Remote Sensing Introduction

Discover the power of Remote Sensing, using satellite - or aircraft- based sensor technologies

What you'll learn
Understand basic concepts of Remote Sensing.
Understand the physical principles behind the interaction of EM radiation and the multiple types of soil cover (vegetation, water, minerals, rocks, etc.).
Understand how atmospheric components can affect a signal recorded by remote sensing platforms and how to correct them.
Download, pre-processing, and satellite image processing.
Remote sensor applications.
Practical examples of remote sensing applications.
Learn Remote Sensing with free software
Requirements
Basic knowledge of Geographic Information Systems.
Have QGIS 3 installed
Description
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 supervisadas––––––––-Lecture 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

Overview

Section 1: Fundamentals of Remote Sensing

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

Students, researchers, professionals, and lovers of the GIS and Remote Sensing world.,Anyone who wishes to use spatial data to solve ecological and environmental issues.,Professionals in forestry, environmental, civil, geography, geology, architecture, urban planning, tourism, agriculture, biology and all those involved in Earth Sciences.