Remote Sensing

Recommended Prerequisites

Basic knowledge on informatics and statistics

Teaching Methods

The fundamental concepts are taught on the theorico-practical classes, along with the resolution of some exercises and tutorials to clarify the subjects. The laboratorial-practical classes include the resolution of exercises and labs using remote sensing software.

Learning Outcomes

This discipline is intended to give to the students the basic knowledge how to use the methods and the techniques for extracting Geographic Information (GI) from space-based multispectral sensors. Additionally, some basic concepts relating to the extraction of GI from georeferenced dada from active sensors (LIDAR and RADAR) will be given. To help students appreciate that their knowledge and skills can be effectively applied in multiple contexts some labs will be presented covering the application areas: i) pré-processing of multispectral satellite images; ii) topographic and thematic map production and updating; iii) Remote Sensing for natural resource management and environmental monitoring.

Work Placement(s)

No

Syllabus

1. Fundamentals of Remote Sensing
a.Electromagnetic radiation principles
b.Sources and characteristics of optical remote sensing data
c.Principal optical remote sensing sensors and platforms

2. Pré-processing of multispectral satellite images
a.Image quality assessment and statistical evaluation
b.Radiometric correction
c.Geometric correction
d.Image enhancements
e.Multispectral transformations

3. Image classification methodologies
a.Supervised classification
b.Unsupervised classification
c.Contextual classification
d.Fuzzy classification
e.Thematic map accuracy assessment

4. Basic principles of active remote sensing
a.LiDAR
b.RADAR

5. Case studies
a.Data fusion and geometric correction of satelite images of high and medium spatial resolution
b.Urban and rural imperviousness from remote sensing data
c.Land-cover/Land-use change detection

Head Lecturer(s)

Gil Rito Gonçalves

Assessment Methods

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

Bibliography

1. John R. Jensen. Introductory Digital Image Processing: A Remote Sensing Perspective. 3^rd Edition. Prentice Hall, 2005. 526 pags.

2. Richards, J., Jia, X. Remote Sensing Digital Image Analysis: an introduction. 2006. 4th Edition. Berlin: Springer. 439 pags.

3. Fonseca, A. e Fernandes, J. Detecção Remota. 2004. Lisboa, Lidel, 224 pags.

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