remote sensing
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Delivery method
- Teaching methods
- Contacts/Info
Knowledge of physics and geology is a suggested background requirement and, possibly, statistics. English language knowledge is a requisite as well. It would be useful to have attended before a basic GIS introduction.
Student evaluation will be based on a written test followed by an oral examination. The written test will be based on the theoretical content of the course. During the oral examination a hypothetical case study will be discussed with the student. An examination held in this way offers both the opportunity to test student’s knowledge of the course content and to drive the student to develop specific skills on a) analysis of complex problems, b) the development of new and original research approach and c) good communications. Both the tests will be evaluated with a mark from 1 to 30 (30/30).
The final mark will be obtained from the weighted sum of the two parts, according to the following:
Written test: 50%
Oral exam: 50%
Teaching objectives
This course includes an introduction to the main techniques of remotely-sensed data acquisition and interpretation, in particular focusing on environmental sciences applications.
Expected outcomes
The student will be able to:
1. manage and analyze imagery from both active and passive remote sensing missions,
2. calculate radiometric indexes
3. Perform supervised and unsupervised classifications.
Main topics are:
01. Remote sensing basics (4h)
02. Optical physics 101 (2h)
03. Sensors and satellite missions (4h)
04. Applications: Botanics, vegetation and landuse (6h)
05. Applications: Hydrology (6h)
06. Applications: Geology (6h)
09. Classification techniques: (supervised e unsupervised) (20h)
Main topics are:
01. Remote sensing basics (4h)
02. Optical physics 101 (2h)
03. Sensors and satellite missions (4h)
04. Applications: Botanics, vegetation and landuse (6h)
05. Applications: Hydrology (6h)
06. Applications: Geology (6h)
09. Classification techniques: (supervised e unsupervised) (20h)
Lessons are basically active lectures, held in one University seat and broadcasted on the other one. Some lectures include some practice on sample data (LANDSAT, SAR scenery and hyperspectral images, stereopaired aerial photographs etc.), using open source software (QGIS, ESA -SENTINEL2 TOOLBOX)
Lessons are basically active lectures, held in one University seat and broadcasted on the other one. Some lectures include some practice on sample data (LANDSAT, SAR scenery and hyperspectral images, stereopaired aerial photographs etc.), using open source software (QGIS, ESA -SENTINEL2 TOOLBOX)
The slides of the course will be uploaded on the University e-learning website as well as all the sample data and SW used during the course.
The following textbooks are strongly suggested:
James B. CAMPBELL Ph.D. – INTRODUCTION TO REMOTE SENSING – Third Ed. – Guilford Press. – course textbook
The slides of the course and any additional texts, will be made available on the on-line course platform.