. "Geographic Information Science"@en . . "Geography"@en . . "Remote Sensing"@en . . "Science of geographical information: remote sensing and gis"@en . . "15.00" . "Objectives and Contextualisation\nThis module aims to create an introductory, broad and specific framework at the same time, to the science and technology of geographic information, focusing on key concepts both of aspects of classical cartography and global positioning, as well as aspects related to remote perception and the use of Geographic Information Systems.\n\nAt the end of the course, the student will be able to:\n\nUnderstand the main functions of different programs used in GIS and Remote Sensing.\nProperly use different data and metadata formats.\nDominate the fundamental concepts of the various disciplines related to the position and representation of elements in space, such as photogrammetry, remote sensing or global positioning systems.\nProperly represent a geographical reality in a digital or analogical cartographic document.\nMaking informed decisions about the use of remote sensing in territorial studies.\nDiscriminate between different types of platforms and sensors according to their characteristics and to know how to choose the appropriate ones according to the objectives of the study to be carried out.\nContent\nPLATFORMS AND SENSORS\n\n1. Platforms: Aircraft.\n2. Platforms: Unmanned Aircraft.\n 2.1. Key points of regulation.\n 2.2. Classification.\n3. Platforms: satellites.\n 3.1. Subsystems satellite.\n 3.2. Launching.\n 3.3. Spatial Orbits.\n 3.4. Orbital maneuvers.\n 3.5. Segment Earth.\n4. Sensors.\n 4.1. Telescopes.\n 4.2. Lidar.\n 4.3. Microwave radiometers and radar.\n 4.3.1. Microwave Remote Sensing.\n 4.3.2. SAR: Synthetic Aperture Radar.\n 4.3.3. Geometry and spatial resolution SAR.\n 4.3.4. \"Performance\" SAR.\n 4.3.5. SAR acquisition modes.\n 4.3.6. Systems airborne and satellite-SAR.\n 4.3.7. Interferometric applications.\n5. Characterization of an instrument/Remote Sensing missions.\n 5.1. Spatial characterization (geometric).\n 5.2. Spectral characterization.\n 5.3. Radiometric Characterization.\n 5.4. Temporal characterization.\n\nPRINCIPLES OF CARTOGRAPHY\n\n1. History of cartographic representation.\n2. Geodesy.\n3. Cartographic projections.\n4. The UTM reference system.\n5. Cartographic products: the maps.\n6. Topographic and thematic mapping.\n\nGEODESY AND POSITIONING SYSTEMS\n\n1. Geodesy and Cartography.\n2. Nomenclature: what is GNSS; other systems besides the GPS.\n3. Introduction to the systems of global positioning and historical development.\n4. Fundamentals of the system.\n 4.1. Sectors or segments.\n 4.2. Basic measures. Code and phase.\n5. Methods of operation.\n6. Type of receivers.\n7. Accuracy.\n8. Applications.\n\nFUNDAMENTALS OF GIS\n\n1. Introduction.\n 1.1. Definition of GIS.\n 1.2. Geographical information and GIS.\n 1.3.Connections and differences between GIS and other systems.\n 1.4. GIS Applications.\n 1.5. Introduction to ArcGIS and MiraMon software.\n2. Models of data.\n 2.1. Raster model.\n 2.2. Vector model.\n 2.3. Topological structure.\n 2.4. Attributes, tables and validation.\n 2.5. Model of observations and measures.\n 2.6. Formats: import and export. CAD model.\n3. Production of data.\n 3.1. Data entry.\n 3.2. Validation and errors.\n4. Data processing.\n 4.1. Classification and reclassification.\n 4.2. Raster transformations - vector: rasterization and vectorization.\n 4.3. Cartographic generalization.\n5. Introduction to the GIS analysis.\n 5.1. Arithmetic and logic operations between layers.\n 5.2. Analytical combinations of layers.\n\nCOMPOSITION AND IMPRESSION OF CARTOGRAPHIC DOCUMENTS\n\nPractical contents based on the use of different software to obtain cartography on paper. It will deal with formal issues of the composition as well as advice aimed at obtaining intelligent impressions and faithful to the reality that one wants to represent.\n\nSYNOPTICAL VIEW OF REMOTE SENSING\n\n1. Introduction. Overview of remote sensing.\n 1.1. Definition.\n 1.2. What tools do we have?\n 1.3. What is intended?\n 1.4. Type of platforms: aerial and satellite, heliosynchronous and geostationary.\n 1.5. Types of sensors according to the way of obtaining the data, the type of information recorded, the spectral region to which they are sensitive, etc.\n 1.6. Typical image processing chain (corrections, improvements, extraction of image information, etc.).\n 1.7. Basics: pixel; space, spectral, radiometric, temporal and angular resolutions; grayscale and palette images, true color and false color renderings.\n 1.8. Visual analysis versus digital processing.\n 1.9. Satellite remote sensing versus aeroported remote sensing and UAV.\n 1.10. Important characteristics and limitations of remote sensing.\n 1.11. Brief history of remote sensing. Remote sensing in Spain and internationally: associations, congresses, publications.\n 1.12. Comment of the recommended bibliography and the main journals.\n2. Electromagnetic spectrum and spectral signatures.\n 2.1. Basic concepts.\n 2.2. Solar radiation; thermal radiation emitted by the Earth; microwave.\n 2.3. Spectral signatures.\n3. Nature of images. Corrections, improvements, transformations.\n 3.1. Nature of the images.\n 3.2. Most common formats in remote sensing.\n 3.3. Geometric corrections.\n 3.4. Radiometric corrections.\n 3.5. Image enhancement.\n 3.6. Transformations: Vegetation indexes, main components, etc.\n4. The interpretation of satellite imagery.\n5. Obtaining information from the images.\n 5.1. Supervised classification.\n 5.2. Unsupervised classification.\n 5.3. Mixedclassification.\n 5.4. Estimation of continuous variables.\n 5.5. Verification of results.\n6. Remote sensing, mapping and geographic information systems.\n\nPHOTOGRAMETRY\n\n1. Fundamentals of photogrammetry.\n 1.1. Introduction.\n 1.2. Air photogrammetry.\n 1.3. Measures on photographs and corrections.\n 1.4. Vertical photography.\n 1.5. Stereoscopic vision.\n 1.6. Stereoscopic parallax.\n 1.7. Rectification.\n 1.8. Restitution.\n2. Topographical photogrammetry.\n 2.1. Phases of a topographic uprising.\n 2.2. Classification of photogrammetric surveys.\n 2.3. Photographic scale.\n 2.4. Planning of work. Flight projects Plan and flight execution.\n 2.5. Post-photogrammetric flight operations (restoration, rectification, generation of digital terrain models, etc.).\n 2.6. Orthophotography versus Rectification.\n\nCompetences\nApply knowledge of remote sensing platforms and sensors to analysing and processing data in different types of studies.\nChoose the most suitable tools and applications to fulfil the objectives of a project in the field of spatial planning or analysis.\nCommunicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nUse different specialised GIS and remote sensing software, and other related software.\nLearning Outcomes\nCommunicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDifferentiate between different types of platforms and sensors based on their characteristics and choose ones that are suited to the aims of the study to be performed.\nHandle different data and metadata formats appropriately.\nMaster the fundamental concepts of the various disciplines related to the position and representation of elements in space, such as photogrammetry, remote sensing and global positioning systems.\nRepresent a real geographic area appropriately in a digital or analogue cartographic document.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nTake informed decisions on the use of remote sensing in land-use studies.\nUnderstand the main functions of different programmes used in GIS and remote sensing.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context." . . "Presential"@en . "TRUE" . . "Methods for obtaining geographical information"@en . . "6.00" . "Objectives and Contextualisation\nAt the end of the course, the student will be able to:\n\nBasic 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.\nProper 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.\n\n\n\nContent\nPHOTOINTERPRETATION\n\nVisual techniques for identifying land uses and land covers.\nRecognition of different types of land uses and land covers.\nPhotointerpretation: Main applications in the study of the natural and artificial environment.\nInterpretation of multispectral images.\nCartography of support for photointerpretation.\nSTATISTICAL METHODS\n\nIntroduction to multivariate data. Characterization of distributions. Normality test. Correlation. Implications in Remote Sensing. Standardization. Principal Component Analysis.\nStatistical distances between individuals, populations and between individuals and populations. Implications of the scaling of the variables. Divergence measures.\nObtaining 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.\nMultiple regression applied to the interpolation of climatic surfaces.\nGeneralized linear models applied to obtaining suitability surfaces based on the ecological niche modelling.\nHierarchical and non-hierarchical classification. Supervised, unsupervised and hybrid classification; fuzzy classification.\nSegmentation of images. Scales and scene models. Processing methods that take spatial information into account. Segmentation methods. Classification by segments.\nNeural networks.\nGeneralization of results in categorical cartography. Direct methods and smart methods.\nVerification of results in binary cartography. Sampling.\nVerification of results in categorical cartography. Sampling.\n\nCompetences\nContinue the learning process, to a large extent autonomously.\nIdentify and propose innovative, competitive applications based on the knowledge acquired.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nUse different specialised GIS and remote sensing software, and other related software.\nUse the different techniques for obtaining information from remote images.\nWrite up and publicly present work done individually or in a team in a scientific, professional context.\nLearning Outcomes\nContinue the learning process, to a large extent autonomously.\nIdentify and propose innovative, competitive applications based on the knowledge acquired.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nShow expertise in using digitalisation and topological structuring tools, modelling tools, and tools for supervised, unsupervised and mixed image classification.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nWork with the statistical concepts underpinning the automatic classification of satellite images, and the most suitable criteria for visually interpreting remote images.\nWrite up and publicly present work done individually or in a team in a scientific, professional context." . . "Presential"@en . "TRUE" . . "Processing remote sensing Images"@en . . "6.00" . "Objectives and Contextualisation\nAt the end of the course, the student will be able to:\n\nMaster different tools primary processing of aerial and satellite imagery.\nDominate the physical principles that govern remote image capture and transformations of the content of the image itself.\nDistinguish the different sources of image geometric deformations and possible signal interference caused by atmospheric captured or lighting effects (topography, etc.).\nCorrectly apply the methodologies to mitigate the different error sources in order to be able to view and extract physical parameters of the received data.\n\nContent\nPHYSICAL PRINCIPLES OF REMOTE SENSING\n\nSolar spectrum\nConcepts: radiation and electromagnetic spectrum, polarization. Fundamental relationships between frequency, length and transported wave energy.\nBasic physical parameters (terminology and symbology, definitions, units): Radiant energy, energy flow, energy intensity, radiance energy excitance, irradiance, reflectance, albedo, transmittance, absorptance; absorbance. spectral magnitudes.\nSpecular reflection, diffuse and lambertiana.\nBlack body (Planck's law, Stefan-Boltzmann law, Wien's displacement law).\nSolar radiation. Exoatmospheric characteristics and the surface of the Earth; interaction with the atmosphere and atmospheric windows.\nSpectral signatures. Main characteristics of water, soil and rocks and vegetation in the visible and infrared non thermal.\nFactors that influence the spectral signature.\nThermal\nThe thermal radiation emitted by the Earth. Remote Sensing approaches.\nPhysical parameters of the thermal infrared region.\nKCL. black body, white body and gray body. selective radiators. Thermal behaviour of an object-related parameters.\nThermal behavior of an object: related parameters.\nSpectral behaviour of the different coverages in the thermal infrared region.\nFactors which influence the emissivity.\nEmissivity measurement. Field measurements.\nEmissivity measurement. Measured from satellite.\nActive microwave\nActive Microwave Remote Sensing: Imaging Radar.\nWave-Matter interaction: Radar Cross Section and Backscattering Coefficient.\nBackscattering Coefficient.\nBackscattering models.\nSAR polarimetry.\nPassive microwave\nPassive Sensors: Fundamentals andPhysical Principles.\nApplications of passive microwave E.O.\nMicrowave Radiometers:\nFigures of Merit: Angular Resolution and Radiometric Resolution.\nCalibration: internal, external, use of multi-look information.\nPresent and future EO Passive Microwave Mission.\nGEOMETRIC CORRECTION OF AERIAL AND SATELLITE IMAGERY\n Geometric corrections. Deformation sources. Orthoimage, orthophoto and orthophoto of authentic orthophotomap concepts. Corrections in vectorial bases.\nPhysical models (collinearity equations orbit models), semi-empirical (polynomial corrections, models of rational functions, Delaunay triangulation) and mixed. Model of radar images: determining the sampling step azimuth and distance. Relief role. Ground control points (GCP), test points, homologous points.\nGeometry of the radar image. Sampling of the image. Geometric distortion of images. Accurate geocoding images using Digital Elevation Models (DEM or DEM). Obtaining DEM and Radar Mapping. Approaches to areas of low relief. Examples.\nBasic correction process. Nearest neighbor, bilinear and bicubic interpolation: Chromatic, radiometric and geometric in image resampling. Considerations about output pixel size.\nSources of GCP. Automatic GCP.\nBasics of physical models. Consideration of the relief.\nBasics of semi-empirical models:\nPolynomial models 1st an 2nd degree. Application cases.\nHigher polynomial model degree. Application cases.\nPolynomial models with consideration relay.\nModels of rational functions.\nDelaunay Triangulation.\nMixed Models: Theory and examples ASTER, MODIS, SSM/I and SMOS.\nErrorestimate.Statistical interpretation of the RMS.\nMosaics and geometry images.\nPractical realization of the main models.\nRADIOMETRIC IMAGE CORRECTION\n\n 1. Radiometric corrections. Calibration sensors. Sources of signal distortion. DN conversion to radiances. Interest and obtaining reflectances.\n 2. Formulation corrections in the visible and infrared non thermal.\n 2.1 Sun and atmspheric roles. Exoatmospheric radiance, transmittance. Variation throughout the year. Spectral variation. Diffuse atmospheric radiation.\n 2.2 Relief role: incidence angle, projected shadows. Celestial sphere. Neighboring reflected radiation.\n 2.3 Combining sensors in the same study. Usability of pseudoinvariant areas (PIA).\n 2.4 Combined use of in situ sensors such as handheld spectroradiometers or sun photometers.\n 3. Corrections based in multispectral and large mount of images: advantages and limitations.\n\nCompetences\nApply different methodologies for the primary processing of images obtained by remote sensors in order to subsequently extract geographic information.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nSolve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nUse different specialised GIS and remote sensing software, and other related software.\nLearning Outcomes\nContinue the learning process, to a large extent autonomously.\nCorrectly apply methodologies to mitigate the different sources of error in order to visualise and extract physical parameters from the data received.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDistinguish the different sources of geometric image deformation, and the possible interferences in the captured signal caused by atmospheric effects or illumination effects (topography, etc.).\nShow expertise in the physics principles that govern remote image capture and transformations made to the content of the image itself.\nShow expertise in using different primary processing tools for aerial and satellite images.\nSolve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context." . . "Presential"@en . "TRUE" . . "Spatial analysis"@en . . "9.00" . "Objectives and Contextualisation\nAt the end of the course, the student will be able to:\n\nDominate at the practical level the different tools related to the interpolation and terrain analysis.\nUse the main applications for the generation of new information from GIS data.\nIdentify the concepts associated with spatial analysis, its applications and its limitations.\n\n\nContent\nANALYSIS IN GIS\n\n1. General Concepts of GIS Analysis\n 1.1 Introduction\n 1.2 Specifications regarding the data model\n 1.3 Combining raster-vector analysis\n\n2. Layers combinations\n 2.1 Variants and possibilities\n 2.2 Vector overlay\n 2.3 Transfer of attributes\n 2.4 Categorical data\n\n3. Map algebra\n 3.1 Previous conditions\n 3.2 Characteristics\n 3.3 NODATA\n 3.4 Multicriteria decision analysis\n\n4. Propagation of errors\n 4.1 Geometric quality criteria\n 4.2 Thematic quality criteria\n 4.3 Elimination of results by criteria of geographical insignificance\n\n5. Analysis of the landscape\n 5.1 Introduction to the conceptual and methodological framework of landscape ecology\n 5.2 Calculation and analysis of landscape indexes at various scales\n 5.3 Analysis of the ecological connectivity of the landscape\n\n6. Spatial autocorrelation\n 6.1 Concepts\n 6.2. Indicators and indexs.\n\n7. Space interpolation\n 6.1 Concepts\n 6.2 Polygons of Thiessen\n 6.3 Trend surfaces\n 6.4 Kriging\n\n8. Logistic regression\n 8.1 Characteristics\n 8.2 Spatial applications\n 8.3 Limitations and adjustments of models\n\n9. Analysis of distances\n 9.1 Cartesian distances and geodesic distances\n 9.2 Generation of continuous and buffer maps\n 9.3 Anisotropic distances and cost analysis\n 9.4 Introduction to network analysis\n\nDIGITAL TERRAIN MODELS\n\n1. Concepts\n 1.1 Fundamental concepts and terminology (DTM, DEM, DSM, etc.)\n 1.2 Models of data: raster, TIN, isolines, etc. BIM and LoD#.\n 1.3 Vertical and geoid duck\n2. Collection ofdata. Primary (field, photogrammetry, lidar, InSAR, etc.) and Secondary\n3. Generation of DTM\n 3.1 Interpolation from points: Inverse of the weighted distance (IDW), splines, kriging. Neighborhood Statistics. Processing of lidar data\n 3.2 Interpolation from isolines\n 3.3 Generation of TIN models\n4. Quality of MDT\n 4.1 Altimeter quality\n 4.2 Control of the error in the DTM\n 4.3 Propagation of error in derivative models\n5. Derived models\n 5.1 Slope, orientations, curvatures, etc.\n 5.2 Hydrographic basins, drainage network\n 5.3 Illumination, shading and solar radiation\n6. Applications\n 6.1 Applications in the processing of remote sensing images: geometric and radiometric image rectification.\n 6.2 Topographical profiles and visibility analysis\n 6.3 Three-dimensional perspectives\n \nINTERFEROMETRY\n\n1. Introduction\n 1.1. Image sensors\n 1.2. Spectral window\n 1.3. SAR missions (Synthetic Aperture Radar)\n2. SAR concept\n 2.1. Classical radar\n 2.2. SAR technical concepts\n3. SAR image\n 3.1. Spectral and reflection characteristics\n 3.2. Geometric distortions\n 3.3. Georeferencing on SAR imagery\n4. SAR Interferometry (InSAR)\n 4.1. Basic formulation\n 4.2. Coherence and noise sources\n 4.3. How to create an Elevation Map using InSAR\n5. Differential Interferometry SAR (DInSAR)\n 5.1. Basic formulation\n 5.2. How to create a ground motion map with DInSAR\n 5.3. Characteristics of DInSARproducts\n6.PSI Techniques (Persistent Scatterer Interferometry)\n 6.1. Basic concepts\n 6.2. PSI Processor Components\n 6.3. Scatterers\n 6.4. Examples of ground motion measurements with PSI\n\nCompetences\nAnalyse and exploit geographic data from different sources to generate new information from pre-existing data.\nCommunicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nUse different specialised GIS and remote sensing software, and other related software.\nUse different techniques and concepts for generating useful information in spatial analysis.\nWrite up and publicly present work done individually or in a team in a scientific, professional context.\nLearning Outcomes\nCommunicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nExploit geographic data through map algebra, layer combination, network analysis and other techniques, taking the right decisions for each problem area based on the knowledge acquired.\nIdentify the concepts associated with spatial analysis, their applications and their limitations.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nShow expertise in using the different tools of terrain analysis and interpolation.\nUse the main applications for generating new information from GIS data.\nWrite up and publicly present work done individually or in a team in a scientific, professional context." . . "Presential"@en . "TRUE" . . "Advanced gis"@en . . "9.00" . "Objectives and Contextualisation\nThis optional module, increases the knowledge acquired in the spatial analysis module of the same master, focusing on the exploitation of geographic databases from the SQL language, as well as in specific practical cases. In addition, it adds concepts associated with the publication of cartography on the Internet taking into account international standards for data and metadata that allow interoperability including semantic, technologic, information, etc.\n\nAt the end of the course, the student will be able to:\n\n1. Use different cartography publication tools on the Internet.\n2. Know the advantages and limitations of the use of standards in the GIS world.\n3. Apply international standards to the edition and publication of data and metadata on the Internet.\n4. Master queries in databases using SQL language.\n5. Design appropriately information systems for the use of data in a scientific, professional or informative context.\n\nContent\nRELATIONAL DATABASES. SQL\n\n1. Introduction to relational databases\n2. Conceptual design of a relational database: entity-relationship model\n 2.1 Foundations of relational databases\n 2.2 Entities, attributes, instances\n 2.3 Primary keys and foreign keys\n 2.4 Types of relationships and classification\n 2.5 Three-valued logic\n3. Logical design of a database\n4. The sample database: IEFC_Garrotxa.mdb\n 4.1 The Ecological and Forest Inventory of Catalonia (IEFC de la Garrotxa)\n5. Conceptual, logical and physical model of the IEFC database\n6. Physical design of a database (standardization)\n7. Practical example of designing a database for a Library\n8. Advantages of a relational database: integrity of entities and referential integrity\n9. Features of a Database Management System (SGDB)\n10. Introduction to the SQL language (management of a BD)\n 10.1 What is SQL?\n 10.2 Advantages of SQL\n11. Introduction to DML (Data Management Language) and DDL (Data Definition Language)\n12. Recovery of data with SQL: SELECT statement\n13. Simple inquiries (SELECT ... FROM)\n 13.1 Management practices with SQL databases (1)\n14. Union consultations (UNION)\n 14.1 Database management practices with SQL (2)\n15. Multi-table questions: compositions\n 15.1 Multi-table queries in SQL1\n 15.2 Internal compositions (INNER JOIN)\n 15.3 External compositions (LEFT, RIGHT and OUTER JOIN)\n 15.4 Self-composition\n 15.5 Management practices with SQL databases (3)\n16. Summary queries\n 16.1 Column functions (GROUP BY)\n 16.2 Conditions in summary queries (HAVING)\n 16.3 Database management practices with SQL (4)\n17. Subqueries\n 17.1 Comparison test with subquery\n 17.2 Proof of belonging to a subset of a subquery\n 17.3 Test of existence\n 17.4 Quantitative comparison test\n 17.5 Database management practices with SQL (5)\n18. Nested consultations\n 18.1 Database management practices with SQL (6)\n19. ODBC Link of a GIS layer with a SQL query (DSN file)\n 19.1 Creation of a DSN file for the database\n 19.2 Creation of a layer of points from the database\n 19.3 Creation of a link via ODBC of a SQL query with MiraMon's point layer\n 19.4 Creation of a link via ODBC from a SQL query with a layer of MiraMon polygons\n20. Transactions (COMMIT ROLLBACK)\n21. Update records:\n 21.1 Insertion of records (INSERT)\n 21.2 Delete and delete with subquery (DELETE)\n 21.3 Modification and modification with subquery (UPDATE)\n 21.4 Database management practices with SQL (7)\n22. DDL (Data Definition Language)\n 22.1 Definition and creation of databases\n 22.2 Definition of tables and views\n 22.3 Definition of fields\n 22.4 Definition of restrictions\n 22.5 Definition of indexes\n 22.6 Changes in the structure of the database\n 22.7 Database management practices with SQL (8)\n \nCASE STUDIES IN GIS IMPLEMENTATIONS\n\nContents based on a series of conferences by representatives of different public and/or private organizations that explain the design and use of the GIS in their work environments\n \nSTANDARDS FOR DISTRIBUTED GEOSERVICES\n\n1. Introduction\n 1.1 Interoperability and IDES\n 1.2 Standardization organizations\n 1.3 UML and XML\nExercise 1: Introduction to XML and XML Schema (Enterprise Architech XMLValidator Buddy)\n\n2. Metadata standards\n 2.1 Introduction\n 2.2 Dublin Core\n 2.3FGDC\n 2.4 ISO (19115, 19139)\n 2.5 IDEC profile\n 2.6 Profile NEM\n 2.7 INSPIRE profile\n 2.8 Metadata management applications\nExercise 2: Metadata documentation\n\n 3. Format standards\n 3.1 Modeling of data: UML and GML\nExercise 3: Introduction to GML, generation schemes from UML\n 3.2 Other format standards (SHP, MMZx, KML, GeoJSON, SWE Common, WaterML, ...)\nExercise 4: Google Earth, Google Maps and KML\n\n 4. GEOSERVICES STANDARDS\n 4.1 Catalog services: CSW\n 4.2 Display services: WMS, WMTS, OWS Context\n 4.3 Download service: WCS, WFS, SOS\n 4.4 Processing service: WPS\nExercise 5: Connection with external WMS servers.\n\nPUBLISHING CARTOGRAPHY ON THE INTERNET\n\n1. Introduction\n 1.1 Protocols\n 1.1.1 Layers of protocol\n 1.1.2 Client server architecture\n 1.1.3 Most commonly used protocol layers\n 1.2 Technological evolution of distributed GIS\n 1.2.1 Static maps (theory for exercise 0)\n 1.2.2 Static webpages (theory for exercise 1)\n 1.2.3 Interactive web maps (theory for exercise 2)\n 1.2.3.a Accelerated JavaScript and JSON\n 1.2.4 Geoservices distributed\n 1.3 Nearby technological examples\n\n2. ISO and OGC standards\n 2.1 Introduction to WxS or OWS\n 2.2 Services for the evaluation of information\n 2.2.1 Review of the Web Map Service (theory for exercises 3, 4, 5)\n 2.2.2 Use of several WMS clients\n 2.3 Services in the cloud (exercise 6)\n\n3. Practice\n 3.1 Introduction to IIS\n 3.2 Static map publication\n 3.3 Dynamic map publication\n\nCompetences\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDesign and apply solutions based on GIS tools for managing and exploiting natural resources or administrative information with a spatial component.\nHandle different data and metadata formats appropriately and take the importance of international standards into account when storing them and publishing them on internet.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nUse different specialised GIS and remote sensing software, and other related software.\nLearning Outcomes\nApply international standards for editing and publishing data and metadata on internet.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDesign suitable information systems for handling data in scientific, professional or general-interest contexts.\nHandle different tools for publishing cartography on internet.\nKnow the advantages and limitations of the use of standards in the GIS field.\nShow expertise in querying databases using the SQL language.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context." . . "Presential"@en . "FALSE" . . "Advanced remote sensing"@en . . "9.00" . "Objectives and Contextualisation\nThis 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.\n\nAt the end of the course, the student will be able to:\n\nApply the methodologies to alleviate the different sources of error in order to visualize and extract physical parameters of the received data.\nApply remote sensing techniques to different fields of research and applied.\nRS & METEOROLOGY. TECHNIQUES & EXAMPLES\n\n1. Introduction\n2. Classical meteorology\n3. Interpretation of satellite images\n 3.1 Images in the visible spectrum\n 3.2 Images in the thermal infrared\n 3.3 Images of water vapor\n 3.4 Compositions RGB\n4. The weather radar\n 4.1 Propagation of the microwave into the atmosphere\n 4.2 The radar equation\n 4.3 Observations of the Doppler radar\n\nRS & OCEANOGRAPHY. TECHNIQUES & EXAMPLES\n\n1. Introduction\n2. Fundamentals of Oceanography\n 2.1 Descriptive oceanography\n 2.2 Dynamic oceanography\n 2.3 Remotely observable phenomena\n3. Observation with passive sensors\n 3.1 Observation in the visible spectrum\n 3.2 Observation in the infrared spectrum\n 3.3 Observation in the microwave spectrum\n4. Observation with active sensors\n 4.1 Generalities\n 4.2 The dispersometer\n 4.3 The SAR\n 4.4 The altimeter\n5. Application: sea currents\n\nRS & GEOLOGY. TECHNIQUES & EXAMPLES\n\nContents 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.\n\nRS & VEGETATION. TECHNIQUES & EXAMPLES\n\n1. The problematic thematic/spectral classes. Land uses and land coverings.\n2. Specific techniques.\n 2.1 Spectral separability\n 2.2 Vegetation indexes\n 2.3 Tasseled Cap Transformation.\n3. Prevention of forestfires.\n4. Active fire.\n5. Techniques of analysis of changes in time.\n 5.1 Assessment of burnt surfaces.\n 5.2 Studies of regeneration of vegetation after forest fires.\n6. Analysis and multitemporal classification of roofs (example of crops)\n 6.1 Spectral signatures\n 6.2 Phenology and temporary signatures\n 6.3 Classification\n 6.4 Analysis of changes\n 6.5 Enrichment of databases\n7. Examples of practical applications\n\nCompetences\nApply different methodologies for the primary processing of images obtained by remote sensors in order to subsequently extract geographic information.\nContinue the learning process, to a large extent autonomously.\nIdentify and propose innovative, competitive applications based on the knowledge acquired.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nUse the different techniques for obtaining information from remote images.\nWrite up and publicly present work done individually or in a team in a scientific, professional context.\nLearning Outcomes\nApply remote sensing techniques to different research and applied-research fields.\nContinue the learning process, to a large extent autonomously.\nCorrectly apply methodologies to mitigate the different sources of error in order to visualise and extract physical parameters from the data received.\nIdentify and propose innovative, competitive applications based on the knowledge acquired.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nWrite up and publicly present work done individually or in a team in a scientific, professional context." . . "Presential"@en . "FALSE" . . "Masters dissertation"@en . . "15.00" . "Objectives and Contextualisation\nAt the end of the course, the student will be able to:\n\nUse concepts from various disciplines studied during the master with special emphasis on the choice of geographic data, whether obtained by remote sensors or in-situ, in order to give the optimal answer to the problems raised at work, be it theoretical or methodological or applied.\nApply remote sensing techniques in the development of the final master's project.\nUse multivariate, geostatistical and interpolation techniques to extract the best knowledge from the available geographic data.\nProperly treat direct and indirect information both in the processing stage and in its preparation for general access through the Internet.\nApply optimal solutions that respond to the challenges and questions posed in the final master's degree project, from the combined principle of environmental sensitivity and technical feasibility.\n\nCompetences\nAnalyse and exploit geographic data from different sources to generate new information from pre-existing data.\nChoose the most suitable tools and applications to fulfil the objectives of a project in the field of spatial planning or analysis.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDesign and apply solutions based on GIS tools for managing and exploiting natural resources or administrative information with a spatial component.\nHandle different data and metadata formats appropriately and take the importance of international standards into account when storing them and publishing them on internet.\nIdentify and propose innovative, competitive applications based on the knowledge acquired.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nUse the different techniques for obtaining information from remote images.\nWrite up and publicly present work done individually or in a team in a scientific, professional context.\nLearning Outcomes\nApply remote sensing techniques in developing the master's dissertation.\nContinue the learning process, to a large extent autonomously.\nDeal suitably with direct and indirect information, both at the processing stage and when preparing it for general publication on internet.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nFind optimal solutions to the challenges and questions posed in the master's degree dissertation, combining environmental sensitivity and technical feasibility from the outset.\nIdentify and propose innovative, competitive applications based on the knowledge acquired.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nUse concepts from various disciplines worked on during the master's programme, especially in choosing the geographic data obtained from remote sensors or in situ, in order to best tackle the problems posed in an assignment, whether this is theoretical, methodological or applied in nature.\nUse multivariate, geostatistical and interpolation techniques to generate maximum knowledge from the available geographic data.\nWrite up and publicly present work done individually or in a team in a scientific, professional context." . . "Presential"@en . "TRUE" . . "Master in Remote Sensing and Geographical Information Systems"@en . . "https://www.uab.cat/web/estudiar/official-master-s-degrees/general-information/remote-sensing-and-geographical-information-systems-1096480962610.html?param1=1345664654736" . "60"^^ . "Presential"@en . "The techniques of remote sensing and Geographic Information Systems (GIS) are essential for studying the Earth and managing its resources, for both academic and business purposes, in such varied fields as the following.\n\nInstruments: satellites, drones, GPS systems.\nGeographical information: cartographic institutes, Bing and Google Maps, OpenStreetMap, etc.\nCataloguing data and metadata in spatial data infrastructures.\nSoftware, such as the free programme MiraMon.\nThis programme has a scientific and technical orientation and gives training in Earth-observation techniques, and in the generation and analysis of information for studying the territory and managing its resources through GIS. It is taught in collaboration with CREAF, GRUMETS, MiraMon and Copernicus Academy."@en . . . . "1"@en . "FALSE" . . "Master"@en . "Thesis" . "5100.00" . "Euro"@en . "6360.00" . "None" . "Specialist in remote sensing and GIS in private companies or public authorities that make wide use of GIS and need personnel with advanced knowledge to manage geospatial data on a daily basis.\nResearcher in both methods and in remote sensing and GIS applications at universities and research centres."@en . "0"^^ . "TRUE" . "Downstream"@en . . . . . . . . . "Spanish"@en . . "Department of Geography"@en . . .