Lecturers: Marco Ciolli, Clara Cattoni
Summary: 18 hours lectures, Basis of Field sampling techniques and Spatial Ecological modelling using GIS and GPS; 10 hours field sampling work with measuring instruments and GPS; 12 hours practical Exercises with basis of GIS Ecological modelling with QGIS and R.
Module 1 (28 hours): Theoretical and practical basis of field sampling techniques and data collection and spatial ecological modelling. Exploration of field data and how they can be used/misused, How to perform sampling in the forest and GPS data collection in the field, basis of spatial ecological modelling, overview of various modelling techniques with special reference to landscape ecology, land use change modelling and scenarios development, Vegetation Analysis and cartography, basis of remote sensing for vegetation. Forest landscape change detection, Markov Chain scenarios and Landscape Ecology indexes and parameters.
Skills acquired: How to select sampling areas in the field and the reliability and limits of field data. Importance of data understanding and of sampling procedures. Data acquisition and critical interpretation of data sources. Improving GPS data sampling in the field.
Module 2 (12 hours): Practical exercises with a Special focus on Land use change, Vegetation Analysis and cartography, basis of remote sensing for vegetation, Past Forest landscape change detection, Production of Markov Chain scenarios future/past and Landscape Ecology indexes and parameters and modelling of different pollution sources. Overview of various modelling techniques. Comparison of different techniques, pros and cons. Alien species modelling and study cases. Interpretation of Model results and validation. GIS and R Practice on sample data.
Skills acquired: It will give a thorough introduction to the How to use and combine the data in a GIS to perform geospatial analysis and ecological modelling. How to use QGIS plugins and basic use of R as a GIS.
Course Schedule to be defined. Further information here.
Registration procedure: in order to register please, please send and e-mail to both dicamphd@unitn.it and phd.aes@unitn.it
Evaluation procedure: Written exam