4.65 (301 reviews)
☑ Download different types of satellite remote sesning data for free
☑ Have thorough knowledge of remote sensing- theoretical concepts and applications
☑ Implement pre-processing techniques using R and QGIS
☑ Carry out unsupervised classification of satellite remote sesning data
☑ Carry out supervised classification of satellite remote sesning 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 avaliable software tools such as Google Earth Engine and SNAP for RS data analysis
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BASIC SATELLITE REMOTE SENSING.
Are you currently enrolled in either of my Core or Intermediate Spatial Data Analysis Courses?
Or perhaps you have prior experience 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.
MY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING DATA WITH OPEN SOURCE TOOLS!
My 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?
I am an Oxford University MPhil (Geography and Environment) graduate. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation).
I have 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 a 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 & 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 a 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.
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
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.
In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!
ENROLL NOW :)
Introduction to Satellite Remote Sensing Data Analysis
Introduction to the Course and Instructor
Data Used in This Course
What is Remote Sensing?
Different Types of Remote Sensing Data
Different Tools for Working with Remote Sensing-Start with R and QGIS
Get Started with SNAP Toolbox-Brief Introduction
Get Started with GRASS GIS-Brief Introduction
Conclusions to Section 1
Section 1 Quiz
Introduction to Optical Remote Sensing Data
Principles Behind Collection of Optical Remote Sensing Data
Different Types of Optical Remote Sensing Data
Downloading and Viewing Landsat Data
Different Landsat Sensors
Downloading and Viewing Optical Data via QGIS
Conclusions to Section 2
Section 2 Quiz
Pre-Processing Optical Data
Why is Pre-Processing Needed for Optical Data?
Implementing Atmospheric Correction on Landsat Data in R
Higher Level Landsat Products
QGIS For Pre-Processing Landsat Data: Semi-Automatic Classification Plugin
Atmospherically Corrected Outputs from QGIS
What Can Pre-Processed Satellite Data Be Used For?
Conclusions to Section 3
Section 3: Quiz
The Many Uses of Optical Data
Rationale for this section
Stacking and Unstacking Bands in QGIS
Band Maths in R and QGIS
Texture Indices-GRASS GIS
Texture Indices-ESA SNAP
Tasseled Cap Transformations-theory
Tasseled Cap Transformations-GRASS GIS
Vegetation Indices in GRASS GIS
Vegetation Indices using RStoolbox
Dimension Reduction-GRASS GIS
Conclusion to Section 4
Section 4 Quiz
Classification of Remote Sensing Satellite Data
Rationale Behind this Section
Theory of Unsupervised Classification
Unsupervised Classification-ESA SNAP
Theory of Supervised Classification
Supervised Classification in QGIS: Preliminary Steps
Classification and Post Classification Accuracy in QGIS
Machine Learning Theory
Create Training Data in QGIS
Apply Machine Learning Techniques on Satellite Data
Conclusion to Section 5
Section 5 Quiz
Introduction to Active Remote Sensing Data: Synthetic Aperture Radar
Why Use Active Remote Sensing Data?
Obtain ALOS PALSAR Data
Pre-processing of ALOS PALSAR data
Filtering for Speckles
Obtain back-scatter values from ALOS PALSAR data
Section 6 Quiz
I really enjoyed the whole class and i understand each point.The instructor gave a wonderful class experience. Nice presentation. she makes difficult concepts understand easily.
An amazing course if you are a beginner,everything is explained in a very easy manner and the tests help you to implement what you have learnt.
Its a very good basic course for everybody. For ones who are new to satellite remote sensing and also for pro who want to brush up their skills. Its a must for everybody.
Excellent course. Very cool teacher, perfect pronunciation (specially for non-native speakers), and very clear explanations. Totally satisfied.
Minerva explains everything in detail which i find great. This course worth every penny. I highly recommend it.
Algunos datos del curso descargados no coinciden con los de los videos del curso, hay cosas que quedan en el aire y no son explicadas.
The delivery of the lecture is a little stilted. Previous courses seem to have a more practiced feel. It is good to see the pitfalls of some of the methods (like the download of LandSat directly into QGIS) but I do not think that it was worth the time. Maybe just a statement at the end of lecture 11 or 12 stating the problems that may occur. There seems to be a disconnect between data sets used in demonstrations within lectures and data sets that are available in the drop box.
Extraordinary course for satellite remote sensing data. Tools are marvelous and very easy to use. Very knowledgeable course.
Great course and examples! It's great to have someone explain the different topics and give real life examples .
Impressive quality of the course. Lectures are short but informative. Given satellite data source are very useful.
This course is a good mix of theory and hands-on implementation of remote sensing with free tools. Its a must have for environmental science, ecology and geography students
It was a good starter course to explain the fundamentals & properly understand the applications of remote sensing
Everything is clear, and step by step! Quite an overload of information for me. I think I really needed to know this.
Yes, absolutely. This course taught a lot of satellites’ detail, remote sensing and also gave the examples of how to use data from them.
I just need exposure to the tools and syntax so that I get good intuition before a specific application.