Google Earth Engine

An Introduction

Examples of output in apps

http://global-surface-water.appspot.com/
https://croplands.org/app/map?lat=0&lng=-0.17578125&zoom=2
These are very pro

Outline

(on your own) Basics of JavaScript for GEE use
User Interface
Add Data
Export data

clip to country
simple band math
change over time
Average over 10 days

Basic of JavaScript

for GEE

Google Earth Engine

Data Repository (data as a service)

Software as a Service

Open Google Earth Engine

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

Personal 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

Tutorial

Let's get started

first open Google Earth Engine

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

Two ways to add data

Import the data (search within GEE)

Add it manually

Search and the

Add data

there are many ways to do this...

Search for data
Let's look at the data available
https://developers.google.com/earth-engine/datasets/catalog/

Text

https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI#bands

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?

// Instantiate an image with the Image constructor. 
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!

Change data

pick another data set
Change the color scheme
- user interface
- javascript


// Instantiate an image with the Image constructor. 
var image = ee.Image('NOAA/NGDC/ETOPO1'); 
//visualize the data in some way - set the visualization parameters. First you need to explore the properties to see what would make sense //here. 
var visParams = {bands: ['ice_surface'], min: -10000, max: 500, palette: ['blue', 'green', 'orange']};

// Display the image on the map.
Map.addLayer(image, visParams);


//printing properties will help you decide what to visualize and how. 
print('ETOPO1 image', image);

Call data from a different dataset

then more change parameters

Let's add one Landsat Image

and one band only

cut paste - what happens?

 //// Load a Landsat 8 image and display the thermal band.
var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318');

Map.setCenter(-122.1899, 37.5010, 13); // SF Bay
Map.addLayer(image, {bands: ['B10'], min: 270, max: 310}, 'image');

Collection=

all of the images!

  // Load a Landsat 5 image collection.
var collection = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
  // Tacoma area.
  .filter(ee.Filter.eq('WRS_PATH', 46))
  .filter(ee.Filter.eq('WRS_ROW', 27))

//render the map

Map.addLayer(collection);

Filter the collection of Landsat Images

    // Load a Landsat 5 image collection.
var collection = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
  // Tacoma area.
  .filter(ee.Filter.eq('WRS_PATH', 46))
  .filter(ee.Filter.eq('WRS_ROW', 27))
  // Filter cloudy scenes.
  .filter(ee.Filter.lt('CLOUD_COVER', 30))
  // Get 20 years of imagery.
  .filterDate('1991-01-01','2011-12-30')
  // Need to have 3-band imagery - trying to highlight urban areas
  .select(['B7', 'B5', 'B3'])
  // Need to make the data 8-bit.
  .map(function(image) {
    return image.multiply(512).uint8();
  });
  //get the map to render in the map pane
  Map.addLayer(collection);

NDVI example

// 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');

Run through and example:

I need the following

Pick dataset
Find and area
date range and filter clouds
Clip area
band math - calculate something
export

Run through and example:

I need the following

Pick dataset: Landsat 8
Find and area: Tonga
date range and filter clouds
Clip area
band math - calculate something
export

Advanced issue found

▲

Center on Tonga

Make True color composite

//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. The print statements means I can use the console and inspector window to read metadata
var image = ee.Image(tonga.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 Tonga and show me in the map
Map.setCenter(-175.2312, -21.1612, 6);
Map.addLayer(trueColor432, trueColor432Vis, 'True Color (432)');

Center on Tonga

Looking at the meta data, I see this image is

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

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

Band Math

https://developers.google.com/earth-engine/getstarted#band-math

Export your data

lots of options!

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