Guide in PDF format
Logo

Methods for Obtaining Geographical Information

Code: 43383
Credits: 6
2026/2027
Degree programme Type Course
Remote Sensing and Geographical Information Systems OB 1

Contact lecturer

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

Teaching staff

Miquel Ninyerola Casals
Robert Benavente Vidal

Teaching staff (external to UAB)

José Ángel Burriel
Agustin Lobo Aleu
Mario Padial

Group languages

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

Prerequisites

Prerequisites are not required

Objectives

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

  • Basic aspects of digitization and advanced aspects of topological structuring, as well as modeling tools, obtaining thematic cartography and quantification of the reliability of the products obtained.
  • Proper use of the statistical concepts that underpin the automatic classification of multivariate data, and in particular those provided by satellite images as well as the most appropriate criteria for the visual interpretation of remote sensor images.

Learning outcomes

  • CA07 (Interpret thematic-categorical spatial information from satellite and airborne sensor images, either manually or through computer procedures to assess its accuracy.) Interpret thematic-categorical spatial information from satellite and airborne sensor images, either manually or through computer procedures to assess its accuracy.
  • CA08 (Design numerical and calculation skills, including advanced statistical and geostatistical analysis aimed at spatial modelling.) Design numerical and calculation skills, including advanced statistical and geostatistical analysis aimed at spatial modelling.
  • CA09 (Relate knowledge to address complexity in the elaboration of judgments based on information that, although incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of such knowledge and judgments.) Relate knowledge to address complexity in the elaboration of judgments based on information that, although incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of such knowledge and judgments.
  • KA09 (Identify the foundations of the cartographic digitisation of the advanced principles of topological structuring in the generation of thematic cartography.) Identify the foundations of the cartographic digitisation of the advanced principles of topological structuring in the generation of thematic cartography.
  • KA10 (Recognise the statistical concepts that underpin cartographic modelling and automatic classification of satellite imagery, as well as the most appropriate criteria for the visual interpretation of remote imagery.) Recognise the statistical concepts that underpin cartographic modelling and automatic classification of satellite imagery, as well as the most appropriate criteria for the visual interpretation of remote imagery.
  • KA11 (Recognise sampling techniques and techniques for assessing the reliability of the geographical products obtained, both from the point of view of the user and the producer, etc., integrating ethical and social reflections, with attention to inequalities on the grounds of language, culture, gender or origin reasons.) Recognise sampling techniques and techniques for assessing the reliability of the geographical products obtained, both from the point of view of the user and the producer, etc., integrating ethical and social reflections, with attention to inequalities on the grounds of language, culture, gender or origin reasons.
  • SA12 (Visually interpret geographical images using photo-interpretation techniques and thematic criteria.) Visually interpret geographical images using photo-interpretation techniques and thematic criteria.
  • SA13 (Use tools for digitisation and topological structuring, cartographic modelling and automatic image classification (supervised, unsupervised and mixed, including parametric and non-parametric methods) for the extraction of coherent and structured historical and contemporary geographical information.) Use tools for digitisation and topological structuring, cartographic modelling and automatic image classification (supervised, unsupervised and mixed, including parametric and non-parametric methods) for the extraction of coherent and structured historical and contemporary geographical information.

Contents

PHOTOINTERPRETATION


  1. Visual techniques for identifying land uses and land covers.
  2. Recognition of different types of land uses and land covers.
  3. Photointerpretation: Main applications in the study of the natural and artificial environment.
  4. Interpretation of multispectral images.
  5. Cartography of support for photointerpretation.


STATISTICAL METHODS


  1. Introduction to multivariate data. Characterization of distributions. Normality test. Correlation. Implications in Remote Sensing. Standardization. Principal Component Analysis.
  2. Statistical distances between individuals, populations and between individuals and populations. Implications of the scaling of the variables. Divergence measures.
  3. Obtaining new information (multitemporality, collateral data, indexes and transformations). Information reduction from the samples and from the variables. Introduction to obtaining continuous variables and categorical variables: linear and non-linear, simple and multiple regression, classification, etc.
  4. Multiple regression applied to the interpolation of climatic surfaces.
  5. Generalized linear models applied to obtaining suitability surfaces based on the ecological niche modelling.
  6. Hierarchical and non-hierarchical classification. Supervised, unsupervised and hybrid classification; fuzzy classification.
  7. Segmentation of images. Scales and scene models. Processing methods that take spatial information into account. Segmentation methods. Classification by segments.
  8. Neural networks.
  9. Generalization of results in categorical cartography. Direct methods and smart methods.
  10. Verification of results in binary cartography. Sampling.
  11. Verification of results in categorical cartography. Sampling.


Learning activities and methodology

Title Hours ECTS Learning outcomes
Personal study 10 0.4
Classroom practices 35 1.4
Master classes / exhibitions 38 1.52
Writing reports 64 2.56
Tutorials 2 0.08
Reading of articles / reports of interest 1 0.04

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
Preparation of works 100 % 0 0 CA07, CA08, CA09, KA09, KA10, KA11, SA12, SA13

The regular evaluation of this module consists is as follows:

  • The accomplishment of different practical works proposed throughout the teaching of the module and delivered within the fixed term, that will be worth the 100 % of the final note. A correct formal presentation and careful preparation will be assessed.

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.
  • When being possible to start the tasks for each evaluation activity, or at the beginning of them, Lecturers will inform about 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 an extraordinary exam the day or days scheduled by the faculty.

The single assessment evaluation of this module is as follows:

This module also includes the possibility of taking advantage of the single assessment option, which must take into account the following aspects:

  • Single assessment assumes a single assessment date but not a single assessment activity.
  • The accomplishment of one or more practical works proposed throughout the teaching of the module and delivered within the fixed term, that will be worth the 100 % of the final note.A correct formal presentation and careful preparation will be assessed.
  • The single assessment will also be competency-based, that is, with the training activities the student must demonstrate that they are capable of carrying out the tasks provided for in the subject and these activities will have the same level of demand as those that are part of the course of continuous evaluation.
  • Students wanting to take the single evaluation will have to communicate it between October the 5th and October the 19th, 2023, and they will not be able to do it at any other time during the course.
  • Single assessment tests may coincide with dates reserved for continuous assessment and, if necessary, one week will be available to carry out face-to-face single assessment tests.

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

Atkinson, P.M. and Tatnall, A.R.L., 1997. Introduction Neural Networks in Remote Sensing. International Journal of Remote Sensing, vol. 18, no. 4, pp. 699-709 DOI 10.1080/014311697218700.

Ball and Hall (1965) ISODATA, a Novel Method of Data Analysis and Pattern Classification. Stanford Research Institute, Menlo Park, Cal.

Benson, B.J. and MacKenzie, M.D. (1995) Effects of sensor spatial resolution on landscape structure parameters. Landscape Ecology, 10: 113-120.

Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford University Press ISBN 0 19 853864

Caetano, M. and Painho, M. (2006) Proceedings of Accuracy 2006. 7th International Symposium on Spatial Accuracy Assessment. Instituto Geográfico Português, 924 p.

Chuvieco, E. (2002) Teledetección Ambiental, Barcelona, Ariel. 453 p.

Chuvieco, Emilio. (1996): Fundamentos de Teledetección Espacial (3rd edition). Editorial Rialp, Madrid. 568 p. ISBN: 84-321-3127-X.

Chuvieco, Emilio. (2010): Teledetección Ambiental (3rd edition). Editorial Ariel, Barcelona. 528 p. ISBN: 978-8-434-43498-1.

Cipolletti, M.P., Delrieux, C.A., Perillo, G.M.E., Piccolo, M.C., (2012) Superresolution border segmentation and measurement in remote sensing images. Computers & Geosciences, 40:87-96.

Clinton, N., Holt, A., Scarborough, J., Yan L., Gong, P. (2010) Accuracy Assessment for Object-Based Image Segmentation Goodness. Photogrammetric Engineering & Remote Sensing 76(3), 289-299.

Congalton, R.G. and Green, K. (2009) Assessing the Accuracy of Remotely Sensed Data—Principles and Practices. CRC Press, Boca Raton, 2ª edició, 183 p.

Cuadras C.M. (1996) Métodos de análisis multivariante. EUB, Barcelona.

Curran, Paul J. (1985): Principles of remote sensing. Longman Scientific and Technical. 282 p. ISBN: 978-0-582-30097-2.

Dalponte, M., Bruzzone, L., Vescovo, L. and Gianelle, D. (2009) The role of spectral resolution and classifier complexityin the analysis of hyperspectral images of forest areas. Remote Sensing of Environment, 113, 2345-2355.

Duda, R.O., Hart, P.E. and Stork, D.G. (2001) Pattern Classification. John Wiley & Sons, New York, 2a Edició, 654 p

Eastman, J.R. (2001) IDRISI32 Release 2: Guide to GIS and Image Processing. Clark University . Worcester, (2 vol.), 161+144 p.

Eklundh, J.O., Yamamoto, H. and Rosenfeld (1980) A relaxation method for multispectral pixel classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-2, 72-75.

Foody, G.M. (2009) Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sensing of Environment, 113: 1658-1653.

Foody, G.M. and Mathur, A. (2004) Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93: 107-117.

Foody, G.M. and Mathur, A. (2007) The use of small training sets containing mixed pixels for accurate hard image classification. Training on mixed spectral responses for classification by a SVM. Remote Sensing of Environment, 103: 179-189.

Franklin, J. (2010). Mapping Species Distributions. Spatial Inference and Prediction. Cambridge University Press, Cambridge.

Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. Elsevier, San Diego, 2nd edition, 591 p.

Goodchild, M. and Gopal, S. (eds) (1989) Accuracy of Spatial Databases. Taylor & Francis, London, 290 p.

Graham, Ron & Koh, Alexander (2002): Digital Aerial Survey, Theory and Practice. Whittles Publishing. 274 p. ISBN: 978-184995-085-5.

Graham, Ron& Read, Roger E. (1990): Manual de fotografía aérea. Omega, Barcelona. 359 p. ISBN: 84-282-0859-X.

Haralick, R. and Shapiro, L. (1985) Image segmentation techniques. Journal of Computer Vision, Graphics and Image Processing. 29:100-132.

Haralick, R.M., Shanmugam, K. and Dinstein, I. (1973) Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-3, 610-621.

Hastie, T., R. Tibshirani and J. Friedman (2009), The Elements of Statistical Learning (2nd edition). Springer-Verlag. 763 p. http://statweb.stanford.edu/~tibs/ElemStatLearn/

Höppner, F., F. Klawonn, R. Kruse and T. Runkler (1999) Fuzzy Cluster Analysis. Wiley, Chichester, 289 p. IEEE (2011) Special volume: Spectral Unmixing of Remotely Sensed Data. IEEE Transactions on Geoscience and Remote Sensing, Vol. 49.11.

Irons, J.R. and Petersen, G.W. (1981) Texture transformations of remote sensing data. Remote Sensing of Environment, 11:359-370.

Jansen, L.L.F. and Molenaar, M. (1995) Terrain objects, their dynamics and their monitoring by integration of GIS and remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 33:749-758.

Jensen, J.R. (2004) Introductory Digital Image Processing. A Remote Sensing Perspective, Prentice Hall, Englewood Cliffs, 3ª edició, 544 p.

Karimi, Y., Prasher, S.O., Patel, R.M. and Kim, S.H. (2006) Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and electronics in agriculture, 51:99-109.

Kaufman and Rousseeuw, (1990) Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley and Sons. 342 p.

Lillesand, T.M. and R.W. Kiefer (2003) Remote Sensing and Image Interpretation. John Wiley & Sons. New York, 5ª edició, 784 p.

Lillesand, T.M., Kiefer, R.W., & Chipman, J. (2015): Remote Sensing and Image Interpretation (7nd edition). John Wiley & Sons, Inc. New York. 768 p. ISBN:978-1-118-34328-9.

Little R.J.A. and Rubin D.B. (2002) Statistical Analysis with Missing Data. John Wiley, New York. 2ª edició, 381 p.

Lobo, A. (1997) Image segmentation and discriminant analysis for the identification of land cover units in ecology IEEE Transactions on Geoscience and Remote Sensing, 35: 1136-1145.

Lobo, A., Chic., O. and Casterad, A. (1996) Classification of mediterranean crops with multisensor data: perpixel versus perobject statistics and image segmentation. International Journal of Remote Sensing, 17: 2385-2400.

Lobo, A., Ibáñez Martí, J.J. and Carrera Giménez Cassina, C. (1997) Regional scale hierachical classification of temporal series of AVHRR vegetation index. International Journal of Remote Sensing, 18: 3167-3193.

Lu, W. and Weng, Q. (2007) A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28: 823 – 870.

Manly, B.F.J. (1994). Multivariate statistical methods. A primer. Chapman and Hall, London. 2nd Edition

Marceau, D., Howarth, P.J., Dubois, J.M.M. and Graton, D.J. (1990) Evaluation of the greylevel cooccurence matrix method for landcover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing, 28: 513-519.

Mas, J.F. and Flores, J.J. (2008) The Application of Artificial Neural Networks to the Analysis of Remotely Sensed Data. International Journal of Remote Sensing, vol. 29, no. 3, pp. 617-663 DOI 10.1080/01431160701352154

Mather, P.M. (2004) Computer Processing of Remotely-Sensed Images J. Wiley & Sons, Chichester, 3rd edition, 324 p..

McCoy, R.M. (2005) Field Methods in Remote Sensing. The Guilford Press, New York. 159 p.

Michie, D., Spiegelhalter, D.J. and Taylor, C.C. (eds) (1994) Machine Learning, Neural and Statistical Classification. Ellis Horwood.

Moré G, Pons X (2008) Influencia del número de imágenes en la calidad de la cartografía detallada de vegetación forestal. Revista deTeledetección, 28: 61-68.

Mountrakis, G., Jungho, I., Ogole, C. (2011) Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66: 247-259

Ninyerola M, Pons X, Roure JM. (2000). A methodologicalapproach of climatological modelling of air temperature and precipitation through GIS techniques. International Journal of Climatology 20: 1823-1841.

Paine, David P. & Kiser, James D. (2012): Aerial Photography and Image Interpretation (3rd edition). John Wiley & Sons Inc, New York. 648 p. ISBN: 978-0-470-87938-2.

Pedley, M. and Curran, P.J. (1991) Perfield classification: an example using SPOTHRV imagery. International Journal of Remote Sensing, 12: 2181-2192.

Persello, C. and Bruzzone, L. (2010) A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1232-1244.

Pons, X. and Arcalís, A. (2012) Diccionari terminològic de teledetecció Enciclopèdia Catalana i Institut Cartogràfic de Catalunya. Barcelona. 597 pàgs.

Richards, J. A. (2013) Remote Sensing Digital Image Analysis. An Introduction. Springer-Verlag, Berlin, 5th edition, 494 p.

Schowengerdt, R. A. (2006) Remote Sensing. Models and methods for image processing. Academic Press, San Diego, California, 2nd edition, 560 p.

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: 1441–1449.

Shahshahani, B.M., Landgrebe, D.A. (1994) The Effect of Unlabeled Samples in Reducing the Small Sample Size Problem and Mitigating the Hughes Phenomenon, IEEE Transactions on Geoscience and RemoteSensing. Vol. 32-5.

Shi, W., Fisher, P. and Goodchild, M. (2002) Spatial Data Quality. Taylor & Francis, 313 p.

Shirabe, T. (2005) Classification of Spatial Properties for Spatial Allocation Modeling. GeoInformatica, 9(3): 269–287.

Sokal, R. i Rohlf, J. 1995. Biometry: the principles and practice of statistics in biological research. 3ª edició. Ed. Freeman and Company. New York.

Sonka, Hlavac, and Boyle, (1993) Image Processing, Analysis, and Machine Vision. Chapman & Hall.

Spiegel, M.R. (1991) Estadística. McGraw Hill, 556 p.

StatSoft, Inc. (1999). STATISTICA for Windows [Computer program manual]. Tulsa, OK: StatSoft, Inc., WEB:http://www.statsoft.com

Stehman, S.V., Arora, M K., Kasetkasem, T., and Varshney, P.K. (2007) Estimation of Fuzzy Error Matrix Accuracy Measures Under Stratified Random Sampling. Photogrammetric Engineering & Remote Sensing, 73(2): 165-173.

Strahler, A., Woodcock, C and Smith., J.A. (1986) On the nature of models in remote sensing. Remote Sensing of Environment, 20: 121-139.

Tso, Br. and Mather, P.M. (2009) Classification methods for remotely sensed data. Taylor and Francis Grup, Boca Raton, 2nd edition.

Vázquez Maure, Francisco & Martín López José (1988): Fotointerpretación. Instituto Geografico Nacional, Madrid. 301 p. ISBN: 84-505-7312-2

Vogelmann, J.E., Tolk, B. and Zhu, Z. (2009) Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sensing of Environment, 113: 1739-1748.

Woodcock, C.E. and Strahler, A.H. (1987) The factor of scale in remote sensing. Remote Sensing of Environment, 21: 311-332.

Yu, Q., Gong, P., Tian, Y.Q., Pu, R. and Yang, J. (2008) Factors Affecting Spatial Variation of Classification Uncertainty in an Image Object-based Vegetation Mapping. Photogrammetric Engineering & Remote Sensing, 74: 1007-1018.

DocumentaciónSIOSE2005. L'Anexo IV i la Guía, amb imatges de cobertes. http://www.ign.es/siose/documentacion.jsp

Manual de Fotointerpretación SIOSE2005. http://www.ign.es/siose/Documentacion/Guia_Tecnica_SIOSE/Manual_Fotointerpretacion_SIOSE2005.pdf

Anexo IV: Fichas Fotointerpretación Zonas Agrícolas y Forestales - Coberturas simples http://www.ign.es/siose/Documentacion/Guia_Tecnica_SIOSE/070206_Manual_Fotointerpretacion_anexoIV_ficha_AgriForestales.pdf

Anexo IV: FichasFotointerpretación Zonas Agrícolas y Forestales – Asociaciones http://www.ign.es/siose/Documentacion/Guia_Tecnica_SIOSE/070122_Manual_Fotointerpretacion_anexoIV_fichas_Asociaciones.pdf

Anexo IV: Fichas Fotointerpretación Coberturas Artificiales http://www.ign.es/siose/Documentacion/Guia_Tecnica_SIOSE/070727_Manual_Fotointerpretacion_anexo_IV_fichas_Artificialcomp.pdf

http://www.ign.es/siose/Documentacion/Guia_Tecnica_SIOSE/061101_Manual_Fotointerpretacion_anexoIV_Tabla_color.pdf

Mapa forestal de España escala 1:25.000 Manual de fotointerpretación. No conté imatges de boscos però és un bon recull de metodologia i de descripció de categories.

http://www.nasdap.ejgv.euskadi.net/contenidos/informacion/inventario_forestal_2011/es_agripes/adjuntos/Manual%20Fotointerpretacion%20MFE25_v5_feb2010_2.pdf

Organización de los Estados Americanos (OEA): \"El Salvador - Zonificación Agrícola - Fase II - Sistema de Información para el Desarrollo\".

http://www.oas.org/dsd/publications/Unit/oea35s/ch26.htm

http://studylib.es/doc/8744482/universidad-nacional-abierta-ya-distancia

https://www.juntadeandalucia.es/medioambiente/portal/landing-page-%C3%ADndice/-/asset_publisher/zX2ouZa4r1Rf/content/gu-c3-ada-t-c3-a9cnica-del-mapa-de-usos-y-coberturas-vegetales-del-suelo-de-andaluc-c3-ada-1-25.000/20151

Software

MiraMon, ArcGIS, QGIS, ENVI, 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 Spanish second semester afternoon
(PAULm) Classroom practices (master) 1 Spanish second semester afternoon