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Dr. Giuseppe Amatulli
Former Postdoctoral Fellow
giuseppe.amatulli@sdstate.edu

Research Interests: forest, forest fire, forest ecology, remote sensing, gis

Giuseppe is a forest scientist and spatial modeller. His research activity is mainly dedicated to wildland fire with special emphasis on wildfire occurrence and pattern recognition, and wildfire risk assessment based on human and bio-physical parameters.  He daily dealing with high resolution data in the context of complex and modern modelling techniques using stand-alone implementation process in under Linux environment.

Education

He obtained  the degree of MSc-Forestry on April 2000 with a specialisation in environment management ans soil conservation. In September 2001 he won a fellowshipgrant from University of Perugia (Italy) in order to follow a MSc in Geo-Information Science at Wageningen University. In April 2003 he defended a MSc thesis dealing in the use of Remote Sensing and GIS to prevent forest fire. In 2005, he held an International PhD dealing with “Non-parametric statistical analysis to estimate fuel models and wildfire occurrence in the framework of fire risk assessment”.

Working experiences

From 2006 to 2011 he had held a post-doc position at the Institute for Environment and Sustainability of the Joint Research Centre. In the framework of the European Forest Fire Information System (EFFIS) he provides scientific and technical support for development, implementation and improvement of the Fire Weather Index applied to the European territory. He has recently dealt with past and future forest fire trends under potential climate change scenarios, handling ECMWF and PRUDENCE climate data. This included modeling forest species distribution, current, potential and future trends using WorldClim dataset. In the beginning of 2012 he starts a post-doc position at GIScCE under the supervision of Prof. David Roy. In particular he will deal with two NASA research projects entitled “Web-enabled Landsat data (WELD) - a consistent seamless near real time MODIS-Landsat data fusion for the terrestrial user community” and “MODIS Burned Area Maintenance, Improvement and Validation”.

Teaching

Teaching intensive practical training in different free and open sources tools to carry out spatial analysis in ecology using remote sensing and GIS data, emphasizing scripting routines to achieve large scale data analysis and standalone implementation processes.  More info at http://www.spatial-ecology.net.