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
- Read in Shapefiles
- Uploading Shapefiles Without Fusion Tables
- Section 3 Quiz
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
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
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
Miscellaneous Lectures
- Quick Primer on Spatial Data
- Github
- GEE Animations
