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.
Lecture 1:Introduction
Lecture 2:Definition and components
Lecture 3:Energy and electromagnetic spectrum
Lecture 4:Main characteristics of sensors optical
Lecture 5:Spectral signature
Lecture 6:Vegetation spectral signature
Lecture 7:Water Spectral Signature
Section 2:Characteristics of the sensors
Lecture 8:Spatial resolution
Lecture 9:Spectral resolution
Lecture 10:Temporary Resolution
Lecture 11:Radiometric resolution
Lecture 12:Relationships between resolutions
Section 3:Download satellite images
Lecture 13:Image Download
Lecture 14:Image Download
Lecture 15:Download of data models
Section 4:Remembering QGIS
Lecture 16:A brief review of QGIS
Lecture 17:Add-ons installation
Lecture 18:Base Maps in QGIS 3
Lecture 19:Introduction to SAGA GIS
Section 5:Pre-processing of satellite images (Improvements)
Lecture 20:Preprocessing
Lecture 21:Display and enhancement of images
Lecture 22:QGIS image cutting
Lecture 23:Multiple image cutting - PlugIn
Lecture 24:Color rendering
Lecture 25:Lecture 25: Pseudocolor Representation
Lecture 26:Spectral Band Composition
Section 6:Satellite Image Pre-Processing (Corrections)
Lecture 27:Corrections to satellite images
Lecture 28:Banding Correction
Lecture 29:Atmospheric correction algorithms
Lecture 30:Topographic correction algorithms
Lecture 31:Topographic Correction in QGIS
Lecture 32:Geometric correction
Lecture 33:Lecture 33: Rectificación de una imagen en QGIS
Section 7:Satellite Image Processing
Lecture 34:What can we extract from satellite images?
Lecture 35:Fusion of images (Pansharpening)
Lecture 36:QGIS image fusion
Lecture 37:Fusion of SAGA images (Brovey, IHS, CPA, spectral)
Lecture 38:Cloud cover mask
Lecture 39:Cloudless images Raster Calculator QGIS
Lecture 40:Cloudless Images - PlugIn
Section 8:Clasificación de imágenes de satélite
Lecture 41:Lecture 41: Clasificación de imágenes de satelite
Lecture 42:Lecture 42: Clasificaciones no supervisadas-----------------
Lecture 43:Interpret and optimize unsupervised classification
Lecture 44:Supervised Classification Configuration and Training Areas
Lecture 45:Supervised Classification - Spectral Signature Chart
Lecture 46:Supervised Classification - Previous Classification
Lecture 47:Supervised Classification - Optimizing the spectral signatures
Lecture 48:Supervised Classification - Minimum distance, Spectral Angle, Maximum Probable
Lecture 49:Supervised Classification - optimizing threshold algorithms
Lecture 50:Supervised Classification - Result with Mask
Lecture 51:Classification Accuracy
Lecture 52:Determination of classification accuracy
Lecture 53:Identification of ceilings with Segmentation
Section 9:Indices espectrales o radiométricos
Lecture 54:Spectral indexes
Lecture 55:Vegetation indices
Lecture 56:NDVI spectral index calculation
Lecture 57:EVI spectral index calculation
Lecture 58:Calculation of 14 vegetation indices in two steps
Section 10:Other tools for image processing and interpretation
Lecture 59:Principal component analysis
Lecture 60:Incremental algorithm, delimiting burned area
Lecture 61:Incremental algorithm, delimiting water-reservoir mirror
Lecture 62:Development of spectral profiles
