Satellite Remote Sensing Data Bootcamp With Opensource Tools
4.3 (34 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
227 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Satellite Remote Sensing Data Bootcamp With Opensource Tools to your Wishlist.

Add to Wishlist

Satellite Remote Sensing Data Bootcamp With Opensource Tools

Pre-process and Analyze Satellite Remote Sensing Data With Free Software
4.3 (34 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
227 students enrolled
Created by Minerva Singh
Last updated 4/2017
English
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 4 hours on-demand video
  • 4 Articles
  • 4 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
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 is the target audience?
  • People with prior expereince of working spatial data
  • GIS analysts
  • Ecologists
  • Forestry and Conservation Practioners
  • Geographers
  • Geologists
Students Who Viewed This Course Also Viewed
Curriculum For This Course
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
About the Instructor
Minerva Singh
4.4 Average rating
375 Reviews
4,570 Students
6 Courses
Bestselling Udemy Instructor & Data Scientist(Cambridge Uni)

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