Satellite Remote Sensing Data Bootcamp With Opensource Tools
4.1 (258 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,790 students enrolled

Satellite Remote Sensing Data Bootcamp With Opensource Tools

Pre-process and Analyze Satellite Remote Sensing Data With Free Software
4.1 (258 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,790 students enrolled
Created by Minerva Singh
Last updated 10/2018
English
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 4 hours on-demand video
  • 4 articles
  • 4 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Requirements
  • Know what spatial data are- different types of spatial data and coordinate reference systems
  • Be able to read spatial data in R
  • Have prior exposure to QGIS- reading in different spatial data and installing plugins
  • Have prior basic know-how of what machine learning can do
  • Interest in learning about satellite remote sesning data-theory, preprocessing and analysis
Description

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 :)

Who this course is for:
  • People with prior expereince of working spatial data
  • GIS analysts
  • Ecologists
  • Forestry and Conservation Practioners
  • Geographers
  • Geologists
Course content
Expand all 50 lectures 04:02:53
+ Introduction to Satellite Remote Sensing Data Analysis
8 lectures 46:33
Data Used in This Course
00:18

This lecture provides a theoretical description of what is remote sensing, basic principles governing it and some of its applications

Preview 05:13

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

Preview 07:40

Provides an overview of the different tools used in this course and a detailed description of R and QGIS packages needed

Different Tools for Working with Remote Sensing-Start with R and QGIS
08:49

Walks the students through installation of SNAP Desktop to reading in data into the software

Get Started with SNAP Toolbox-Brief Introduction
08:32

Walks the students through installation of GRASS GIS to establishing file locations and reading in data into the software

Get Started with GRASS GIS-Brief Introduction
07:27
Section 1 Quiz
3 questions
+ Introduction to Optical Remote Sensing Data
6 lectures 33:35
Principles Behind Collection of Optical Remote Sensing Data
04:07
Different Landsat Sensors
07:44
Downloading and Viewing Optical Data via QGIS
06:36
Section 2 Quiz
4 questions
+ Pre-Processing Optical Data
7 lectures 27:57
Implementing Atmospheric Correction on Landsat Data in R
06:02
Higher Level Landsat Products
00:04
QGIS For Pre-Processing Landsat Data: Semi-Automatic Classification Plugin
04:53
Atmospherically Corrected Outputs from QGIS
02:19
Section 3: Quiz
3 questions
+ The Many Uses of Optical Data
14 lectures 41:21
Stacking and Unstacking Bands in QGIS
03:17
Band Maths in R and QGIS
04:48
Texture Indices-GRASS GIS
03:14
Texture Indices-ESA SNAP
03:18
Tasseled Cap Transformations-GRASS GIS
02:48
Vegetation Indices in GRASS GIS
02:21
Vegetation Indices using RStoolbox
03:55
Dimension Reduction-theory
02:33
Dimension Reduction-QGIS
02:10
Dimension Reduction-GRASS GIS
02:29
Section 4 Quiz
4 questions
+ Classification of Remote Sensing Satellite Data
10 lectures 01:14:50
Unsupervised Classification-ESA SNAP
03:36
Supervised Classification in QGIS: Preliminary Steps
16:28
Classification and Post Classification Accuracy in QGIS
07:21
Machine Learning Theory
08:38
Create Training Data in QGIS
11:45
Apply Machine Learning Techniques on Satellite Data
17:25
Section 5 Quiz
4 questions
+ Introduction to Active Remote Sensing Data: Synthetic Aperture Radar
5 lectures 18:36
Pre-processing of ALOS PALSAR data
03:08
Filtering for Speckles
03:02
Obtain back-scatter values from ALOS PALSAR data
03:17
Section 6 Quiz
3 questions