. "Remote Sensing"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Advanced methods in digital remote sensing"@en . . "7.5" . "Photo interpretation. Physical principles of remote sensing. Multispectral, Hyperspectral, Thermal and Radar imaging.\nEarth, Sea, Atmosphere observation satellites. International Programmes.\nGeometric and Radiometric Image Reduction\nAlgorithms for Digital Analysis and Classification of Remote Sensing Imagery\nAdvanced Classification Methods. Object Oriented Remote Sensing Image Analysis, applications.\nDigital terrain models and geomorphometric feature extraction\nPattern Detection, Representation, Description and Recognition. Area Segmentation, Mathematical Morphology. Computer Vision in Remote Sensing.\nApplications to Earth, Sea, and Atmosphere." . . "Presential"@en . "FALSE" . . "Digital methods in photogrammetry"@en . . "7.5" . "Digital image processing\nMeasurement automation techniques\nDigital matching\nEdge detection\nPattern recognition\nFeature extraction\nApplications of digital methods\nDigital products" . . "Presential"@en . "FALSE" . . "Photogrammetry"@en . . "5" . "- reach the basic knowledges about principles of photogrammetric measuring\n- know to choose the optimal methods of aerophotogrammetric measuring\n- to undestand the technology of aerophotogrammetric instrumentation\n- to know the procedures of photogrammetric data processing and measuring of im- \n -compare photogrammetric measuring methods to other surveying measuring methods\n- know the relevant features of metric cameras, and classify them according their metric features\n- Use the possibilities of digital photographic camera to achieve technical photography.\n- Define the coordinate systems in photogrammetry. Transform photogrammetric measurements between different coordinate systems in photogrammetry.\n- Evaluate the abilities of the human eye in the photogrammetric survey. \n -Interpret photogrammetric image and identify the orientation point.\n- measure photogrammetric images at the digital monocomparator.\n- Use the principles of stereoscopic vision to measure photogrammetric images.\n- Recognise elements of metric images and use them in the photogrammetric survey." . . "Presential"@en . "TRUE" . . "Remote sensing"@en . . "5" . "Students through lectures acquire knowledge about the following topics:\nOverview and definition of remote sensing. Features of the physical fields that are used in remote sensing. Sensors and systems for recording, the impact of platforms and environments. \n Usable characteristics of sensors. Electro - optical digital matrix cameras, line scanner, thermal cameras, multi-spectral cameras, hyperspectral scanner. Spatial resolution, modulation\ntransfer function, the minimum discriminable contrast, the minimum resolved temperature difference, calibration. Synthetic aperture radar, interferometric and polarimetric mode, usable features. Improving of images. Enhencement, ranking and reduce the amount of features. The method of principal components. Unsupervised classification. Supervised classification.\nEvaluation of the classification results. Registration and geocoding. Joining of images. Using of softwers for remote sensing in geoscience. Analysis and evaluation of interpretation results. Confusion matrix.\nStudents through practical work on exercies neet to acquire proficiency in the following skills:\nUsing of softwer tools (TNTlite, ImageJ, MiltiSpec) for remote sensing. Improving the images. Geometric transformations,\njoining of images, geocoding. Feature enhencement. Segmentation. Transformation of images in principal components (PCA).\nUnsupervised and supervised classification. Interpretation of multispectral images (visible, infrared, thermal). Interpretation of hyperspectral and radar imagesknow and distinguish the features of physical fields which were base of remote sensing, characteristics of remote sensing\nfeatures in different wavelength regions (multi-spectral, radar, hyperspectral, thermal), principles, methods and technology of the recording, interpretations\n- apply knowledge and understanding of the scene based on multisensor recordings, data processing and interpretation by\naddressing selected problems within the independent assignments in the remote sensing\n- applying initial skills for interpretation of multisensor, multispectral and hyperspectral images\n- independently drawing the conclusions about the quality and reliability of interpretation\n- publicly present selected problem and its solution through the example from remote sensing\n- identify areas, methods and techniques where necessary lifelong learning\n- used independently one of leading software tool for remote sensing." . . "Presential"@en . "TRUE" . . "Data science in remote sensing"@en . . "6" . "In the beginning of the course students can select a topic which they start to solve in a smaller group. Every group has a supervisor. Course is based on a problem based learning method. Additionally lectures about various remote sensing applications will be held.\n\nOutcome:\nAfter the end of the course:\r\n- students have the overview about principles used in passive, radar and lidar remote sensing and their respective application fields;\r\n- knows the principles of spectral measurements (knows the terms spectrometer, radiance, irradiance, reflectance, atmospheric correction, calibration),\r\n- knows the principles in water remote sensing (bio-optical modelling, adjacency effect)\r\n- knows the principles in vegetation remote sensing (optical properties of the leaf, contribution of various features to the reflectance, leaf angles, various indices).\r\n- student knows how to download, process and analyse remote sensing and possibly ancillary data and apply this knowledge to solve various exercises.\r\n- understands the differences in remote sensing and field data, how to combine them and use for spatio-temporal analyses and supporting the sustainable development goals (SDG) and international environmental frameworks.\r\n- have gained experience how to plan and conduct groupwork, share responsibilities inside small group, present results." . . "Hybrid"@en . "FALSE" . . "Satellite remote sensing and earth observation"@en . . "20" . "This module is taught within three broad areas. The first (i) introduces the main concepts of satellite remote sensing including electromagnetic radiation and its interaction at different wavelengths with the atmosphere and surface for both passive and active sensors. A second area (ii) focuses on sensor technology and data acquisition systems of the primary space based remote sensing platforms including; the COPERNICUS missions; Landsat; geostationary satellites; commercial platforms. The final component (iii) focuses on digital image processing - i.e. how images acquired by different satellites are analysed and interpreted to provide information on the Earth. The module is a combination of theoretical and practical based sessions using both commercial and open source software.\n\nLecture Topics include; Applications of Remote Sensing; Historical Development of Remote Sensing; Electromagnetic radiation; Interaction of electromagnetic radiation with atmosphere; Interaction of electromagnetic radiation with a surface; Passive Remote Sensing; Active Remote Sensing; Resolution in Remote Sensing; Pre-processing digital satellite data; Image correction techniques; Spectral Ratioing; Pixel and Object based Classification; Convolution filters; Change detection, Spatial Models, Accuracy assessments.\n\nOutcome:\nOn successful completion of the module, students should be able to:\r\nExplain the factors influencing the generation of electromagnetic radiation.\r\nDemonstrate knowledge of human visual systems, waveband selection and analysis.\r\nDifferentiate remote sensing functionality possible in the visible, near infrared, thermal infrared and microwave portions of the EM spectrum.\r\nIdentify and source the correct satellite datasets for specific applications.\r\nManipulate imagery through rectification, correction and visualisation.\r\nApply and evaluate classification/change detection imagery and quantify accuracy of outputs.\r\nAutomate image processing flow lines for scalable processing of large datasets." . . "Presential"@en . "TRUE" . . "Principles and practice of remote sensing"@en . . "5" . "Description\n\nThe module will provide an introduction to the basic concepts and principles of remote sensing. It will include 3 components: i) radiometric principles underlying remote sensing: electromagnetic radiation; basic laws of electromagnetic radiation; absorption, reflection and emission; atmospheric effects; radiation interactions with the surface, radiative transfer; ii) assumptions and trade-offs for particular applications: orbital mechanics and choices; spatial, spectral, temporal, angular and radiometric resolution; data pre-processing; scanners; iii) time- resolved remote sensing including: RADAR principles; the RADAR equation; RADAR resolution; phase information and SAR interferometry; LIDAR remote sensing, the LIDAR equation and applications.\n\nThe course aims to:\n\nProvide knowledge and understanding of the fundamental concepts, principles and applications of remote sensing, particularly the electromagnetic spectrum – what it is, how it is measured, and what it tells us;\nProvide examples of applications of principles to a variety of topics in remote sensing, particularly related to climate and environment\nDevelop a detailed understanding of the fundamental trade-offs in the design and applications of remote sensing tools: spatial, spectral, orbital etc.\nIntroduce new technologies, missions and opportunities, including ground-based sensing, lidar at multiple scales, radar, UAVs, new science and commercial missions, open data and the tools that are emerging to exploit these opportunities;\nIntroduce the principles of the radiative transfer problem that underpins most remote sensing measurements and how it is modelled and solved; applications of radiative transfer modelling to terrestrial vegetation;\nIntroduce students to wider remote sensing organisations, policy and careers through invited seminars from professionals in the field, including former RSEM students.\nSessions .\n\nIntroduction to remote sensing\nRadiation principles, EM spectrum, blackbody\nEM spectrum terms, definitions and concepts\nRadiative transfer principles and assumptions\nSpatial, spectral resolution and sampling\nPre-processing chain, ground segment, radiometric resolution, scanners; poster discussion\nActive remote sensing: LIDAR – principles and applications\nActive remote sensing: RADAR –principles and applications\nNew missions and technologies including LIDAR, UAVs, Copernicus etc.\nApplication discussions around assessed posters" . . "Presential"@en . "TRUE" . . "Remote sensing of the environment"@en . . "5" . "Short Description\nIntroductory principles of remote sensing and examples of data sets. Processing and interpretation of remotely sensed data.\nLearning Outcomes of Course\nBy the end of this course students will be able to:\n\n■ explain the principles of remote sensing, with reference to a range of examples;\n\n■ explain methods for processing remotely sensed data to generate environmental information;\n\n■ explain sources of error in remote sensing data;\n\n■ explain examples of the use of remote sensing data to detect and quantify environmental change;\n\n■ explain how remote sensing data can be integrated with other data sources;\n\n■ discuss the limitations of using remotely sensed data to detect environmental change;\n\n■ write scientific reports, including use of established conventions for the reporting of results and analysis and the appropriate use and referencing of relevant published material." . . "Presential"@en . "TRUE" . . "Sensing technologies"@en . . "5" . "This course provides an overview on the principles and applications of spaceborne, airborne and terrestrial sensing technologies for geographic data acquisition as well as techniques for processing and information extraction from the acquired data. \n\nAfter the course the student is able to\n\n• describe different sensing techniques, from different platforms, for acquiring geospatial data\n• explain the applied metrics for quality assessment of the acquired data\n• apply data processing and machine learning techniques on the (self) acquired geographic data\n• analyse the applied data processing/ information extraction algorithms and evaluate their capabilities/drawbacks\n• compare different sensing techniques for their suitability in different application domains from their efficiency and quality points of view\n• select appropriate sensing technologies for tackling a spatial decision making problem" . . "Presential"@en . "TRUE" . . "Photogrammetry and 3d computer visioin"@en . . "5" . "Photogrammetry and 3D computer vision aim at recovering the structure of real-world objects/scenes from images. This course is about the theories, methodologies, and techniques of 3D computer vision for the built environment. In the term of this course, students will learn the basic knowledge and algorithms in 3D computer vision through a series of lectures, reading materials, lab exercises, and group assignments. The topics cover the whole pipeline of reconstructing 3D models from images:\n- Cameras models: how a point from the real world gets projected onto the image plane and how to recover the camera parameters from a set of observations;\n- Epipolar geometry: the geometric relations between 3D points and their images points; the constraints between the image points;\n- Image matching: define and match image features (SIFT) to establish correspondences between images;\n- Structure from motion: recover/refine geometry and camera parameters from a set of images;\n- Multi-view stereo and learning-based approaches for recovering dense geometry (e.g., point clouds) from images;\n- Surface reconstruction: obtain 3D surface models of real-world objects from point clouds.\n\n \nAfter finishing this course, the students will be able to:\n- apply linear algebra knowledge to implement basic 3D computer vision algorithms;\n- explain the main concepts in 3D computer vision (i.e., camera models, epipolar constraints, fundamental matrix, image matching, triangulation, structure from motion, bundle adjustment, multi-view stereo, and surface reconstruction);\n- explain the principles of the state-of-the-art 3D computer vision pipelines for 3D dense reconstruction from images;\n- evaluate methods for reconstructing smooth surfaces and piecewise planar objects, and choose applicable methods to solve specific reconstruction problems;\n- propose and implement solutions for reconstructing real-world buildings from images." . . "Presential"@en . "TRUE" . . "Microwave remote sensing of the earths surface"@en . . "5" . "By the end of this course, students will be able to work with, and perform research based on real or synthetic data from current\nand imminent microwave missions (e.g. Sentinel-1, ROSE-L, CIMR, Harmony, Metop ASCAT/SG-SCA) to study processes\nrelevant in land, ocean and cryosphere applications, in particular processes related to surface-atmosphere interactions.\nMicrowave dielectric properties of natural materials\nMicrowave remote sensing of soil and vegetation.\nThe relation between surface soil moisture, root zone soil moisture and vegetation water content variations and their role in land-\natmosphere exchanges of water, energy and carbon. Modelling the influence of dielectric properties and geometry on emission\nand scattering from vegetated surfaces.\nSoil moisture estimation from passive and active microwave remote sensing.\nMonitoring biomass and vegetation water status using passive and active microwave remote sensing.\nLand-atmosphere interactions over ice/snow\nThe relation between physical snow/firn/ice properties and land/atmosphere interactions for ice\nThe relation between snow/firn/ice properties and the EM properties in the MW region\nRadiative transfer models to translate snow/firn/ice properties into remote sensing signals for both passive/active MW RS\nsensors\nRetrieval of snow/firn/ice properties from MW RS data\nOcean\nThe relation between wind, marine boundary layer conditions, and directional surface wave spectra.\nTheoretical models relating wave-spectra and the resulting directional roughness to the radar scattering intensities and\nmicrowave emissivity.\nEmpirical Geophysical Model Functions (GMF) relating surface winds and/or wind stress to radar scattering and Doppler\nRetrieval of surface wind and surface wave information using data from radar-scatter meters, Synthetic Aperture Radars, and\nmicrowave radiometers.\nStudy Goals After completing this module, students will be able to:\n1. Explain and describe the relations between surface properties and processes, and observations that can be obtained using\nmicrowave remote sensing.\n2. Apply state-of-the-art models and retrieval techniques to simulate microwave observables and retrieve states of interest in\nland, ocean and cryosphere applications .\n3. Compare different forward modelling or retrieval techniques to estimate variables of interest at the surface\n4. Select and defend the choice of a product/technique/model to capture a process relevant in land, ocean and cryosphere\napplications" . . "Presential"@en . "FALSE" . . "Remote sensing"@en . . "7.50" . "After successful completing this course, you will be able:\nTo provide an overview of the most important, currently available remote sensing techniques and sensors for the earth sciences;\nTo explain the physics of (imaging) spectroscopy and other Earth observation methods and the use of spectral libraries to aid image interpretation;\nTo illustrate the study and interpretation of spatial patterns and time series data in remote sensing;\nTo instruct on the use of current desktop- and cloud-based image processing tools\nDuring the course you will develop and train the following skills:\nGiving academic oral presentations about an applied remote sensing topic.\nWritten reporting about image processing and interpretation.\nAnalyze and interpret various types of satellite images using the theoretical knowledge acquired during the lectures.\nHands on use of advanced image processing software to process, interpret, classify and analyze a range of different earth observation images.\nThe student is expected to:\nUnderstand the fundamentals of imaging spectroscopy and its applications;\nBe able to analyze and interpret remote sensing information in their spatial and temporal contexts;\nBe able to do Earth observation image processing and interpretation using available software and effectively use build-in or online documentation to compose their own analyses.\nTo critically evaluate remote sensing products passing your desk.\nContent\nRemote sensing, or Earth observation, is a fast developing and innovative technique of exceptional importance for all geo-disciplines. Earth observation is now widely used to study the dynamics of system Earth and deliver important input in global change models, ocean current models water balance models and at regional level for modeling catchment discharges and erosion processes. Remote sensing enables the collection of information about the spatial distribution of objects at the Earth surface such as crops, vegetation, soil types, rock types, alteration zones, snow, surface water, to identify object properties (vegetation cover, type of crops, soil mineral contents) and to investigate their temporal changes (seasonal or long-term). A wide range of sensors (optical, thermal, radar, lidar) are now orbiting the earth or are available in aircrafts. These basics are presented and discussed during the bachelor course and here we continue with more advanced techniques for information extraction from imagery by hands-on exercises." . . "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" . . "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" . . "Methods and models for analysing remote sensing data"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Advanced processing of remote sensing data"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Photogrammetry"@en . . "5" . "no data" . . "Presential"@en . "TRUE" . . "Remote sensing"@en . . "5" . "no data" . . "Presential"@en . "TRUE" . . "Remote sensing and photogrammetry"@en . . "5" . "LO: students gain knowledge for planning and\nmanagement of remote sensing and\nphotogrammetric projects as well as for\npractical accomplishment of procedures\n• they learn to connect theory and practice, they\nare able of deeper understanding of modern\ntechnologies of remote sensing and\nphotogrammetric processes, they become\naware of technological advancement\n• they develop the sense for scientific-research\nwork\n• students are encouraged to work in a team and\nto manage practical projects\n• they improve skills how to search and use\nprofessional literature, improve their research\napproach as well as written and oral reporting\n• they combine and use of knowledge acquired in\nother courses\n• they are able to analyse, interpret and logically\ncombine different data sources in a professional\napproach" . . "Presential"@en . "TRUE" . . "Python for remote sensing"@en . . "no data" . "N.A." . . "Presential"@en . "TRUE" . . "Transitional work on geo-informatics in remote sensing"@en . . "no data" . "N.A." . . "Presential"@en . "TRUE" . . "Satellite remote sensing l"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Satellite remote sensing p"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "High resolution remote sensing l"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "High resolution remote sensing p"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "State of the art remote sensing technologies"@en . . "4" . "LO: The course deals with the state of the art topics in\nthe field of remote sensing. It adapts to the trend of\ndevelopment of systems and data processing\nprocedures.\nRadar systems operation\nRadar data\nRadar data pre-processing\nRadar imaging geometry\nRadar interferometry (InSAR)\nProduction of digital elevation models\nDetermination of surface displacements (DInSAR)\nPermanent scatterers (PS InSAR and SBAS)\nDeformation analysis\nOblique aerial images\n3D reconstruction\nApplications\n3D point clouds\nSatellite images time series analysis\nIntersensor calibration\nTemporal smoothing and production of composites\nMultitemporal classification\nMachine learning\nExercises\nApplication of selected data processing technology\nUse of dedicated software\nESA SNAP\nENVI SARScape\nSocetGX" . . "Presential"@en . "FALSE" . . "Geodetic monitoring and remote sensing"@en . . "6" . "no data" . . "Presential"@en . "TRUE" . . "Photogrammetry and remote sensing"@en . . "no data" . "no data" . . "Presential"@en . "FALSE" . . "Earth remote sensing"@en . . "4" . "The aim of the study course is to ensure the systematic and integrative acquisition of competencies on the application aspects and application possibilities of modern remote sensing methods and technologies in various fields of Geography and Earth Sciences. Tasks of the study course: to look in detail at the basic questions about the interaction of natural objects and electromagnetic radiation, as well as equipment and techniques for obtaining images in remote sensing. To acquaint students with the geometric and informative properties of the obtained images, image processing and decoding of various objects and their condition (interpretation of information). Language of course teaching: English Latvian.\nResults Knowledge: 1. Understands fields in natural sciences, principles of remote sensing. 2. Understands the application and limitations of photogrammetry and remote sensing in to geography and Earths sciences. Skills: 3. Performs measurements in logos and digital remote sensing images, views, evaluates and analyzes stereo images, performs simple image conversion. 4. Works with remote sensing materials, using various methods, techniques and basic tools and their application in modern applied aspects and research directions. Competence: 5. Evaluates the possibilities of researching the interaction of natural structures, as well as man-made structures and phenomena, using remote sensing materials, as well as the possibilities to analyze the changes of territories in time and space." . . "Presential"@en . "TRUE" . . "Photogrammetry, lidar and uav"@en . . "4" . "The goal of the course is to introduce students with photogrammetry, LiDAR data acquisition and processing, unmanned aerial vehicles (UAV) research methods. Tasks of the course, that includes acquisition of knowledge, skills and competences are: 1) to learn photogrammetric data processing methods. 2) to learn basic LiDAR data processing and various interpretation methods. 3) identify shortcomings and advantages of remote sensing methods 4) to obtain competences in practical application of UAV platforms. The course is being studied in Latvian and English\r\nResults\tKnowledge 1.Gain basic knowledge about theoretical background of photogrammetry. 2.Are familiar with LiDAR data processing and interpretation methods. 3.Characterize UAV applications in the context of close-range remote sensing. Skills 4.Are able to work with UAV platforms. 5.Are able to process obtained measurements. 6.Are able to calculate data precision and accuracy. Competence 7.Are capable of selecting most suitable close-range remote sensing method for solution of specific problem. 8.Are capable of integrating close-range remote sensing data in preparation of geospatial information." . . "Presential"@en . "FALSE" . . "Applied remote sensing"@en . . "6" . "The aim of the study course is to provide students with theoretical knowledge and practical skills in the application of remote sensing materials for support of land resource management. The goals of the course are to explain the principles of optical and radar (SAR) image processing and interpretation; to demonstrate applications in solving problems in marine, agricultural, forestry and urban domains; to introduce into various automatic image classification options; to explain quality evaluation of the obtained results. The course focuses on the possibilities of freely available satellite data, with a special focus on the EU Copernicus program Sentinel 1 and Sentinel 2 satellite data. Course languages: English, Latvian\nCourse responsible lecturer Zaiga Krišjāne\nResults Knowledge 1. knows the peculiarities of interaction of the optical and microwave part of the electromagnetic spectrum with the Earth's surface and objects on it; 2. understands the reasons for differences in the spectral properties of plants; 3. describes the most popular remote sensing data acquisition platforms; 4. describes various automatic classification methods; 5. explains the possible causes of changes in SAR image series; 6. is familiar with the use of SAR in marine applications; 7. describes the possibilities of remote sensing in agricultural and forestry monitoring; 8. understands the limitations of remote sensing methods in urban research. Skills 9. retrieves EU Copernicus satellite data from the data repository; 10. visualizes multispectral and hyperspectral images; 11. prepares SAR images for interpretation; 12. analyzes remote sensing images using automatic classification methods; 13. calculates quality indicators of the prepared maps; 14. uses SAR and optical data for operational monitoring of floods, marine pollution, deforestation and rural development; 15. evaluates the economic activity of the region from remote sensing materials. Competences 16. chooses the most appropriate data source and analysis method for solving a specific business problem in a reasoned manner;\n17. critically evaluates the results obtained from remote sensing materials and provides reasoned recommendations for improving the results.\nCourse plan 1.Introduction to applied remote sensing L4, S2, P2 2.Interaction of electromagnetic radiation with living and non-living objects, Earth's surfaceL4, P4 3.Automatic interpretation of imagesL16, P16 4.Quality assessment of image classification L4, P4 5.Basics of synthetic aperture radarL6, P6 6.Synthetic aperture radar applications in marine domain L4, P4 7.Remote sensing opportunities in agriculture and forestryL6,P6 8.Possibilities of remote sensing in urban areas L2, P2 9.Characterization of the sample area from remote sensing data S4 L - lectures; P - practical work; S - seminar." . . "Presential"@en . "FALSE" . . "Satellites remote sensing: acquisition system and data processing methods"@en . . "9" . "Cap. 1 - Generalities on the remote sensing and physics principles. Introduction, remote sensing system, Properties of \r\nthe electromagnetic radiations, source of the electromagnetic radiation, Interaction with matter, remote sensing \r\nindicators, Interaction of the electromagnetic radiation and the terrestrial atmosphere – – Equation of the radiative \r\ntransport (RTE) – Estimate of the surface temperature. Description of the General Split Window Technique, using the \r\nthermal emission for estimating the sub-surface characteristics. Appendices: A – Beer law, scattering, absorption bands, \r\nrefraction, surface backscattering, B – Description of the General Split Window Technique, C – Using the thermal \r\nemission for estimating the sub-surface characteristics. Exercises: Software PclnWin e PCModWin. \r\nCap. 2 - Remote sensing sensors. Photographic & electro-optical sensors. Micro-wave systems (active and passive), \r\nLidar. Calibration techniques. \r\nCap. 3 – The remote sensing and the space environment. The terrestrial upper-atmosphere – the San Marco satellites \r\ndata. The Space Debris – Techniques for the observation and monitoring. The atmosphere of the outer planets (Mercury, \r\nVenus, Mars, the giant planets). \r\nCap. 4 – Principle of remote sensing of the terrestrial atmosphere. Atmosphere sounding. Satellite based measurement \r\nof the atmospheric ozone. Occultation techniques with active systems. \r\nCap. 5 – Remote sensing orbits. Orbit properties. Orbit perturbations. The requirements of the orbits for remote sensing. \r\nGround tracks. Remote sensing satellite constellations. Exercises: Software STK, Matlab Orbital Mechanics. \r\nRemote sensing systems (Landsat, SPOT, NOAA, Sentinel, MSG). Appendices: A – Drift of the orbit operational \r\nparameters, B – Computation of the acquisition times at the ground station, C – Design of an orbit crossing a given \r\nstation at a given crossing time. Tutorial: Software STK, Matlab Orbital Mechanics. \r\nCap. 6 – Acquisition systems and satellite images pre-processing. Ground receiving station, Image re-construction, \r\nenhancement and information extraction. Image registration. Map projection. Appendices: A - pixel Geo-location, B – \r\nStatistical analysis and enhancement of the images (Discrete Fourier Transform applied to the images, Wavelet, \r\nPrincipal Components, Maximum auto-correlation factors, MAF). Tutorial: Software ENVI, MATLAB Image \r\nProcessing tool. \r\nCap. 7 – Theory and practices of image processing. Selection of the classification algorithms (Unsupervised and \r\nSupervised classification). Topographic models. Image registration (Ground Control Points, Mutual Information, \r\ninvariant moments, contour matching). Change detection (algebraic methods, Multivariational Alteration Detection, \r\nMAD). Introduction to the processing of hyperspectral images (Modeling the measurements, linear un-mixing, pure \r\npixels). Object recognition (Mathematic Morphology, Hough Transform). Tutorial: Software ENVI, Arcview, Image \r\nProcessing tool di MATLAB. \r\nCap. 8 – Project of a Remote Sensing Sensor." . . "Presential"@en . "TRUE" . . "Remote sensing application"@en . . "3" . "Overview of advanced remote sensing techniques. Pre\u0002processing and processing of data for the purposes of \r\nsolving issues related to environmental analyses con\u0002ducted with use of remote sensing. Data, ways of their \r\nprocessing, and methods of analysing remote sensing \r\ninformation to solve various problems related to monitor\u0002ing the natural environment." . . "Presential"@en . "TRUE" . . "Photogrammetry in engineering"@en . . "4" . "Preparing and elaborating geodata for tasks in the field \r\nof civil engineering. Preparing measurement data, in\u0002cluding obtaining and pre-processing to the form of complete point clouds. Application of point clouds in \r\nvarious tasks related to geospatial engineering with use \r\nof photogrammetric and remote sensing data." . . "Presential"@en . "FALSE" . . "Low-altitude photogrammetry"@en . . "4" . "Processing digital images obtained at low altitudes in \r\nthe visible and near infrared spectrum with use of non\u0002metric cameras installed on UAVs. Photogrammetric \r\ndevelopment of data obtained at low altitudes and the \r\nuse of specialist photogrammetric software. Introduction \r\nto low-altitude photogrammetry. Assessment of the qual\u0002ity of images obtained at low altitude" . . "Presential"@en . "FALSE" . . "Environmental remote sensing"@en . . "5" . "Basic and advanced issues related to contemporary \r\ntechniques of processing remote sensing data and their \r\napplication in environmental research. Basic and ad\u0002vanced methods, techniques, and tools used in contem\u0002porary remote sensing, including the integration of data \r\nobtained from various sources, radar remote sensing, \r\nand spectral analyses." . . "Presential"@en . "FALSE" . . "Close-range photogrammetry"@en . . "5" . "Close-range photogrammetry. Techniques of terrestrial \r\nimaging techniques in the visual spectrum. Analogue \r\nand digital ground-based photogrammetric cameras and \r\nthe adaptation of non-metric digital cameras for the \r\ntasks of engineering and industrial photogrammetry as \r\nan alternative to geodesic measurement technologies, \r\nincluding laser scanning. Unmanned aerial platforms to \r\nobtain image data that enable spatial modelling of close\u0002range objects and their surroundings." . . "Presential"@en . "FALSE" . . "Fundamentals of photogrammetry"@en . . "2" . "Photogrammetry - definition. Aerial cameras. Measurement properties of aerial photos. Elements of internal camera orientation: elements of the central projection, the image coordinate system of the\nanalogue camera, basic elements of internal camera orientation, spatial camera coordinate system, the image coordinate system of a digital camera. Radial distortion of the lens. Tangential distortion of\nthe lens. Points and line of interest of a tilted image. Geometric properties of the aerial photo. Elements of external orientation of a photo. Systematic errors of photos. Aerial photos acquisition, quality, basic parameters of the photo block, designing the scale of photos, time of day and photographic season, photo mission management system. The quality of modern aerial photographs. Comparison of the spatial resolution of analogue and digital photos. Country coverage by aerial photographs. Stereoscopy - observations vs. measurement. Conditions for stereoscopic observations. The concept of a\nmeasurement mark. Stereoscopic observations and stereoscopic measurement. Simplified altitude compilation of the stereogram of aerial photographs. Image coordinate system of photos: definition and measurement. Automatic photo measurement (image matching). Introduction to analytical photogrammetry. Elementary analytical operations on photos: photo rotation matrix, spatial coordinate\nsystem of a photo (camera), the condition of collinearity, spatial resection (calculation of elements of external camera orientation), spatial intersection. Introduction to aerotriangulation." . . "Presential"@en . "TRUE" . . "Photogrammetric measurement technologies"@en . . "5" . "Stereoplotter processing of the stereogram of analog aerial photos. Image Matching. Digital photogrammetric workstation - DPW. Aerotriangulation. Digital Terrain Model - DTM. Airborne laser scanning (ALS). Digital orthophotomap. Vector studies. Photogrammetric supply of topographic databases. Satellite imaging in the optical range. Satellite imaging with a very high resolution (VHRS). 3D\r\nmodeling. City model." . . "Presential"@en . "TRUE" . . "Photogrammetric measurement technologies 2"@en . . "2" . "Stereoplotter processing of the stereogram of analog aerial photos. Image Matching. Digital photogrammetric workstation - DPW. Aerotriangulation. Digital Terrain Model - DTM. Airborne laser scanning\r\n(ALS). Digital orthophotomap. Vector studies. Photogrammetric supply of topographic databases. Satellite imaging in the optical range. Satellite imaging with a very high resolution (VHRS). 3D\r\nmodeling. City model." . . "Presential"@en . "TRUE" . . "Remote sensing"@en . . "3" . "Lecture: Physical basics of remote sensing. Energy relations between Sun - object - sensor. Absorption bands in the electromagnetic spectrum and atmospheric windows used in remote sensing. Spectral\ncharacteristics of objects: measurement methods, spectral curves of typical objects and the influence of various factors on their course, the meaning of spectral characteristics knowledge in remote\nsensing. Aerial images: panchromatic, black-and-white infrared, color, color-infrared and multispectral. Characteristics of images in terms of interpretation tasks. Methodology of aerial image interpretation, typical relations: object - the look of object in different images. Visual and digital methods of interpretation, the logic of image interpretation. Aerial and satellite scanners: methods of imaging using scanners, the essence of digital format, image structure in digital format. Basic information on meteorological, optical and radar satellites. Characteristics of selected satellite systems,\nincluding Landsat, SPOT, Sentinel-2, WorldView, GeoEye, Plejades, Radarsat, TerraSAR-X. General information concerning digital image processing, color composite, image classification, creating a satellite map. Examples of remote sensing techniques usage in various fields of the economy. Remote sensing data as a data source for GIS. Exercises: Recognition and interpretation of objects in aerial\nimages in selected band of the visible spectrum and black-and-white infrared images, the relation between spectral characteristics of object and its shade of grey in the image. Relations between shades of grey in the optical (visible spectrum) and infrared images. The update of selected elements in indicated spatial database using open-access remote sensing data. Basics of creating color composites.\nLandscape analysis on Sentinel-2 color composites. Creating image interpretation key based on satellite images." . . "Presential"@en . "TRUE" . . "Remote sensing 2"@en . . "2" . "General Introduction to software. Raster images - basic features, notation, formats, metadata. Image visualization, the concept, and role of the histogram. Image quality improvement; contrast enhancement with a linear function and non-linear functions, visual evaluation of the quality of processed images. The creation of color compositions in various combinations and the overall assessment of the information content - the importance of selecting specific channels, selecting the contrast enhancement function, and the RGB filters assignment method. Interpretation of the image of color\ncomposition and the knowledge of spectral characteristics of objects. Vegetation condition analysis using the NDVI and TASSCAP index.\nCombining panchromatic and multispectral data - examples using the following methods: RGB transformation => HLS => RGB, val. the mean of (MS + PAN). Digital classification of land cover forms in a supervised approach - initial assumptions, class definition, preparation of training fields, analysis of statistics (signatures), assessment of the correctness of classes and preparation of training fields, classification with the use of selected algorithms. Assessment of the accuracy of the thematic digital classification of land cover classes." . . "Presential"@en . "TRUE" . . "Facultative class 3 - selected topics of environment remote sensing"@en . . "2" . "The course is divided into two parts. In the first, the student becomes acquainted with selected methods of satellite image processing (multi, super and hyperspectral), such as: creating color composites for the needs of environmental analyzes - selecting the proper spectral ranges for specific purposes, creating masks of selected objects (e.g. water mask), statistical analysis of satellite images in global and local terms - advantages and disadvantages of both approaches, calculation of vegetation and soil indicies and their role in the natural environment research. In the second part of the course, students, in small teams, carry out a project aimed at analyzing and assessing the changes in the environment with the use of LANDSAT or Sentinel satellite data, using e.g. supervised classification and/or masking." . . "Presential"@en . "FALSE" . . "Facultative class 4 - fundametals of engineering photogrammetry"@en . . "3" . "Discussion of the technological scheme of photogrammetric measurement: factors affecting the choice of the method - the type of desired information, the object's geometry, the type of coof the object. Issues of cntrol elements, the representativeness and accuracy of the measurement results and methods of registration alibration of digital cameras. Problems of spatial modelling of architectural and engineering objects. Fundamentals of ground scanning. Examples of photogrammetric systems used in various branches of the economy. Basics of digital image processing used in various engineering applications." . . "Presential"@en . "FALSE" . . "Facultative class 5 - application of photogrammetry and remote sensing"@en . . "3" . "The use of satellite images for the condition assessment and Earth monitoring, - multispectral images and their applications, - super- and hyperspectral images and their applications, - thermal remote sensing and its applications, - SAR images and their applications. Aerial data and their products available in Poland - status and coverage (digital terrain models, orthoimages,\r\ntopographic databases) - coverage status, parameters, availability. Airborne laser scanning - a source of data not only for hydrologists and surveyors - the use of ALS in technical, natural and human\r\nsciences. Near-range photogrammetry in engineering and cultural heritage. Data acquisition from UAV and their applications." . . "Presential"@en . "FALSE" . . "Remote sensing"@en . . "10" . "Radar and radiometer systems are used from satellites and aircrafts to measure and monitor the surface of the Earth, including the land surfaces, the oceans and the atmosphere. This is called remote sensing or Earth observation and is of utmost importance for monitoring the Earth’s environment and climate.\r\nThe general course objectives are to provide the students with an understanding of those radar and radiometer techniques and systems that are used for remote sensing, with a special emphasis on the technical description of the sensors and on the application of these techniques for measuring and monitoring of properties of the surface of the Earth. This also includes knowledge about the necessary data processing techniques." . . "Presential"@en . "FALSE" . . "Geological remote sensing"@en . . "7" . "no data" . . "Presential"@en . "TRUE" . . "Thermal infrared remote sensing: from theory to applications"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Radar remote sensing"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Quantitative remote sensing of vegetation parameters"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Remote sensing and modelling of primary productivity and plant growth"@en . . "5" . "no data" . . "Presential"@en . "FALSE" . . "Remote sensing: physics and methods"@en . . "3" . "no data" . . "Presential"@en . "TRUE" . . "Remote sensing: applications"@en . . "3" . "no data" . . "Presential"@en . "TRUE" . . "Remote sensing project"@en . . "3" . "no data" . . "Presential"@en . "TRUE" . . "Radiometry and remote sensing"@en . . "6" . "Antenna theory\n- LiDAR Remote Sensing\n- Microwave Remote Sensing\n- Optical imaging -overview of Earth observation satellite imaging systems in the reflective domain.\n- Atmospheric remote sensing\n- Land surface remote sensing -this lecture is an introduction to the characterization of terrestrial surfaces by remote sensing,\nmainly in the solar domain. At first, the different modes of interaction of solar radiation with continental\nsurfaces are discussed. The second part of the lecture is devoted to the determination of the biochemical\nand structural parameters of vegetation by hyperspectral and multiangular remote sensing, from the leaf\nscale to the ecosystem. In the last part, we discuss the quantification of energy balance on the surface of\nthe Earth and its importance in climate models. Emphasis is put on physical modeling at different scales." . . "Presential"@en . "TRUE" . . "Principles of remote sensing (5 ects)"@en . . "5" . "no data" . . "Presential"@en . "TRUE" . . "Applications of remote sensing"@en . . "10" . "Not found" . . "Presential"@en . "TRUE" . . "Fundamentals of remote sensing"@en . . "5" . "Not found" . . "Presential"@en . "TRUE" . . "Remote sensing"@en . . "10.00" . "Remote Sensing is a core focus of contemporary GIS application, both in research and professional / business contexts. The purpose of this course is to provide adequate knowledge pertaining to concepts, principles and utility of Remote Sensing technology and to prepare students to apply this technology to their discipline of interest. It will also provide a sound understanding of principles and applications of remotely sensed digital image processing. The specification and use of digital imagery for investigating Earth resources and environmental applications will be discussed. Digital image processing of aerial/space borne sensors including radiometric and geometric correction, image enhancement and interpretation, mosaicking, segmentation a swell as classification techniques and its integration with GIS will be covered.\n\nLearning Outcomes:\nOn completion of the module you will have gained the following skills:\n\n- Understanding of theoretical remote sensing considerations and technical information pertaining to a range of sensor platforms.\n- Ability to use the complete range of remote sensing tools for a broad range of operational and application tasks.\n- Ability to efficiently and accurately correct and interpret remotely sensed digital imagery.\n- Understanding on the use of statistics pertainning to radiometric/geometric correction and classification as well as segmentation techniques.\n- Knowledge and application of image enhancement techniques.\n- Knowledge to use the electromagnetic spectrum to generate a variety of image products.\n- Ability to discuss the interaction of remotely sensed data in a GIS and vice versa at a philosophical and practical level." . . "Blended"@en . "FALSE" . . "Fundamentals of remote sensing"@en . . "20" . "This module will provide a comprehensive understanding of the physical principles of remote sensing. You’ll gain an understanding of the nature of electromagnetic radiation, its key properties and the processes that influence its propagation through space, atmospheres and its physical interaction with matter. It will also provide an introduction to different remote sensing platforms, sensors and data types and practical experience in basic techniques for processing, analysing and visualising remotely sensed data. On successful completion of the module, you should be able to:\n \n demonstrate critical knowledge and understanding of electromagnetic radiation including its characteristics in free space and interactions with matter;\n demonstrate critical knowledge and understanding of the physical principles underlying measurements of electromagnetic radiation using passive and active sensors;\n demonstrate critical knowledge and understanding of the design, technical specifications and deployment modes of different photographic, electro-optical and microwave sensors and how this influences their suitability for different applications;\n demonstrate and apply critical knowledge and understanding of methodologies for sourcing, processing, analyzing and visualising remotely sensed data;\n demonstrate advanced ICT skills for the processing, analysis and visualisation of data and the ability to critically evaluate datasets in numerical and graphical form;\n communicate effectively to a range of audiences with different levels of knowledge and expertise." . . "Presential"@en . "TRUE" . . "Advanced remote sensing"@en . . "6" . "no data" . . "Presential"@en . "TRUE" . . "Fundamentals of remote sensing"@en . . "6.0" . "### Working language\n\nPortuguese and English\n\n### Goals\n\nThis UC aims to introduce the basic concepts of Remote Sensing (RD), which will serve as the basis for the frequency of specific UC DR, from the 2nd semester of the 1st year.\n\nPretend that students:\n\n1) Acquire basic knowledge about the physical principles of remote sensing, in particular about radiometry and the interaction of radiation with the atmosphere and the Earth's surface.\n\n2) Get to know the immense potential of remote sensing in Earth observation.\n\n3) Get to know the main characteristics of the orbits of Remote Sensing satellites.´\n\n4) Get to know the vast set of satellite data available and be able to identify the most suitable one for solving a given problem.\n\n5) Know the specific characteristics of microwave sensors versus optical and thermal sensors, advantages and tolerances of each type.\n\n### Learning outcomes and skills\n\nStudents must:\n\n1) Be able to identify the strengths and limitations of remote sensing in Earth observation, in particular: the physical principles of remote sensing; the main characteristics of DR satellite orbits and how they affect the ability to acquire DR data; main satellites and sensors and their characteristics..\n\n2) Know the vast set of available satellite data and be able to identify the most appropriate one to solve a given problem.\n\n### Working mode\n\nin person\n\n### Program\n\n1\\. Introduction to Remote Sensing.\ntwo\\. Energy sources and radiometric concepts\n3\\. Interaction of energy with the atmosphere\n4\\. Interaction of energy with the Earth's surface\n5\\. Orbits of remote sensing satellites.\n6\\. Earth Observation Satellites\n6.1 Characteristics of satellites and sensors; Types of Sensors.\n6.2 Environmental satellites\n6.3 Oceanographic satellites\n6.4 Weather satellites\n6.5 High resolution satellites (spatial and/or spectral)\n7\\. Microwave sensors.\n\n### Mandatory Bibliography\n\nJensen John R.; [Remote sensing of the environment](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000285191 \"Remote sensing of the environment (Abre numa nova janela)\"). ISBN: 0-13-188950-8 \nLillesand, T.M., Kiefer, R.W.; Remote Sensing and Image Interpretation, John Wiley and Sons, 7th Edition,, 2015 \nRees, W. G; Physical Principles of Remote Sensing, University of Cambridge, 3rd Edition , 2013 \nProst Gary L.; [Remote sensing for geoscientists](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000297467 \"Remote sensing for geoscientists (Abre numa nova janela)\"). ISBN: 978-1-4665-6175-5 ebook \n\n### Complementary Bibliography\n\nRichards, J.A., Jia, X; Remote Sensing Digital Image Analysis - An Introduction, Fifth Edition, Springer-Verlag, 2013 \nGonzalez, R.C., Woods, R.E. ; Digital Image Processing, Addison-Wesley, 2008 \n\n### Teaching methods and learning activities\n\nTheoretical content classes are given based on Power Point presentations. In the practical classes, it is proposed to solve exercises that aim to apply and complement the knowledge given in the theoretical ones, especially those referring to points 2) and 5) of the program. At the beginning of each class, a period will be reserved for students to raise questions about the content of the previous class, thus trying to encourage continuous and regular study.\n\nSince the objectives of this UC are the transmission of basic knowledge of Remote Sensing, it does not include the use of any type of software, this component being covered by the UC Digital Image Processing and Computing for Remote Sensing.\n\n### Type of evaluation\n\nEvaluation by final exam (100%)\n\n### Occupation Components\n\nSelf-study (hours): 120.00\n\nFrequency of classes (hours): 42.00\n\n**Total:**: 162.00\n\n### Get Frequency\n\nClass attendance is mandatory. Students may lose attendance if they exceed the number of absences provided by law.\n\n### Final classification calculation formula\n\nAssessment is carried out in a final exam (EF), with two components: Theoretical (T) and Practical (P).\n\nFinal: CF=T \\*0.7 + P\\*0.3.\n\nMore information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479403" . . "Presential"@en . "TRUE" . . "Computing for remote sensing"@en . . "6.0" . "### Working language\n\nPortuguese and English\n\n### Goals\n\nIt is intended that students acquire skills and knowledge of programming/scientific computing that will allow them to develop tools and applications dedicated to the areas of Remote Sensing (RD).\n\n### Learning outcomes and skills\n\nThe program covers the Python language as a programming language, as well as the use of scientific computing libraries for manipulation and visualization of geospatial information for the development of applications.\n\n### Working mode\n\nIn person\n\n### Prerequisites (prior knowledge) and co-requisites (concurrent knowledge)\n\nNo prerequisites.\n\n### Program\n\nIntroduction to the Python language\n\nIntroduction to the command line for interactive computing – IPython\n\nData Types (variables of type int, float, byte…, strings, lists, dictionaries…)\n\nControl flow (loops, if-then conditions)\n\nCode organization (functions, modules, packages)\n\nFile writing and reading, data input-output\n\nIntroduction to the _numpy_ module\n\nUnderstand data structuring with N-dimensions\n\narray creation\n\nArray indexing, joining and cutting with indexes, masks\n\nBasic operations and manipulation of N-dimensional arrays\n\nIntroduction to the 2D visualization module – _matplotlib_\n\nControl of colors, axes and legends\n\nCreating scatter, line, and bar charts\n\nStatistical graphs, histograms\n\nLevel curves, 2.5D visualization\n\nSub-figures, graphic organization\n\nIntroduction to the _s__ci__p__y_ module for scientific computing\n\ntrigonometric functions\n\nstatistical functions\n\nLinear Algebra, vectors and matrices\n\nLinear, polynomial and spline interpolation\n\ndata input-output\n\nVisualization of georeferenced information – _b__asemap_ and _c__artopy_\n\nmap creation\n\ncartographic projections\n\nCoastlines, political boundaries, land-sea, lakes and rivers\n\nMapping of vector information through shapefiles\n\nData structuring in time series and dataframes – module _p__andas_\n\nData input-output in pandas\n\nStructured information 1D (series) and 2D (dataframes)\n\nData organization, aggregation and indexing criteria\n\nComputing and analyzing information in pandas\n\nControl of dates and times, module _astropy_\n\n2D visualization and matplotlib integrated in Pandas\n\nMulti-dimensional arrays and datasets with _pandas_ and _xarray_\n\nInput-output of structured information in netCDF\n\nData indexing and selection\n\nExtraction and manipulation of variables\n\nStatistical analysis by dimensions and time series\n\nReorganization and visualization\n\n### Mandatory Bibliography\n\nMark Lutz; [Programming Python](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000228063 \"Programming Python (Opens in a new window)\"). ISBN: 0-596-00085-5\n\n### Complementary Bibliography\n\nMark Lutz; [Python pocket reference](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000189758 \"Python pocket reference (Opens in a new window)\"). ISBN: 978-1-56592-500-7\nMatt A. Wood; [Python and Matplotlib essentials for scientists and engineers](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000855875 \"Python and Matplotlib essentials for scientists and engineers (Opens in a new window )\"). ISBN: 978-1-62705-619-9\nHans Peter Langtangen; [A primer on scientific programming with Python](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000291612 \"A primer on scientific programming with Python (Opens in a new window)\" ). ISBN: 978-3-642-02474-0\n\n### Teaching methods and learning activities\n\nClasses are based on Powerpoint presentations and Notebooks with practical exercises exemplifying the use of the various modules addressed.\n\n### Software\n\nPython interpreter\nvirtualbox\n\n### Type of evaluation\n\nEvaluation by final exam\n\n### Assessment Components\n\nExam: 100.00%\n\n**Total:**: 100.00\n\n### Occupation Components\n\nSelf-study: 50.00 hours\nFrequency of classes: 50.00 hours\n\n**Total:**: 100.00\n\n### Get Frequency\n\nClass attendance is mandatory.\nStudents may lose attendance if they exceed the number of absences provided by law.\n\n### Final classification calculation formula\n\nFinal exam (100%).\n\nMore information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479402" . . "Presential"@en . "TRUE" . . "Seminars in remote sensing"@en . . "3.0" . "### Working language\n\nPortuguese and English\n\n### Goals\n\nThe objective of this UC is to provide students with a cycle of Lectures on various specialty topics that are not covered in the remaining UC of the CE. These Lectures are intended to be a training and didactic complement and will be delivered predominantly by specialists, either researchers (national or foreign who travel to Porto within the scope of projects. or collaborations with the CE teaching staff), or professionals from companies with activities linked to the DR. Former students of the Masters in Remote Sensing at FCUP, which operated between 2000 and 2007, whose activity is linked to DR, will also be invited, with the aim of transmitting their academic and professional experience to students.\n\n### Learning outcomes and skills\n\nAt the end of the UC, it is intended that students have broadened their knowledge to other domains of DR, contacted researchers and professionals with different experiences, broadened their critical and analytical sense, as well as their ability to carry out autonomous work.\n\n### Working mode\n\nIn person\n\n### Program\n\nSeminars on topics related to the Master's theme, which may be of a more scientific or more technical content, given predominantly by invited national or foreign specialists. Since the master's degree covers a wide range of applications, the possible topics are very wide and will naturally vary from one year to the next.\n\nExamples of possible topics:\n\n\\- Coastal bathymetry with lidar\n\n\\- Sensors on board unmanned aerial vehicles (UAV) and range of applications\n\n\\- Gravimetric Satellites\n\n\\- Innovative applications of integrated GNSSS/INS systems\n\n\\- Remote detection of vegetation\n\n\\- Remote detection of urban areas\n\n\\- etc.\n\n### Mandatory Bibliography\n\nD. Stammer and A. Cazenave, (eds); Satellite Altimetry Over Oceans and Land Surfaces, CRC PressI, 2017. ISBN: 9781498743457\nJackson, C.R., Apel, J.; Synthetic Aperture Radar Marine User’s Manual, available online at http://www.sarusersmanual.com, 2004\nRobinson Ian S.; [Measuring the oceans from space](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000261491 \"Measuring the oceans from space (Opens in a new window)\"). ISBN: 3-540-42647-7\nVaughan Robin A. 340; [Remote sensing applications in meteorology and climatology](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000256498 \"Remote sensing applications in meteorology and climatology (Opens in a new window)\" ). ISBN: 90-277-2502-0\nJones, G.H., Vaughan, R.; Remote sensing of vegetation. Principles, techniques, and applications. , Oxford University Press, Oxford New York, 2010\nFerretti, A.; Satellite InSAR Data – Reservoir Monitoring from Space, EAGE Publications, 2016\n\n### Bibliographic Notes\n\nBibliographic support documents will be provided for each seminar.\n\nExamples of reference books for some areas of application are given.\n\n### Teaching methods and learning activities\n\nThe TP will consist of seminars that will be organized in advance, taking into account the availability of the guests and the relevance of the themes for the EC.\n\nStudents will participate in person to the various Lectures given by national or foreign specialists in the area of DR. After the Lectures, students will be motivated to research one of the topics addressed, under the guidance of the UC professor and/or the specialist who presented the lecture.\n\nIn the “Other” classes, doubts will be answered about the topics covered and support will be given to carrying out individual work.\n \n\n### Type of evaluation\n\nEvaluation by final exam\n\n### Assessment Components\n\nOral test: 30.00%\nWritten work: 70.00%\n\n**Total:**: 100.00%\n\n### Occupation Components\n\nSelf-study: 35.00 hours\nFrequency of classes: 21.00 hours\nResearch work: 25.00 hours\n\n**Total:**: 81.00 hours\n\n### Get Frequency\n\nClass attendance is mandatory. Students may lose attendance if they exceed the number of absences provided by law.\n\n### Final classification calculation formula\n\nThe evaluation is done through an individual work, in article format, on a theme chosen from the list of themes of the various seminars presented. The assessment includes an oral presentation and discussion of the work.\n\nThe classification of the course is the grade of the work carried out with a weighting of 70% for the written work (T) and 30% for the oral discussion (O).\n\nCF=T \\*0.7 + O\\*0.3.\n\nMore information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479388" . . "Presential"@en . "TRUE" . . "Airborne sensors and photogrammetry"@en . . "3.0" . "### Working language\n\nPortuguês - Suitable for English-speaking students\n\n### Goals\n\nThis UC presents the main concepts related to the acquisition, georeferencing and extraction of geometric information from images obtained by aerial cameras and other sensors, transported by manned aircraft or by UAVs (unmanned aerial vehicles).\n\n### Learning outcomes and skills\n\nIt is intended that students:\n1) Get to know the essentials of aerial photography geometry.\n2) Know the orientation procedures for single optical images and stereoscopic pairs.\n3) Get to know the processes for obtaining three-dimensional information and orthorectification of images.\n4) Perform simple processing of images obtained with UAV.\n5) Understand the geometry of other airborne sensors and understand the need for adequate mathematical models.\n\n### Working mode\n\nIn person\n\n### Program\n\n1\\. Geometry of aerial photography.\ntwo\\. External guidance.\n3\\. Stereoscope pairs, image blocks and aerial triangulation.\n4\\. Automatic image correlation methods.\n5\\. Point clouds and digital surface models.\n6\\. Orthorectification and composition of mosaics.\n7\\. Image processing exercise obtained with UAV.\n8\\. Geometry of other sensors: Lidar, linear sensors and aerial SAR.\n\n### Mandatory Bibliography\n\nBerberan Antonio; [Elements of photogrammetry](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000294337 \"Elements of photogrammetry (Opens in a new window)\"). ISBN: 972-95873-5-3\nWolf Paul R.; [Elements of photogrammetry](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000228090 \"Elements of photogrammetry (Opens in a new window)\"). ISBN: 0-07-292454-3\n\n### Complementary Bibliography\n\nLinder Wilfried; [Digtal photogrammetry](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000280780 \"Digtal photogrammetry (Opens in a new window)\")\nSchenk Toni; [Digital photogrammetry](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000245819 \"Digital photogrammetry (Opens in a new window)\")\n\n### Teaching methods and learning activities\n\nAttendance in 2/3 of classes\n\n### Type of evaluation\n\nEvaluation by final exam\n\n### Assessment Components\n\nExam: 100.00%\n\n**Total:**: 100.00\n\n### Occupation Components\n\nFrequency of classes: 100.00 hours\n\n**Total:**: 100.00 hours\n\n### Get Frequency\n\nSince this is a UC mainly for the transmission of concepts, classes are given based on Power Point presentations and some deductions or calculations on the board. Practical exercises for processing images obtained by UAV with specific software will be launched, with a view to implementing the concepts learned. In the complementary classes of typology “Others”, questions will be clarified and support will be given to carrying out these exercises and analyzing the results.\n\n### Final classification calculation formula\n\n100% final exam\n\nMore information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479385" . . "Presential"@en . "TRUE" . . "Active and multitemporal remote sensing"@en . . "3" . "understand the principles of active and multitemporal remote sensing;\n remember of opportunities offered those recent sensors available in remote sensing;\n process the data provided by such sensors;\n perform data analysis to address specific methodological tasks;\n use dedicated software." . . "Presential"@en . "TRUE" . . "Introduction to remote sensing"@en . . "15.0" . "EGM713 – Introduction to Remote Sensing (15 credits) – this module runs in weeks 1-6 of semester 1 and is a compulsory module.\n\nThis module offers students the opportunity to study the principles and applications of remote sensing and image analysis and to explore links between remote sensing and GIS. Students will become familiar with theoretical foundations of remote sensing and will develop technical skills through a series of software-based practical exercises and assignments using ERDAS Imagine." . . "Presential"@en . "TRUE" . . "Photogrammetry and advanced Image analysis"@en . . "15.0" . "EGM702 – Photogrammetry and Advanced Image Analysis (15 credits)\n\nThis module covers advanced topics in visible remote sensing and image analysis, including photogrammetry and digital elevation models, image processing and manipulation, advanced classification techniques such as object-based image analysis (OBIA), and time series analysis using Google Earth Engine. It builds on the topics covered in EGM713, complements the topics covered in EGM722, and provides a foundation for further study in remote sensing." . . "Presential"@en . "FALSE" . . "Programming for gis & remote sensing"@en . . "15.0" . "EGM722 – Programming for GiS & Remote Sensing (15 credits)\n\nThis module develops programming skills using the python programming language. The module seeks to provide students with key skills in the development of repeatable, automated analyses of GIS applications. The module also aims to develop academic writing skills in preparation for the MSc degree." . . "Presential"@en . "FALSE" . . "Fundamentals of sensing and measurement"@en . . "20.0" . "FUNDAMENTALS OF SENSING AND MEASUREMENT PHYS5044\nAcademic Session: 2023-24\nSchool: School of Physics and Astronomy\nCredits: 20\nLevel: Level 5 (SCQF level 11)\nTypically Offered: Semester 1\nAvailable to Visiting Students: Yes\nShort Description\nStudents will receive training in fundamental aspects of sensing and transduction across all modalities and the generalised concepts and parameters pertinent for transduction of physical phenomena into an electrical signal. The course will provide instruction in the characteristics of sensing and measurement across domains that will enable students to appraise and select appropriate task-specific sensing and imaging modalities and to be able to design and model high-level systems.\n\nTimetable\nTBC\n\nExcluded Courses\nNone\n\nCo-requisites\nNone\n\nAssessment\n1. Written examination\n\na. Unseen examination, comprising compulsory short questions and a choice of 1 from 2 long questions.\n\n2. Written coursework\n\na. One individual assignment\n\n3. Written reports on one problem-based group project and individual project\n\n \n\nDescription of \"Other\" Summative Assessment Method:\n\n4. Oral presentation on problem-based project\n\nMain Assessment In: December\n\nAre reassessment opportunities available for all summative assessments? No\n\nReassessment of the main diet examination is normally available for students on PGT degree programmes if they do not achieve an overall course grade of C3 at their first attempt. Reassessment of the main diet examination is not normally available for students on Honours degree programmes.\n\n \n\nReassessment is not normally allowed, for practical reasons, for any other assessed components of coursework.\n\nCourse Aims\nThe aims of this course are:\n\n \n\na. To provide training in fundamental and general concepts in transduction and sensing\n\nb. Familiarise the student with the salient characteristics of sensing across the main physical domains of electromagnetism (radio, optical), electrical, magnetic, ionising radiation, gravitational, biological, chemical\n\nc. To develop understanding of transduction in electrical signals and signal conditioning\n\nd. To provide understanding of the process of imaging with sensing\n\ne. To provide training in solving problems associating with sensing and imaging\n\nIntended Learning Outcomes of Course\nBy the end of this course students will be able to:\n\n \n\na. Propose and assess a range of solutions to a sensing and imaging problem against pertinent criteria\n\nb. Analyse and evaluate data provided by a range of sensors and imaging systems\n\nc. Demonstrate an understanding of the fundamental limitations of a range of sensing and imaging techniques.\n\nd. Demonstrate an understanding of the physical origins of phenomena to be measured\n\nMinimum Requirement for Award of Credits\nStudents must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.\n\n\nMore information at: https://www.gla.ac.uk/postgraduate/taught/sensorandimagingsystems/?card=course&code=PHYS5044" . . "Presential"@en . "TRUE" . . "Remote sensing and earth observation"@en . . "6" . "Principles of remote sensing, image processing, and trends in earth observation. Upon completion of this course, it is expected that the learner will be able to: (1) show a deeper understanding of theory and practice of remote sensing, (2) comprehend the information content of remotely sensed data and means for retrieving that information, (3) decide which remote sensing techniques suit specific needs." . . "Presential"@en . "TRUE" . . "Digital Imaging, photogrammetry & computer vision"@en . . "6" . "Familiarization with basic principles of photogrammetry and computer vision techniques, along with their applications in data gathering using cameras and image processing. Upon completion of this course, it is expected that the learner will be able to: (1) relate photogrammetric data in a cartographic production process, (2) predict the quality of photogrammetric processes and products, as well as prioritize the influence of variables in the final product quality, and (3) use digital photogrammetric systems." . . "Presential"@en . "TRUE" . . "Remote sensing I"@en . . "5" . "Introduction. Basic concepts and Philosophy of Photointerpretation and Remote Sensing. Basics from physics and mathematics. Electromagnetic radiation. Sensors and images. Photointerpretation and Remote Sensing instruments and measurements. Satellite Remote Sensing Programs and operational applications. Possibilities and constraints. Prospects. Photointerpretation and Remote Sensing analogue and digital methods and techniques for Earth Observation and Monitoring by airborne and satellite systems. Applications in the scientific/technical and professional fields of the Spatial Infrastructure and Geomatics Engineer. Remote Sensing and GIS Integration Applications for Land and Environment Inventories, Mapping and Monitoring." . . "Presential"@en . "TRUE" . . "Photogrammetry I"@en . . "5" . "Introduction to the photogrammetric process-Photogrammetry and Surveying. Applications and subdivision of Photogrammetry. Data collection-Geometry of the camera. Measurement and corrections of image-coordinates-Interior orientation. Photogrammetric cameras. Image and space coordinate systems. Exterior orientation. Collinearity equation. Monoplotting. Parallax and elevation determination. Geometry of stereopair-General principles of photogrammetric instruments. Stereoplotting instruments. Relative and absolute orientation. General principles of analytical and digital instruments, DTM’s, orthophotos and aerialtriangulation." . . "Presential"@en . "TRUE" . . "Remote sensing II"@en . . "4" . "Pre-processing steps: geometric, radiometric corrections. Atmospheric Correction theory-algorithms -Computational Image Interpretation. Image Histogram. Contrast enhancement and stretching, linear histogram stretching, histogram equalisation, histogram saturation. Display alternatives, colour processing. Filters, edge enhancement, high pass filtering, smoothing, low pass filtering, gradient, Laplacian. Spatial registration, geometric manipulation, co-ordinate transformation, interpolation. Feature extraction: spectral rationing, principal component analysis, vegetation indices. Mathematical concepts for image classification, discriminant functions, Bayes theory, Density slicing. Supervised training and classification: parallelepiped, table look-up, decision tree, minimum distance, maximum likelihood. Unsupervised training and clustering, Algorithms: K-means, ISODATA. Post-classification processing. Classification accuracy. Data merging, Geographic information systems. Change detection. Applications. Introduction to computer vision. -The students will have the opportunity to apply most of the pre-processing and post-processing techniques to the following satellite imagery of Cyprus: Quickbird, Ikonos, Landsat TM & ETM+, and SPOT etc. Spectroradiometric Measurements." . . "Presential"@en . "TRUE" . . "Photogrammetry II"@en . . "5" . "Review of Photogrammetry I: image and stereopair orientation, coplanarity condition, analogue stereo plotters. Aerial cameras. Planning and specifications of flight. Analytical processing of photogrammetric measurements. Bundle adjustment. Principles and methods of aerotiangulation and phototriangulation. Principles, types, work-flow and potential of analytical stereo plotters. Photogrammetric mapping and types of photogrammetric products. Photogrammetric production of digital elevation models. Monoplotting. Aspects of digital photogrammetric techniques. Geometric transformations of digital images, digital products. Orthophotography: method, specifications, accuracy. Planimetric and heighting accuracy. Accuracy specifications of photogrammetric mapping. Close-range photogrammetry." . . "Presential"@en . "TRUE" . . "Radiometry & microwave remote sensing"@en . . "4" . "Introduction. Atmospheric radiative transfer process. Electro-Optical sensors. Radiometric instruments. Measurements and applications in the scientific field of the Surveying & Geomatics Engineer. Hyperspectrometry and applications. Radar fundamentals. Geometry of the SAR images.\nSAR imagery processing and interpretation. SAR interferometry. Digital Terrain Models based on the interferometry. Comparison with other methods. Applications of SAR imagery analysis and processing methods and techniques in land use/cover inventories mapping and monitoring. Applications of the SAR imagery processing and interpretation in Hydrology, Oceanography, Geology and Forestry." . . "Presential"@en . "TRUE" . . "Photogrammetry III"@en . . "5" . "Introduction to digital procedures. The digital image. Digital image acquisition, instrumentation for data collection. Elements of digital image processing. Measurements on the digital image. Digital image matching. Automation of photogrammetric procedures including interior relative and absolute orientation, DTM collection, automatic aerial triangulation, digital orthophoto production and object recognition. Presentation of digital photogrammetric systems. Digital photogrammetric products and applications." . . "Presential"@en . "FALSE" .