Table of Contents

• 1  Create an interactive map
• 2  Add basemaps
• 3  Add WMS and XYZ tile layers
• 4  Add Earth Engine data layers
• 5  Search Earth Engine data catalog
• 6  Search Earth Engine API documentation
• 7  Use Inspector tool
• 8  Use Plotting tool
• 9  Create a split-panel map
• 10  Add marker cluster
• 11  Add customized legends
• 12  Use Drawing tools
• 13  Convert JavaScripts to Python
• 14  Use shapefiles
• 15  Create Landsat timelapse
• 16  Use time-series inspector
• 17  Export images

Uncomment the following line to install geemap if needed.

In [ ]:

# !pip install geemap

# !pip install geemap

In [ ]:

import ee
import geemap

import ee import geemap

Create an interactive map¶

In [ ]:

Map = geemap.Map(center=(40, -100), zoom=4)
Map

Map = geemap.Map(center=(40, -100), zoom=4) Map

Add basemaps¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

Map.add_basemap('HYBRID')

Map.add_basemap('HYBRID')

In [ ]:

Map.add_basemap('OpenTopoMap')

Map.add_basemap('OpenTopoMap')

In [ ]:

Map = geemap.Map()
Map.basemap_demo()
Map

Map = geemap.Map() Map.basemap_demo() Map

Add WMS and XYZ tile layers¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

# https://viewer.nationalmap.gov/services/
url = 'https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}'
Map.add_tile_layer(url, name='Google Satellite', attribution='Google')

# https://viewer.nationalmap.gov/services/ url = 'https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}' Map.add_tile_layer(url, name='Google Satellite', attribution='Google')

In [ ]:

naip_url = 'https://services.nationalmap.gov/arcgis/services/USGSNAIPImagery/ImageServer/WMSServer?'
Map.add_wms_layer(
    url=naip_url, layers='0', name='NAIP Imagery', format='image/png', shown=True
)

naip_url = 'https://services.nationalmap.gov/arcgis/services/USGSNAIPImagery/ImageServer/WMSServer?' Map.add_wms_layer( url=naip_url, layers='0', name='NAIP Imagery', format='image/png', shown=True )

Add Earth Engine data layers¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

# Add Earth Engine dataset
dem = ee.Image('USGS/SRTMGL1_003')
landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover')
landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')
states = ee.FeatureCollection("TIGER/2018/States")

# Set visualization parameters.
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine layers to Map
Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5)
Map.addLayer(landcover, {}, 'Land cover')
Map.addLayer(
    landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7'
)
Map.addLayer(states, {}, "US States")

# Add Earth Engine dataset dem = ee.Image('USGS/SRTMGL1_003') landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover') landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003') states = ee.FeatureCollection("TIGER/2018/States") # Set visualization parameters. vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } # Add Earth Engine layers to Map Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5) Map.addLayer(landcover, {}, 'Land cover') Map.addLayer( landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7' ) Map.addLayer(states, {}, "US States")

Search Earth Engine data catalog¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

Map.search_locations

Map.search_locations

In [ ]:

Map.search_loc_geom

Map.search_loc_geom

In [ ]:

location = Map.search_loc_geom
# print(location.getInfo())

location = Map.search_loc_geom # print(location.getInfo())

Search Earth Engine API documentation¶

In [ ]:

geemap.ee_search()

geemap.ee_search()

Use Inspector tool¶

In [ ]:

Map = geemap.Map()

# Add Earth Engine dataset
dem = ee.Image('USGS/SRTMGL1_003')
landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover')
landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')
states = ee.FeatureCollection("TIGER/2018/States")

# Set visualization parameters.
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine layers to Map
Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5)
Map.addLayer(landcover, {}, 'Land cover')
Map.addLayer(
    landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7'
)
Map.addLayer(states, {}, "US States")

Map

Map = geemap.Map() # Add Earth Engine dataset dem = ee.Image('USGS/SRTMGL1_003') landcover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover') landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003') states = ee.FeatureCollection("TIGER/2018/States") # Set visualization parameters. vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } # Add Earth Engine layers to Map Map.addLayer(dem, vis_params, 'SRTM DEM', True, 0.5) Map.addLayer(landcover, {}, 'Land cover') Map.addLayer( landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200}, 'Landsat 7' ) Map.addLayer(states, {}, "US States") Map

Use Plotting tool¶

In [ ]:

Map = geemap.Map()

landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select([0, 1, 2, 3, 4, 6])
landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(landsat7, landsat_vis, "LE7_TOA_5YEAR/1999_2003")

hyperion = ee.ImageCollection('EO1/HYPERION').filter(
    ee.Filter.date('2016-01-01', '2017-03-01')
)
hyperion_vis = {
    'min': 1000.0,
    'max': 14000.0,
    'gamma': 2.5,
}
Map.addLayer(hyperion, hyperion_vis, 'EO1/HYPERION')

Map

Map = geemap.Map() landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select([0, 1, 2, 3, 4, 6]) landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4} Map.addLayer(landsat7, landsat_vis, "LE7_TOA_5YEAR/1999_2003") hyperion = ee.ImageCollection('EO1/HYPERION').filter( ee.Filter.date('2016-01-01', '2017-03-01') ) hyperion_vis = { 'min': 1000.0, 'max': 14000.0, 'gamma': 2.5, } Map.addLayer(hyperion, hyperion_vis, 'EO1/HYPERION') Map

In [ ]:

Map.set_plot_options(plot_type='bar', add_marker_cluster=True)

Map.set_plot_options(plot_type='bar', add_marker_cluster=True)

Create a split-panel map¶

In [ ]:

Map = geemap.Map()
Map.split_map(left_layer='HYBRID', right_layer='ROADMAP')
Map

Map = geemap.Map() Map.split_map(left_layer='HYBRID', right_layer='ROADMAP') Map

In [ ]:

Map = geemap.Map()
Map.split_map(
    left_layer='NLCD 2016 CONUS Land Cover', right_layer='NLCD 2001 CONUS Land Cover'
)
Map

Map = geemap.Map() Map.split_map( left_layer='NLCD 2016 CONUS Land Cover', right_layer='NLCD 2001 CONUS Land Cover' ) Map

In [ ]:

nlcd_2001 = ee.Image('USGS/NLCD/NLCD2001').select('landcover')
nlcd_2016 = ee.Image('USGS/NLCD/NLCD2016').select('landcover')

left_layer = geemap.ee_tile_layer(nlcd_2001, {}, 'NLCD 2001')
right_layer = geemap.ee_tile_layer(nlcd_2016, {}, 'NLCD 2016')

Map = geemap.Map()
Map.split_map(left_layer, right_layer)
Map

nlcd_2001 = ee.Image('USGS/NLCD/NLCD2001').select('landcover') nlcd_2016 = ee.Image('USGS/NLCD/NLCD2016').select('landcover') left_layer = geemap.ee_tile_layer(nlcd_2001, {}, 'NLCD 2001') right_layer = geemap.ee_tile_layer(nlcd_2016, {}, 'NLCD 2016') Map = geemap.Map() Map.split_map(left_layer, right_layer) Map

Add marker cluster¶

In [ ]:

import geemap
import json
import os
import requests
from geemap import geojson_to_ee, ee_to_geojson
from ipyleaflet import GeoJSON, Marker, MarkerCluster

import geemap import json import os import requests from geemap import geojson_to_ee, ee_to_geojson from ipyleaflet import GeoJSON, Marker, MarkerCluster

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

file_path = os.path.join(os.getcwd(), 'us_cities.json')

if not os.path.exists(file_path):
    url = 'https://github.com/gee-community/geemap/raw/master/examples/data/us_cities.json'
    r = requests.get(url)
    with open(file_path, 'w') as f:
        f.write(r.content.decode("utf-8"))

with open(file_path) as f:
    json_data = json.load(f)

file_path = os.path.join(os.getcwd(), 'us_cities.json') if not os.path.exists(file_path): url = 'https://github.com/gee-community/geemap/raw/master/examples/data/us_cities.json' r = requests.get(url) with open(file_path, 'w') as f: f.write(r.content.decode("utf-8")) with open(file_path) as f: json_data = json.load(f)

In [ ]:

maker_cluster = MarkerCluster(
    markers=[
        Marker(location=feature['geometry']['coordinates'][::-1])
        for feature in json_data['features']
    ],
    name='Markers',
)

maker_cluster = MarkerCluster( markers=[ Marker(location=feature['geometry']['coordinates'][::-1]) for feature in json_data['features'] ], name='Markers', )

In [ ]:

Map.add_layer(maker_cluster)

Map.add_layer(maker_cluster)

In [ ]:

ee_fc = geojson_to_ee(json_data)
Map.addLayer(ee_fc, {}, "US Cities EE")

ee_fc = geojson_to_ee(json_data) Map.addLayer(ee_fc, {}, "US Cities EE")

Add customized legends¶

In [ ]:

Map = geemap.Map()
Map.add_basemap('HYBRID')
landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover')
Map.addLayer(landcover, {}, 'NLCD Land Cover')
Map.add_legend(builtin_legend='NLCD')
Map

Map = geemap.Map() Map.add_basemap('HYBRID') landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover') Map.addLayer(landcover, {}, 'NLCD Land Cover') Map.add_legend(builtin_legend='NLCD') Map

In [ ]:

Map = geemap.Map()
Map.add_basemap('HYBRID')
Map.add_basemap('FWS NWI Wetlands')
Map.add_legend(builtin_legend='NWI')
Map

Map = geemap.Map() Map.add_basemap('HYBRID') Map.add_basemap('FWS NWI Wetlands') Map.add_legend(builtin_legend='NWI') Map

In [ ]:

Map = geemap.Map()

legend_dict = {
    '11 Open Water': '466b9f',
    '12 Perennial Ice/Snow': 'd1def8',
    '21 Developed, Open Space': 'dec5c5',
    '22 Developed, Low Intensity': 'd99282',
    '23 Developed, Medium Intensity': 'eb0000',
    '24 Developed High Intensity': 'ab0000',
    '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f',
    '41 Deciduous Forest': '68ab5f',
    '42 Evergreen Forest': '1c5f2c',
    '43 Mixed Forest': 'b5c58f',
    '51 Dwarf Scrub': 'af963c',
    '52 Shrub/Scrub': 'ccb879',
    '71 Grassland/Herbaceous': 'dfdfc2',
    '72 Sedge/Herbaceous': 'd1d182',
    '73 Lichens': 'a3cc51',
    '74 Moss': '82ba9e',
    '81 Pasture/Hay': 'dcd939',
    '82 Cultivated Crops': 'ab6c28',
    '90 Woody Wetlands': 'b8d9eb',
    '95 Emergent Herbaceous Wetlands': '6c9fb8',
}

landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover')
Map.addLayer(landcover, {}, 'NLCD Land Cover')

Map.add_legend(legend_title="NLCD Land Cover Classification", legend_dict=legend_dict)
Map

Map = geemap.Map() legend_dict = { '11 Open Water': '466b9f', '12 Perennial Ice/Snow': 'd1def8', '21 Developed, Open Space': 'dec5c5', '22 Developed, Low Intensity': 'd99282', '23 Developed, Medium Intensity': 'eb0000', '24 Developed High Intensity': 'ab0000', '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f', '41 Deciduous Forest': '68ab5f', '42 Evergreen Forest': '1c5f2c', '43 Mixed Forest': 'b5c58f', '51 Dwarf Scrub': 'af963c', '52 Shrub/Scrub': 'ccb879', '71 Grassland/Herbaceous': 'dfdfc2', '72 Sedge/Herbaceous': 'd1d182', '73 Lichens': 'a3cc51', '74 Moss': '82ba9e', '81 Pasture/Hay': 'dcd939', '82 Cultivated Crops': 'ab6c28', '90 Woody Wetlands': 'b8d9eb', '95 Emergent Herbaceous Wetlands': '6c9fb8', } landcover = ee.Image('USGS/NLCD/NLCD2016').select('landcover') Map.addLayer(landcover, {}, 'NLCD Land Cover') Map.add_legend(legend_title="NLCD Land Cover Classification", legend_dict=legend_dict) Map

In [ ]:

# https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1
Map = geemap.Map()

ee_class_table = """

Value	Color	Description
0	1c0dff	Water
1	05450a	Evergreen needleleaf forest
2	086a10	Evergreen broadleaf forest
3	54a708	Deciduous needleleaf forest
4	78d203	Deciduous broadleaf forest
5	009900	Mixed forest
6	c6b044	Closed shrublands
7	dcd159	Open shrublands
8	dade48	Woody savannas
9	fbff13	Savannas
10	b6ff05	Grasslands
11	27ff87	Permanent wetlands
12	c24f44	Croplands
13	a5a5a5	Urban and built-up
14	ff6d4c	Cropland/natural vegetation mosaic
15	69fff8	Snow and ice
16	f9ffa4	Barren or sparsely vegetated
254	ffffff	Unclassified

"""

landcover = ee.Image('MODIS/051/MCD12Q1/2013_01_01').select('Land_Cover_Type_1')
Map.setCenter(6.746, 46.529, 2)
Map.addLayer(landcover, {}, 'MODIS Land Cover')

legend_dict = geemap.legend_from_ee(ee_class_table)
Map.add_legend(legend_title="MODIS Global Land Cover", legend_dict=legend_dict)

Map

# https://developers.google.com/earth-engine/datasets/catalog/MODIS_051_MCD12Q1 Map = geemap.Map() ee_class_table = """ Value Color Description 0 1c0dff Water 1 05450a Evergreen needleleaf forest 2 086a10 Evergreen broadleaf forest 3 54a708 Deciduous needleleaf forest 4 78d203 Deciduous broadleaf forest 5 009900 Mixed forest 6 c6b044 Closed shrublands 7 dcd159 Open shrublands 8 dade48 Woody savannas 9 fbff13 Savannas 10 b6ff05 Grasslands 11 27ff87 Permanent wetlands 12 c24f44 Croplands 13 a5a5a5 Urban and built-up 14 ff6d4c Cropland/natural vegetation mosaic 15 69fff8 Snow and ice 16 f9ffa4 Barren or sparsely vegetated 254 ffffff Unclassified """ landcover = ee.Image('MODIS/051/MCD12Q1/2013_01_01').select('Land_Cover_Type_1') Map.setCenter(6.746, 46.529, 2) Map.addLayer(landcover, {}, 'MODIS Land Cover') legend_dict = geemap.legend_from_ee(ee_class_table) Map.add_legend(legend_title="MODIS Global Land Cover", legend_dict=legend_dict) Map

Use Drawing tools¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

# Add Earth Engine dataset
image = ee.Image('USGS/SRTMGL1_003')

# Set visualization parameters.
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine DEM to map
Map.addLayer(image, vis_params, 'SRTM DEM')

states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, 'US States')

# Add Earth Engine dataset image = ee.Image('USGS/SRTMGL1_003') # Set visualization parameters. vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } # Add Earth Engine DEM to map Map.addLayer(image, vis_params, 'SRTM DEM') states = ee.FeatureCollection("TIGER/2018/States") Map.addLayer(states, {}, 'US States')

In [ ]:

Map.draw_features

Map.draw_features

Convert JavaScripts to Python¶

You can simply copy and paste your GEE JavaScripts into a code block wrapped with trip quotes and pass it to a variable.

For example, you can grap GEE JavaScripts from GEE Documentation.

In [ ]:

js_snippet = """
// Load an image.
var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318');

// Define the visualization parameters.
var vizParams = {
  bands: ['B5', 'B4', 'B3'],
  min: 0,
  max: 0.5,
  gamma: [0.95, 1.1, 1]
};

// Center the map and display the image.
Map.setCenter(-122.1899, 37.5010, 10); // San Francisco Bay
Map.addLayer(image, vizParams, 'false color composite');

"""

js_snippet = """ // Load an image. var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318'); // Define the visualization parameters. var vizParams = { bands: ['B5', 'B4', 'B3'], min: 0, max: 0.5, gamma: [0.95, 1.1, 1] }; // Center the map and display the image. Map.setCenter(-122.1899, 37.5010, 10); // San Francisco Bay Map.addLayer(image, vizParams, 'false color composite'); """

In [ ]:

geemap.js_snippet_to_py(
    js_snippet, add_new_cell=True, import_ee=True, import_geemap=True, show_map=True
)

geemap.js_snippet_to_py( js_snippet, add_new_cell=True, import_ee=True, import_geemap=True, show_map=True )

In [ ]:

import ee
import geemap

Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Define the visualization parameters.
vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]}

# Center the map and display the image.
Map.setCenter(-122.1899, 37.5010, 10)
# San Francisco Bay
Map.addLayer(image, vizParams, 'False color composite')
Map

import ee import geemap Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Define the visualization parameters. vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]} # Center the map and display the image. Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(image, vizParams, 'False color composite') Map

In [ ]:

import ee
import geemap

Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Define the visualization parameters.
vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]}

# Center the map and display the image.
Map.setCenter(-122.1899, 37.5010, 10)
# San Francisco Bay
Map.addLayer(image, vizParams, 'False color composite')
Map

import ee import geemap Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Define the visualization parameters. vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]} # Center the map and display the image. Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(image, vizParams, 'False color composite') Map

In [ ]:

import ee
import geemap

Map = geemap.Map()
ee.Initialize()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Define the visualization parameters.
vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]}

# Center the map and display the image.
Map.setCenter(-122.1899, 37.5010, 10)
# San Francisco Bay
Map.addLayer(image, vizParams, 'False color composite')
Map

import ee import geemap Map = geemap.Map() ee.Initialize() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Define the visualization parameters. vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]} # Center the map and display the image. Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(image, vizParams, 'False color composite') Map

In [ ]:

js_snippet = """

// Load an image.
var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318');

// Create an NDWI image, define visualization parameters and display.
var ndwi = image.normalizedDifference(['B3', 'B5']);
var ndwiViz = {min: 0.5, max: 1, palette: ['00FFFF', '0000FF']};
Map.addLayer(ndwi, ndwiViz, 'NDWI', false);

"""

js_snippet = """ // Load an image. var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318'); // Create an NDWI image, define visualization parameters and display. var ndwi = image.normalizedDifference(['B3', 'B5']); var ndwiViz = {min: 0.5, max: 1, palette: ['00FFFF', '0000FF']}; Map.addLayer(ndwi, ndwiViz, 'NDWI', false); """

In [ ]:

geemap.js_snippet_to_py(js_snippet)

geemap.js_snippet_to_py(js_snippet)

In [ ]:

import ee
import geemap

Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)
Map

import ee import geemap Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.addLayer(ndwi, ndwiViz, 'NDWI', False) Map

In [ ]:

import ee
import geemap

Map = geemap.Map()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)
Map

import ee import geemap Map = geemap.Map() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.addLayer(ndwi, ndwiViz, 'NDWI', False) Map

In [ ]:

import ee
import geemap

Map = geemap.Map()
ee.Initialize()

# Load an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')

# Create an NDWI image, define visualization parameters and display.
ndwi = image.normalizedDifference(['B3', 'B5'])
ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']}
Map.addLayer(ndwi, ndwiViz, 'NDWI', False)
Map

import ee import geemap Map = geemap.Map() ee.Initialize() # Load an image. image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318') # Create an NDWI image, define visualization parameters and display. ndwi = image.normalizedDifference(['B3', 'B5']) ndwiViz = {'min': 0.5, 'max': 1, 'palette': ['00FFFF', '0000FF']} Map.addLayer(ndwi, ndwiViz, 'NDWI', False) Map

Use shapefiles¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

countries_shp = '../data/countries.shp'
countries = geemap.shp_to_ee(countries_shp)

countries_shp = '../data/countries.shp' countries = geemap.shp_to_ee(countries_shp)

In [ ]:

countries_shp = '../data/countries.shp'
countries = geemap.shp_to_ee(countries_shp)
Map.addLayer(countries, {}, 'Countries')

countries_shp = '../data/countries.shp' countries = geemap.shp_to_ee(countries_shp) Map.addLayer(countries, {}, 'Countries')

In [ ]:

states_shp = '../data/us_states.shp'
states = geemap.shp_to_ee(states_shp)
Map.addLayer(states, {}, 'US States')

states_shp = '../data/us_states.shp' states = geemap.shp_to_ee(states_shp) Map.addLayer(states, {}, 'US States')

In [ ]:

cities_shp = '../data/us_cities.shp'
cities = geemap.shp_to_ee(cities_shp)
Map.addLayer(cities, {}, 'US Cities')

cities_shp = '../data/us_cities.shp' cities = geemap.shp_to_ee(cities_shp) Map.addLayer(cities, {}, 'US Cities')

In [ ]:

geemap.ee_to_shp(countries, filename='../data/countries_new.shp')

geemap.ee_to_shp(countries, filename='../data/countries_new.shp')

In [ ]:

geemap.ee_export_vector(states, filename='../data/states.csv')

geemap.ee_export_vector(states, filename='../data/states.csv')

Create Landsat timelapse¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

label = 'Urban Growth in Las Vegas'
Map.add_landsat_ts_gif(
    label=label,
    start_year=1985,
    bands=['Red', 'Green', 'Blue'],
    font_color='white',
    frames_per_second=10,
    progress_bar_color='blue',
)

label = 'Urban Growth in Las Vegas' Map.add_landsat_ts_gif( label=label, start_year=1985, bands=['Red', 'Green', 'Blue'], font_color='white', frames_per_second=10, progress_bar_color='blue', )

Use time-series inspector¶

In [ ]:

naip_ts = geemap.naip_timeseries(start_year=2009, end_year=2018)

naip_ts = geemap.naip_timeseries(start_year=2009, end_year=2018)

In [ ]:

layer_names = ['NAIP ' + str(year) for year in range(2009, 2019)]
print(layer_names)

layer_names = ['NAIP ' + str(year) for year in range(2009, 2019)] print(layer_names)

In [ ]:

naip_vis = {'bands': ['N', 'R', 'G']}

naip_vis = {'bands': ['N', 'R', 'G']}

In [ ]:

Map = geemap.Map()
Map.ts_inspector(
    left_ts=naip_ts,
    right_ts=naip_ts,
    left_names=layer_names,
    right_names=layer_names,
    left_vis=naip_vis,
    right_vis=naip_vis,
)
Map

Map = geemap.Map() Map.ts_inspector( left_ts=naip_ts, right_ts=naip_ts, left_names=layer_names, right_names=layer_names, left_vis=naip_vis, right_vis=naip_vis, ) Map

Export images¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

image = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')

landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(image, landsat_vis, "LE7_TOA_5YEAR/1999_2003", True, 0.7)

image = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003') landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4} Map.addLayer(image, landsat_vis, "LE7_TOA_5YEAR/1999_2003", True, 0.7)

In [ ]:

# Draw any shapes on the map using the Drawing tools before executing this code block
feature = Map.draw_last_feature

if feature is None:
    geom = ee.Geometry.Polygon(
        [
            [
                [-115.413031, 35.889467],
                [-115.413031, 36.543157],
                [-114.034328, 36.543157],
                [-114.034328, 35.889467],
                [-115.413031, 35.889467],
            ]
        ]
    )
    feature = ee.Feature(geom, {})

roi = feature.geometry()

# Draw any shapes on the map using the Drawing tools before executing this code block feature = Map.draw_last_feature if feature is None: geom = ee.Geometry.Polygon( [ [ [-115.413031, 35.889467], [-115.413031, 36.543157], [-114.034328, 36.543157], [-114.034328, 35.889467], [-115.413031, 35.889467], ] ] ) feature = ee.Feature(geom, {}) roi = feature.geometry()

In [ ]:

out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')
filename = os.path.join(out_dir, 'landsat.tif')

out_dir = os.path.join(os.path.expanduser('~'), 'Downloads') filename = os.path.join(out_dir, 'landsat.tif')

In [ ]:

geemap.ee_export_image(
    image, filename=filename, scale=90, region=roi, file_per_band=False
)

geemap.ee_export_image( image, filename=filename, scale=90, region=roi, file_per_band=False )

In [ ]:

geemap.ee_export_image(
    image, filename=filename, scale=90, region=roi, file_per_band=True
)

geemap.ee_export_image( image, filename=filename, scale=90, region=roi, file_per_band=True )

In [ ]:

loc = ee.Geometry.Point(-99.2222, 46.7816)
collection = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(loc)
    .filterDate('2008-01-01', '2020-01-01')
    .filter(ee.Filter.listContains("system:band_names", "N"))
)

loc = ee.Geometry.Point(-99.2222, 46.7816) collection = ( ee.ImageCollection('USDA/NAIP/DOQQ') .filterBounds(loc) .filterDate('2008-01-01', '2020-01-01') .filter(ee.Filter.listContains("system:band_names", "N")) )

In [ ]:

out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')

out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')

In [ ]:

geemap.ee_export_image_collection(collection, out_dir=out_dir)

geemap.ee_export_image_collection(collection, out_dir=out_dir)

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Last update: 2023-04-06
Created: 2020-05-24