QGIS and Google Earth Engine Python API for Spatial Analysis

QGIS and Google Earth Engine Python API for Spatial Analysis, available at $54.99, has an average rating of 4.2, with 29 lectures, based on 99 reviews, and has 595 subscribers.

You will learn about 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 This course is ideal for individuals who are This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS or People who want to understand various satellite image processing techniques using Python or Anyone who wants to learn accessing visualizing and extracting information from satellites or People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2 or Anyone who wants to apply for GIS or Remote Sensing Specialist job position It is particularly useful for This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS or People who want to understand various satellite image processing techniques using Python or Anyone who wants to learn accessing visualizing and extracting information from satellites or People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2 or Anyone who wants to apply for GIS or Remote Sensing Specialist job position.

Enroll now: QGIS and Google Earth Engine Python API for Spatial Analysis

Summary

Title: QGIS and Google Earth Engine Python API for Spatial Analysis

Price: $54.99

Average Rating: 4.2

Number of Lectures: 29

Number of Published Lectures: 29

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS

People who want to understand various satellite image processing techniques using Python

Anyone who wants to learn accessing visualizing and extracting information from satellites

People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2

Anyone who wants to apply for GIS or Remote Sensing Specialist job position

Target Audiences

This course is meant for professionals who want to harness the power Google Earth Engine Python API and QGIS

People who want to understand various satellite image processing techniques using Python

Anyone who wants to learn accessing visualizing and extracting information from satellites

People who are working with satellite remote sensing data such as Landsat, MODIS, and Sentinel-2

Anyone who wants to apply for GIS or Remote Sensing Specialist job position

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.

Course Curriculum

Chapter 1: Introduction to Earth Engine Python API

Lecture 1: Welcome

Lecture 2: Sign Up on Earth Engine

Lecture 3: Install Earth Engine Plugin in QGIS

Lecture 4: Load Landsat Images

Lecture 5: Calculate NDVI

Lecture 6: Map Image Collection

Lecture 7: Landsat Cloud Mask

Chapter 2: Geospatial Data Visualization

Lecture 1: Earth Observation Satellites

Lecture 2: Landsat Visualization

Lecture 3: MODIS Land Cover Visualization

Lecture 4: NLCD Land Cover Visualization

Lecture 5: NDVI Visualization

Lecture 6: NDWI Visualization

Lecture 7: Terrain Visualization

Chapter 3: Access Raster Data Using Earth Engine Python API

Lecture 1: Sentinel

Lecture 2: CHIRPS

Lecture 3: VIIRS Nighttime Light

Lecture 4: MODIS NDVI

Chapter 4: Images in Earth Engine Python API

Lecture 1: Download

Lecture 2: Clipping

Lecture 3: Image Metadata

Chapter 5: Machine Learning in Earth Engine Python API

Lecture 1: Clustering

Lecture 2: CART Classification

Chapter 6: Advanced Algorithms

Lecture 1: Spectral Unmixing

Lecture 2: Linear Regression

Lecture 3: Object-based Detection

Lecture 4: SMAP Soil Moisture

Chapter 7: Final Project

Lecture 1: Final Project

Chapter 8: Bonus Lectures

Lecture 1: Bonus

Instructors

Dr. Alemayehu Midekisa
Geospatial Data Scientist

Rating Distribution

1 stars: 3 votes

2 stars: 6 votes

3 stars: 23 votes

4 stars: 38 votes

5 stars: 29 votes

Frequently Asked Questions

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