QGIS and Google Earth Engine Python API for Spatial Analysis

QGIS and Google Earth Engine Python API for Spatial Analysis udemy course

Harness the power of big spatial data with Earth Engine Python API and QGIS

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 plugins. Then, you will have access to satellite data using the Python API.

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 scripts and data such that you will be able to access, download, visualize big data, and extract 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 scripts 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.