Satellite Remote Sensing Data Bootcamp With Opensource Tools

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

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54 on-demand videos & exercises
Level: Intermediate
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What to know about this course

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 -

Who's this course for?

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

What you'll 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.

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.

Course Curriculum

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.

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