CoCalc -- 00_geemap_key

GitHub Repository: giswqs/geemap
Path: blob/master/docs/notebooks/00_geemap_key_features.ipynb
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Kernel: Python 3

Uncomment the following line to install geemap if needed.

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# !pip install geemap

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import ee
import geemap

Create an interactive map

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

Add basemaps

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Map = geemap.Map()
Map

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Map.add_basemap("HYBRID")

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Map.add_basemap("OpenTopoMap")

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

Add WMS and XYZ tile layers

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Map = geemap.Map()
Map

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# 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")

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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

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Map = geemap.Map()
Map

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# 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

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Map = geemap.Map()
Map

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Map.search_locations

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Map.search_loc_geom

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

Search Earth Engine API documentation

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geemap.ee_search()

Use Inspector tool

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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

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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

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

Create a split-panel map

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

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

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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

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

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Map = geemap.Map()
Map

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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)

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

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Map.add_layer(maker_cluster)

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

Add customized legends

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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

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

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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

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# 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

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Map = geemap.Map()
Map

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# 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")

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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 grab GEE JavaScripts from GEE Documentation.

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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');

"""

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

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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

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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

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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

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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);

"""

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geemap.js_snippet_to_py(js_snippet)

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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

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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

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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

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Map = geemap.Map()
Map

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

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

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

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

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

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

Create Landsat timelapse

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Map = geemap.Map()
Map

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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

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

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

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

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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

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Map = geemap.Map()
Map

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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)

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# 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()

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

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

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

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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"))
)

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

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geemap.ee_export_image_collection(collection, out_dir=out_dir)

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