The aim of the course is to introduce and describe some basic Geomatics techniques for data acquisition and the methodologies in order to develop basic understanding of spatially referenced data analysis and to explore the potential of Geographic Information Systems application in environmental and social studies. The course builds up on the practical skills related to climate change, geospatial data mining and mapping. It also offers more detailed discussion and wider range of geospatial data visualization methods and introduces quantitative analysis of geospatial phenomena. Besides, the course aims at providing both a theoretical understanding and a practical introduction to the use of Geomatics technologies for monitoring and analysis of environmental problems. Students are exposed to the basic techniques and practical skills of extracting relevant information also from digital imagery. Educational Approach: The course consists of computer class-based instructor presentations, supervised in-class exercises, individual consultations and a major individual term project. The course is based on "learn-by-doing" approach: basic approaches and principles presented by instructors during laboratory-based classes will be accompanied by students' extensive individual work. Some outdoor activities will be carried out for improving the practical skill of the students and for applying what they have studied during the lessons.

In this course, the students will learn: • Basic concepts about data acquisition techniques • Basic concepts about statistics • Theory of spatial analysis • Correlation and probability estimation • Interpolation methods • Filtering techniques After attending the course, students will be able to: • Select the appropriate technique for data acquisition according to the selected purposes • Process and interpret the collected data • Describe and characterize one sample with the main statistical parameters • Estimate correlation between variables or parameters • Use the appropriate interpolation method as function of the considered problem • Apply different filtering techniques The lab activities deal with the environmental spatial analysis using climate change, hydraulic/hydrologic and petroleum data.

Pre-requisites are basic knowledge of geological and hydrogeological concepts, computing and geospatial data acquisition and visualization techniques.

The program is divided into 5 modules of lectures, for a total duration of 60 hours, and a series of laboratories and outdoor activities. DELIVERY MODES Lessons (30 h): Module 1: How to represent and collect data. The importance of reference systems and why is important to select a proper reference system will be described in order to perform correct analyses from spatial data. Some different Geomatics techniques will be described as possible methods to collect data in a direct way or for extracting secondary information (e.g. the precipitable water vapour from GNSS measurements). Module 2: Basic concepts of theory of observations Definitions of basic concepts on measurement and estimation of a random 1D/2D variable, mean, median, standard deviation, probability density function (PDF), cumulative density function (CDF). In this module, some aspects about the measurement of real objects and statistical interpretation are considered. Moreover, it will focused the attention on correlation, accuracy and precision concepts. Module 3: The interpolation/approximation methods The interpolation methods play a crucial role in environmental spatial analysis. Different interpolation techniques such as IDW, correlogram, variogram, different Kriging (simple, universal, linear, co-Kriging) methods will be deeply analyzed and considered, focusing the attention on different kinds of applications especially related to petroleum and hydrological activities. Module 4: Data visualization and interpretation Basic concepts related to GIS/database Design procedure will be provided. The modelling procedure, external model, conceptual model (Entity/Relationship), logical model (Relational DB), internal model, cardinality, basic rules for data modelling (complex attributes, multi-values fields), 3D GIS support and visualization Digital Terrain model and digital Surface model Definition of DTM and DSM, contents (point cloud, breaklines, dead zone, …), dense model, irregular (TIN) and regular grids, estimation of surface parameters (slope, aspect, contour lines, curvature,…), visibility analysis (line of sight, skyline, viewshade, observer, …) Module 5: Filtering, model building and inversion techniques. In this module, different filtering techniques (e.g. low-pass/high pass filter) and inverse problems will be considered, especially considering hydraulic and petroleum applications. During geological modelling, for instance, it is frequent the use of filtering techniques on well log data in order to identify characteristics of the log electrical signals, improve stratigraphic correlation or remove unwanted parts of the registration. On 2D or 3D datasets (e.g. maps, distributed petrophysical values) filters can be applied in order to identify the contribution of each spatial component of a given property, remove/enhance property features or calculate trends used during petrophysical distribution. During the lesson different filtering techniques are going to be presented and analyzed that are commonly used during well data analysis and 3D geological modelling. Lab activities (30 h): The lab activities take about half of the course and are focused on themes considered during the lessons. Specific applications will be done using Geomatics and GIS software especially for geospatial analysis, focusing on analysis tools, 3D data, working with rasters, projections, and environment variables. The works are carried out in team; finally, a special report must be completed.

The course is divided in 30h of lessons and 30h of lab activities, including outdoor sessions.

The main text consists of the lecture notes provided by the teacher and slides presented during the classes.

Modalità di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;

Exam: Compulsory oral exam; Group essay;

... The exam consists of 3 parts: 1. a discussion of exercises on the entire process of environmental spatial analysis with theoretical subjects (vote/30) 2. a discussion of exercises on hydraulic problems made during practical activities (vote/30) 3. a discussion of exercises on petroleum engineering made during practical activities (vote/30). The final grade is a weighted average of the results of the 3 previous evaluations with weights: P1=0.50, P2 =0.25, P3=0.25.

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.

Modalità di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;

The exam consists of 3 parts: 1. a discussion of exercises on the entire process of environmental spatial analysis with theoretical subjects (vote/30) 2. a discussion of exercises on hydraulic problems made during practical activities (vote/30) 3. a discussion of exercises on petroleum engineering made during practical activities (vote/30). The final grade is a weighted average of the results of the 3 previous evaluations with weights: P1=0.50, P2 =0.25, P3=0.25.

Modalità di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;

The exam consists of 3 parts: 1. a discussion of exercises on the entire process of environmental spatial analysis with theoretical subjects (vote/30) 2. a discussion of exercises on hydraulic problems made during practical activities (vote/30) 3. a discussion of exercises on petroleum engineering made during practical activities (vote/30). The final grade is a weighted average of the results of the 3 previous evaluations with weights: P1=0.50, P2 =0.25, P3=0.25.