Description

Pre-process and Analyze Satellite Remote Sensing Data with Free Software

Enroll in my latest course on how to learn all about Basic Satellite Remote Sensing or perhaps you have prior experiences in GIS or tools like R and QGIS? You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis? The next step for you is to gain proficiency in satellite remote sensing data analysis. This course is hands-on training with real remote sensing data with open source tools! This course provides a foundation to carry out practical, real-life remote sensing analysis tasks in popular and free software frameworks with real spatial data. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis. Why Should You Take My Course? The author is an Oxford University MPhil (Geography and Environment), graduate. She also completed a Ph.D. at Cambridge University (Tropical Ecology and Conservation). She has several years of experience in analyzing real-life spatial remote sensing data from different sources and producing publications for international peer-reviewed journals. In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA will be used to give practical hands-on experience of working with remote sensing and understanding what kind of questions remote sensing can help us answer. This course will ensure you learn and put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis. Remote sensing software tools are very expensive, and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at risk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using a number of popular, open-source GIS tools such as R, QGIS, GRASS, and ESA-SNAP. All of which are in great demand in the geospatial sector and improving your skills in these is a plus for you. You will also learn about the different sources of remote sensing data there are and how to obtain these free of charge and process them using free software. All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Satellite-Remote-Sensing-Data-Bootcamp-with-Opensource-Tools

What You Will Learn

Download different types of satellite remote sensing data for free
Have a thorough knowledge of remote sensing- theoretical concepts and applications
Implement pre-processing techniques using R and QGIS
Carry out the unsupervised classification of satellite remote sensing data
Carry out the supervised classification of satellite remote sensing data
Implement machine learning algorithms on satellite remote sensing data in R
Carry out habitat suitability mapping using remote sensing and machine learning
Use other freely available software tools such as Google Earth Engine and SNAP for RS data analysis

Audience

This course is for people with prior experience of working spatial data such as GIS analysts, Ecologists, Forestry and Conservation Practioners, Geographers, and Geologists.

Approach

This is an introductory course, i.e. we will focus on learning the most important and widely encountered remote sensing data processing and analyzing tasks in R, QGIS, GRASS and ESA-SNAP.

Key Features

A good starter course to explain the fundamentals & properly understand the applications of remote sensing * Short lectures and practicals which are good to ensure the attention of the audience is still there.

Github Repo

https://github.com/packtpublishing/satellite-remote-sensing-data-bootcamp-with-opensource-tools

About the Author

Minerva Singh

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.

Course Outline

1. Introduction to Satellite Remote Sensing Data Analysis

1. Introduction to the Course and Instructor

Introduction to Satellite Remote Sensing Data Analysis: Introduction to the Course and Instructor

2. What is Remote Sensing?

Introduction to Satellite Remote Sensing Data Analysis: What is Remote Sensing?

3. Different Types of Remote Sensing Data

Introduction to Satellite Remote Sensing Data Analysis: Different Types of Remote Sensing Data

4. Different Tools for Working with Remote Sensing-Start with R and QGIS

Introduction to Satellite Remote Sensing Data Analysis: Different Tools for Working with Remote Sensing-Start with R and QGIS

5. Get Started with SNAP Toolbox-Brief Introduction

Introduction to Satellite Remote Sensing Data Analysis: Get Started with SNAP Toolbox-Brief Introduction

6. Get Started with GRASS GIS-Brief Introduction

Introduction to Satellite Remote Sensing Data Analysis: Get Started with GRASS GIS-Brief Introduction

7. Conclusions to Section 1

Introduction to Satellite Remote Sensing Data Analysis: Conclusions to Section 1

2. Introduction to Optical Remote Sensing Data

1. Principles Behind Collection of Optical Remote Sensing Data

Introduction to Optical Remote Sensing Data: Principles Behind Collection of Optical Remote Sensing Data

2. Different Types of Optical Remote Sensing Data

Introduction to Optical Remote Sensing Data: Different Types of Optical Remote Sensing Data

3. Downloading and Viewing Landsat Data

Introduction to Optical Remote Sensing Data: Downloading and Viewing Landsat Data

4. Different Landsat Sensors

Introduction to Optical Remote Sensing Data: Different Landsat Sensors

5. Downloading and Viewing Optical Data via QGIS

Introduction to Optical Remote Sensing Data: Downloading and Viewing Optical Data via QGIS

6. Conclusions to Section 2

Introduction to Optical Remote Sensing Data: Conclusions to Section 2

3. Pre-Processing Optical Data

1. Why is Pre-Processing Needed for Optical Data?

Pre-Processing Optical Data: Why is Pre-Processing Needed for Optical Data?

2. Implementing Atmospheric Correction on Landsat Data in R

Pre-Processing Optical Data: Implementing Atmospheric Correction on Landsat Data in R

3. QGIS For Pre-Processing Landsat Data: Semi-Automatic Classification Plugin

Pre-Processing Optical Data: QGIS For Pre-Processing Landsat Data: Semi-Automatic Classification Plugin

4. Atmospherically Corrected Outputs from QGIS

Pre-Processing Optical Data: Atmospherically Corrected Outputs from QGIS

5. What Can Pre-Processed Satellite Data Be Used For?

Pre-Processing Optical Data: What Can Pre-Processed Satellite Data Be Used For?

6. Conclusions to Section 3

Pre-Processing Optical Data: Conclusions to Section 3

4. The Many Uses of Optical Data

1. Stacking and Unstacking Bands in QGIS

The Many Uses of Optical Data: Stacking and Unstacking Bands in QGIS

2. Band Maths in R and QGIS

The Many Uses of Optical Data: Band Maths in R and QGIS

3. Texture Indices-Theory

The Many Uses of Optical Data: Texture Indices-Theory

4. Texture Indices-GRASS GIS

The Many Uses of Optical Data: Texture Indices-GRASS GIS

5. Texture Indices-ESA SNAP

The Many Uses of Optical Data: Texture Indices-ESA SNAP

6. Tasseled Cap Transformations-theory

The Many Uses of Optical Data: Tasseled Cap Transformations-theory

7. Tasseled Cap Transformations-GRASS GIS

The Many Uses of Optical Data: Tasseled Cap Transformations-GRASS GIS

8. Vegetation Indices in GRASS GIS

The Many Uses of Optical Data: Vegetation Indices in GRASS GIS

9. Vegetation Indices using RStoolbox

The Many Uses of Optical Data: Vegetation Indices using RStoolbox

10. Dimension Reduction-theory

The Many Uses of Optical Data: Dimension Reduction-theory

11. Dimension Reduction-QGIS

The Many Uses of Optical Data: Dimension Reduction-QGIS

12. Dimension Reduction-GRASS GIS

The Many Uses of Optical Data: Dimension Reduction-GRASS GIS

13. Conclusion to Section 4

The Many Uses of Optical Data: Conclusion to Section 4

5. Classification of Remote Sensing Satellite Data

1. Theory of Unsupervised Classification

Classification of Remote Sensing Satellite Data: Theory of Unsupervised Classification

2. Unsupervised Classification-ESA SNAP

Classification of Remote Sensing Satellite Data: Unsupervised Classification-ESA SNAP

3. Theory of Supervised Classification

Classification of Remote Sensing Satellite Data: Theory of Supervised Classification

4. Supervised Classification in QGIS: Preliminary Steps

Classification of Remote Sensing Satellite Data: Supervised Classification in QGIS: Preliminary Steps

5. Classification and Post Classification Accuracy in QGIS

Classification of Remote Sensing Satellite Data: Classification and Post Classification Accuracy in QGIS

6. Machine Learning Theory

Classification of Remote Sensing Satellite Data: Machine Learning Theory

7. Create Training Data in QGIS

Classification of Remote Sensing Satellite Data: Create Training Data in QGIS

8. Apply Machine Learning Techniques on Satellite Data

Classification of Remote Sensing Satellite Data: Apply Machine Learning Techniques on Satellite Data

9. Conclusion to Section 5

Classification of Remote Sensing Satellite Data: Conclusion to Section 5

6. Introduction to Active Remote Sensing Data: Synthetic Aperture Radar

1. Why Use Active Remote Sensing Data?

Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Why Use Active Remote Sensing Data?

2. Obtain ALOS PALSAR Data

Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Obtain ALOS PALSAR Data

3. Pre-processing of ALOS PALSAR data

Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Pre-processing of ALOS PALSAR data

4. Filtering for Speckles

Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Filtering for Speckles

5. Obtain back-scatter values from ALOS PALSAR data

Introduction to Active Remote Sensing Data: Synthetic Aperture Radar: Obtain back-scatter values from ALOS PALSAR data

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Satellite Remote Sensing Data Bootcamp With Opensource Tools

By Packt

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