Satellite Remote Sensing Data Bootcamp With Opensource Tools [Video]
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-
Free ChapterIntroduction to Satellite Remote Sensing Data Analysis
-
Introduction to Optical Remote Sensing Data
-
Pre-Processing Optical Data
-
The Many Uses of Optical Data
- Stacking and Unstacking Bands in QGIS
- Band Maths in R and QGIS
- Texture Indices-Theory
- 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-theory
- Dimension Reduction-QGIS
- Dimension Reduction-GRASS GIS
- Conclusion to Section 4
-
Classification of Remote Sensing Satellite Data
- 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
-
Introduction to Active Remote Sensing Data: Synthetic Aperture Radar
About this
video
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 -
https://github.com/PacktPublishing/Satellite-Remote-Sensing-Data-Bootcamp-with-Opensource-Tools
- Publication date:
- November 2019
- Publisher
- Packt
- Duration
- 4 hours 0 minutes
- ISBN
- 9781839213236