Week 12 geemap in python

Week 12: geemap in Python

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What is Google Earth Engine (GEE)¶

Google Earth Engine (GEE) is a cloud-based platform developed by Google for processing and analyzing geospatial data at a planetary scale. It enables users to analyze and visualize vast amounts of satellite imagery and geospatial datasets using a web-based JavaScript API or a Python API. GEE is widely used for environmental and climate research, urban planning, natural resource management, and disaster response, among other fields.

What is geemap¶

Geemap is a Python package that extends the functionality of Google Earth Engine (GEE), making it easier for users to interact with GEE data and perform spatial analysis within a Jupyter Notebook or other Python environments

Onlnie Learning Source¶

geemap online textbook

Installing geemap¶

1. Installing with conda
2. Installing with pip
3. Installing from source

Earth Engine authentication¶

1. Create a google engine account

2. Click 'Get Started to create the project'

3. Go to google cloud console to check your project's information

4. Create secret key with 'ID' of your project

In [ ]:

%pip install -U geemap

%pip install -U geemap

In [ ]:

import ee

ee.Authenticate()

import ee ee.Authenticate()

Initialize a project¶

https://console.cloud.google.com/cloud-resource-manager?walkthrough_id=resource-manager--create-project#step_index=1

Replace the key value for 'ee_project' key in Secrets

In [ ]:

from google.colab import userdata
ee.Initialize(project = userdata.get('ee_project'))

from google.colab import userdata ee.Initialize(project = userdata.get('ee_project'))

Load basemap¶

In [ ]:

import geemap

Map = geemap.Map(center = [42.2602, -71.7980], zoom = 11)
Map

import geemap Map = geemap.Map(center = [42.2602, -71.7980], zoom = 11) Map

Metadata about Earth Engine Data¶

gee dataset

Earth Engine Data Catelog

ee.ImageCollection

In Google Earth Engine, an ee.ImageCollection is a data structure used to handle a collection of multiple ee.Image objects. It’s often used for time-series analysis, multi-temporal composites, or batch processing of satellite imagery over a specified region

ee.Image constructor

In [ ]:

landsat_9 = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')
landsat_9.size().getInfo()

landsat_9 = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') landsat_9.size().getInfo()

aggregate_array

In [ ]:

landsat_9.limit(10).aggregate_array('system:index').getInfo()

landsat_9.limit(10).aggregate_array('system:index').getInfo()

In [ ]:

single_image = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_001004_20220709').select(
    ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7']
)

single_image = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_001004_20220709').select( ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7'] )

In [ ]:

geemap.image_props(single_image).getInfo()

geemap.image_props(single_image).getInfo()

Plot Satellite iamge¶

Image Visualization

Specify the bands combination

Image.geometry

This list will contain 21 coordinate pairs, each defining a point on the boundary of the image. This data can help you understand the precise extent of the image’s coverage area in geographic space.

In [ ]:

single_image.geometry()

single_image.geometry()

In [ ]:

Map = geemap.Map(center = [78.04207655029894,-10.322619721660091], zoom = 6)

single_image = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_001004_20220709').select(
    ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7']
)

landsat_vis = {'bands': ['SR_B4', 'SR_B3', 'SR_B2'],'gamma': 1.4}

Map.addLayer(single_image, landsat_vis, "Landsat")
Map

Map = geemap.Map(center = [78.04207655029894,-10.322619721660091], zoom = 6) single_image = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_001004_20220709').select( ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7'] ) landsat_vis = {'bands': ['SR_B4', 'SR_B3', 'SR_B2'],'gamma': 1.4} Map.addLayer(single_image, landsat_vis, "Landsat") Map

Using the plot tool¶

ee.Geometry.Point

Define the center point of your study area in [x,y]

In [ ]:

location = ee.Geometry.Point([-71.80613, 42.27189])

location = ee.Geometry.Point([-71.80613, 42.27189])

In [ ]:

landsat9_subset = landsat_9.filterBounds(location).filterMetadata('CLOUD_COVER', 'less_than', 30)
landsat9_subset.size().getInfo()

landsat9_subset = landsat_9.filterBounds(location).filterMetadata('CLOUD_COVER', 'less_than', 30) landsat9_subset.size().getInfo()

In [ ]:

landsat9_subset.limit(10).aggregate_array('system:index').getInfo()

landsat9_subset.limit(10).aggregate_array('system:index').getInfo()

In [ ]:

Map = geemap.Map(center = [42.27189, -71.80613], zoom = 6)

single_image = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_012031_20220722').select(
    ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7']
)

landsat_vis = {'bands': ['SR_B3', 'SR_B2', 'SR_B1'],'gamma': 1.4}

Map.addLayer(single_image, landsat_vis, "Landsat")
Map

Map = geemap.Map(center = [42.27189, -71.80613], zoom = 6) single_image = ee.Image('LANDSAT/LC09/C02/T1_L2/LC09_012031_20220722').select( ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7'] ) landsat_vis = {'bands': ['SR_B3', 'SR_B2', 'SR_B1'],'gamma': 1.4} Map.addLayer(single_image, landsat_vis, "Landsat") Map

Visualizing raster data¶

Single-band images¶

Explore the pre-defined tool

In [ ]:

Map = geemap.Map(center=[12, 69], zoom=3)
dem = ee.Image('USGS/SRTMGL1_003')
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}
Map.addLayer(dem, vis_params, 'SRTM DEM')
Map

Map = geemap.Map(center=[12, 69], zoom=3) dem = ee.Image('USGS/SRTMGL1_003') vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } Map.addLayer(dem, vis_params, 'SRTM DEM') Map

Visualizing vector data¶

Search TIGER keyword in data catalog

Define style of vector data

In [ ]:

Map = geemap.Map(center = [42.27189, -71.80613], zoom = 6)
states = ee.FeatureCollection("TIGER/2016/Roads")

vis_params = {
    'color': '0000FFFF',
    'width': 1,
    'lineType': 'solid',
}

Map.addLayer(states.style(**vis_params), {}, "US Roads")
Map

Map = geemap.Map(center = [42.27189, -71.80613], zoom = 6) states = ee.FeatureCollection("TIGER/2016/Roads") vis_params = { 'color': '0000FFFF', 'width': 1, 'lineType': 'solid', } Map.addLayer(states.style(**vis_params), {}, "US Roads") Map

Creating legends¶

Custom legends - vector¶

In [ ]:

labels = ['US Roads']

# colors can be defined using either hex code or RGB (0-255, 0-255, 0-255)
colors = ['#0000FF']
# legend_colors = [(255, 0, 0), (127, 255, 0), (127, 18, 25), (36, 70, 180), (96, 68 123)]

Map.add_legend(
    labels=labels, colors=colors, position='bottomright'
)
Map

labels = ['US Roads'] # colors can be defined using either hex code or RGB (0-255, 0-255, 0-255) colors = ['#0000FF'] # legend_colors = [(255, 0, 0), (127, 255, 0), (127, 18, 25), (36, 70, 180), (96, 68 123)] Map.add_legend( labels=labels, colors=colors, position='bottomright' ) Map

Built-in legends¶

In [ ]:

from geemap.legends import builtin_legends

for legend in builtin_legends:
    print(legend)

from geemap.legends import builtin_legends for legend in builtin_legends: print(legend)

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
Map.add_basemap('HYBRID')

nlcd = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019')
landcover = nlcd.select('landcover')

Map.addLayer(landcover, {}, 'NLCD Land Cover 2019')
Map.add_legend(
    title="NLCD Land Cover Classification", builtin_legend='NLCD', height='465px'
)
Map

Map = geemap.Map(center=[40, -100], zoom=4) Map.add_basemap('HYBRID') nlcd = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019') landcover = nlcd.select('landcover') Map.addLayer(landcover, {}, 'NLCD Land Cover 2019') Map.add_legend( title="NLCD Land Cover Classification", builtin_legend='NLCD', height='465px' ) Map

Earth Engine class table¶

ESA WorldCover data

In [ ]:

Map = geemap.Map()

dataset = ee.ImageCollection("ESA/WorldCover/v100").first()
Map.addLayer(dataset, {'bands': ['Map']}, "ESA Landcover")

ee_class_table = """
Value	Color	Description
10	006400	Trees
20	ffbb22	Shrubland
30	ffff4c	Grassland
40	f096ff	Cropland
50	fa0000	Built-up
60	b4b4b4	Barren / sparse vegetation
70	f0f0f0	Snow and ice
80	0064c8	Open water
90	0096a0	Herbaceous wetland
95	00cf75	Mangroves
100	fae6a0	Moss and lichen
"""

legend_dict = geemap.legend_from_ee(ee_class_table)
Map.add_legend(title="ESA Land Cover", legend_dict=legend_dict)
Map

Map = geemap.Map() dataset = ee.ImageCollection("ESA/WorldCover/v100").first() Map.addLayer(dataset, {'bands': ['Map']}, "ESA Landcover") ee_class_table = """ Value Color Description 10 006400 Trees 20 ffbb22 Shrubland 30 ffff4c Grassland 40 f096ff Cropland 50 fa0000 Built-up 60 b4b4b4 Barren / sparse vegetation 70 f0f0f0 Snow and ice 80 0064c8 Open water 90 0096a0 Herbaceous wetland 95 00cf75 Mangroves 100 fae6a0 Moss and lichen """ legend_dict = geemap.legend_from_ee(ee_class_table) Map.add_legend(title="ESA Land Cover", legend_dict=legend_dict) Map

Creating color bars¶

Adjust palette in tools

In [ ]:

Map = geemap.Map()
dem = ee.Image('USGS/SRTMGL1_003')
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']
}

Map.addLayer(dem, vis_params, 'SRTM DEM')

Map.add_colorbar(vis_params, label="Elevation (m)", layer_name="SRTM DEM")

Map

Map = geemap.Map() dem = ee.Image('USGS/SRTMGL1_003') vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'] } Map.addLayer(dem, vis_params, 'SRTM DEM') Map.add_colorbar(vis_params, label="Elevation (m)", layer_name="SRTM DEM") Map

Displaying labels¶

US Census States 2016

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4, add_google_map=False)
states = ee.FeatureCollection("TIGER/2016/States")
style = {'color': 'black', 'fillColor': "00000000"}
Map.addLayer(states.style(**style), {}, "US States")

Map.add_labels(
    data=states,
    column="NAME",
    font_size="12pt",
    font_color="blue",
    font_family="arial",
    font_weight="bold",
    draggable=True,
)
Map

Map = geemap.Map(center=[40, -100], zoom=4, add_google_map=False) states = ee.FeatureCollection("TIGER/2016/States") style = {'color': 'black', 'fillColor': "00000000"} Map.addLayer(states.style(**style), {}, "US States") Map.add_labels( data=states, column="NAME", font_size="12pt", font_color="blue", font_family="arial", font_weight="bold", draggable=True, ) Map

Timeseries inspector¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
collection = ee.ImageCollection('USGS/NLCD_RELEASES/2019_REL/NLCD').select('landcover')
vis_params = {'bands': ['landcover']}
years = collection.aggregate_array('system:index').getInfo()
years

Map = geemap.Map(center=[40, -100], zoom=4) collection = ee.ImageCollection('USGS/NLCD_RELEASES/2019_REL/NLCD').select('landcover') vis_params = {'bands': ['landcover']} years = collection.aggregate_array('system:index').getInfo() years

In [ ]:

Map.ts_inspector(
    left_ts=collection,
    right_ts=collection,
    left_names=years,
    right_names=years,
    left_vis=vis_params,
    right_vis=vis_params,
    width='80px',
)
Map

Map.ts_inspector( left_ts=collection, right_ts=collection, left_names=years, right_names=years, left_vis=vis_params, right_vis=vis_params, width='80px', ) Map

Practice 1: Create a Time Series Slider¶

Data Source

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
collection = ee.ImageCollection('MODIS/006/MOD44B').select('Percent_Tree_Cover')
vis_params = {
    'min': 0,
    'max': 100,
    'palette': ['bbe029', '0a9501', '074b03']
}
years = collection.aggregate_array('system:index').getInfo()
years

Map = geemap.Map(center=[40, -100], zoom=4) collection = ee.ImageCollection('MODIS/006/MOD44B').select('Percent_Tree_Cover') vis_params = { 'min': 0, 'max': 100, 'palette': ['bbe029', '0a9501', '074b03'] } years = collection.aggregate_array('system:index').getInfo() years

In [ ]:

Map.addLayer(collection, vis_params)
Map.ts_inspector(
    left_ts=collection,
    right_ts=collection,
    left_names=years,
    right_names=years,
    left_vis=vis_params,
    right_vis=vis_params,
    width='80px',
)

Map.add_colorbar(vis_params, label="Percent_Tree_Cover", layer_name="Percent_Tree_Cover")
Map

Map.addLayer(collection, vis_params) Map.ts_inspector( left_ts=collection, right_ts=collection, left_names=years, right_names=years, left_vis=vis_params, right_vis=vis_params, width='80px', ) Map.add_colorbar(vis_params, label="Percent_Tree_Cover", layer_name="Percent_Tree_Cover") Map