QGIS and Google Earth Engine Python API for Spatial Analysis
Updated on
Course overview
- Provider
- Udemy
- Course type
- Paid course
- Level
- All Levels
- Duration
- 4 hours
- Lessons
- 29 lessons
- Certificate
- Available on completion
- Course author
- Dr. Alemayehu Midekisa
-
- Students will access and sign up the Google Earth Engine Python API platform
- Download, and install QGIS
- Access satellite data in Earth Engine
- Export geospatial Data
- Access image collections
- Learn to access and analyze various satellite data including, MODIS, Sentinel and Landsat
- Cloud masking of Landsat images
- Visualize time series images
- Extract information from satellite data
Description
Do you want to access satellite sensors using Earth Engine Python API?
Do you want to learn the QGIS Earth Engine plugin?
Do you want to visualize and analyze satellite data in Python?
Enroll in my new QGIS and Google Earth Engine Python API for Spatial Analysis course.
I will provide you with hands-on training with example data, sample scripts, and real-world applications. By taking this course, you be able to install QGIS and Earth Engine plugin. Then, you will have access to satellite data using the Python API.
What makes me qualified to teach you?
I am Dr. Alemayehu Midekisa, PhD. I am a geospatial data scientist, instructor and author. I have over 15 plus years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM and other remote sensing products. I am also the recipient of one the prestigious NASA Earth and Space Science Fellowship. I teach over 10,000 students on Udemy.
In this QGIS and Google Earth Engine Python API for Spatial Analysis course, I will help you get up and running on the Earth Engine Python API and QGIS. By the end of this course, you will have access to all example script and data such that you will be able to accessing, downloading, visualizing big data, and extracting information.
In this course we will cover the following topics:
Introduction to Earth Engine Python API
Install the QGIS Earth Engine Plugin
Load Landsat Satellite Data
Cloud Masking Algorithm
Calculate NDVI
Access Sentinel, Landsat, MODIS, CHIRPS, and VIIRS data
Export images and videos
Process image collections
CART classification
Clustering analysis
Linear regression
Global Land Cover Products (NLCD, and MODIS Land Cover)
One of the common problems with learning image processing is the high cost of software. In this course, I entirely use the Google Earth Engine Python API and QGIS open source tools. All sample data and script will be provided to you as an added bonus throughout the course.
Jump in right now to enroll. To get started click the enroll button.
Best,
Dr. Alemayehu Midekisa
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