Applied Remote Sensing

Year
1
Academic year
2021-2022
Code
02002518
Subject Area
Geospatial Information
Language of Instruction
Portuguese
Other Languages of Instruction
English
Mode of Delivery
Face-to-face
Duration
SEMESTRIAL
ECTS Credits
6.0
Type
Compulsory
Level
2nd Cycle Studies - Mestrado

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 consist 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 aply the Remote Sensing Techniques in the extraction of Geographic Information (GI) from images captured by mutispectral optical sensors located in spatial platforms. The basic knowledge to the extraction of GI from grorreferenced 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 pre-processing; ii) production and update of thematic and topographic cartografy of rural and urban areas and iii) management of natural resources 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

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

 

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

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

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