CoCalc -- GEE_Workshop

giswqs

GitHub Repository: giswqs/geemap
Path: blob/master/docs/workshops/GEE_Workshop_2024.ipynb
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Kernel: Python 3

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

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

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

From GeoDataFrame

In [ ]:

import geopandas as gpd

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

In [ ]:

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

To GeoJSON

In [ ]:

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

In [ ]:

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

To GeoDataFrame

In [ ]:

gdf = geemap.ee_to_gdf(fc)
gdf

In [ ]:

gdf.explore()

To DataFrame

In [ ]:

df = geemap.ee_to_df(fc)
df

To CSV

In [ ]:

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)

In [ ]:

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

In [ ]:

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

In [ ]:

geemap.shp_to_gdf("dem.shp")

In [ ]:

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

In [ ]:

geemap.csv_to_df("landsat.csv")

Extracting pixel values along a transect

In [ ]:

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

In [ ]:

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

In [ ]:

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

Check image projection.

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

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"))
)

In [ ]:

collection.aggregate_array("system:index")

To local drive

In [ ]:

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

To Google Drive

In [ ]:

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

Exporting feature collections

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

gdf = geemap.ee_to_gdf(fc)
gdf

In [ ]:

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)

In [ ]:

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)

In [ ]:

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

In [ ]:

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

In [ ]:

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

In [ ]:

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)

In [ ]:

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)

Introduction

Agenda

Prerequisites

Technical requirements

Install packages

Import libraries

Colab setup

Vector data conversion

From GeoJSON

From Shapefile

From GeoDataFrame

To GeoJSON

To Shapefile

To GeoDataFrame

To DataFrame

To CSV

Raster data extraction

Extracting values to points

Extracting pixel values along a transect

Exporting Earth Engine data

Exporting images

Exporting image collections

Exporting feature collections

Creating timelapse animations

Landsat timelapse

Sentinel-2

MODIS

GOES

Product

Resources

Company