On successful completion of the course students are expected to be able to (i.e. learning outcomes):
1. Describe the types of measurements in remote sensing and explain why satellite images can be used to produce geographic information
2. Develop in an autonomous way a project to produce information based on satellite images with a spatial resolution from 1m to 1000m
3. Select the satellite and sensor more adequate to use on the production of different types of information on different spatial scales
4. Describe and apply classification algorithms of spectral, spatial and temporal patterns of satellite images in order to derive information
5. Assess and interpret the error within information derived from satellite images
6. Describe and evaluate the social economic benefits of remote sensing
Roberto André Pereira Henriques
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
There are no specific course prerequisites.
Jensen, J.R., 2015. Introductory Digital Image Processing: a Remote Sensing Perspective, 4ª Edição. New Jersey: Prentice-Hall.
Jensen, J.R., 2006. Remote sensing of the environment: an earth resource perspective, 2ª Edição. New Jersey: Prentice Hall.
Warner, T., M. Nellis and G. Foody (Eds.), 2009. The SAGE Handbook of Remote Sensing. London: SAGE Publications Ltd.
CCRS, Canadian Centre for Remote Sensing, 2007.Fundamentals of Remote Sensing , [Online]. Disponível em http://www.nrcan.gc.ca/sites/www.nrcan.gc.ca.earth-sciences/files/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf. Access date: Feb 13, 2013; Ustin, S., 2004.
Giri, C. (Ed.), 2012. Remote Sensing of Land Use and Land Cover: Principles and Applications. CRC Press.
The course has lectures and laboratory sessions. In the lectures, the instructor uses slides to illustrate the theory. The laboratory sessions consists on the use of a image processing software for deriving a thematic map based on spectral and/or temporal pattern analysis. The course also includes seminars to discuss the socioeconomic benefits of remote sensing.
The professor promotes an active and collaborative learning based on real world problem solving
The professor in one of the first classes will discuss with the student the evaluation method to be applied in this course. This semester, for continuous evaluation and for exam season, it was decided as follows:
- Test - 30%;
- Essay - Essay on socio-economic benefits of remote sensing within a specific theme selected by the student from a list provided by the Professor (individual) - 30%
- Project - Group project to solve a real problem by using satellite images and an image software chosen by the group. The problem has to be defined by the group - 40%
The project and the essay are to be presented in a seminar of the course. The students have to deliver one report for each work. The delivery date is defined by together by professor and students.
If the student fails in one of the components during continuos evaluation, he/she has to do all components in the xaem season, including the ones where he/she had success during continuous evaluation.
- Introduction to remote sensing and to the course
- Remote sensing principles
- Characteristics of Earth observation satellites and sensors
- Exploratory analysis
- Image pre-processing
- Band transformations
- Image information extraction
- Multi-temporal image analysis
- Socioeconomic benefits of remote sensing
- Practical exercises on satellite image processing
- Real world problem solving based on satellite image processing
Programs where the course is taught:
- Master degree program in Geospatial Technologies
- Master of Geographic Information Systems and Science
- PostGraduate in Smart Cities
- PostGraduate in Geographic Information Systems and Science
- PostGraduate in Information Systems Governance
- PostGraduate in Information Management and Business Intelligence in Healthcare
- PostGraduate in Geospatial Intelligence
- PostGraduate in Enterprise Information Systems