Earth Engine Data Converters

Notebook: https://geemap.org/workshops/GEE_Workshop_2024
Earth Engine: https://earthengine.google.com
Geemap: https://geemap.org

Introduction¶

This notebook contains the materials for the workshop Earth Engine Data Converters at the 第七届地球空间大数据与云计算前沿会议与集中学习.

Agenda¶

This workshop covers the following topics:

Colab setup
Vector data conversion
Raster data extraction
Exporting Earth Engine data
Creating timelapse animations

Prerequisites¶

To use geemap and the Earth Engine Python API, you must register for an Earth Engine account and follow the instructions here to create a Cloud Project. Earth Engine is free for noncommercial and research use. To test whether you can use authenticate the Earth Engine Python API, please run this notebook on Google Colab.

Technical requirements¶

Install packages¶

conda create -n gee python=3.11
conda activate gee
conda install -c conda-forge mamba
mamba install -c conda-forge pygis

In [ ]:

# %pip install geemap pygis mapclassify

Import libraries¶

In [ ]:

import ee
import geemap

In [ ]:

geemap.ee_initialize()

Colab setup¶

Uncomment the following line to get the Earth Engine authorization token. Please treat your token with care and don't share it with anyone. Copy the token to the clipboard.

In [ ]:

# geemap.get_ee_token()

Open your Google Colab notebook and click on the secrets tab.
Create a new secret with the name EARTHENGINE_TOKEN.
Paste the content from the clipboard into the Value input box of the created secret.
Toggle the button on the left to allow notebook access to the secret.

Vector data conversion¶

From GeoJSON¶

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in_geojson = "https://github.com/gee-community/geemap/blob/master/examples/data/countries.geojson"
m = geemap.Map()
fc = geemap.geojson_to_ee(in_geojson)
m.add_layer(fc.style(**{"color": "ff0000", "fillColor": "00000000"}), {}, "Countries")
m

From Shapefile¶

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url = "https://github.com/gee-community/geemap/blob/master/examples/data/countries.zip"
geemap.download_file(url, overwrite=True)

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in_shp = "countries.shp"
fc = geemap.shp_to_ee(in_shp)

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m = geemap.Map()
m.add_layer(fc, {}, "Countries")
m

From GeoDataFrame¶

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import geopandas as gpd

gdf = gpd.read_file(in_shp)
fc = geemap.gdf_to_ee(gdf)

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m = geemap.Map()
m.add_layer(fc, {}, "Countries")
m

To GeoJSON¶

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m = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.eq("NAME", "Alaska"))
m.add_layer(fc, {}, "Alaska")
m.center_object(fc, 4)
m

In [ ]:

geemap.ee_to_geojson(fc, filename="Alaska.geojson")

To Shapefile¶

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geemap.ee_to_shp(fc, filename="Alaska.shp")

To GeoDataFrame¶

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gdf = geemap.ee_to_gdf(fc)
gdf

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gdf.explore()

To DataFrame¶

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df = geemap.ee_to_df(fc)
df

To CSV¶

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geemap.ee_to_csv(fc, filename="Alaska.csv")

Raster data extraction¶

Extracting values to points¶

In [ ]:

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

dem = ee.Image("USGS/SRTMGL1_003")
landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003")

vis_params = {
    "min": 0,
    "max": 4000,
    "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}

m.add_layer(
    landsat7,
    {"bands": ["B4", "B3", "B2"], "min": 20, "max": 200, "gamma": 2},
    "Landsat 7",
)
m.add_layer(dem, vis_params, "SRTM DEM", True, 1)
m

In [ ]:

in_shp = "us_cities.shp"
url = "https://github.com/giswqs/data/raw/main/us/us_cities.zip"
geemap.download_file(url)

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in_fc = geemap.shp_to_ee(in_shp)
m.add_layer(in_fc, {}, "Cities")

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geemap.extract_values_to_points(in_fc, dem, out_fc="dem.shp")

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geemap.shp_to_gdf("dem.shp")

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geemap.extract_values_to_points(in_fc, landsat7, "landsat.csv")

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geemap.csv_to_df("landsat.csv")

Extracting pixel values along a transect¶

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m = geemap.Map(center=[40, -100], zoom=4)
m.add_basemap("TERRAIN")

image = ee.Image("USGS/SRTMGL1_003")
vis_params = {
    "min": 0,
    "max": 4000,
    "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
m.add_layer(image, vis_params, "SRTM DEM", True, 0.5)
m

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line = m.user_roi
if line is None:
    line = ee.Geometry.LineString(
        [[-120.2232, 36.3148], [-118.9269, 36.7121], [-117.2022, 36.7562]]
    )
    m.add_layer(line, {}, "ROI")
m.centerObject(line)

In [ ]:

reducer = "mean"
transect = geemap.extract_transect(
    image, line, n_segments=100, reducer=reducer, to_pandas=True
)
transect

In [ ]:

geemap.line_chart(
    data=transect,
    x="distance",
    y="mean",
    markers=True,
    x_label="Distance (m)",
    y_label="Elevation (m)",
    height=400,
)

In [ ]:

transect.to_csv("transect.csv")

Exporting Earth Engine data¶

Exporting images¶

Add a Landsat image to the map.

In [ ]:

m = geemap.Map()

image = ee.Image("LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318").select(
    ["B5", "B4", "B3"]
)

vis_params = {"min": 0, "max": 0.5, "gamma": [0.95, 1.1, 1]}

m.center_object(image)
m.add_layer(image, vis_params, "Landsat")
m

Add a rectangle to the map.

In [ ]:

region = ee.Geometry.BBox(-122.5955, 37.5339, -122.0982, 37.8252)
fc = ee.FeatureCollection(region)
style = {"color": "ffff00ff", "fillColor": "00000000"}
m.add_layer(fc.style(**style), {}, "ROI")

To local drive

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geemap.ee_export_image(image, filename="landsat.tif", scale=30, region=region)

Check image projection.

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projection = image.select(0).projection().getInfo()
projection

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crs = projection["crs"]
crs_transform = projection["transform"]

Specify region, crs, and crs_transform.

In [ ]:

geemap.ee_export_image(
    image,
    filename="landsat_crs.tif",
    crs=crs,
    crs_transform=crs_transform,
    region=region,
)

To Google Drive

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geemap.ee_export_image_to_drive(
    image, description="landsat", folder="export", region=region, scale=30
)

In [ ]:

geemap.download_ee_image(image, "landsat.tif", scale=90)

Exporting image collections¶

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point = ee.Geometry.Point(-99.2222, 46.7816)
collection = (
    ee.ImageCollection("USDA/NAIP/DOQQ")
    .filterBounds(point)
    .filterDate("2008-01-01", "2018-01-01")
    .filter(ee.Filter.listContains("system:band_names", "N"))
)

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collection.aggregate_array("system:index")

To local drive

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geemap.ee_export_image_collection(collection, out_dir=".", scale=10)

To Google Drive

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geemap.ee_export_image_collection_to_drive(collection, folder="export", scale=10)

Exporting feature collections¶

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m = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.eq("NAME", "Alaska"))
m.add_layer(fc, {}, "Alaska")
m.center_object(fc, 4)
m

To local drive

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geemap.ee_to_shp(fc, filename="Alaska.shp")

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geemap.ee_export_vector(fc, filename="Alaska.shp")

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geemap.ee_to_geojson(fc, filename="Alaska.geojson")

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geemap.ee_to_csv(fc, filename="Alaska.csv")

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gdf = geemap.ee_to_gdf(fc)
gdf

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df = geemap.ee_to_df(fc)
df

To Google Drive

In [ ]:

geemap.ee_export_vector_to_drive(
    fc, description="Alaska", fileFormat="SHP", folder="export"
)

Creating timelapse animations¶

Landsat timelapse¶

In [ ]:

m = geemap.Map(center=[64.838721, -147.763366], zoom=11)
m

Pan and zoom the map to an area of interest. Use the drawing tools to draw a rectangle on the map. If no rectangle is drawn, the default rectangle shown below will be used.

In [ ]:

roi = m.user_roi
if roi is None:
    roi = ee.Geometry.BBox(-147.9701, 64.7733, -147.5849, 64.8717)
    m.add_layer(roi)
    m.center_object(roi)

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timelapse = geemap.landsat_timelapse(
    roi,
    out_gif="Fairbanks.gif",
    start_year=2000,
    end_year=2023,
    start_date="06-01",
    end_date="09-01",
    bands=["SWIR1", "NIR", "Red"],
    frames_per_second=5,
    title="Landsat Timelapse",
    progress_bar_color="blue",
    mp4=True,
)
geemap.show_image(timelapse)

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m = geemap.Map(center=[64.838721, -147.763366], zoom=11)
m.add_gui("timelapse")
m

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m = geemap.Map()
roi = ee.Geometry.BBox(-115.5541, 35.8044, -113.9035, 36.5581)
m.add_layer(roi)
m.center_object(roi)
m

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timelapse = geemap.landsat_timelapse(
    roi,
    out_gif="las_vegas.gif",
    start_year=1984,
    end_year=2023,
    bands=["NIR", "Red", "Green"],
    frames_per_second=5,
    title="Las Vegas, NV",
    font_color="blue",
)
geemap.show_image(timelapse)

In [ ]:

m = geemap.Map()
roi = ee.Geometry.BBox(113.8252, 22.1988, 114.0851, 22.3497)
m.add_layer(roi)
m.center_object(roi)
m

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timelapse = geemap.landsat_timelapse(
    roi,
    out_gif="hong_kong.gif",
    start_year=1990,
    end_year=2022,
    start_date="01-01",
    end_date="12-31",
    bands=["SWIR1", "NIR", "Red"],
    frames_per_second=3,
    title="Hong Kong",
)
geemap.show_image(timelapse)

Sentinel-2¶

In [ ]:

m = geemap.Map(center=[64.838721, -147.763366], zoom=11)
m

Pan and zoom the map to an area of interest. Use the drawing tools to draw a rectangle on the map. If no rectangle is drawn, the default rectangle shown below will be used.

In [ ]:

roi = m.user_roi
if roi is None:
    roi = ee.Geometry.BBox(-147.9701, 64.7733, -147.5849, 64.8717)
    m.add_layer(roi)
    m.center_object(roi)

In [ ]:

timelapse = geemap.sentinel2_timelapse(
    roi,
    out_gif="sentinel2.gif",
    start_year=2017,
    end_year=2023,
    start_date="06-01",
    end_date="09-01",
    frequency="year",
    bands=["SWIR1", "NIR", "Red"],
    frames_per_second=3,
    title="Sentinel-2 Timelapse",
)
geemap.show_image(timelapse)

MODIS¶

MODIS vegetation indices

In [ ]:

m = geemap.Map()
m

In [ ]:

roi = m.user_roi
if roi is None:
    roi = ee.Geometry.BBox(-18.6983, -36.1630, 52.2293, 38.1446)
    m.add_layer(roi)
    m.center_object(roi)

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timelapse = geemap.modis_ndvi_timelapse(
    roi,
    out_gif="ndvi.gif",
    data="Terra",
    band="NDVI",
    start_date="2000-01-01",
    end_date="2022-12-31",
    frames_per_second=3,
    title="MODIS NDVI Timelapse",
    overlay_data="countries",
)
geemap.show_image(timelapse)

MODIS temperature

In [ ]:

m = geemap.Map()
m

In [ ]:

roi = m.user_roi
if roi is None:
    roi = ee.Geometry.BBox(-171.21, -57.13, 177.53, 79.99)
    m.add_layer(roi)
    m.center_object(roi)

In [ ]:

timelapse = geemap.modis_ocean_color_timelapse(
    satellite="Aqua",
    start_date="2018-01-01",
    end_date="2020-12-31",
    roi=roi,
    frequency="month",
    out_gif="temperature.gif",
    overlay_data="continents",
    overlay_color="yellow",
    overlay_opacity=0.5,
)
geemap.show_image(timelapse)

GOES¶

In [ ]:

roi = ee.Geometry.BBox(167.1898, -28.5757, 202.6258, -12.4411)
start_date = "2022-01-15T03:00:00"
end_date = "2022-01-15T07:00:00"
data = "GOES-17"
scan = "full_disk"

In [ ]:

timelapse = geemap.goes_timelapse(
    roi, "goes.gif", start_date, end_date, data, scan, framesPerSecond=5
)
geemap.show_image(timelapse)

In [ ]:

roi = ee.Geometry.BBox(-159.5954, 24.5178, -114.2438, 60.4088)
start_date = "2021-10-24T14:00:00"
end_date = "2021-10-25T01:00:00"
data = "GOES-17"
scan = "full_disk"

In [ ]:

timelapse = geemap.goes_timelapse(
    roi, "hurricane.gif", start_date, end_date, data, scan, framesPerSecond=5
)
geemap.show_image(timelapse)

In [ ]:

roi = ee.Geometry.BBox(-121.0034, 36.8488, -117.9052, 39.0490)
start_date = "2020-09-05T15:00:00"
end_date = "2020-09-06T02:00:00"
data = "GOES-17"
scan = "full_disk"

In [ ]:

timelapse = geemap.goes_fire_timelapse(
    roi, "fire.gif", start_date, end_date, data, scan, framesPerSecond=5
)
geemap.show_image(timelapse)