GEE Intro 2018.pptx

Google Earth Engine
Introduction
1

Outline
• What is GEE?
• Practical to get you started
– introduce some functionality to GEE API
• Advanced practical on queries
– Use satellite data and shapefiles to query something
• Extra bonus
– At end is a run through of how to get shapefiles into GEE
2

What is GEE?
• Cloud-based platform for geospatial analysis
• Access over 40 years of satellite imagery
• Upload own data sets to integrate with publicly available data
• Apply range of common algorithms to data
• Export images, tables, charts, map outputs
3
Elevation
Sentinel-1
Sentinel-2
Night-time
lights
GlobCover

What can you do in the API
• Run algorithms on image data:
– Simple Band combinations such as NDVI
– Masking bands using existing bands
– Classifying satellite data
– Subsetting data spatially using vector data
– Long list of options – most would appear in ArcMap, ENVI,
Erdas Imagine, R.
4

Example: Time Series in Kenya
• Is it possible to estimate NDVI time series of
agricultural fields in Kenya?
• Method:
– VHR Land use classification (QuickBird).
– Create mask of agricultural fields
– Mask landsat 5 time series from 2002-2012 to the
field level.
5

Results
• DOH!
– Landsat 5 has no data for a time series
7

Results
• MODIS data exists but, resolution too coarse
8

Home page
• https://earthengine.google.com/
• https://explorer.earthengine.google.com/#workspace
9

11
EarthEngine API introduction

API Introduction
• API requried to analyse data more fully
• Mainly uses a version of JavaScript but some Python
• Do not have to be a coding genius to use GEE
• Many helpful tutorials:
– https://developers.google.com/earth-engine/getstarted
– https://developers.google.com/earth-engine/tutorials
12

The API
Code editor
Code examples,
saved scripts, Code management,
debugging etc
13
https://code.earthengine.google.com/

Available Algorithms
• Docs tab > ee.Image
– Shows the algorithms that can be run on an image
– Eg: Add
– Click to see an explanation of the algorithm.
– var image3 = image1.add(image2)
• Sums the pixels in the first raster to the second raster
creating a new variable (var) called image3.
14

15
EarthEngine API Practical 1
“Differences in NDVI"

Before you start
• Have two Script windows open
• Use one to paste in each new set of instructions
• Once you are happy with what they are doing add them to the
second script window and add a comment. Save the script for
future use.
16

Tutorial 1 – can copy all into EE
//add a single Landsat 8 image and center the
map view to San Francisco. First line has two
functions add layer to map and load an image
//Add a single Landsat 8 Image and center it
Map.addLayer(ee.Image('LC8_L1T/LC804403420
14077LGN00'));
Map.setCenter(-122.44, 37.77, 12);
17

Tutorial 1 –
//change the visualisation settings by adding in
second parameter describing how image should
be visualised, a third parameter selects the
bands
Map.addLayer(ee.Image('LC8_L1T/LC804403420
14077LGN00'),
{'min':6000, 'max':18000, 'bands':['B5', 'B4',
'B3']});
Map.setCenter(-122.44, 37.77, 12);
18

Tutorial 1 – Image collections
/// image collections are sets of images such as
time series or multiple landsat tiles
//open these using ee.ImageCollection rather than
ee.Image
//by default the most recent pixel is shown at the
top
Map.addLayer(ee.ImageCollection('LANDSAT/LC8_L
1T'),
{'bands': ['B5', 'B4', 'B3'], 'min':6000, 'max':18000});
Map.setCenter(-122.44, 37.77, 7);
19

Tutorial 1 – improving the image view
resulting image is messy due to overlapping
scenes. Two ways to deal with this:
1. first is to select only a specific time period of
images to display
2. second is to tell GEE how to deal with
overlapping areas.
20

Tutorial 1 – Filter by Date
• filter by date using .filterDate
• filterDate is a method which ee.ImageCollection knows
about.
• Can find full list of methods in the docs tab under
ee.ImageCollection.
• but all methods start with .methodName()
Map.addLayer(ee.ImageCollection('LANDSAT/LC8_L1T').fil
terDate('2013-06-01','2013-12-31'),
{'bands': ['B5', 'B4', 'B3'], 'min':6000, 'max':18000});
Map.setCenter(-122.44, 37.77, 7);
21

Tutorial 1 – Reducers -
• changing the way GEE deals with overlaping pixels.
• over the 6 month period in our date range there will be
approx 12 images (more in overlap regions) default is to
select most recent pixel.
• change this using EE reducers. include median pixel value in
the stack. median value will remove high value cloud pixels
and shadows (low value)
Map.addLayer(ee.ImageCollection('LANDSAT/LC8_L1T')
.filterDate('2013-06-01','2013-12-31')
.median(),
{'bands': ['B5', 'B4', 'B3'], 'min':5000, 'max':18000});
Map.setCenter(-122.44, 37.77, 7);
22

Tutorial 1 – Variables
• The code above is getting too messy
• so now use variables to hold data in
• and reduce the amount of instructions in two
lines
• variables store values such as strings, numbers
and can be called in the script
• to define a variable start with var
23

Tutorial 1 – Variables
var landsat8 =
ee.ImageCollection('LANDSAT/LC8_L1T');
var secondHalf2013 = landsat8.filterDate('2013-
06-01', '2013-12-31');
var median = secondHalf2013.median()
Map.addLayer(median, {'bands':['B5', 'B4', 'B3'],
'min':5000, 'max':18000});
Map.setCenter(-119.84, 37.83, 8);
24

Tutorial 1 – Image Band Math
//load the Landsat 5 32 day NDVI composite.
var collection =
ee.ImageCollection('LANDSAT/LT5_L1T_32DAY_NDVI');
//filter by date for the year 2000
var filtered2000 = collection.filterDate('2000-01-01',
'2000-12-31');
//reducer to select the median pixel value
var ndvi = filtered2000.median();
print(ndvi)
? How does that look?
25

Tutorial 1 – Image Band Math 2 add to previous
Map.setCenter(-122.44, 37.74, 13);
//when adding the layer can also add a palette to
display
//can also add in min and max display values. to find
out these can switch to inspector tab (top right) and
click on a pixel
Map.addLayer(ndvi, {palette: '000000, 00FF00',
min:0, max:0.7});
26

Tutorial 1 – Image Band Math 3
//basic band math compare the ndvi in 2000 and 2010
//load the NDVI composite again
var collection =
ee.ImageCollection('LANDSAT/LT5_L1T_32DAY_NDVI');
//filter for the year 2000
var filtered2000 = collection.filterDate('2000-01-01', '2000-12-
31');
//filter for the year 2010
var filtered2010 = collection.filterDate('2010-01-01', '2010-12-
31');
27

//reducer to identify the median pixel value per year for
2000 and 2010. median means clouds and shadows are
ignored.
var ndvi2000 = filtered2000.median();
Add these to the bottom of the previous slides code…
28

//band math. subtract the 2000 ndvi values from the 2010
ndvi values
var difference = ndvi2010.subtract(ndvi2000);
Map.setCenter(-122.44, 37.74, 13);
Map.addLayer(difference,
{palette: 'FF0000, 000000, 00FF00', min: -0.3, max: 0.3});
//the palette shows areas in red where vegetation
decreased and green where vegetation increased.
29

Tutorial 1 – Masking
//but we have water in this area still. so next we mask out
the water areas. use a binary mask approach where the
mask band will have zero value over water
// run the same script as above but with added mask
creation.
30

Tutorial 1 – Masking – add to bottom of script
• //use a MODIS land cover product as the basis
for the mask.
• //load the MODIS land cover classification
product
var classifiedImage =
ee.Image('MODIS/051/MCD12Q1/2001_01_01');
print(classifiedImage)
31

Tutorial 1 – Masking – add to bottom of script
//type 1 is 1 of these where water has a value of zero in the product so
we just have to load this land cover as a mask and then use with the
ndvi to remove any water (zero value) pixels.
var mask = classifiedImage.select(['Land_Cover_Type_1']);
//apply the mask to the difference image
var maskedDifference = difference.updateMask(mask);
Map.setCenter(-122.44, 37.74, 13);
Map.addLayer(maskedDifference,
{palette: 'FF0000, 000000, 00FF00', min: -0.3, max: 0.3});
Hint – check layer tab
32

• //problem we have now is that we lose a lot of
land due to the 500 m MODIS pixels.
• //use a 30 m land use/land cover product.
• //can use the hansen global forest change
product for this purpose, but the water is not
coded with a value of 0. it has a value of 2.
• //land is 1 and no data is 0.
33

//load in the hansen forest change product
var hansenImage =
ee.Image('UMD/hansen/global_forest_change_2013');
//from hansen dataset select the datamask - a band to mask no land
var data = hansenImage.select('datamask');
//create a mask of water using the operator .eq() which means that
the water Mask selects only pixels with a value of 1 (land) so any water
or no data pixels will not be brought into this image and therefore will
automatically get a pixel value of 0.
var waterMask = data.eq(1);
Map.setCenter(-122.44, 37.74, 13);
Map.addLayer(waterMask);
34

// use this mask instead to mask out water in ndvi difference.
var collection = ee.ImageCollection('LANDSAT/LT5_L1T_32DAY_NDVI');
var filtered2000 = collection.filterDate('2000-01-01', '2000-12-31');
var filtered2010 = collection.filterDate('2010-01-01', '2010-12-31');
var difference = ndvi2010.subtract(ndvi2000);
var hansenImage = ee.Image('UMD/hansen/global_forest_change_2013');
var data = hansenImage.select('datamask');
var mask = data.eq(1);
//apply the hansen land mask to the ndvi difference
var maskedDifference = difference.updateMask(mask);
Map.setCenter(-122.44, 37.74, 13);
Map.addLayer(maskedDifference,
{palette: 'FF0000, 000000, 00FF00', min: -0.3, max: 0.3}, ‘masked difference’);
35

Sharing Scripts
• https://code.earthengine.google.com/232f90
0373b96d22696f796c7b06348a
36

Review
• Display images
• Difference between Image and
ImageCollection
• Simple mathemetical operators applied to
images
– Band math in ENVI
• Filtered on date
• masking
37

Tutorial 2
Features and Fusion Tables
38

What is a Fusion Table?
• ”Experimental data visualisation web application”
– Worrying?
• Those familiar with GIS - Fusion tables are the way that
shapefiles or polygons can be imported into EE.
• Known as Feature (single) or Feature Class (multiple).
39

What is a Fusion Table
• Can create them by importing:
– Spreadsheet or delimited text file (.csv; .tsv; .txt)
– KML file (google earth)
– Shapeescape tool (.shp to fusion table)
• Automated tool to convert into a Fusion Table.
40

Denmark Fusion Table
Table ID: 1l8U580LAtM9d-fYUUfd5kRaK1BlNVZKAP8SmhEOi
This is a Fusion Table for parts of Denmark
• GADM Admin Level 1 boundaries for a few places
(http://gadm.org/)
41

Display Denmark FT
//Display Denmark GADM 1 Levels
var ft = ee.FeatureCollection('ft:1l8U580LAtM9d-
fYUUfd5kRaK1BlNVZKAP8SmhEOi');
Map.addLayer(ft);
Map.centerObject(ft);
42

Use the Denmark FT
Identifying greennest area
43

Which Danish Area is most green?
• Use the fusion table in EE to find out
– Average NDVI in 2015 for each Danish polygon.
44

find out the median NDVI of Midtjylland in 2011
• // 1. filter the feature collection
var midt = ft.filter(ee.Filter.eq('NAME_1', 'Midtjylland'));
Map.addLayer(midt, {'color':'FF0000'});
• // 2. load in NDVI MODIS 16-Day Composite
var NDVI =
ee.ImageCollection('MODIS/MCD43A4_NDVI').filterDate('2011-
01-01','2011-12-31');
• //reduce the NDVImidt to median
var median = NDVI.median();
45

find out the median NDVI of Midtjylland in 2011
• //clip to Midtjylland
var midtNDVI = median.clip(midt);
Map.addLayer(midtNDVI, {palette:'000000, 00FF00', min:0,
max:0.8});
• //this shows us the median pixel value in 2011 for all pixels in
Midtjylland.
46

• //what about average value of Midtjylland as a whole
compared to the other areas?
• //use reduceRegion()
var average =
midtNDVI.reduceRegion(ee.Reducer.mean(),midt, 500);
print(average);
This gives us an average printed in the console
47

What about for all the polygons in FT?
• Repeat the same process just using the FT for denmark
rather than midt?
var NDVI2015 =
ee.ImageCollection('MODIS/MCD43A4_NDVI').filterDate('
2015-01-01','2015-12-31');
var median2 = NDVI2015.median();
var average2 =
median2.reduceRegion(ee.Reducer.mean(), ft, 500);
print(average2);
48

Whoops!
• this has averaged the median NDVI over the entire Denmark
shapefile or fusion table, which is not what we wanted.
– We only got a single NDVI value again
• to extract the mean NDVI for 2015 we need to create a
function to estimate the mean for 1 region and then map it to
the rest of the regions that we have in the dataset.
49

Mapping functions
• This is not mapping in the normal sense. Instead we define a
function for a feature in a collection and then we apply it to
the entire feature collection
var averageNDVI = ft.map(function(feature) {
var Districtaverage = median2.reduceRegion(ee.Reducer.mean(),
feature.geometry(), 500);
return feature.set({'ndvi':Districtaverage});
});
print(averageNDVI);
• //we have added a new property to each of the features within the
fusion table giving the average NDVI in 2015.
50

Review
• Created fusion table from csv and shp file.
• Uploaded and displayed in EE API
• Performed basic queries on NDVI data using FT.
• Wrote a Function and mapped it to a collection.
51

Comments
• The fusion tables are experimental – what does that mean?
• There are limits to the file sizes for upload
• Difficult to get polygons uploaded without using shapeescape
• Difficult to create your own data in GoogleEarth and then
converting it to fusion table as the geometry and name
variables require specific types.
52

Good to know
• Fusion table creation is temperamental – general advice if get
errors is to try again and vary a few things:
– Eg - Chrome browser doesnt always seem to work for
creating fusion tables
• FT’s are public. You can protect them with invite only status
but you still have to upload your data onto a public google
cloud
– Check the data use policies before doing this!
• Uploading to shapeescape could also be against data
providers policies.
53

Benefits of GEE?
• ”GEE does the same things as the software i am used to”
– Uses the Google Cloud Server capabilities so the analysis is
run on the fly.
– You do not need to download and process gb’s of data.
– Example:
• NDVI time series for 12 months at 8-day repeat?
• In ENVI ~ 46 images per tile
• GEE – run the analysis on the 46 images without having
to download.
54

Create a Fusion Table
From csv
56

Create Fusion Table
• First we need a spatial data file.
• Open the Kenya_points.csv file
SiteNumber Name elevation measurement Decription latitude longitude
1MasaiMaraReserve1 1516 5game reserve -1.41685 34.91468
3MasaiMaraReserve3 1578 6reserve nr river -1.40123 34.98155
4NarokMountainZone 2106 54mountainregion -1.16799 35.39791
5MasaiMaraReserve4 1482 51riparian -1.53514 35.02524
6KisumuAgriculture 1225 99agriculture -0.05499 34.94508
7LabootFoothills 2925 79forest_ag -0.98413 34.63178
8KarunaAg 2199 58agroforest -0.73683 35.45333
9coastal 23 35forest -1.84156 41.13778
10savannah 342 57Shrub -3.16688 38.89925
57

Create Fusion Table
• First we need a spatial data file.
• Open the Kenya_points.csv file
SiteNumber Name elevation measurement Decription latitude longitude
3MasaiMaraReserve3 1578 6reserve nr river -1.40123 34.98155
4NarokMountainZone 2106 54mountainregion -1.16799 35.39791
5MasaiMaraReserve4 1482 51riparian -1.53514 35.02524
6KisumuAgriculture 1225 99agriculture -0.05499 34.94508
7LabootFoothills 2925 79forest_ag -0.98413 34.63178
8KarunaAg 2199 58agroforest -0.73683 35.45333
9coastal 23 35forest -1.84156 41.13778
10savannah 342 57Shrub -3.16688 38.89925
58

• Open Google Drive
• Select new and Fusion table option
59

Create Fusion Table
• If not an option select connect more apps and search for
fusion table
60

• In drive Select new and the Fusion table option
• Navigate to the kenya_points.csv file you just created using
the browse button and select next
61

• Import new table? Check the formatting looks ok
62

• This is the Fusion Table – ready for use in EE.
63

• Check Map of Latitude to check if the points look in a
reasonable location
64

• How do we get the Fusion Table into EE?
• Need the ID.
• File > About this table > ID
65

Fusion Table ID
• This is a unique code for your table. Anyone who has this code
can access your file.
• Notice that the ID also appears in the address bar
You can copy this ID in the address bar
66

Fusion Table ID
• If you forget the ID? Or copy it wrong?
• It is in Drive so you can always check again
67

Display Kenya FT
// Load in the Kenya Samples Fusion Table and display
var kenya = ee.FeatureCollection('ft:1-1p5mlDeaqxcPENYI0-
iaurkOfkZR-nQAptma_Dw
');
Map.addLayer(kenya, {'color': 'FF0000'});
Map.centerObject(kenya);
print(kenya);
Map.centerObject(); - useful when you are unsure of the location
of a dataset or the zoom level to use.
68

Shapefile to Fusion Table
• ShapeEscape (http://www.shpescape.com/)
• Can convert ArcMap .shp file to FusionTable.
• Put all of the files associated with an ArcMap .shp file into a
folder and zip
• Upload the .zip to ShapeEscape and wait.
• Automatically uploads the files and provides the Fusion Table
ID.
• Click the link to explore the fusion table.
69

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