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 :)
This lecture provides a theoretical description of what is remote sensing, basic principles governing it and some of its applications
This lecture is a theoretical introduction to the different types of remote sensing data out there defined in terms of the sensors used, spatial, spectral and temporal resolution
Provides an overview of the different tools used in this course and a detailed description of R and QGIS packages needed
Walks the students through installation of SNAP Desktop to reading in data into the software
Walks the students through installation of GRASS GIS to establishing file locations and reading in data into the software
Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler