REMOTE SENSING

It builds upon and extends the cartographic and GIS competencies typically acquired at Bachelor's level, developing advanced skills in the acquisition, processing and interpretation of remotely sensed data from satellite, airborne and UAV platforms. The course equips students with the technical and methodological tools required for environmental and territorial monitoring, in direct support of the programme's green transition objectives. The competencies gained are applicable in professional and research contexts related to land use planning, natural resource management, natural hazard assessment and climate change monitoring.

1. Knowledge and understanding

- Know the physical principles of electromagnetic radiation and its interaction with natural surfaces and the atmosphere.
- Know the main sensor families (multispectral, hyperspectral, thermal, SAR/radar) and operational platforms (Sentinel, Landsat, MODIS, UAVs).
- Know the main pre-processing methodologies (geometric, atmospheric and radiometric correction) and approaches for image classification and change detection.
- Know the techniques for extracting thematic information related to geomorphological, vegetational, urban and environmental features.

2. Ability to apply knowledge and understanding

- Apply techniques to process and analyse digital images acquired from different platforms.
- Extract geomorphological and environmental information through spectral indices, classification algorithms and multi-temporal analysis.
- Use image processing software (SNAP, Google Earth Engine, QGIS) and integrate remotely sensed data within GIS platforms.
- Select the most appropriate sensor and methodology for a given environmental monitoring objective.

3. Making judgements

- Critically evaluate the quality and suitability of remote sensing products, accounting for their limitations and uncertainties.

4. Communication skills

- Present and discuss remote sensing analyses and results in written and oral form using appropriate technical terminology in English.

5. Learning skills

- Autonomously consult technical documentation and scientific literature to keep up to date with new platforms, sensors and methodologies.

A basic knowledge of GIS systems and spatial data, as well as fundamentals of physics (electromagnetic waves) and mathematics (linear algebra, basic statistics), as normally acquired in Bachelor's Degree Programmes in environmental engineering or related fields, is recommended. No prior knowledge of remote sensing is required. Familiarity with at least one programming environment (e.g., Python) is useful for laboratory activities but not mandatory.

Module 1 – Physical foundations of remote sensing (approx. 12 h): electromagnetic spectrum and radiation laws; spectral reflectance of natural surfaces (vegetation, soil, water, snow, urban areas); atmospheric effects and correction strategies; geometric principles and spatial resolution.
Module 2 – Sensors and platforms (approx. 10 h): passive optical multispectral and hyperspectral sensors; active SAR and LiDAR sensors; thermal remote sensing; major operational satellite constellations (Sentinel-1/2/3, Landsat, MODIS, Planet) and UAV systems; data access and open-data repositories.
Module 3 – Image pre-processing (approx. 10 h): radiometric calibration and conversion to reflectance; atmospheric correction; geometric correction and orthorectification; cloud masking; analysis-ready data (ARD).
Module 4 – Image analysis and classification (approx. 14 h): spectral indices (NDVI, EVI, NDWI, NBR, LST); unsupervised (k-means, ISODATA) and supervised classification (Random Forest, SVM, Maximum Likelihood); accuracy assessment; change detection; time-series analysis.
Module 5 – Environmental applications (approx. 14 h): land use/land cover mapping; vegetation monitoring; water body mapping; urban heat islands; natural hazard monitoring (floods, wildfires, landslides); carbon stock estimation; integration with climate and environmental models.
Module 6 – Laboratory and project work (approx. 12 h): hands-on exercises with SNAP, QGIS and Google Earth Engine (Python API); individual or group project on an environmental monitoring case study; presentation and discussion of results.

- J.R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, 4th ed., Pearson, 2015.
- Lecture notes and teaching materials provided by the instructor, available on Moodle.

Learning is assessed through a combination of laboratory practical work and a final examination. During the course, students carry out guided exercises and develop an individual or group practical project, consisting in the design and execution of a full remote sensing analysis pipeline applied to an environmental monitoring case study. The project is submitted as a written technical report. The final examination, in written and/or oral form, covers the theoretical and methodological content of the course. Students are required to demonstrate knowledge of the physical principles, technologies and methodologies covered, and to critically discuss the methodological choices made in their practical project. The final grade takes into account both the project assessment (40%) and the written/oral examination (60%). Attendance at laboratory sessions is required to submit the project for assessment.

written and oral

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

18–22: The student demonstrates adequate knowledge of the main course topics and a sufficient ability to apply basic remote sensing techniques. The practical project is completed correctly but shows limited critical interpretation. Oral exposition is clear but not always supported by precise technical terminology.
23–26: The student demonstrates good knowledge of the theoretical and methodological content and a fair ability to apply remote sensing techniques. The project shows a sound methodological approach and reasonable ability to interpret results. Oral exposition is fluent and uses appropriate scientific terminology.
27–30: The student demonstrates thorough and well-integrated knowledge of the course content, with excellent applied and critical skills. The project shows methodological rigour and depth of interpretation. Oral exposition is precise and well-structured.
30 with honours: The student demonstrates outstanding mastery of all course content, exceptional critical thinking and the ability to connect concepts across the course with current professional and research practice. Both the project and final examination are of exceptional quality.

The course is structured into frontal lectures (approx. 46 h) and computersessions (approx. 26 h). Lectures cover the theoretical and methodological content of the course, supported by slides, real imagery examples and environmental case studies. Materials are made available on the University e-learning platform (moodle.unive.it) before each session. Laboratory sessions allow students to apply the techniques presented in lectures to real datasets using SNAP, QGIS and Google Earth Engine (Python API). Lab exercises are accompanied by detailed instruction sheets available on Moodle. The course places particular emphasis on connecting theoretical understanding with operational practice, consistent with Master's Degree level: students are expected not only to apply processing workflows but to justify methodological choices and critically interpret results in their environmental engineering context. Guest seminars by professionals or researchers may be organised.

The course is taught entirely in English. All teaching materials, slides and exercise datasets are provided in English via Moodle. Students are expected to read technical documentation and scientific papers in English autonomously. Office hours are communicated at the beginning of the course and updated on the course webpage.