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Complete Google Earth Engine for Remote Sensing & GIS

Video Introducing this tutorial


Introduction to Google Earth Engine (GEE) :
What is Google Earth Engine?
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Scripts For the Course

Get Started with GEE :
Explore the Google Earth Engine (GEE) Interface
Sign-up for GEE
Explore the Datasets in Google Earth Engine (GEE)
GEE Explorer for Satellite Data Analysis
Code Editor of GEE

Introduction to the GEE Code Editor :
Hello to Javascript
Read in Display Single-Band Raster Data
Read & Visualize Multi-Band Raster Data
Start With Image Collections
Visualize Vector Data
More Feature Data Manipulation
Create Google Fusion Table from KML File
Read in Shapefiles
Section 3 Quiz
1 question

Common GIS Operations Using Google Earth Engine (GEE) :
Filter a Feature Collection
Create a Buffer Around a Feature Collection
Compute Zonal Statistics on Feature Data
Filter an Image Collection
Filter an Image Collection According to Path and Row
Filter and Apply Statistical Function on Each Band
Select & Display a Specific Image
User Defined ROI
Create a Categorical DEM Map
Deriving Topographic Products from Elevation Data
Section 4 Quiz
1 question

More GIS Operations in GEE :
Clipping a Raster Using a Feature
Band Arithmetic on Raster Data in GEE
User Defined Functions
More Arithmetic Operations in GEE
Threshold Operations on Raster Data
Threshold With Canny Edge Detector
Resampling a Raster
Change Raster Resolution
Raster to Vector Conversion
Vector to Raster Conversion

Plotting and Exporting GEE Data :
Use of Reducer Function
Plot Temporal Variation
Spectral Signatures Over Time & Space
Grouped Means for Two Raster Bands
Apply Simple Linear Regression
Export Raster Data
Export Data in CSV Format
Section 6 Quiz
2 questions

Working with Optical Data-Landsat :
Principles Behind Collection of Optical Remote Sensing Data
Why Do We Need Pre-Processing of Landsat Data
Different Landsat Sensors
Apply Atmospheric Correction to Landsat Data
Pan Sharpening Landsat Images
More Pan-Sharpening
Create a Landsat Composite
Texture Indices-Theory
Compute Texture Indices From an Image
Spectral Unmixing for Mapping
Unsupervised Classification- Theory
Unsupervised Classification-K Means Clustering
Supervised Classification-Theory

Common Remote Sensing Applications :
Read in and Visualize Socio-Economic Data
Hansen Forest Loss Data
Compute Forest Loss at Country Scale with Hansen
Compute Forest Loss at Sub-Country Scale with Hansen
Section 8 Quiz

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