Subject objectives
This course is intended to introduce students to the characteristics of remotely sensed imagery and the main techniques available for their processing. After successfully passing this course, students should be able to:
Know the main satellite platforms and their sensors.
Be familiar with the characteristics of remotely sensed imagery.
Use digital image processing techniques like automatic and semi-automatic classification, rectification and orthorectification, use of spectral indexes.
Contents
Basic contents included in the programme documentation:
Fundamentals and physical principles of remote sensing.
Correction of remotely sensed imagery.
Information extraction: classification and spectral indexes.
Quality assessment.
These basic contents are developed along the course duration following the following structure of theoretical lessons:
Unit 1. Platforms, sensors and associated images. The Copernicus programme (3 h).
Unit 2. Image correction levels (1.5 h).
Unit 3. Data processing I: spectral indexes (1.5 h).
Unit 4. Data processing II: classification (3 h).
And the following list of practical sessions using Google Earth Engine:
Session 1 - Introduction (3 h).
Session 2 - Image collections (3 h).
Session 3 - Spectral indexes (3 h).
Session 4 - Classification (3 h).
Basic and complementary bibliography
Basic bibliography
Schowengerdt, R. A. (2007). Remote Sensing. Models and Methods for Image Processing (3rd ed.). Academic Press, Elsevier.
Wegmann, M., Benjamin Leutner, Stefan Dech (eds.), 2016. Remote sensing and GIS for ecologists : using open source software. Pelagic Publishing.
Liu, J.G., Philippa J. Mason, 2016. Image Processing and GIS for Remote Sensing: Techniques and Applications, Second Edition. Wiley/Blackwell.
Chuvieco Salinero, Emilio, 2008. Teledetección ambiental : la observación de la Tierra desde el espacio. Ariel.
Complementary bibliography
Camara, G., Simoes, R., Souza, F., Sanchez, A., Santos, L., Andrade, P.R., Peletier, Ch., Carvalho, A., Ferreira, K., Queiroz, G., Maus, V., 2022. sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series.
Simoes, R., Camara, G., Queiroz, G., Souza, F., Andrade, P.R., Santos, L., Carvalho, A., Ferreira, K. 2021. Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sensing 13, p. 2428.
Thenkabail, P.S, John G. Lyon, Alfredo Huete (Eds.), 2019. Hyperspectral indices and image classifications for agriculture and vegetation. CRC Press.
Jensen, John R., 2014. Remote sensing of the environment : an earth resource perspective. Pearson Education.
Richards, John A., 2013. Remote sensing digital image analysis : an introduction. Springer.
Chuvieco, E., Alfredo Huete, 2010. Fundamentals of satellite remote sensing. Taylor & Francis.
Competencies
General and basic competences
CG01 - Capacity to search and select the information that is useful to resolve complex problems, handling fluently the bibliographic sources and the available statistics.
CG02 - Capacity to apply the theoretical knowledge to address research problems in the field of the geographic and territorial analysis.
CG03 - Capacity of acquire pertinent geographical information from different sources and integrate it in geospatial databases.
CG04 - Capacity to schedule and carry out research activities in the field of the studies and territorial analyses.
CB7 - To know how to apply the acquired knowledges and to address problems in new contexts, or in less known contexts inside extended (or multidisciplinary) ones, that are related with his area of study.
CB8 - To be able to integrate knowledges and confront to the complexity to make judgements from an information that, being incomplete or limited, includes aspects from the social and ethical responsibilities.
CB9 - To know how to communicate his conclusions, together with their rationales, to both specialists and no specialist in a clear way.
CB10 - To possess the skills of learning that allow students to continue studying in an autonomous way.
Transversal competences
CT01 - Capacity of researching and selecting information.
CT04 - Knowledge of the main cartographic sources and of territorial information.
CT05 - Handling the main software of Geographic Information Systems.
Specific competences
CE05 - Training to extract, analyse and present the necessary information for taking decisions at planning, territorial and environmental management.
CE09 - Capacity for the design of politics of planning and land use management, as well as of property management.
Teaching methodology
Theoretical lectures (competences CG02, CG04, CB7, CB8, CB9, CB10, CT04, CE09)
Practical sessions (competences CG01, CG03, CB7, CB8, CB9, CB10, CT01, CT04, CT05, CE05, CE09)
Theoretical and practical sessions will be complemented by:
Use of the virtual campus (Moodle).
Practical cases and projects.
Individualized and group tutoring.
Autonomous study.
Evaluation of competence.
Evaluation system
Assessment of students’ performance will be based on two components:
Continuous assessment, based on practical assignments along the duration of the course. Will account for 70% of final grade.
Written test. Will account for 30% of final grade.
A minimum of 5 points out of 10 will be required for a passing grade. There will be no minimum required grade in each of the two components. The criteria and requirements will be the same in 1st and 2nd opportunities. Students enrolled for a second time in the course can ask for the grade of one of the elements to be kept.
Students exempt of attendance to classes will follow the same assessment system.
The USC Norm for Assessment of Academic Performance will be automatically applied if fraud or fabrication of assessment materials is detected.
Studying time and personal work
This course includes 9 hours of theoretical lectures, 12 hours of practical lectures, and implies around 54 hours of personal work.
Subject study recommendations
It is advisable that students have access to a personal computer in order to install the software applications used in class.
Observations
