Remote Sensing Introduction AulaGEO

Remote Sensing Introduction – AulaGEO, available at $39.99, has an average rating of 3.75, with 62 lectures, based on 19 reviews, and has 105 subscribers.

You will learn about 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 This course is ideal for individuals who are Students, researchers, professionals, and lovers of the GIS and Remote Sensing world. or Anyone who wishes to use spatial data to solve ecological and environmental issues. or Professionals in forestry, environmental, civil, geography, geology, architecture, urban planning, tourism, agriculture, biology and all those involved in Earth Sciences. It is particularly useful for Students, researchers, professionals, and lovers of the GIS and Remote Sensing world. or Anyone who wishes to use spatial data to solve ecological and environmental issues. or Professionals in forestry, environmental, civil, geography, geology, architecture, urban planning, tourism, agriculture, biology and all those involved in Earth Sciences.

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Summary

Title: Remote Sensing Introduction – AulaGEO

Price: $39.99

Average Rating: 3.75

Number of Lectures: 62

Number of Published Lectures: 62

Number of Curriculum Items: 62

Number of Published Curriculum Objects: 62

Original Price: $94.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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: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

Course Curriculum

Chapter 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

Chapter 2: Characteristics of the sensors

Lecture 1: Spatial resolution

Lecture 2: Spectral resolution

Lecture 3: Temporary Resolution

Lecture 4: Radiometric resolution

Lecture 5: Relationships between resolutions

Chapter 3: Download satellite images

Lecture 1: Image Download

Lecture 2: Image Download

Lecture 3: Download of data models

Chapter 4: Remembering QGIS

Lecture 1: A brief review of QGIS

Lecture 2: Add-ons installation

Lecture 3: Base Maps in QGIS 3

Lecture 4: Introduction to SAGA GIS

Chapter 5: Pre-processing of satellite images (Improvements)

Lecture 1: Preprocessing

Lecture 2: Display and enhancement of images

Lecture 3: QGIS image cutting

Lecture 4: Multiple image cutting – PlugIn

Lecture 5: Color rendering

Lecture 6: Lecture 25: Pseudocolor Representation

Lecture 7: Spectral Band Composition

Chapter 6: Satellite Image Pre-Processing (Corrections)

Lecture 1: Corrections to satellite images

Lecture 2: Banding Correction

Lecture 3: Atmospheric correction algorithms

Lecture 4: Topographic correction algorithms

Lecture 5: Topographic Correction in QGIS

Lecture 6: Geometric correction

Lecture 7: Lecture 33: Rectificación de una imagen en QGIS

Chapter 7: Satellite Image Processing

Lecture 1: What can we extract from satellite images?

Lecture 2: Fusion of images (Pansharpening)

Lecture 3: QGIS image fusion

Lecture 4: Fusion of SAGA images (Brovey, IHS, CPA, spectral)

Lecture 5: Cloud cover mask

Lecture 6: Cloudless images Raster Calculator QGIS

Lecture 7: Cloudless Images – PlugIn

Chapter 8: Clasificación de imágenes de satélite

Lecture 1: Lecture 41: Clasificación de imágenes de satelite

Lecture 2: Lecture 42: Clasificaciones no supervisadas–

Lecture 3: Interpret and optimize unsupervised classification

Lecture 4: Supervised Classification Configuration and Training Areas

Lecture 5: Supervised Classification – Spectral Signature Chart

Lecture 6: Supervised Classification – Previous Classification

Lecture 7: Supervised Classification – Optimizing the spectral signatures

Lecture 8: Supervised Classification – Minimum distance, Spectral Angle, Maximum Probable

Lecture 9: Supervised Classification – optimizing threshold algorithms

Lecture 10: Supervised Classification – Result with Mask

Lecture 11: Classification Accuracy

Lecture 12: Determination of classification accuracy

Lecture 13: Identification of ceilings with Segmentation

Chapter 9: Indices espectrales o radiométricos

Lecture 1: Spectral indexes

Lecture 2: Vegetation indices

Lecture 3: NDVI spectral index calculation

Lecture 4: EVI spectral index calculation

Lecture 5: Calculation of 14 vegetation indices in two steps

Chapter 10: Other tools for image processing and interpretation

Lecture 1: Principal component analysis

Lecture 2: Incremental algorithm, delimiting burned area

Lecture 3: Incremental algorithm, delimiting water-reservoir mirror

Lecture 4: Development of spectral profiles

Instructors

AulaGEO Academy
Specialists in GIS – BIM – LAND and Smart process training

Luis Eduardo Perez Graterol
Ingeniero en Recursos Naturales

Golgi Alvarez
Land Administration and AEC Specialist

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 4 votes

4 stars: 8 votes

5 stars: 3 votes

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