Overview

Remote Sensing (RS) contains a set of remote capture techniques and information analysis that allows us to know the territory without being present. The abundance of Earth observation data allows us to address many urgent environmental, geographical and geological issues.Students will have a solid understanding of the physical principles of Remote Sensing, including the concepts of electromagnetic radiation (EM), and will also explore in detail the interaction of EM radiation with the atmosphere, water, vegetation, minerals and other types. of land from a remote sensing perspective. We will review several fields where Remote Sensing can be used, including agriculture, geology, mining, hydrology, forestry, the environment and many more.#AulaGEO This course guides you to learn and implement data analysis in Remote Sensing and improve your geospatial analysis skills.Content:Lecture 1:IntroductionLecture 2:Definition and componentsLecture 3:Energy and electromagnetic spectrumLecture 4:Main characteristics of sensors opticalLecture 5:Spectral signatureLecture 6:Vegetation spectral signatureLecture 7:Water Spectral SignatureSection 2:Characteristics of the sensorsLecture 8:Spatial resolutionLecture 9:Spectral resolutionLecture 10:Temporary ResolutionLecture 11:Radiometric resolutionLecture 12:Relationships between resolutionsSection 3:Download satellite imagesLecture 13:Image DownloadLecture 14:Image DownloadLecture 15:Download of data modelsSection 4:Remembering QGISLecture 16:A brief review of QGISLecture 17:Add-ons installationLecture 18:Base Maps in QGIS 3Lecture 19:Introduction to SAGA GISSection 5:Pre-processing of satellite images (Improvements)Lecture 20:PreprocessingLecture 21:Display and enhancement of imagesLecture 22:QGIS image cuttingLecture 23:Multiple image cutting - PlugInLecture 24:Color renderingLecture 25:Lecture 25: Pseudocolor RepresentationLecture 26:Spectral Band CompositionSection 6:Satellite Image Pre-Processing (Corrections)Lecture 27:Corrections to satellite imagesLecture 28:Banding CorrectionLecture 29:Atmospheric correction algorithmsLecture 30:Topographic correction algorithmsLecture 31:Topographic Correction in QGISLecture 32:Geometric correctionLecture 33:Lecture 33: Rectificación de una imagen en QGISSection 7:Satellite Image ProcessingLecture 34:What can we extract from satellite images?Lecture 35:Fusion of images (Pansharpening)Lecture 36:QGIS image fusionLecture 37:Fusion of SAGA images (Brovey, IHS, CPA, spectral)Lecture 38:Cloud cover maskLecture 39:Cloudless images Raster Calculator QGISLecture 40:Cloudless Images - PlugInSection 8:Clasificación de imágenes de satéliteLecture 41:Lecture 41: Clasificación de imágenes de sateliteLecture 42:Lecture 42: Clasificaciones no supervisadasLecture 43:Interpret and optimize unsupervised classificationLecture 44:Supervised Classification Configuration and Training AreasLecture 45:Supervised Classification - Spectral Signature ChartLecture 46:Supervised Classification - Previous ClassificationLecture 47:Supervised Classification - Optimizing the spectral signaturesLecture 48:Supervised Classification - Minimum distance, Spectral Angle, Maximum ProbableLecture 49:Supervised Classification - optimizing threshold algorithmsLecture 50:Supervised Classification - Result with MaskLecture 51:Classification AccuracyLecture 52:Determination of classification accuracyLecture 53:Identification of ceilings with SegmentationSection 9:Indices espectrales o radiométricosLecture 54:Spectral indexesLecture 55:Vegetation indicesLecture 56:NDVI spectral index calculationLecture 57:EVI spectral index calculationLecture 58:Calculation of 14 vegetation indices in two stepsSection 10:Other tools for image processing and interpretationLecture 59:Principal component analysisLecture 60:Incremental algorithm, delimiting burned areaLecture 61:Incremental algorithm, delimiting water-reservoir mirrorLecture 62:Development of spectral profiles

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