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Advanced Remote Sensing

Code: 43382
Credits: 9
2026/2027
Degree programme Type Course
Remote Sensing and Geographical Information Systems OP 1

Contact lecturer

Name :
Xavier Pons Fernandez
Email :
xavier.pons@uab.cat

Teaching staff

Pere Serra Ruiz

Teaching staff (external to UAB)

Mario Padial
Carolina Gabarró
Jordi Isern
Bernat Codina
Giorgi Khazaradze
Ricardo Díaz-Delgado
Albert García Benadí
Joan Bech

Group languages

You can consult this information at the end of the document.

Prerequisites

Prerequisites are not required

Objectives

This optional module, expands the knowledge acquired in the module of obtaining geographic information of this same master's degree from the study of techniques and applications specific to remote sensing in fields such as meteorology, oceanography, geology and the study of vegetation.

At the end of the course, the student will be able to:

  • Apply the methodologies to alleviate the different sources of error in order to visualize and extract physical parameters of the received data.
  • Apply remote sensing techniques to different fields of research and applied.

Learning outcomes

  • CA12 (Create Remote Sensing products, demonstrating a comprehensive view of problems, proposing innovative solutions and making appropriate decisions based on their knowledge and judgments.) Create Remote Sensing products, demonstrating a comprehensive view of problems, proposing innovative solutions and making appropriate decisions based on their knowledge and judgments.
  • CA13 (Prepare technical and scientific reports for the analysis of the territory, which facilitate the presentation and explanation of the information in a clear and appropriate way to expert and amateur audiences.) Prepare technical and scientific reports for the analysis of the territory, which facilitate the presentation and explanation of the information in a clear and appropriate way to expert and amateur audiences.
  • KA15 (Define the physical principles, sensors, and types of imagery used in meteorological Remote Sensing, including Doppler radar, visible, infrared, water vapour images and RGB compositions.) Define the physical principles, sensors, and types of imagery used in meteorological Remote Sensing, including Doppler radar, visible, infrared, water vapour images and RGB compositions.
  • KA16 (Recognise both passive and active sensor techniques (SAR, altimeters), for the observation of ocean parameters and marine dynamic phenomena such as currents or surface temperatures.) Recognise both passive and active sensor techniques (SAR, altimeters), for the observation of ocean parameters and marine dynamic phenomena such as currents or surface temperatures.
  • KA17 (Select uses of Remote Sensing in geological analysis and the study of vegetation using satellite images, taking into account the various forms of social, cultural, linguistic, gender or origin inequality.) Select uses of Remote Sensing in geological analysis and the study of vegetation using satellite images, taking into account the various forms of social, cultural, linguistic, gender or origin inequality.
  • SA17 (Use different types of meteorological images (visible, infrared, water vapour) and different remote sensors (passive and active) to identify atmospheric patterns relevant to the weather and climate analysis, and to ocean variables such as temperature, currents or other phenomena.) Use different types of meteorological images (visible, infrared, water vapour) and different remote sensors (passive and active) to identify atmospheric patterns relevant to the weather and climate analysis, and to ocean variables such as temperature, currents or other phenomena.
  • SA18 (Apply methodologies to detect relevant geological changes (volcanic activity, floods, snow and ice) through the practical interpretation of satellite image time series.) Apply methodologies to detect relevant geological changes (volcanic activity, floods, snow and ice) through the practical interpretation of satellite image time series.
  • SA19 (Use different spectral and multitemporal analysis techniques in order to evaluate the state and evolution of vegetation cover in various contexts (drought, fires, etc.), as well as the general dynamics of land use and cover.) Use different spectral and multitemporal analysis techniques in order to evaluate the state and evolution of vegetation cover in various contexts (drought, fires, etc.), as well as the general dynamics of land use and cover.

Contents

RS & METEOROLOGY. TECHNIQUES & EXAMPLES

1. Introduction
2. Classical meteorology
3. Interpretation of satellite images
  3.1. Images in the visible spectrum
  3.2. Images in the thermal infrared
  3.3. Images of water vapor
  3.4. Compositions RGB
4. The weather radar
  4.1. Propagation of the microwave into the atmosphere
  4.2. The radar equation
  4.3. Observations of the Doppler radar


RS & OCEANOGRAPHY. TECHNIQUES & EXAMPLES


1. Introduction
2. Fundamentals of Oceanography
  2.1. Descriptive oceanography
  2.2. Dynamic oceanography
  2.3. Remotely observable phenomena
3. Observation with passive sensors
  3.1. Observation in the visible spectrum
  3.2. Observation in the infrared spectrum
  3.3. Observation in the microwave spectrum
4. Observation with active sensors
  4.1. Generalities
  4.2. The dispersometer
  4.3. The SAR
  4.4. The altimeter
5. Application: sea currents


RS & GEOLOGY. TECHNIQUES & EXAMPLES

Contents based on a series of guided practical exercises dedicated to showing examples of the use of Remote Sensing in the monitoring of volcanoes, episodes of floods, monitoring of the evolution of snow and ice, etc.

RS & VEGETATION. TECHNIQUES & EXAMPLES

1. The problematic thematic/spectral classes. Land uses and land coverings.
2. Specific techniques.
  2.1. Spectral separability
  2.2. Vegetation indexes
  2.3. Tasseled Cap Transformation.
3. Prevention of forestfires.
4. Active fire.
5. Techniques of analysis of changes in time.
  5.1. Assessment of burnt surfaces.
  5.2. Studies of regeneration of vegetation after forest fires.
6. Analysis and multitemporal classification of roofs (example of crops)
  6.1. Spectral signatures
  6.2. Phenology and temporary signatures
  6.3. Classification
  6.4. Analysis of changes
  6.5. Enrichment of databases
7. Examples of practical applications

Learning activities and methodology

Title Hours ECTS Learning outcomes
Tutorials 4 0.16
Classroom practices 60 2.4
Master classes / exhibitions 48 1.92
Personal study 22 0.88
Personal study 90 3.6

Principal working language: spanish (spa), although the bibliographic materials may be in other languages, mostly English.

In this module there are 3 groups of learning activities:

  • Targeted activities consist of classes of theory and practices that will be carried out in a specialized computer room. At the beginning of each of the subjects that make up the module, the teachers will explain the structure of the theoretical-practical contents, as well as the evaluation method.
  • Supervised activities consist of classroom practices that will allow you to prepare the work and exercises of each subject, as well as tutorial sessions with the teachers in case the students request it.
  • Autonomous activities are a set of activities related to the elaboration of works, exercises and exams, such as the study of different material in the form of journal articles, reports, data, etc., defined according to the needs of autonomous work of each student.
Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Practical works 80%- 90% 0 0 CA12, CA13, KA15, KA16, KA17, SA17, SA18, SA19
Theoretical exam 10%-20% 1 0.04 CA12, KA15, KA16, SA17

This module does not incorporate single assessment.

The evaluation of this module consists of the following system:

  • The realization of 2 exams, that will be between 10 % and 20 % of the final mark and that will include the theoretical and practical contents carried out. An exam not reaching the minimum mark of 5 out of 10 must be repeated the day assigned by the teacher of the subject.
  • The accomplishment of different practical works proposed throughout the teaching of the module and delivered within the fixed term, that will be between 80 % and 90 % of the final mark. A correct formal presentation and careful preparation will be taken to account.

Aspects to take into account.

  • Regular class attendance is highly recommended in order to follow the lessons properly. Follow on through streaming is only justified in cases of physical impossibility for face-to-face assistance, since an important part of the experiences and learning are fully achieved through contact with the teaching staff and classmates.
  • If you have to deliver practical work, this delivery must be done within the deadlines for them to be evaluated.
  • On carrying out each evaluation activity, Lecturers will inform of the procedures to be followed for reviewing all grades awarded, and the date on which such a review will take place.

Extraordinary exams.

  • The exams or other evaluation procedures not reaching the minimum mark of 5 out of 10 must be repeated. This extraordinary exam is unique.
  • Students will have the opportunity to take a extraordinary exam the day or days scheduled by the faculty.

Cheating: Copies and plagiarisms.

  • The commission of any irregularity in an assessment activity (academic fraud, plagiarism or improper use of AI, unless such use is expressly authorised in the course guide) that may lead to a significant change in the grade will result in that assessment activity being graded with a 0. If the course guide stipulates that obtaining a minimum grade in that assessment activity is an essential requirement for passing the course, or if several irregularities occur in the assessment activities of the same course, the final grade for that course will be 0. In addition, disciplinary proceedings may be initiated against any student who incurs any of these irregularities.

Bibliography

RS & GEOLOGY. TECHNIQUES & EXAMPLES

Cea C, Cristóbal J, Pons X (2007) An improved methodology to map snow cover by means of Landsat and MODIS imagery. Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE Internacional, 4217 - 4220. DOI 10.1109/IGARSS.2007.4423984, p2037.pdf Barcelona.
Cea C, Cristóbal J, Pons X (2006) Mejoras en la determinación de la cubierta nival mediante imágenes Landsat y Modis. Camacho Olmedo MT, Cañete Pérez JA, Lara Valle JJ (eds.) El acceso a la información espacial y las nuevas tecnologías geográficas, 65-78 (edición en CD-ROM). ISBN: 84-333-3944-6. Depósito legal: GR-1855-2006. XII Congreso Nacional de Tecnologías de la Información Geográfica, Granada.
Cea C, Cristóbal J, Serra P, Pons X, Pastor J, Monterde M (2006) Determinació de la superfície nival a Catalunya mitjançant la Teledetecció. Publicado en Internet: http://www.icc.es/allaus/jortec2.html. 2a Jornada Tècnica de Neu i Allaus, Barcelona.
Dozier J, Painter T.H. 2004. Multispectral and hyperspectral remote sensing of alpine snow properties. Annu. Rev. Earth Planet. Sci. 2004. 32:465-94.
Dozier J. 1989. Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sens. Environ.28:9-22
Gabarró C, Hughes N, Wilkinson J, Bertino L, Bracher A, Diehl T, Dierking W, Gonzalez-Gambau V, Lavergne T, Madurell T, Malnes E, Wagner P. M. (2023). Improving satellite-based monitoring of the polar regions: Identification of research and capacity gaps. Front. Remote Sens., Volume 4 - 2023 | https://doi.org/10.3389/frsen.2023.952091
Hall D.K et al. 2005. Estimation of snow extent and snow properties. Encyclopedia of Hydrological Sciencies. Edited by M G Anderson
König M et al .2001. Measuring snow and glacier ice properties from satellite.Reviw of Geophysics, 39, 1/February 2001.1-27.
Seidel K, Martinec J. 2004. Remote sensing of snow hydrology. Runoff Modelling, Effect of climate change. Praxis
Snow, hydrology and forests in High alpine Areas. 1991. IAHS Publication No.205.
Ulaby, F. T., Long, D. G. (2014). Microwave Radar and Radiometric Remote Sensing. United States. University of Michigan Press.

RS & VEGETATION. TECHNIQUES & EXAMPLES

Díaz-Delgado R, Lloret F, Pons X (2004) Spatial patterns of fire occurrence in Catalonia, NE, Spain. Landscape Ecology 19: 731-745. http://digital.csic.es/bitstream/10261/60332/1/Diaz-Delgado_etal_2004b.pdf.
Díaz-Delgado R, Lloret F, Pons X (2004) Statistical analysis of fire frequency models for Catalonia (NE Spain, 1975-1998) based on fire scar maps from Landsat MSS data. International Journal of Wildland Fire 13: 89-99. DOI: 10.1071/WF02051.
Díaz-Delgado R, Lloret F, Pons X (2003) Influence of fire severity on plant regeneration by means of remote sensing imagery. International Journal of Remote Sensing 24 (8): 1751-1763. http://digital.csic.es/bitstream/10261/60313/1/Diaz-Delgado_etal_2003.pdf
Díaz-Delgado R, Pons X (2001) Spatial patterns of forest fires in Catalonia (NE of Spain) along the period 1975-1995. Analysis of vegetation recovery after fire. Forest Ecology and Management 147 (1): 67-74. DOI: 10.1016/S0378-1127(00)00434-5
Moré G, Serra P, Pons X (2011) Multitemporal flooding dynamics of rice fields by means of discriminant analysis of radiometrically corrected remote sensing imagery. International Journal of Remote Sensing 32 (7): 1983-2011 DOI: 10.1080/01431161003645816.
Pons X, Cristóbal J, González O, Riverola A, Serra P, Cea C, Domingo C, Díaz P, Monterde M, Velasco E (2012) Ten years of Local Water Resource Management: Integrating Satellite Remote Sensing and Geographical Information Systems. European Journal of Remote Sensing 45: 317-332. DOI: 10.5721/EuJRS20124528. http://www.aitjournal.com/articleView.aspx?ID=567
Serra P, Salvati L, Queralt E, Pin C, González-Guerrero O, Pons X (2016) Estimating water consumption and irrigation requirements in a long-established Mediterranean rural community by remote sensing and field data. Irrigation and Drainage. DOI: 10.1002/ird.1978. http://onlinelibrary.wiley.com/doi/10.1002/ird.1978/pdf
Serra P, Pons X (2015)Uncertainty visualization of remote sensing crop maps enriched at parcel scale: A contribution for a more conscious GIS dataset usage Journal of Maps 15 Nov 2015. DOI: 10.1080/17445647.2015.1113390. http://www.tandfonline.com/doi/pdf/10.1080/17445647.2015.1113390
Serra P, Pons X (2013) Two Mediterranean irrigation communities in front of water scarcity: A comparison using satellite image time series. Journal of Arid Environments 98: 41-51. DOI: j.jaridenv.2013.07.011.
Serra P, Moré G, Pons X (2009) Thematic accuracy consequences in cadaster land-cover enrichment from a pixel and from a polygon perspective. Photogrammetric Engineering and Remote Sensing 75 (12): 1441-1449.http://eserv.asprs.org/PERS/2009journal/dec/2009_dec_1441-1449.pdf
Serra P, Pons X (2008) Monitoring farmers' decisions on Mediterranean irrigated crops using satellite image time series. International Journal of Remote Sensing 29 (8): 2293 - 2316. DOI: 10.1080/01431160701408444
Serra P, Pons X, Saurí D (2003) Post-classification change detection with data from different sensors: some accuracy considerations. International Journal of Remote Sensing 24 (16): 3311-3340.

RS & OCEANOGRAPHY. TECHNIQUES & EXAMPLES

Apel 1987. Principles of ocean physics. Academic Press.
Stewart 2008. Introduction to physical oceanography. http://oceanworld.tamu.edu/resources/ocng textbook/contents.html.
The Open University Team 1989. Ocean Circulation. Open University.
The Open University Team 1989. Waves, Tides and Shallow wàter processes. Open University.
Talley, Pickard, Emery and Swift 2011. Descriptive PhysicalOceanography. 6th ed. Elsevier.
Pond, Pickard 1995. Introductory Dynamical Oceanography. 2nd ed. Pergamon Press
Robinson 2004. Measuring the oceans from space. SpringerPraxis
Robinson 2010. Discovering the Ocean from Space. SpringerPraxis
Martin 2014. An Introduction to Ocean remote Sensing. Cambridge 2n Ed.

RS & METEOROLOGY. TECHNIQUES & EXAMPLES

Atlas, D., ed. 1990, Radar in Meteorology, American Meteorological Society, 806 pp
Bech, J, Chau J.L., 2012: Doppler radar observations. Intech. Open Access book http://dx.doi.org/10.5772/2036
Collier, C.G., 1996, Applications of Weather Radar Systems, Wiley, 390 pp
Doviak, V.A., and D.S. Zrnic, 1993, Doppler radar and weather observations, Academic Press, 562 pp
Meischner P. (editor), 2003: Weather Radar : Principles and Advanced Applications. Springer; 1 edition (August 22, 2005) . ISBN: 3540003282, 337 pp.
O'Hora F, J Bech, 2007, Improving weather radar observations using pulse-compression techniques.
Meteorological Applications 14:389 – 401. http://dx.doi.org/10.1002/met.38
Rinehart, R.E., 1991, Radar for Meteorologists, Rinehart, P.O. Box 6124, Grand Forks, ND, 58206-6124, US, 335 pp
Skolnik, M.I., 1981, Introduction to radar systems, McGraw-Hill, New York, 581 pp

 

Software

MiraMon, ArcGIS, QGIS, SNAP, Office Microsoft

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TEm) Theory (master) 1 Catalan/Spanish second semester afternoon
(PAULm) Classroom practices (master) 1 Catalan/Spanish second semester afternoon