Beginner's Cookbook

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Author(s): TC25  Published: Nov 11, 2019
In this tutorial, we will introduce several types of geospatial data, and enumerate key Earth Engine functions for analyzing and visualizing them. This cookbook was originally created as a workshop during Yale-NUS Data 2.0 hackathon, and later updated for Yale GIS Day 2018 and 2019.

Introduction

GIS or Geographic Information System is the collection, visualization, and analysis of geographical or spatial data. In this section, we will cover the data types commonly used in GIS applications.

Vector data

Vector data represent objects on the Earth's surface using their longitude and latitude, as well as combinations of the pairs of coordinates (lines, polylines, polygons, etc.).

Point data

A pair of coordinates (longitude, latitude), that represents the location of points on the Earth's surface.

Example: Location of drop boxes, landmarks, etc.

Points

Lines

A series of points that represents a line (straight or otherwise) on the Earth's surface.

Example: Center of roads, rivers, etc.

Lines

Polygons

A series of points (vertices) that define the outer edge of a region. Example: Outlines of cities, countries, continents, etc.

Polygons

Raster data

Raster data represent objects/variables on the Earth's surface as a matrix of values, in the form of pixels, cells, or grids.

Layers and bands

A raster is an image with a matrix of values representing the values of some observed attribute. Bands of a raster correspond to different variables, usually using the same matrix structure.

Example: Spatial variability of temperature, elevation, rainfall, etc. over a region.

Raster

Image sources: https://gisgeography.com/spatial-data-types-vector-raster/

The Google Earth Engine platform

Introductory video

Code editor

What is Earth Engine?

  • A cloud-based platform for planetary scale geospatial analysis
  • Uses Google's computational resources to reduce processing time
  • A massive archive of remote sensing data

The Earth Engine Code Editor (source: developers.google.com)

Basic functions

Declaring variables

var variableName = ee.ContainerType(value);

A container object (usually in the form ee.SomeVariableType) is used to wrap a native JavaScript object so that Google's servers can perform operations on it.

Centering the map

Map.setCenter(long, lat, zoomLevel);

Zoom level varies from 0 (no zoom) to 20 (highest zoom level)

Displaying metadata

print(variableName);

The print operation is also useful for printing data and getting debugging info. Note: You cannot print more than 5,000 elements at once.

Adding a layer to the map

Map.addLayer(variableName);

Common Earth Engine data types

Strings

var str = ee.String('This is a string. Or is it? It is.');

Numbers

var num = ee.Number(5);

Arrays

var arr = ee.Array([[5, 2, 3], [-2, 7, 10], [6, 6, 9]]);

Lists

var lis = ee.List([5, 'five', 6, 'six']);

Dictionaries

var dict = ee.Dictionary({five: 5, six: 6});

And the fun stuff...

  • ee.Geometry
  • ee.Feature
  • ee.FeatureCollection
  • ee.Image
  • ee.ImageCollection

Declaring geometries

Points

var poi = ee.Geometry.Point(0, 45);

Multi points

var multi = ee.Geometry.MultiPoint(0, 45, 5, 6, 70, -56);

Line string

var lineStr = ee.Geometry.LineString([[0, 45], [5, 6], [70, -56]]);

Multi-line string

var mLineStr =
ee.Geometry.MultiLineString([[[0, 45], [5, 6], [70, -56]], [[0, -45], [-5, -6], [-70, 56]]]);

Linear ring

var linRin = ee.Geometry.LinearRing(0, 45, 5, 6, 70, -56, 0, 45);

Rectangle

var rect = ee.Geometry.Rectangle(0, 0, 60, 30);

Polygon

var poly = ee.Geometry.Polygon([[[0, 0], [6, 3], [5, 5], [-30, 2], [0, 0]]]);

Multi-polygon

var multiPoly =
ee.Geometry.MultiPolygon([ee.Geometry.Polygon([[0, 0], [6, 3], [5, 5], [-30, 2], [0, 0]]),
ee.Geometry.Polygon([[0, 0], [-6, -3], [-5, -5], [30, -2], [0, 0]])]);

Features and FeatureCollections

  • Features are geometries associated with specific properties.
  • Feature collections are groups of features.

Counties in the contiguous United States

Functions and mapping

A function is a set of instructions to perform a specific task:

function functionName(Arguments) {
statements;
};

Calling a function

var result = functionName(input);

Mapping a function over a collection

var result = input.map(functionName);

Mapping a function over a collection applies the function to every element in the collection.

Common operations on geometries

Finding the area of a geometry

var geoArea = geometry.area(maxError);

By default, all units in Earth Engine are in meters.

Finding the length of a line

var linLen = lineString.length(maxError);

Finding the perimeter of a geometry

var geoPeri = geometry.perimeter(maxError);

Reducing number of vertices in geometry

var simpGeo = geometry.simplify(maxError);

Finding the centroid of a geometry

var centrGeo = geometry.centroid(maxError);

Creating buffer around a geometry

var buffGeo = geometry.buffer(radius, maxError);

Finding the bounding rectangle of a geometry

var bounGeo = geometry.bounds(maxError);

Finding the smallest polygon that can envelope a geometry

var convexGeo = geometry.convexHull(maxError);

Finding common areas between two or more geometries

var interGeo = geometry1.intersection(geometry2, maxError);

Finding the area that includes two or more geometries

var unGeo = geometry1.union(geometry2, maxError);

Example: Geometry operations

Let's run of some these operations over the the state of Connecticut, US using geometries of the public US counties feature collection available on Earth Engine:

1. We begin by zooming to the region of interest and loading/creating the geometries of interest by extracting them from the corresponding features.

// Set map center over the state of CT.
Map.setCenter(-72.6978, 41.6798, 8);
// Load US county dataset.
var countyData = ee.FeatureCollection('TIGER/2018/Counties');
// Filter the counties that are in Connecticut (more on filters later).
var countyConnect = countyData.filter(ee.Filter.eq('STATEFP', '09'));
// Get the union of all the county geometries in Connecticut.
var countyConnectDiss = countyConnect.union(100);
// Create a circular area using the first county in the Connecticut  FeatureCollection.
var circle = ee.Feature(countyConnect.first()).geometry().centroid(100).buffer(50000, 100);
// Add the layers to the map with a specified color and layer name.
Map.addLayer(countyConnectDiss, {color: 'red'}, 'CT dissolved');
Map.addLayer(circle, {color: 'orange'}, 'Circle');

2. Using the bounds() function, we can find the rectangle that emcompasses the southernmost, westernmost, easternmost, and northernmost points of the geometry.

var bound = countyConnectDiss.geometry().bounds(100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(bound, {color: 'yellow'}, 'Bounds');

3. In the same vein, but not restricting ourselves to a rectangle, a convex hull (convexHull()) is a polygon covering the extremities of the geometry.

var convex = countyConnectDiss.geometry().convexHull(100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(convex, {color: 'blue'}, 'Convex Hull');

4. Moving on to some basic operations to combine multiple geometries, the intersection (intersection()) is the area common to two or more geometries.

var intersect = convex.intersection(circle, 100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(intersect, {color: 'green'}, 'Circle and convex intersection');

5. The union (union()) is the area encompassing two or more features.

// number is the maximum error in meters.
var union = convex.union(circle, 100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(union, {color: 'purple'}, 'Circle and convex union');

6. We can also find the spatial difference (difference()) between two geometries.

var diff = convex.difference(circle, 100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(diff, {color: 'brown'}, 'Circle and convex difference');
Difference Union Intersection

7. Finally, we can calculate and display the area, length, perimeter, etc. of our geometries.

// Find area of feature.
var ar = countyConnectDiss.geometry().area(100);
print(ar);
// Find length of line geometry (You get zero since this is a polygon).
var length = countyConnectDiss.geometry().length(100);
print(length);
// Find perimeter of feature.
var peri = countyConnectDiss.geometry().perimeter(100);
print(peri);

Example: Mapping over a feature collection

By mapping over a collection, one can apply the same operation on every element in a collection. For instance, let's run the same geometry operations on every county in Connecticut:

1. Similar to the previous example, we start by zooming into the map and loading the feature collection of CT counties.

// Set map center over the state of CT.
Map.setCenter(-72.6978, 41.6798, 8);
// Load US county dataset.
var countyData = ee.FeatureCollection('TIGER/2018/Counties');
// Filter the counties that are in Connecticut.
var countyConnect = countyData.filter(
  ee.Filter.eq('STATEFP', '09'));
// Add the layer to the map with a specified color and layer name.
Map.addLayer(countyConnect, {color: 'red'}, 'Original Collection');

2. We define the function, which will perform the geometry operation on a feature. Try changing the operation being performed within the function to test what it does to the final output.

function performMap(feature) {
 // Reduce number of vertices in geometry; the number is to specify maximum
 // error in meters. This is only for illustrative purposes, since Earth Engine
 // can handle up to 1 million vertices.
 var simple = feature.simplify(10000);
 // Find centroid of geometry.
 var center = simple.centroid(100);
 // Return buffer around geometry; the number represents the width of buffer,
 // in meters.
 return center.buffer(5000, 100);
}

3. Finally, we map the defined function over all the features in the collection. This parallelization is generally much faster than performing operations sequentially over each element of the collection.

var mappedCentroid = countyConnect.map(performMap);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(mappedCentroid, {color: 'blue'}, 'Mapped buffed centroids');

Operations on features

Creating a feature with a specific property value

var feat = ee.Feature(geometry, {Name: 'featureName', Size: 500});

Creating a feature from an existing feature, renaming a property

var featNew = feature.select(['name'], ['descriptor']);

Extracting values of a property from a Feature

var featVal = feature.get('size');

Example: Feature operations

Let's create a feature from scratch and play around with its properties:

// Create geometry.
var varGeometry = ee.Geometry.Polygon(0, 0, 40, 30, 20, 20, 0, 0);
// Create feature from geometry.
var varFeature = ee.Feature(varGeometry, {
 name: ['Feature name', 'Supreme'],
 size: [500, 1000]
});
// Get values of a property.
var arr = varFeature.get('size');
// Print variable.
print(arr);
// Select a subset of properties and rename them.
var varFeaturenew = varFeature.select(['name'], ['descriptor']);
// Print variable.
print(varFeaturenew);

Filtering

Filtering by property values

var bFilter = ee.Filter.eq(propertyName, value);

or .neq , .gt , .gte , .lt , and .lte

Filtering based on maximum difference from a threshold

var diffFilter = ee.Filter.maxDifference(threshold, propertyName, value);

Filtering by text property

var txtFilter = ee.Filter.stringContains(propertyName, stringValue);

or .stringStartsWith, and .stringEndsWith

Filtering by a value range

var rangeFilter = ee.Filter.rangeContains(
  propertyName, stringValue, minValue, maxValue);

Filtering by specific property values

var listFilter = ee.Filter.listContains(
  propertyName, value1, propertyName2, value2);

.inList to test against a list of values

Filtering by date range

var dateFilter = ee.Filter.calendarRange(startDate, stopDate);

Filtering by particular days of the year

var dayFilter = ee.Filter.dayOfYear(startDay, stopDay);

Filtering by a bounding area

var boundsFilter = ee.Filter.bounds(geometryOrFeature);

Combining and inversing filters

var newFilterAnd = ee.Filter.and(listOfFilters);
var newFilterOr = ee.Filter.or(listOfFilters);
var inverseFilter = ee.Filter.not(filter);

Operations on images

Selecting the bands of an image

var band = image.select(bandName);

Creating masks

var mask = image.eq(value);

or .neq or .gt or .gte or .lt or .lte

Applying image masks

var masked = image.updateMask(mask);

Performing pixelwise calculations

var results = image.add(value);

or .subtract , .multiply , .divide , .max , .min , .abs , .round , .floor , .ceil , .sqrt , .exp, .log, .log10, .sin , .cos , .tan , .sinh , .cosh , .tanh , .acos, .asin

Shift pixels of an image

newImage = oldImage.leftShift(valueOfShift);

or .rightShift

Reducers

Reducers are objects in Earth Engine for data aggregation. They can be used for aggregating across time, space, bands, properties, etc. Reducers range from basic statistical indices (like ee.Reducer.mean(), ee.Reducer.stdDev(), ee.Reducer.max(), etc.), to standard measures of covariance (like ee.Reducer.linearFit(),ee.Reducer.spearmansCorrelation(), ee.Reducer.spearmansCorrelation(), etc.), to descriptors of variable distributions (like ee.Reducer.skew(), ee.Reducer.frequencyHistogram(),ee.Reducer.kurtosis(), etc.). To get the first (or only) value for a property, use ee.Reducer.first().

Reducing an image collection to an image

var outputImage = imCollection.reduce(reducer);

Reducing an image to a statistic for an area of interest

var outputDictionary = varImage.reduceRegion(reducer, geometry, scale);

Alternatively, reduceRegions can be used to compute image statistics for all elements of a collection at once:

var outputCollection = varImage.reduceRegions(reducer, collection, scale);

Note that for large collections, this may be less efficient than mapping over the collection and using reduceRegion.

Applying a reducer to each element of a collection

var outputDictionary = reduceColumns(reducer, selectors);

Applying a reducer to the neighborhoods of each pixel

var outputImage = reduceNeighborhood(reducer, kernel);

Applying a reducer to each element of an array pixel

var outputImage = arrayAccum(axis, reducer);

Convert the properties of a vector into a raster

var outputImage = reduceToImage(properties, reducer);

Convert a raster into a vector

var outputCollection = reduceToVectors(reducer);

Operations on image collections

Selecting the first n images in a collection (based on property)

var selectedImages = imCollection.limit(n, propertyName, order);

Selecting images based on particular properties

var selectedIm = imCollection.filterMetadata(propertyName, operator, value);

Operators include: "equals", "less_than", "greater_than", "not_equals", "not_less_than", "not_greater_than", "starts_with", "ends_with", "not_starts_with", "not_ends_with", "contains", "not_contains".

Selecting images within a date range

var selectedIm = imCollection.filterDate(startDate, stopDate);

Selecting images within a bounding geometry

var selectedIm = imCollection.filterBounds(geometry);

Performing pixelwise calculations for all images in a collection

var sumOfImages = imCollection.sum();

or product(), max(), min(), mean(), mode(), median(), count().

Alternatively, using reducers:

var sumOfImages = imCollection.reduce(ee.Reducer.sum());

Compositing images in collection with the last image on top

var mosaicOfImages = imCollection.mosaic();

Alternatively, using reducers:

var sumOfImages = imCollection.reduce(ee.Reducer.first());

Example: Image and image collection operations

Let's analyze images over a region of interest (the counties of Connecticut):

1. As before, we start by loading in the feature and image collections of interest.

// Set map center over the state of CT.
Map.setCenter(-72.6978, 41.6798, 8);
// Load the MODIS MYD11A2 (8-day LST) image collection.
var raw = ee.ImageCollection('MODIS/006/MYD11A2');
// Load US county dataset.
var countyData = ee.FeatureCollection('TIGER/2018/Counties');
// Filter the counties that are in Connecticut.
// This will be the region of interest for the image operations.
var roi = countyData.filter(ee.Filter.eq('STATEFP', '09'));
// Examine image collection.
print(raw);

2. We select the bands and images in the collection we are interested in.

// Select a band of the image collection using either indexing or band name.
var bandSel1 = raw.select(0);
var bandSel2 = raw.select('LST_Day_1km');
// Filter the image collection by a date range.
var filtered = raw.filterDate('2002-12-30', '2004-4-27');
// Print filtered collection.
print(filtered);
// Limit the image collection to the first 50 elements.
var limited = raw.limit(50);
// Print collections.
print(limited);
print(bandSel1);

3. We calculate the mean of all the images in the collection, clip it to the geometry of interest and scale it to convert it from digital number to degree Celsius.

// Calculate mean of all images (pixel-by-pixel) in the collection.
var mean = bandSel1.mean();
// Isolate image to region of interest.
var clipped = mean.clip(roi);
// mathematical operation on image pixels to convert from digital number
// of satellite observations to degree Celsius.
var calculate = clipped.multiply(0.02).subtract(273.15);
// Add the layer to the map with a specified color palette and layer name.
Map.addLayer(calculate, {min: 15, max: 20, palette: ['blue', 'green', 'red']}, 'LST');

4. We mask out parts of the image to display regions above and below certain temperature thresholds.

// Select pixels in the image that are greater than 30.8.
var mask = calculate.gt(18);
// Add the mask to the map with a layer name.
Map.addLayer(mask, {}, 'mask');
// Use selected pixels to update the mask of the whole image.
var masked = calculate.updateMask(mask);
// Add the final layer to the map with a specified color palette and layer name.
Map.addLayer(masked,
  {min: 18, max: 25, palette: ['blue', 'green', 'red']}, 'LST_masked');

Masked LST image

Exporting data

Exporting a collection to Google Drive, Earth Engine Asset, or Google Cloud

Export.image.toDrive({
  collection: varImage, description: 'fileName', region: geometry, scale: 1000
});

or Export.image.toCloudStorage(), Export.image.toAsset(), Export.table.toDrive(), Export.table.toCloudStorage(), Export.video.toCloudStorage(), Export.video.toDrive().

Example: Exporting data

1. Define a function to find the mean value of pixels in each feature of a collection.

// Function to find mean of pixels in region of interest.
var getRegions = function(image) {
  // Load US county dataset.
  var countyData = ee.FeatureCollection('TIGER/2018/Counties');
  // Filter the counties that are in Connecticut.
  // This will be the region of interest for the operations.
  var roi=countyData.filter(ee.Filter.eq('STATEFP', '09'));
  return image.reduceRegions({
    // Collection to run operation over.
    collection: roi,
    // Calculate mean of all pixels in region.
    reducer: ee.Reducer.mean(),
    // Pixel resolution used for the calculations.
    scale: 1000
  });
};

2. Load image collection, filter collection to date range, select band of interest, calculate mean of all images in collection, and multiply by scaling factor.

var image = ee.ImageCollection('MODIS/MYD13A1')
    .filterDate('2002-07-08', '2017-07-08')
    .select('NDVI')
    .mean()
    .multiply(.0001);
// Print final image.
print(image);
// Call function.
var coll = getRegions(image);

3. Export the table created to your Google Drive

Export.table.toDrive({
 collection: coll,
 description: 'NDVI_all',
 fileFormat: 'CSV'
});
// Print final collection.
print(coll);

Bonus: Timelapse example

// Timelapse example (based on google API example);
// Create rectangle over Dubai.
var geometry = ee.Geometry.Rectangle([55.1, 25, 55.4, 25.4]);
// Add layer to map.
Map.addLayer(geometry);
// Load Landsat image collection.
var allImages = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
 // Filter row and path such that they cover Dubai.
 .filter(ee.Filter.eq('WRS_PATH', 160))
 .filter(ee.Filter.eq('WRS_ROW', 43))
 // Filter cloudy scenes.
 .filter(ee.Filter.lt('CLOUD_COVER', 30))
 // Get required years of imagery.
 .filterDate('1984-01-01', '2012-12-30')
 // Select 3-band imagery for the video.
 .select(['B4', 'B3', 'B2'])
 // Make the data 8-bit.
 .map(function(image) {
  return image.multiply(512).uint8();
 });
Export.video.toDrive({
 collection: allImages,
 // Name of file.
 description: 'dubaiTimelapse',
 // Quality of video.
 dimensions: 720,
 // FPS of video.
 framesPerSecond: 8,
 // Region of export.
 region: geometry
});

Dubai timelapse

Urban growth in Dubai

Example applications

What can you do with Google Earth Engine?

Additional resources