Forest Mapping in Earth Engine Python API and Colab



What you’ll learn

  • Download, process and visualize various satellite data for forest monitoring application

  • Understand the advantages of using satellites for forest monitoring

  • Calculate Forest cover gain and loss using satellite data

  • Map global forest cover using SAR data


  • This course has no requirement.

Welcome to the Forest Mapping in Google Earth Engine Python API and Colab course.

This Earth Engine course is without a doubt the most comprehensive course for anyone who wants to apply remote sensing in forest cover mapping and monitoring. Even if you have zero programming experience, this course will take you from beginner to mastery.

Here’s why:

  • The course is taught by an experienced spatial data scientist and former NASA fellow.
  • The course has been updated to be 2021 ready and you’ll be learning the latest tools available on the cloud.
  • We’ve taught over 16,000 students how to code and apply spatial data science and cloud computing.
  • You will have access to example data and sample scripts.

In this course we will cover the following topics:

  • Introduction to Earth Engine Python API
  • Explore Earth Engine Python API
  • Sign Up with Earth Engine
  • Global Forest and Non-forest Mapping using SAR Data
  • Global Forest Cover Mapping using the Hansen Data
  • Forest Cover Map for Gabon
  • Forest Cover gain and Loss for Gabon
  • Calculate Area of Forest Cover Gain and Loss for Gabon
  • MODIS Burned Area
  • Forest Fire Map in California
  • NBRT Burn ratio Map
  • Net Primary Productivity (NPP) Map

The course includes HD video tutorials. We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know to apply remote sensing and cloud computing for forest monitoring application.

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So, what are you waiting for? Click the buy now button and join the course.

Who this course is for:

  • This course is meant for professionals who want to apply remote sensing data for forest mapping and monitoring.
  • Anyone who wants to learn remote sensing for forest applications.

Lead Instructor & Geospatial Data Scientist

Dr. Alemayehu Midekisa, PhD is an applied remote sensing scientist with 15 plus years of expertise in big Earth observation data and various methods such as machine learning, time series analysis, deep learning, and cloud computing. He is proficient in different scripting languages including Python, JavaScript, R, and Google Earth Engine. He is a former NASA Earth and Space Science fellow. With global experience in USA, Europe and Africa, his research focus is in the application of multi-sensor remote sensing data utilizing Landsat, VIIRS, Sentinel 2, MODIS, GPM, and SMAP to answer complex environmental problems in land use, water resource, agriculture, and public health.

Learn Geospatial Data Science Skills!

Spatial eLearning provides online courses teaching remote sensing, GIS, machine learning, cloud computing and spatial data science skills. Our mission is to make highly valuable geospatial data science skills accessible and affordable to anyone and anywhere around the world. We teach 17,000 plus students in over 170 countries around the world. Spatial eLearning’s valuable learning resources include webinars, books, free tutorials, and online courses.

Forest Mapping in Earth Engine Python API and Colab, Free Tutorials Download

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