Applied Remote Sensing

Recommended Prerequisites

Basic knowledge on informatics and statistics.

Teaching Methods

The fundamental concepts are taught on the theoretical-practical classes, along with the resolution of exercises and tutorials to clarify the subjects. The laboratorial-practical classes include the resolution of exercises and labs using remote sensing software. The evaluation includes a practical component and an exam. The practical component consists on the execution of labs, initiated in the laboratorial-practical classes and concluded by the students outside the classes, which are accompanied by the elaboration of reports describing the theoretical context and all the work performed.

Learning Outcomes

The aim of the curricular unit is to give the students’ knowledge on how to apply the Remote Sensing Techniques in the extraction of Geographic Information (GI) from images captured by multispectral optical sensors located in spatial platforms. The basic knowledge to the extraction of GI from georeferenced data captured by active sensors (LiDAR and RADAR) is also studied. From the applications point of view, the students are expected to perform laboratory work in the following areas: i) satellite image preprocessing; ii) production and update of thematic and topographic cartography of rural and urban areas and iii) management of natural resources and environmental monitoring.

Work Placement(s)

No

Syllabus

1. Fundamentals of Remote Sensing
   1. Electromagnetic radiation principles
   2. Sources and characteristics of optical remote sensing data
   3. Principal optical remote sensing sensors and platforms
2. Preprocessing of multispectral satellite images
   1. Image quality assessment and statistical evaluation
   2. Radiometric correction
   3. Geometric correction
   4. Image enhancements
   5. Multispectral transformations
3. Image classification methodologies
   1. Supervised classification
   2. Unsupervised classification
   3. Contextual classification
   4. Fuzzy classification
   5. Thematic map accuracy assessment
4. Basic principles of active remote sensing
   1. LiDAR
   2. RADAR
5. Case studies
   1. Data fusion and geometric correction of satellite images of high and medium spatial resolution
   2. Urban and rural imperviousness from remote sensing data
   3. Land-cover/Land-use change detection

Head Lecturer(s)

Cidália Maria Parreira da Costa Fonte

Assessment Methods

Assessment
Laboratory work or Field work: 40.0%
Exam: 60.0%

Assessment
Exam: 40.0%
Laboratory work or Field work: 60.0%

Bibliography

Chuvieco, E., 2020. Fundamentals of Satellite Remote Sensing : An Environmental Approach. CRC Press, Boca Raton. 432 pages, ISBN: 978-0-42-950648-2

James B. Campbell and Randolph H. Wynne. Introduction to Remote Sensing. Sixth Edition. The GuilfordPress, 2022. 675 pages. ISBN 978-0-13-405816-0

John R. Jensen. Introductory Digital Image Processing: A Remote Sensing Perspective. 4th Edition. Prentice Hall, 2015. 526 pages. ISBN 978-0-13-405816-0

Richards, J.A., 2022. Remote Sensing Digital Image Analysis. Springer International Publishing, Cham. 567 pages. ISBN: 978-3-030-82326-9

Kurt Menke. Discover QGIS 3.x. Second Edition. Locate Press. 2022. 430 pages. ISBN: 978-0-98-680524-0

 

Gonçalves, G, (2013). Cadernos de Detecção Remota. FCTUC