Google Earth Engine

An Introduction

for Land Use Change Science

Outline

  • What is Google Earth Engine?
  • User Interface
  • define polygon or bounding box on your country
  • Browse and add Data
    • Modis 
    • Sentinel 
    • processed datasets
    • Looking up data parameters 
  • Color scheme and legend
  • Export data 

 

 

What is Google Earth Engine? 

  • Computing platform and runs in your internet browser
    • with a code editor,
    • offers a compiler that runs in the Google cloud,
    • hosts data on Google servers.
  • Integrated Development Environment (IDE)
  • Read more here

want more tutorials? https://tutorials.geemap.org/Image/image_visualization/

advanced https://courses.spatialthoughts.com/end-to-end-gee.html

 

Google Earth Engine is a

Data Repository (data as a service)

Software as a Service (SaaS)

Platform as a service (PaaS)

 

 

 

Examples of output in apps

Basic of JavaScript

for GEE

Feel free to dig deeper on your own here 

https://developers.google.com/earth-engine/tutorials/tutorials

 

Quick tip

copy code and keep notes in a text editor,

save as js files

keep what works

I like to use https://www.sublimetext.com/3

Any text editor will due!

Tutorial

Let's get started

first open Google Earth Engine

I hope you signed up for an account already!

https://code.earthengine.google.com/

 

 

User Interface

Quick tip: Find lat long

Click anywhere in the map pane and then

look in the "Inspector"  window

Two ways to add data

Import the data (search within GEE)

 

Add it manually (cut and paste)

 

Search and the

Add data

there are many ways to do this...

Text

Text

Text

Search Land Use datasets in the Google Earth Engine Data Catelog 

ESA WorldCover 10m

Scroll down - click "Open in Code Editor"

Then hit run. What happens? 

Generate a bounding box

Find lat long coordinates for your country.

I will show you how to: 

Zoom to your country.

Place a box around it using the "geometry tools".

Change the color to transparent

it will create a new variable called "geometry"

alternatively - Use this tool geojson.io

Center the map on your country 

change to:

Map.centerObject(geometry);

 

Then click Run 

What do you think this script does?


var image = ee.Image('CGIAR/SRTM90_V4'); 
Map.setCenter(-112.8598, 36.2841, 9);
Map.addLayer(image); 

Do the comments match what you thought?

// define the variable to instantiate an image with the Image constructor - calling a Digital Elevation Model
var image = ee.Image('CGIAR/SRTM90_V4'); 
// Center on the Grand Canyon and zoom there. 
Map.setCenter(-112.8598, 36.2841, 9); 
// Display the image on the map. 
Map.addLayer(image); 

cut and paste this into

Google Earth Engine and run it!

Now let's clip the region to your country 

// define the variable to instantiate an image with the Image constructor - calling a Digital Elevation Model
var image = ee.Image('CGIAR/SRTM90_V4'); 

// Load a country border as a region of interest (roi) and then select your country
var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
var roi = countries.filterMetadata('country_na', 'equals', 'Aruba');

//center map on that country
Map.centerObject(roi);
//render it to the map
Map.addLayer(image); 

Now let's add some visualizations based on elevation and slope 

// define the variable to instantiate an image with the Image constructor - calling a Digital Elevation Model
var image = ee.Image('CGIAR/SRTM90_V4'); 

// Calculate slope.
var slope = ee.Terrain.slope(image);
var elevation = image.select('elevation');

// Load a country border as a region of interest (roi).
var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
var roi = countries.filterMetadata('country_na', 'equals', 'Aruba');

// Clip the image to the region of interest.
slope = slope.clip(roi);
var DEM = elevation.clip(roi)

// Displaying slope and elevation for the region of interest and naming the layers
var visualization_slope = {min: 0, max: 45, palette:'yellow,red'};
Map.centerObject(roi);
Map.addLayer(slope, visualization_slope, "slope");

//display elevation - stretch based on the highest and lowest value in the country 
var visualization_DEM = {min: 0, max: 190, palette:'yellow,red'};
Map.addLayer(DEM, visualization_DEM, "DEM");

Add a legend 

// define// define the variable to instantiate an image with the Image constructor - calling a Digital Elevation Model
var image = ee.Image('CGIAR/SRTM90_V4'); 

// Calculate slope.
var slope = ee.Terrain.slope(image);
var elevation = image.select('elevation');

// Load a country border as a region of interest (roi).
var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
var roi = countries.filterMetadata('country_na', 'equals', 'Aruba');

// Clip the image to the region of interest.
slope = slope.clip(roi);
var DEM = elevation.clip(roi)

// Displaying slope and elevation for the region of interest and naming the layers
var visualization_slope = {min: 0, max: 40, palette:'yellow,red'};
Map.centerObject(roi);
Map.addLayer(slope, visualization_slope, "slope");

var visualization_DEM = {min: 0, max: 190, palette:'yellow,red'};
Map.addLayer(DEM, visualization_DEM, "DEM");

//you will need to update this value based on the highest elevation in your country
var palette = ["yellow", "red"]
var vis = {min: 0, max: 190, palette: palette};

var nSteps = 10
// Creates a color bar thumbnail image for use in legend from the given color palette
function makeColorBarParams(palette) {
  return {
    bbox: [0, 0, nSteps, 0.1],
    dimensions: '100x10',
    format: 'png',
    min: 0,
    max: nSteps,
    palette: palette,
  };
}

var nSteps = 10
// Create the colour bar for the legend
var colorBar = ui.Thumbnail({
  image: ee.Image.pixelLonLat().select(0).int(),
  params: makeColorBarParams(vis.palette),
  style: {stretch: 'horizontal', margin: '0px 8px', maxHeight: '24px'},
});

// Create a panel with three numbers for the legend
var legendLabels = ui.Panel({
  widgets: [
    ui.Label(vis.min, {margin: '4px 8px'}),
    ui.Label(
        ((vis.max-vis.min) / 2+vis.min),
        {margin: '4px 8px', textAlign: 'center', stretch: 'horizontal'}),
    ui.Label(vis.max, {margin: '4px 8px'})
  ],
  layout: ui.Panel.Layout.flow('horizontal')
});

// Legend title
var legendTitle = ui.Label({
  value: 'Elevation (meters)',
  style: {fontWeight: 'bold'}
});

// Add the legendPanel to the map
var legendPanel = ui.Panel([legendTitle, colorBar, legendLabels]);
Map.add(legendPanel);

Let's change the visualization parameters to make it pop

two ways to do this...

click on the layer itself,

or programmatically 

Want to export the Digital Elevation Model to incorporate in ArcGIS? 

/ Export the image
Export.image.toDrive({
  image: slope,
  region: roi,
  description: 'Slope',
  scale: 30,

});


Export.image.toDrive({
image: DEM,
region: roi,
description: 'DEM',
scale: 30,

});

style will not export

unless you convert the file

We are not going to worry about this right now...

Let's add Sentinel 2 data

Modify this example code to

1. center on your country

2. Then also render a false color composite layer

3. Change the date of images

 

 

Aruba Example of a true color composite 


// What does each line of code do?
var S2_collection = ee.ImageCollection("COPERNICUS/S2")
  .filterBounds(geometry)
  .filterDate('2020-01-01', '2020-12-31')
  .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10);

var S2_display = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000};

print(S2_collection);

var S2_mosaic = S2_collection.median().clip(geometry);

Map.addLayer(S2_mosaic, S2_display, "Sentinel-2");

NDVI example with Modis Data

// Normalized Difference example.
//
// Compute Normalized Difference Vegetation Index over MOD09GA product.
// NDVI = (NIR - RED) / (NIR + RED), where
// RED is sur_refl_b01, 620-670nm
// NIR is sur_refl_b02, 841-876nm

var img = ee.Image('MOD09GA/MOD09GA_005_2012_03_09');
var ndvi = img.normalizedDifference(['sur_refl_b02', 'sur_refl_b01']);
var palette = ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
               '74A901', '66A000', '529400', '3E8601', '207401', '056201',
               '004C00', '023B01', '012E01', '011D01', '011301'];

Map.setCenter(-94.84497, 39.01918, 8);
Map.addLayer(img.select(['sur_refl_b01', 'sur_refl_b04', 'sur_refl_b03']),
         {gain: '0.1, 0.1, 0.1'}, 'MODIS bands 1/4/3');
Map.addLayer(ndvi, {min: 0, max: 1, palette: palette}, 'NDVI');

Call data from a different dataset

then more change parameters

https://developers.google.com/earth-engine/datasets/catalog 

notice how many datasets focus on the Netherlands! 

Run through and example: 

I need the following

  • Pick dataset
  • Find and area
  • date range and filter clouds
  • Clip area
  • NDVI
  • export

Center on Aruba

Make True color composite

//calling landsat 8 images - only looking for imagery that covers a single point. Searching for images between these dates
var aruba = ee.ImageCollection('LANDSAT/LC08/C01/T1')
                 .filterBounds(ee.Geometry.Point(-69.9735, 12.5037))
                  .filterDate('2014-01-01', '2019-12-31');

  // Sort by a cloud cover property, get the least cloudy image. The print statements means I can use the console and inspector window to read metadata
var image = ee.Image(aruba.sort('CLOUD_COVER').first());
print('Least cloudy image: ', image);

//only show me the following bands represetned as a true color composite in the map display

var trueColor432 = image.select(['B4', 'B3', 'B2']);
var trueColor432Vis = {
  min: 0.0,
  max: 30000.0,
};

//Centered on Aruba
Map.setCenter(-69.9735, 12.5037, 10);

//add map layer
Map.addLayer(trueColor432, trueColor432Vis, 'True Color (432)');

What do you see in the map?

Why do you think this happened?

What needs to be fixed?LANDSAT/LC08/C01/T1/LC08_069075_20190705

make false color composite

what is possible? 

examples

https://gisgeography.com/landsat-8-bands-combinations/

 

This band combination is also called the near infrared (NIR) composite. It uses near-infrared (5), red (4) and green (3). Because chlorophyll reflects near infrared light, this band composition is useful for analyzing vegetation. In particular, areas in red have better vegetation health. Dark areas are water and urban areas are white.

//calling landsat 8 images - only looking for imagery that covers a single point. Searching for images between these dates
var tonga = ee.ImageCollection('LANDSAT/LC08/C01/T1')
                 .filterBounds(ee.Geometry.Point(-175.2312, -21.1612))
                  .filterDate('2014-01-01', '2019-12-31');

  // Sort by a cloud cover property, get the least cloudy image.
var image = ee.Image(tonga.sort('CLOUD_COVER').first());
print('Least cloudy image: ', image);

//only show me the following bands to highlight vegitation NIR composite in the map display - notice I change the variable names

var NIR = image.select(['B5', 'B4', 'B3']);
var NIRColor543Vis = {
  min: 0.0,
  max: 30000.0,
};

//Centered on Tonga and show me in the map changed variable names
Map.setCenter(-175.2312, -21.1612, 6);
Map.addLayer(NIR, NIRColor543Vis, 'veg (543)');

Calculate NDVI for this specific image

Export your data

lots of options!

https://developers.google.com/earth-engine/exporting

You may want to export data to use in ArcGIS/QGIS

Make a map for your wall

share on social media...

Export your data

lots of options!

Fill in this quiz related to the cartography lecture and 

Share ideas and an example of your work 

Here

Google Earth Engine and Land Use Change

By Britta Ricker

Google Earth Engine and Land Use Change

Introduction to GEE for Land Use Science course at UU

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