Laboratory course on geographic information systems (GIS) and remote sensing.
Intro on geographic information, coordinate systems and geographic projections, geographic information analysis, vector and raster systems, data analysis functions.
Intro of remote sensing (RS). Physical principals of remote sensing, main satellites and sensors, image correction techniques. Analysis of Landsat, Sentinel and SAR data. Indexes. Final exercise on GIS and RS data analysis.
Handouts and learning material provided by the teacher, selected scientific papers (on the Moodle platform), ArcGIS (ESRI) user manuals.
Learning Objectives
The course provides the basis for the student to develop an independent capability of understanding, designing and implementing GIS and RS tools for the analysis of geological and geomorphological data typical of the activities of the skilled professional or scholar geologist.
Prerequisites
A basic knowledge of optics and computer science is recommended.
Teaching Methods
The course is based on short lectures followed by practical computer lab sessions on the daily topics, to be done by using ArcGIS (ESRI) as well as other open source sw (Google Earth, SAGA GIS) on real datasets.
Type of Assessment
Final examination is based on the discussion and defense of a practical work, to be done alone or in small groups by the students, concerning the analysis of a chosen area with GIS and RS tools.
Course program
Geographic data intro. First law of geography, data autocorrelation and basics of geostatistics. Carthographic projections, coordinate systems, the Proj.4 standard.
Definition and components of a GIS. Types of data. Vector, raster, alphanumeric, multimedia. Functional, conceptual and logical analysis of a GIS. Basic principles of geographic data elaboration with practical exercises in ArcGIS (ESRI).
Intro to remote sensing. Type of sensors and satellites. Physics of RS, fundamental laws, electromagnetic spectrum, concepts of spectral band and spectral signature, active and passive sensors. Example of RS data and their use in geology.
Spatial, radiometric, spectral and temporal resolutions in RS data. RS data analysis. Geometric, spectral and calibration corrections in RS. Optical data analysis with Landsat 7/8 and Sentinel-2 images. Radiometric indexes for vegetation, water and rock types. RS image classification with supervised and unsupervised methods.
Intro on radar image analysis. SAR, interferometry and multi-interferometry exploitation for landscape monitoring and for the mitigation of geological and natural risks.