Google Earth Engine (GEE) Workshop

Google Earth Engine is a remote-sensing big data platform for geospatial processing service. It can solve some global challenges that involve large geospatial datasets.

It is free for research, education, and non-profit.

(1) Cloud-based platform for geospatial analysis

(2) Access over 40 years of satellite imagery (over 50 Petabytes data)

(3) Easy to upload own datasets and export results generated via GEE

GEE contains nearly 1000 datasets and it is still growing ( Earth Engine Data Catalog) .

Landset, MODIS, Sentinel, DEM, Land cover, and so on.

These are some examples showing in the GEE.

You can achieve your research goal via your own codes.

Firstly, you need to sign up a GEE account for accessing.

Here is the link:    https://signup.earthengine.google.com 

Here, I recommend you use the education or similar email for signing up with academic purpose.

Once you have signed up and get the permission of GEE, you can write your own code in Google Earth Engine Code Editor.

Here is the link:  https://code.earthengine.google.com/ 

In Code Editor, the acceptable language is JavaScript, and it is modified to be suitable for GEE. Besides, Google opens the API for Python use so you can code in Jupyter Notebook with Python API.

Here are some useful websites for assisting your GEE exploration!

The guides are written primarily for JavaScript in the  Code Editor  with examples for  Python in Colab  where applicable. The  JavaScript Quickstart  guide and the  tutorials  describe basic concepts and example workflows to help beginning users.

Here is the link:  https://developers.google.com/earth-engine/guides 

Geemap is a Python package for interactive mapping with  Google Earth Engine  (GEE), which is a cloud computing platform with a  multi-petabyte catalog  of satellite imagery and geospatial datasets.  It is developed by  Dr. Qiusheng Wu  in University of Tennessee.

Geemap is intended for students and researchers, who would like to utilize the Python ecosystem of diverse libraries and tools to explore Google Earth Engine. It is also designed for existing GEE users who would like to transition from the GEE JavaScript API to Python API. The automated JavaScript-to-Python  conversion module  of the geemap package can greatly reduce the time needed to convert existing GEE JavaScripts to Python scripts and Jupyter notebooks.

Here is the link:  https://geemap.org/ 

In order to use Earth Engine well, you will need to develop basic skills in remote sensing and programming. The language of this book is JavaScript, and you will begin by learning how to manipulate variables using it. With that base, you’ll learn about viewing individual satellite images, viewing collections of images in Earth Engine, and how common remote sensing terms are referenced and used in Earth Engine.

Here is the link:  https://www.eefabook.org/ 

Most of the world’s glaciers are currently experiencing mass loss, which will contribute to a host of global environmental problems, such as increasing glacier meltwater, rising sea level, and increasing water scarcity.

Mountain glaciers form a critical component of the cryosphere and are sensitive to climate change.

 Columbia Glacier Retreat 

The glacier equilibrium line altitude (ELA), which is representative of the annual glacier mass balance and the response of that glacier to climate change, is defined as the glacier position where its annual ablation equals accumulation.However, this field-based approach is unfeasible for monitoring many mountain glaciers due to their remote environments and limited observation sites.

The SLA is defined as the glacier position that marks the maximum surface extent of snow cover, with bare ice present below the SLA and snow cover present above the SLA. Therefore, observations of the glacier SLA can be used to assume the ELA.

More details and codes can be found here: Li, X.; Wang, N.; Wu, Y. Automated Glacier Snow Line Altitude Calculation Method Using Landsat Series Images in the Google Earth Engine Platform. Remote Sens. 2022, 14, 2377.  https://doi.org/10.3390/rs14102377 

 GEEWorkshop Code Website 

 Needed shapefile 

Thank you very much!

Email: xiang11@usf.edu