically detailed habitat mapping over extensive areas. These methods will be further developed to incorporate information about temporal variation in reflectance and in vegetation, and potential extension to marine and freshwater habitat mapping will be explored. Application software will be created, which will be applied to focal observatory sites, applied more generally in WP4, and made available for wider dissemination.
Focus 2 – Integrative analyses of distribution status and trends The Post-Doc researcher should perform integrative analyses and metaanalyses of species distribution data in relation to land use and climate data, including remotely sensed predictors (based mostly on optical multi- and hyperspectral imagery as well as LiDAR data) of biodiversity. These analyses will incorporate spatial and also phylogenetic relationships. To address the multiscale nature of the problem, we will apply existing as well as new methods, including hierarchical models, hybrid models (combining niche models and geometric models) as well as a set of statistical models (AquaMaps, GLM, Boosted Regression Trees). Ultimately we aim to cover various species belonging to different taxa across the terrestrial, freshwater, and marine domains in a single modelling approach.
- Classification of remotely sensed data and advanced modelling techniques
- Species Distribution modelling and application of remote sensing for biodiversity estimate
- Development of theoretical and empirical algorithms for spatial ecology under free and open source software
- Writing papers and reports on spatial statistical achievements
Project collaboration contract, 22 months (Contratto di collaborazione coordinata e continuativa a progetto)
Deadline for application: September 30th, 2013.