Geemap

17. Geemap#

17.1. Overview#

This lecture introduces cloud-based geospatial analysis using the Google Earth Engine (GEE) API in combination with the geemap Python package. We will cover core concepts of Earth Engine, visualization techniques, and practical workflows to perform analyses within a Jupyter environment.

17.2. Learning Objectives#

By the end of this lecture, you will be able to:

Explain the fundamentals of the Google Earth Engine platform, including its data types and cloud-based capabilities
Set up and configure the geemap library for conducting geospatial analyses in the cloud
Perform essential geospatial operations, including filtering, visualizing, and exporting data from Earth Engine
Access and manipulate both raster and vector data using Earth Engine
Create timelapse animations from satellite imagery
Create interactive charts from Earth Engine data

17.2.1. Prerequisites#

Before beginning with geemap and Google Earth Engine, ensure the following steps are completed:

Register for a Google Earth Engine account: Go to https://code.earthengine.google.com/register to sign up for a GEE account. After registering, you will need to set up a Google Cloud Project and enable the Earth Engine API by following the instructions here. GEE is free for noncommercial and research use.
Verify API Authentication: To confirm that your Earth Engine account is set up correctly, try running this test notebook in Google Colab.

17.3. Introduction to Google Earth Engine#

17.3.1. Google Earth Engine Overview#

Google Earth Engine is a cloud-computing platform that enables scientific analysis and visualization of large-scale geospatial datasets. It provides a rich data catalog and processing capabilities, allowing users to analyze satellite imagery and geospatial data at planetary scale.

Earth Engine is free for noncommercial and research use. Nonprofit organizations, research institutions, educators, Indigenous governments, and government researchers can continue using Earth Engine for free, as they have for more than a decade. However, commercial users may require a paid license.

17.3.2. Installing Geemap#

The geemap package simplifies the use of Google Earth Engine in Python, offering an intuitive API for visualization and analysis in Jupyter notebooks. In Google Colab, geemap is pre-installed, but you may need to install additional dependencies for certain features. To install the latest version with optional dependencies, use the following command:

# %pip install -U "geemap[workshop]"

17.3.3. Import Libraries#

To start, import the necessary libraries for working with Google Earth Engine (GEE) and geemap.

import ee
import geemap

17.3.4. Authenticate and Initialize Earth Engine#

To authenticate and initialize your Earth Engine environment, you’ll need to create a Google Cloud Project and enable the Earth Engine API for the project if you have not done so already. You can find detailed setup instructions here.

geemap.ee_initialize()

Running the code above will prompt you to authenticate your Earth Engine account. Follow the instructions to complete the authentication process on Colab. To avoid this step in the future, you can set up a Colab secret with the name EARTHENGINE_TOKEN and your Earth Engine token as the value, which can be obtained by running geemap.get_ee_token().

# geemap.get_ee_token()

17.3.5. Creating Interactive Maps#

Let’s create an interactive map using the ipyleaflet plotting backend. The geemap.Map class inherits from the ipyleaflet.Map class, so the syntax is similar to creating an interactive map with ipyleaflet.Map.

m = geemap.Map()

To display the map in a Jupyter notebook, simply enter the map object:

To customize the map’s display, you can set various keyword arguments, such as center (latitude and longitude), zoom, width, and height. By default, the width is 100%, filling the entire width of the Jupyter notebook cell. The height parameter can be a number (in pixels) or a string with a pixel format, e.g., 600px.

17.3.5.1. Example Maps#

Map of the Contiguous United States

To center the map on the contiguous United States, use:

m = geemap.Map(center=[40, -100], zoom=4, height="600px")
m

Map of the state of Tennessee

m = geemap.Map(center=[35.746512, -86.209818], zoom=8)
m

Map of the city of Knoxville, TN

m = geemap.Map(center=[35.959111, -83.909463], zoom=13)
m

To hide specific map controls, set the corresponding control argument to False, such as draw_ctrl=False to hide the drawing control.

m = geemap.Map(data_ctrl=False, toolbar_ctrl=False, draw_ctrl=False)
m

17.3.6. Adding Basemaps#

There are several ways to add basemaps to a map in geemap. You can specify a basemap in the basemap keyword argument when creating the map, or you can add additional basemap layers using the add_basemap method. Geemap provides access to hundreds of built-in basemaps, making it easy to add layers to a map with a single line of code.

To create a map with a specific basemap, use the basemap argument as shown below. For example, Esri.WorldImagery provides an Esri world imagery basemap.

m = geemap.Map(basemap="Esri.WorldImagery")
m

17.3.6.1. Adding Multiple Basemaps#

You can add multiple basemaps to a map by calling add_basemap multiple times. For example, the following code adds the Esri.WorldTopoMap and OpenTopoMap basemaps to the existing map:

m.add_basemap("Esri.WorldTopoMap")

m.add_basemap("OpenTopoMap")

17.3.6.2. Listing Available Basemaps#

To view the first 10 available basemaps, use:

basemaps = list(geemap.basemaps.keys())
len(geemap.basemaps)

basemaps[:10]

17.3.7. Google Basemaps#

Due to licensing restrictions, Google basemaps are not included in geemap by default. However, users can add Google basemaps manually at their own discretion using the following URLs:

    ROADMAP:  https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}
    SATELLITE: https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}
    TERRAIN: https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}
    HYBRID: https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}

For example, to add Google Satellite as a tile layer:

m = geemap.Map()
url = "https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}"
m.add_tile_layer(url, name="Google Satellite", attribution="Google")
m

You can also add text to the map using the add_text method. The text will be displayed at the specified location on the map.

m.add_text(text="Hello from Earth Engine", position="bottomright")

17.4. Introduction to Interactive Maps and Tools#

17.4.1. Basemap Selector#

The basemap selector allows you to choose from various basemaps via a dropdown menu. Adding it to your map provides easy access to different map backgrounds.

m = geemap.Map()
m.add("basemap_selector")
m

17.4.2. Layer Manager#

The layer manager provides control over layer visibility and transparency. It enables toggling layers on and off and adjusting transparency with a slider, making it easy to customize map visuals.

m = geemap.Map(center=(40, -100), zoom=4)
dem = ee.Image("USGS/SRTMGL1_003")
states = ee.FeatureCollection("TIGER/2018/States")
vis_params = {
    "min": 0,
    "max": 4000,
    "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
m.add_layer(dem, vis_params, "SRTM DEM")
m.add_layer(states, {}, "US States")
m.add("layer_manager")
m

17.4.3. Inspector Tool#

The inspector tool allows you to click on the map to query Earth Engine data at specific locations. This is helpful for examining the properties of datasets directly on the map.

m = geemap.Map(center=(40, -100), zoom=4)
dem = ee.Image("USGS/SRTMGL1_003")
landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003")
states = ee.FeatureCollection("TIGER/2018/States")
vis_params = {
    "min": 0,
    "max": 4000,
    "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
m.add_layer(dem, vis_params, "SRTM DEM")
m.add_layer(
    landsat7,
    {"bands": ["B4", "B3", "B2"], "min": 20, "max": 200, "gamma": 2.0},
    "Landsat 7",
)
m.add_layer(states, {}, "US States")
m.add("inspector")
m

17.4.4. Layer Editor#

With the layer editor, you can adjust visualization parameters of Earth Engine data for better clarity and focus. It supports single-band images, multi-band images, and feature collections.

17.4.4.1. Single-Band image#

m = geemap.Map(center=(40, -100), zoom=4)
dem = ee.Image("USGS/SRTMGL1_003")
vis_params = {
    "min": 0,
    "max": 4000,
    "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
m.add_layer(dem, vis_params, "SRTM DEM")
m.add("layer_editor", layer_dict=m.ee_layers["SRTM DEM"])
m

17.4.4.2. Multi-Band image#

m = geemap.Map(center=(40, -100), zoom=4)
landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003")
m.add_layer(
    landsat7,
    {"bands": ["B4", "B3", "B2"], "min": 20, "max": 200, "gamma": 2.0},
    "Landsat 7",
)
m.add("layer_editor", layer_dict=m.ee_layers["Landsat 7"])
m

17.4.4.3. Feature Collection#

m = geemap.Map(center=(40, -100), zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
m.add_layer(states, {}, "US States")
m.add("layer_editor", layer_dict=m.ee_layers["US States"])
m

17.4.5. Draw Control#

The draw control feature allows you to draw shapes directly on the map, converting them automatically into Earth Engine objects. Access the drawn features as follows:

To return the last drawn feature as an ee.Geometry(), use m.user_roi
To return all drawn features as an ee.FeatureCollection(), use m.user_rois

m = geemap.Map(center=(40, -100), zoom=4)
dem = ee.Image("USGS/SRTMGL1_003")
vis_params = {
    "min": 0,
    "max": 4000,
    "palette": "terrain",
}
m.add_layer(dem, vis_params, "SRTM DEM")
m.add("layer_manager")
m

if m.user_roi is not None:
    image = dem.clip(m.user_roi)
    m.layers[1].visible = False
    m.add_layer(image, vis_params, "Clipped DEM")

17.5. The Earth Engine Data Catalog#

The Earth Engine Data Catalog hosts an extensive collection of geospatial datasets. Currently, he catalog includes over 1,000 datasets with a combined size exceeding 100 petabytes. Notable datasets include Landsat, Sentinel, MODIS, and NAIP. For a comprehensive list of datasets in CSV or JSON format, refer to the Earth Engine Datasets List.

17.5.1. Searching Datasets on the Earth Engine Website#

To browse datasets directly on the Earth Engine website:

View the full catalog: https://developers.google.com/earth-engine/datasets/catalog
Search by tags: https://developers.google.com/earth-engine/datasets/tags

17.5.2. Searching Datasets Within Geemap#

The Earth Engine Data Catalog can also be searched directly from geemap. Use keywords to filter datasets by name, tag, or description. For instance, searching for “elevation” will display only datasets containing “elevation” in their metadata, returning 52 datasets. You can scroll through the results to locate the NASA SRTM Digital Elevation 30m dataset.

m = geemap.Map()
m

Each dataset page contains detailed information such as availability, provider, Earth Engine snippet, tags, description, and example code. The Image, ImageCollection, or FeatureCollection ID is essential for accessing the dataset in Earth Engine’s JavaScript or Python APIs.

17.5.3. Using the Datasets Module in Geemap#

Geemap offers a built-in datasets module to access specific datasets programmatically. For example, to access the USGS GAP Alaska dataset:

from geemap.datasets import DATA

m = geemap.Map()
dataset = ee.Image(DATA.USGS_GAP_AK_2001)
m.add_layer(dataset, {}, "GAP Alaska")
m.centerObject(dataset, zoom=4)
m

To retrieve metadata for a specific dataset, use the get_metadata function:

from geemap.datasets import get_metadata

get_metadata(DATA.USGS_GAP_AK_2001)

17.6. Earth Engine Data Types#

Earth Engine objects are server-side entities, meaning they are stored and processed on Earth Engine’s servers rather than locally. This setup is comparable to streaming services (e.g., YouTube or Netflix) where the data remains on the provider’s servers, allowing us to access and process geospatial data in real time without downloading it to our local devices.

Image: The core raster data type in Earth Engine, representing single images or scenes.
ImageCollection: A collection or sequence of images, often used for time series analysis.
Geometry: The fundamental vector data type, including shapes like points, lines, and polygons.
Feature: A Geometry with associated attributes, used to add descriptive data to vector shapes.
FeatureCollection: A collection of Features, similar to a shapefile with attribute data.

17.7. Earth Engine Raster Data#

17.7.1. Image#

In Earth Engine, raster data is represented as Image objects. Each Image is composed of bands, with each band having its own name, data type, scale, mask, and projection. Metadata for each image is stored as a set of properties.

17.7.1.1. Loading Earth Engine Images#

To load images, use the Earth Engine asset ID within the ee.Image constructor. Asset IDs can be found in the Earth Engine Data Catalog. For example, to load the NASA SRTM Digital Elevation dataset:

image = ee.Image("USGS/SRTMGL1_003")
image

17.7.1.2. Visualizing Earth Engine Images#

To visualize an image, you can specify visualization parameters such as minimum and maximum values and color palettes.

m = geemap.Map(center=[21.79, 70.87], zoom=3)
image = ee.Image("USGS/SRTMGL1_003")
vis_params = {
    "min": 0,
    "max": 6000,
    "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],  # 'terrain'
}
m.add_layer(image, vis_params, "SRTM")
m

17.7.2. ImageCollection#

An ImageCollection represents a sequence of images, often used for temporal data like satellite image time series. ImageCollections are created by passing an Earth Engine asset ID into the ImageCollection constructor. Asset IDs are available in the Earth Engine Data Catalog.

17.7.2.1. Loading Image Collections#

To load an ImageCollection, such as the Sentinel-2 surface reflectance collection:

collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")

17.7.2.2. Filtering Image Collections#

You can filter ImageCollections by location and time. For example, to filter images covering a specific location with low cloud cover:

geometry = ee.Geometry.Point([-83.909463, 35.959111])
images = collection.filterBounds(geometry)
images.size()

images.first()

images = (
    collection.filterBounds(geometry)
    .filterDate("2024-07-01", "2024-10-01")
    .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 5))
)
images.size()

To view the filtered collection on a map:

m = geemap.Map()
image = images.first()

vis = {
    "min": 0.0,
    "max": 3000,
    "bands": ["B4", "B3", "B2"],
}

m.add_layer(image, vis, "Sentinel-2")
m.centerObject(image, 8)
m

17.7.2.3. Visualizing Image Collections#

To visualize an ImageCollection as a single composite image, you need to aggregate the collection. For example, using the collection.median() method creates an image representing the median value across the collection.

m = geemap.Map()
image = images.median()

vis = {
    "min": 0.0,
    "max": 3000,
    "bands": ["B8", "B4", "B3"],
}

m.add_layer(image, vis, "Sentinel-2")
m.centerObject(geometry, 8)
m

17.8. Earth Engine Vector Data#

A FeatureCollection is a collection of Features. It functions similarly to a GeoJSON FeatureCollection, where features include associated properties or attributes. For example, a shapefile’s data can be represented as a FeatureCollection.

17.8.1. Loading Feature Collections#

The Earth Engine Data Catalog includes various vector datasets, such as U.S. Census data and country boundaries, as feature collections. Feature Collection IDs are accessible by searching the data catalog. For example, to load the TIGER roads data by the U.S. Census Bureau:

m = geemap.Map()
fc = ee.FeatureCollection("TIGER/2016/Roads")
m.set_center(-83.909463, 35.959111, 12)
m.add_layer(fc, {}, "Census roads")
m

17.8.2. Filtering Feature Collections#

The filter method allows you to filter a FeatureCollection based on certain attribute values. For instance, we can filter for specific states using the eq filter to select “Tennessee”:

m = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.eq("NAME", "Tennessee"))
m.add_layer(fc, {}, "Tennessee")
m.center_object(fc, 7)
m

To retrieve properties of the first feature in the collection, use:

feat = fc.first()
feat.toDictionary()

You can also convert a FeatureCollection to a Pandas DataFrame for easier analysis with:

geemap.ee_to_df(fc)

m = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.inList("NAME", ["California", "Oregon", "Washington"]))
m.add_layer(fc, {}, "West Coast")
m.center_object(fc, 5)
m

region = m.user_roi
if region is None:
    region = ee.Geometry.BBox(-88.40, 29.88, -77.90, 35.39)

fc = ee.FeatureCollection("TIGER/2018/States").filterBounds(region)
m.add_layer(fc, {}, "Southeastern U.S.")
m.center_object(fc, 6)

A FeatureCollection can be used to clip an image. The clipToCollection method clips an image to the geometry of a feature collection. For example, to clip a Landsat image to the boundary of France:

m = geemap.Map(center=(40, -100), zoom=4)
landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003")
countries = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level0")
fc = countries.filter(ee.Filter.eq("ADM0_NAME", "Germany"))
image = landsat7.clipToCollection(fc)
m.add_layer(
    image,
    {"bands": ["B4", "B3", "B2"], "min": 20, "max": 200, "gamma": 2.0},
    "Landsat 7",
)
m.center_object(fc, 6)
m

17.8.3. Visualizing Feature Collections#

Once loaded, feature collections can be visualized on an interactive map. For example, visualizing U.S. state boundaries from the census data:

m = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
m.add_layer(states, {}, "US States")
m

Feature collections can also be styled with additional parameters. To apply a custom style, specify options like color and line width:

m = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
style = {"color": "0000ffff", "width": 2, "lineType": "solid", "fillColor": "FF000080"}
m.add_layer(states.style(**style), {}, "US States")
m

Using add_styled_vector, you can apply a color palette to style different features by attribute:

m = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
vis_params = {
    "color": "000000",
    "colorOpacity": 1,
    "pointSize": 3,
    "pointShape": "circle",
    "width": 2,
    "lineType": "solid",
    "fillColorOpacity": 0.66,
}
palette = ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"]
m.add_styled_vector(
    states, column="NAME", palette=palette, layer_name="Styled vector", **vis_params
)
m

17.9. More Tools for Visualizing Earth Engine Data#

17.9.1. Using the Plotting Tool#

The plotting tool in geemap enables visualization of Earth Engine data layers. In this example, Landsat 7 and Hyperion data are added with specific visualization parameters, allowing for comparison of these datasets. The plot GUI is then added to facilitate detailed exploration of the data.

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

landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003").select(
    ["B1", "B2", "B3", "B4", "B5", "B7"]
)

landsat_vis = {"bands": ["B4", "B3", "B2"], "gamma": 1.4}
m.add_layer(landsat7, landsat_vis, "Landsat")

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,
}
m.add_layer(hyperion, hyperion_vis, "Hyperion")
m.add_plot_gui()
m

Set the plotting options for Landsat to add marker clusters and overlays, enhancing the interactivity of plotted data on the map.

m.set_plot_options(add_marker_cluster=True, overlay=True)

Adjust the plotting options for Hyperion data by setting a bar plot type with marker clusters, suitable for visualizing Hyperion’s data values in bar format.

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

17.9.2. Legends#

17.9.2.1. Built-in Legends#

Geemap provides built-in legends that can be easily added to maps for better interpretability of data classes. Here, an NLCD legend is added to both a WMS layer and an Earth Engine land cover layer, giving quick visual reference to the data’s classification scheme.

from geemap.legends import builtin_legends

Print out all available built-in legends, which can be used with various data layers to enhance map readability.

for legend in builtin_legends:
    print(legend)

Add an NLCD WMS layer along with its corresponding legend, which appears as an overlay, providing users with an informative legend display.

m = geemap.Map(center=[40, -100], zoom=4)
m.add_basemap("Esri.WorldImagery")
m.add_basemap("NLCD 2021 CONUS Land Cover")
m.add_legend(builtin_legend="NLCD", max_width="100px", height="455px")
m

Add an Earth Engine layer for NLCD land cover and display its legend, specifying title, legend type, and dimensions for user convenience.

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

nlcd = ee.Image("USGS/NLCD_RELEASES/2021_REL/NLCD/2021")
landcover = nlcd.select("landcover")

m.add_layer(landcover, {}, "NLCD Land Cover 2021")
m.add_legend(
    title="NLCD Land Cover Classification", builtin_legend="NLCD", height="455px"
)
m

17.9.2.2. Custom Legends#

Create a custom legend by defining unique labels and colors for each class, allowing for flexible map customization. This example uses a color palette for labeling several map categories.

m = geemap.Map(add_google_map=False)

keys = ["One", "Two", "Three", "Four", "etc"]

# colors can be defined using either hex code or RGB (0-255, 0-255, 0-255)
colors = ["#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3"]
# legend_colors = [(255, 0, 0), (127, 255, 0), (127, 18, 25), (36, 70, 180), (96, 68 123)]

m.add_legend(keys=keys, colors=colors, position="bottomright")
m

Define a custom legend using a dictionary that links specific colors to labels. This approach provides flexibility to represent specific categories in the data, such as various land cover types.

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

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",
}

nlcd = ee.Image("USGS/NLCD_RELEASES/2021_REL/NLCD/2021")
landcover = nlcd.select("landcover")

m.add_layer(landcover, {}, "NLCD Land Cover 2021")
m.add_legend(title="NLCD Land Cover Classification", legend_dict=legend_dict)
m

17.9.3. Color Bars#

Add a horizontal color bar representing elevation data. This example demonstrates how to add an SRTM elevation layer and overlay a color bar for quick visual reference of elevation values.

m = geemap.Map()

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

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

Add a vertical color bar for elevation data, adjusting its orientation and dimensions to fit the map layout.

m.add_colorbar(
    vis_params,
    label="Elevation (m)",
    layer_name="SRTM DEM",
    orientation="vertical",
    max_width="100px",
)

Make the color bar’s background transparent, which improves visual integration with the map when adding color bars for data layers.

m.add_colorbar(
    vis_params,
    label="Elevation (m)",
    layer_name="SRTM DEM",
    orientation="vertical",
    max_width="100px",
    transparent_bg=True,
)

17.9.4. Split-panel Maps#

Create a split-panel map with basemaps, allowing users to compare two different basemaps side by side.

m = geemap.Map()
m.split_map(left_layer="Esri.WorldTopoMap", right_layer="OpenTopoMap")
m

Use Earth Engine layers in a split-panel map to compare NLCD data from 2001 and 2021. This layout is effective for examining changes between datasets across time.

m = geemap.Map(center=(40, -100), zoom=4, height=600)

nlcd_2001 = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2001").select("landcover")
nlcd_2021 = ee.Image("USGS/NLCD_RELEASES/2021_REL/NLCD/2021").select("landcover")

left_layer = geemap.ee_tile_layer(nlcd_2001, {}, "NLCD 2001")
right_layer = geemap.ee_tile_layer(nlcd_2021, {}, "NLCD 2021")

m.split_map(left_layer, right_layer)
m

17.9.5. Linked Maps#

Set up a 2x2 grid of linked maps showing Sentinel-2 imagery in different band combinations, ideal for comparing multiple visual perspectives. Note that this feature may not work in Colab.

image = (
    ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
    .filterDate("2024-07-01", "2024-10-01")
    .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 5))
    .map(lambda img: img.divide(10000))
    .median()
)

vis_params = [
    {"bands": ["B4", "B3", "B2"], "min": 0, "max": 0.3, "gamma": 1.3},
    {"bands": ["B8", "B11", "B4"], "min": 0, "max": 0.3, "gamma": 1.3},
    {"bands": ["B8", "B4", "B3"], "min": 0, "max": 0.3, "gamma": 1.3},
    {"bands": ["B12", "B12", "B4"], "min": 0, "max": 0.3, "gamma": 1.3},
]

labels = [
    "Natural Color (B4/B3/B2)",
    "Land/Water (B8/B11/B4)",
    "Color Infrared (B8/B4/B3)",
    "Vegetation (B12/B11/B4)",
]

geemap.linked_maps(
    rows=2,
    cols=2,
    height="300px",
    center=[35.959111, -83.909463],
    zoom=12,
    ee_objects=[image],
    vis_params=vis_params,
    labels=labels,
    label_position="topright",
)

17.9.6. Timeseries Inspector#

Retrieve the available years in the NLCD collection for setting up a timeseries. This step helps confirm available time points for inspecting changes over time.

m = 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

Use a timeseries inspector to compare changes in NLCD data across different years. This tool is ideal for temporal data visualization, showing how land cover changes in an interactive format.

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

17.9.7. Time Slider#

Create a time slider to explore MODIS vegetation data, setting the slider to visualize NDVI values over a specific period. This feature allows users to track vegetation changes month-by-month.

m = geemap.Map()

collection = (
    ee.ImageCollection("MODIS/MCD43A4_006_NDVI")
    .filter(ee.Filter.date("2018-06-01", "2018-07-01"))
    .select("NDVI")
)
vis_params = {
    "min": 0.0,
    "max": 1.0,
    "palette": "ndvi",
}

m.add_time_slider(collection, vis_params, time_interval=2)
m

Add a time slider for visualizing NOAA weather data over a 24-hour period, using color-coding to show temperature variations. The slider enables temporal exploration of hourly data.

m = geemap.Map()

collection = (
    ee.ImageCollection("NOAA/GFS0P25")
    .filterDate("2018-12-22", "2018-12-23")
    .limit(24)
    .select("temperature_2m_above_ground")
)

vis_params = {
    "min": -40.0,
    "max": 35.0,
    "palette": ["blue", "purple", "cyan", "green", "yellow", "red"],
}

labels = [str(n).zfill(2) + ":00" for n in range(0, 24)]
m.add_time_slider(collection, vis_params, labels=labels, time_interval=1, opacity=0.8)
m

Add a time slider to visualize Sentinel-2 imagery with specific bands and cloud cover filtering. This feature enables temporal analysis of imagery data, allowing users to explore seasonal and other changes over time.

m = geemap.Map()

collection = (
    ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
    .filterBounds(ee.Geometry.Point([-83.909463, 35.959111]))
    .filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 10)
    .filter(ee.Filter.calendarRange(6, 8, "month"))
)

vis_params = {"min": 0, "max": 4000, "bands": ["B8", "B4", "B3"]}

m.add_time_slider(collection, vis_params)
m.set_center(-83.909463, 35.959111, 12)
m

17.10. Processing of Vector Data#

17.10.1. From GeoJSON#

Load GeoJSON data into an Earth Engine FeatureCollection. This example retrieves countries data from a remote URL, converting it to a FeatureCollection and visualizing it with a specified style.

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

17.10.2. From Shapefile#

Download and load a shapefile of country boundaries. The shapefile is converted to a FeatureCollection for visualization on the map.

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

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

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

17.10.3. From GeoDataFrame#

Read a shapefile into a GeoDataFrame using geopandas, then convert the GeoDataFrame to an Earth Engine FeatureCollection for mapping.

import geopandas as gpd

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

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

17.10.4. To GeoJSON#

Filter U.S. state data to select Tennessee and save it as a GeoJSON file, which can be shared or used in other GIS tools.

m = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.eq("NAME", "Tennessee"))
m.add_layer(fc, {}, "Tennessee")
m.center_object(fc, 7)
m

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

17.10.5. To Shapefile#

Export the filtered Tennessee FeatureCollection to a shapefile format for offline use or compatibility with desktop GIS software.

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

17.10.6. To GeoDataFrame#

Convert an Earth Engine FeatureCollection to a GeoDataFrame for further analysis in Python or use in interactive maps.

gdf = geemap.ee_to_gdf(fc)
gdf

gdf.explore()

17.10.7. To DataFrame#

Transform an Earth Engine FeatureCollection into a pandas DataFrame, which can then be used for data analysis in Python.

df = geemap.ee_to_df(fc)
df

17.10.8. To CSV#

Export the FeatureCollection data to a CSV file, useful for spreadsheet applications and data reporting.

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

17.11. Processing of Raster Data#

17.11.1. Extract Pixel Values#

17.11.1.1. Extracting Values to Points#

Load and visualize SRTM DEM and Landsat 7 imagery. Points from a shapefile of U.S. cities are added, and the tool extracts DEM values to these points, saving the results in a shapefile.

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

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

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

geemap.shp_to_gdf("dem.shp")

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

geemap.csv_to_df("landsat.csv")

17.11.1.2. Extracting Pixel Values Along a Transect#

Visualize SRTM DEM with a terrain basemap. Define a line transect, either interactively or manually, and extract elevation values along this line. Display the elevation profile in a line chart, then export it as a CSV file for further analysis.

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

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)

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

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

transect.to_csv("transect.csv")

17.11.2. Zonal Statistics#

17.11.2.1. Zonal Statistics with an Image and a Feature Collection#

This section demonstrates the use of zonal statistics to calculate the mean elevation values within U.S. state boundaries using NASA’s SRTM DEM data and a 5-year Landsat composite. The geemap.zonal_stats function exports results to CSV files for analysis.

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

# Add NASA SRTM
dem = ee.Image("USGS/SRTMGL1_003")
dem_vis = {
    "min": 0,
    "max": 4000,
    "palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
m.add_layer(dem, dem_vis, "SRTM DEM")

# Add 5-year Landsat TOA composite
landsat = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003")
landsat_vis = {"bands": ["B4", "B3", "B2"], "gamma": 1.4}
m.add_layer(landsat, landsat_vis, "Landsat", False)

# Add US Census States
states = ee.FeatureCollection("TIGER/2018/States")
style = {"fillColor": "00000000"}
m.add_layer(states.style(**style), {}, "US States")
m

out_dem_stats = "dem_stats.csv"
geemap.zonal_stats(
    dem, states, out_dem_stats, statistics_type="MEAN", scale=1000, return_fc=False
)

out_landsat_stats = "landsat_stats.csv"
geemap.zonal_stats(
    landsat,
    states,
    out_landsat_stats,
    statistics_type="MEAN",
    scale=1000,
    return_fc=False,
)

17.11.2.2. Zonal Statistics by Group#

Here, zonal statistics are applied to NLCD land cover data, calculating the area of each land cover type within each U.S. state. The results are saved to CSV files as both raw totals and percentages. This provides insights into the spatial distribution of land cover categories across states.

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

# Add NLCD data
dataset = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019")
landcover = dataset.select("landcover")
m.add_layer(landcover, {}, "NLCD 2019")

# Add US census states
states = ee.FeatureCollection("TIGER/2018/States")
style = {"fillColor": "00000000"}
m.add_layer(states.style(**style), {}, "US States")

# Add NLCD legend
m.add_legend(title="NLCD Land Cover", builtin_legend="NLCD")
m

nlcd_stats = "nlcd_stats.csv"

geemap.zonal_stats_by_group(
    landcover,
    states,
    nlcd_stats,
    statistics_type="SUM",
    denominator=1e6,
    decimal_places=2,
)

nlcd_stats = "nlcd_stats_pct.csv"

geemap.zonal_stats_by_group(
    landcover,
    states,
    nlcd_stats,
    statistics_type="PERCENTAGE",
    denominator=1e6,
    decimal_places=2,
)

17.11.2.3. Zonal Statistics with Two Images#

This example calculates the mean elevation values within different NLCD land cover types using DEM and NLCD data. The geemap.image_stats_by_zone function provides summary statistics (e.g., mean and standard deviation), which can be exported to CSV files for further analysis or visualization.

m = geemap.Map(center=[40, -100], zoom=4)
dem = ee.Image("USGS/3DEP/10m")
vis = {"min": 0, "max": 4000, "palette": "terrain"}
m.add_layer(dem, vis, "DEM")
m

landcover = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select("landcover")
m.add_layer(landcover, {}, "NLCD 2019")
m.add_legend(title="NLCD Land Cover Classification", builtin_legend="NLCD")

stats = geemap.image_stats_by_zone(dem, landcover, reducer="MEAN")
stats

stats.to_csv("mean.csv", index=False)

geemap.image_stats_by_zone(dem, landcover, out_csv="std.csv", reducer="STD")

17.11.3. Map Algebra#

This example demonstrates basic map algebra by computing the Normalized Difference Vegetation Index (NDVI) for a 5-year Landsat composite and the Enhanced Vegetation Index (EVI) for a Landsat 8 image. These indices are visualized with color scales to highlight areas of vegetation.

m = geemap.Map()

# Load a 5-year Landsat 7 composite 1999-2003.
landsat_1999 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003")

# Compute NDVI.
ndvi_1999 = (
    landsat_1999.select("B4")
    .subtract(landsat_1999.select("B3"))
    .divide(landsat_1999.select("B4").add(landsat_1999.select("B3")))
)

vis = {"min": 0, "max": 1, "palette": "ndvi"}
m.add_layer(ndvi_1999, vis, "NDVI")
m.add_colorbar(vis, label="NDVI")
m

# Load a Landsat 8 image.
image = ee.Image("LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318")

# Compute the EVI using an expression.
evi = image.expression(
    "2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))",
    {
        "NIR": image.select("B5"),
        "RED": image.select("B4"),
        "BLUE": image.select("B2"),
    },
)

# Define a map centered on San Francisco Bay.
m = geemap.Map(center=[37.4675, -122.1363], zoom=9)

vis = {"min": 0, "max": 1, "palette": "ndvi"}
m.add_layer(evi, vis, "EVI")
m.add_colorbar(vis, label="EVI")
m

17.12. Working with Local Geospatial Data#

17.12.1. Raster Data#

Single-band and multi-band raster data can be loaded from local files. Here, a digital elevation model (DEM) is loaded and displayed with a terrain color scheme, and another raster is displayed with a false-color composite to highlight different features.

17.12.1.1. Single-Band Raster Data#

url = "https://github.com/giswqs/data/raw/main/raster/srtm90.tif"
filename = "dem.tif"
geemap.download_file(url, filename)

17.12.1.2. Multi-Band Raster Data#

m = geemap.Map()
m.add_raster(filename, cmap="terrain", layer_name="DEM")
vis_params = {"min": 0, "max": 4000, "palette": "terrain"}
m.add_colorbar(vis_params, label="Elevation (m)")
m

url = "https://github.com/giswqs/data/raw/main/raster/cog.tif"
filename = "cog.tif"
geemap.download_file(url, filename)

m = geemap.Map()
m.add_raster(filename, indexes=[4, 1, 2], layer_name="False color")
m

17.12.2. Vector Data#

Various vector data formats can be loaded and visualized with geemap, including GeoJSON, Shapefile, GeoDataFrame, and GeoPackage formats. GeoJSON data is styled dynamically with custom colors based on attributes, while Shapefiles and GeoDataFrames allow for simple, structured addition of geographic features to the map.

17.12.2.1. GeoJSON#

in_geojson = (
    "https://github.com/opengeos/datasets/releases/download/vector/cables.geojson"
)
m = geemap.Map()
m.add_geojson(in_geojson, layer_name="Cable lines", info_mode="on_hover")
m

m = geemap.Map()
m.add_basemap("CartoDB.DarkMatter")
callback = lambda feat: {"color": feat["properties"]["color"], "weight": 2}
m.add_geojson(in_geojson, layer_name="Cable lines", style_callback=callback)
m

url = "https://github.com/opengeos/datasets/releases/download/world/countries.geojson"
m = geemap.Map()
m.add_geojson(
    url, layer_name="Countries", fill_colors=["red", "yellow", "green", "orange"]
)
m

import random

m = geemap.Map()


def random_color(feature):
    return {
        "color": "black",
        "weight": 3,
        "fillColor": random.choice(["red", "yellow", "green", "orange"]),
    }


m.add_geojson(url, layer_name="Countries", style_callback=random_color)
m

m = geemap.Map()

style = {
    "stroke": True,
    "color": "#0000ff",
    "weight": 2,
    "opacity": 1,
    "fill": True,
    "fillColor": "#0000ff",
    "fillOpacity": 0.1,
}

hover_style = {"fillOpacity": 0.7}

m.add_geojson(url, layer_name="Countries", style=style, hover_style=hover_style)
m

17.12.2.2. Shapefile#

url = "https://github.com/opengeos/datasets/releases/download/world/countries.zip"
geemap.download_file(url, overwrite=True)

m = geemap.Map()
in_shp = "countries.shp"
m.add_shp(in_shp, layer_name="Countries")
m

17.12.2.3. GeoDataFrame#

import geopandas as gpd

m = geemap.Map(center=[40, -100], zoom=4)
gdf = gpd.read_file("countries.shp")
m.add_gdf(gdf, layer_name="Countries")
m

17.12.2.4. GeoPackage#

m = geemap.Map()
data = "https://github.com/opengeos/datasets/releases/download/world/countries.gpkg"
m.add_vector(data, layer_name="Countries")
m

17.12.2.5. CSV to Vector#

Geemap enables easy conversion from CSV files to vector data formats like GeoJSON, Shapefile, and GeoPackage. The data from a CSV is visualized on a map, where cities are displayed with markers styled by region, and a legend is added for clear reference.

data = "https://github.com/gee-community/geemap/blob/master/examples/data/us_cities.csv"
geemap.csv_to_df(data)

geemap.csv_to_geojson(
    data, "cities.geojson", latitude="latitude", longitude="longitude"
)

geemap.csv_to_shp(data, "cities.shp", latitude="latitude", longitude="longitude")

geemap.csv_to_vector(data, "cities.gpkg", latitude="latitude", longitude="longitude")

gdf = geemap.csv_to_gdf(data, latitude="latitude", longitude="longitude")
gdf

cities = (
    "https://github.com/gee-community/geemap/blob/master/examples/data/us_cities.csv"
)
m = geemap.Map(center=[40, -100], zoom=4)
m.add_points_from_xy(cities, x="longitude", y="latitude")
m

regions = "https://github.com/gee-community/geemap/blob/master/examples/data/us_regions.geojson"

m = geemap.Map(center=[40, -100], zoom=4)
m.add_geojson(regions, layer_name="US Regions")
m.add_points_from_xy(
    cities,
    x="longitude",
    y="latitude",
    layer_name="US Cities",
    color_column="region",
    icon_names=["gear", "map", "leaf", "globe"],
    spin=True,
    add_legend=True,
)
m

m = geemap.Map(center=[40, -100], zoom=4)
m.add_circle_markers_from_xy(
    data,
    x="longitude",
    y="latitude",
    radius=8,
    color="blue",
    fill_color="black",
    fill_opacity=0.5,
)
m

17.13. Accessing Cloud Optimized GeoTIFFs#

17.13.1. Cloud Optimized GeoTIFFs (COG)#

This section shows how to add Cloud Optimized GeoTIFF (COG) layers to a map using URLs, which allows for efficient loading and visualization of large raster files stored on the cloud. In this example, a pre-event image of the 2020 California wildfire is added to a map, allowing for remote sensing analysis without local file storage.

m = geemap.Map()
url = "https://opendata.digitalglobe.com/events/california-fire-2020/pre-event/2018-02-16/pine-gulch-fire20/1030010076004E00.tif"
m.add_cog_layer(url, name="Fire (pre-event)")
m

geemap.cog_center(url)

The COG functions also allow inspection of data, such as viewing the bounds and available bands in the GeoTIFF file. The pre- and post-event images can be compared in a split map view to analyze changes due to the fire.

geemap.cog_bands(url)

url2 = "https://opendata.digitalglobe.com/events/california-fire-2020/post-event/2020-08-14/pine-gulch-fire20/10300100AAC8DD00.tif"
m.add_cog_layer(url2, name="Fire (post-event)")

m = geemap.Map(center=[39.4568, -108.5107], zoom=12)
m.split_map(left_layer=url2, right_layer=url)
m

17.13.2. SpatioTemporal Asset Catalog (STAC)#

STAC collections can be accessed and visualized similarly, enabling dynamic mapping of spatiotemporal data. By specifying STAC URLs, you can view dataset bounds, center the map on a dataset, and select specific bands to add as layers. This example shows adding panchromatic and false-color imagery for enhanced visual analysis.

url = "https://tinyurl.com/22vptbws"

geemap.stac_bounds(url)

geemap.stac_center(url)

geemap.stac_bands(url)

m = geemap.Map()
m.add_stac_layer(url, bands=["pan"], name="Panchromatic")
m.add_stac_layer(url, bands=["B3", "B2", "B1"], name="False color")
m

17.14. Exporting Earth Engine Data#

17.14.1. Exporting Images#

This section demonstrates exporting Earth Engine images. First, a Landsat image is added to the map for visualization, and a rectangular region of interest (ROI) is specified. Exporting options include saving the image locally with specified scale and region or exporting to Google Drive. It’s possible to define custom CRS and transformation settings when saving the image.

Add a Landsat image to the map.

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.

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

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

Check image projection.

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

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

Specify region, crs, and crs_transform.

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

To Google Drive

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

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

17.14.2. Exporting Image Collections#

Image collections, like time series data, can be filtered and exported as multiple images. Here, a National Agriculture Imagery Program (NAIP) collection is filtered by date and location. The collection can then be saved locally or sent to Google Drive, making it easier to handle large datasets.

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"))
)

collection.aggregate_array("system:index")

To local drive

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

To Google Drive

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

17.14.3. Exporting Feature Collections#

Feature collections, such as state boundaries, are exportable in multiple formats (e.g., Shapefile, GeoJSON, and CSV). This example exports the Alaska state boundary as different vector formats both locally and to Google Drive. Additionally, exported data can be directly loaded into a GeoDataFrame for further manipulation in Python.

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

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

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

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

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

gdf = geemap.ee_to_gdf(fc)
gdf

df = geemap.ee_to_df(fc)
df

To Google Drive

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

17.15. Creating Timelapse Animations#

17.15.1. Landsat Timelapse#

The Landsat timelapse tool creates animations of changes over time. Here, we use a defined region of interest (ROI) to generate timelapse GIFs for Las Vegas (1984-2023):

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

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)

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

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)

17.15.2. Sentinel-2 Timelapse#

This example generates a timelapse of Sentinel-2 data for a selected ROI, with options to draw a custom ROI on the map. The timelapse spans 2017-2023, focusing on the June to September period each year.

m = geemap.Map(center=[41.718934, -86.894547], zoom=12)
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.

roi = m.user_roi
if roi is None:
    roi = ee.Geometry.BBox(-87.0492, 41.6545, -86.7903, 41.7604)
    m.add_layer(roi)
    m.center_object(roi)

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)

17.15.3. MODIS Timelapse#

The MODIS timelapse can showcase NDVI (vegetation index) or ocean color data over time. In the vegetation example, we visualize NDVI from 2000-2022 with country overlays. The temperature example animates Aqua satellite data for monthly temperature trends from 2018-2020 with continent overlays.

m = geemap.Map()
m

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)

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

m = geemap.Map()
m

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)

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)

17.15.4. GOES Timelapse#

GOES timelapse generation can animate atmospheric phenomena in near real-time. We create animations for different ROIs, including hurricane tracking and fire events using GOES-17 data with custom time windows and frame rates.

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"

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

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"

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

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"

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

17.16. Charting#

17.16.1. Charting Features#

17.16.1.1. Import libraries#

To create charts for Earth Engine features, import required libraries like calendar, ee, geemap, and chart.

import calendar
import ee
import geemap
from geemap import chart

Initialize Earth Engine using geemap.ee_initialize().

geemap.ee_initialize()

17.16.1.2. feature_by_feature#

This function allows feature plotting along the x-axis, with values from selected properties represented along the y-axis. Ecoregions data is used to chart average monthly temperature across regions, where months serve as series columns.

ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")
features = ecoregions.select("[0-9][0-9]_tmean|label")

geemap.ee_to_df(features)

x_property = "label"
y_properties = [str(x).zfill(2) + "_tmean" for x in range(1, 13)]

labels = calendar.month_abbr[1:]  # a list of month labels, e.g. ['Jan', 'Feb', ...]

colors = [
    "#604791",
    "#1d6b99",
    "#39a8a7",
    "#0f8755",
    "#76b349",
    "#f0af07",
    "#e37d05",
    "#cf513e",
    "#96356f",
    "#724173",
    "#9c4f97",
    "#696969",
]
title = "Average Monthly Temperature by Ecoregion"
x_label = "Ecoregion"
y_label = "Temperature"

fig = chart.feature_by_feature(
    features,
    x_property,
    y_properties,
    colors=colors,
    labels=labels,
    title=title,
    x_label=x_label,
    y_label=y_label,
)
fig

17.16.1.3. feature.by_property#

Displays average precipitation by month for each ecoregion. Properties represent precipitation values for each month, allowing for visual comparison across regions by month.

ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")
features = ecoregions.select("[0-9][0-9]_ppt|label")

geemap.ee_to_df(features)

keys = [str(x).zfill(2) + "_ppt" for x in range(1, 13)]
values = calendar.month_abbr[1:]  # a list of month labels, e.g. ['Jan', 'Feb', ...]

x_properties = dict(zip(keys, values))
series_property = "label"
title = "Average Ecoregion Precipitation by Month"
colors = ["#f0af07", "#0f8755", "#76b349"]

fig = chart.feature_by_property(
    features,
    x_properties,
    series_property,
    title=title,
    colors=colors,
    x_label="Month",
    y_label="Precipitation (mm)",
    legend_location="top-left",
)
fig

17.16.1.4. feature_groups#

Plots groups of features based on property values, showing average January temperature for ecoregions divided into “Warm” and “Cold” categories.

ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")
features = ecoregions.select("[0-9][0-9]_ppt|label")

features = ee.FeatureCollection("projects/google/charts_feature_example")
x_property = "label"
y_property = "01_tmean"
series_property = "warm"
title = "Average January Temperature by Ecoregion"
colors = ["#cf513e", "#1d6b99"]
labels = ["Warm", "Cold"]

chart.feature_groups(
    features,
    x_property,
    y_property,
    series_property,
    title=title,
    colors=colors,
    x_label="Ecoregion",
    y_label="January Temperature (°C)",
    legend_location="top-right",
    labels=labels,
)

17.16.1.5. feature_histogram#

Generates a histogram displaying July precipitation distribution across a region, showing pixel count for different precipitation levels, useful for understanding data spread.

source = ee.ImageCollection("OREGONSTATE/PRISM/Norm91m").toBands()
region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14)
features = source.sample(region, 5000)

geemap.ee_to_df(features.limit(5).select(["07_ppt"]))

property = "07_ppt"
title = "July Precipitation Distribution for NW USA"

fig = chart.feature_histogram(
    features,
    property,
    max_buckets=None,
    title=title,
    x_label="Precipitation (mm)",
    y_label="Pixel Count",
    colors=["#1d6b99"],
)
fig

17.16.2. Charting Images#

17.16.2.1. image_by_region#

Displays monthly temperature for each ecoregion using the image_by_region function, aggregating data to visualize average temperature by month across regions.

ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")
image = (
    ee.ImageCollection("OREGONSTATE/PRISM/Norm91m").toBands().select("[0-9][0-9]_tmean")
)

labels = calendar.month_abbr[1:]  # a list of month labels, e.g. ['Jan', 'Feb', ...]
title = "Average Monthly Temperature by Ecoregion"

fig = chart.image_by_region(
    image,
    ecoregions,
    reducer="mean",
    scale=500,
    x_property="label",
    title=title,
    x_label="Ecoregion",
    y_label="Temperature",
    labels=labels,
)
fig

17.16.2.2. image_regions#

Generates a chart showing average monthly precipitation across regions, using properties to represent months and comparing precipitation levels.

ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")
image = (
    ee.ImageCollection("OREGONSTATE/PRISM/Norm91m").toBands().select("[0-9][0-9]_ppt")
)

keys = [str(x).zfill(2) + "_ppt" for x in range(1, 13)]
values = calendar.month_abbr[1:]  # a list of month labels, e.g. ['Jan', 'Feb', ...]

x_properties = dict(zip(keys, values))
title = "Average Ecoregion Precipitation by Month"
colors = ["#f0af07", "#0f8755", "#76b349"]

fig = chart.image_regions(
    image,
    ecoregions,
    reducer="mean",
    scale=500,
    series_property="label",
    x_labels=x_properties,
    title=title,
    colors=colors,
    x_label="Month",
    y_label="Precipitation (mm)",
    legend_location="top-left",
)

17.16.2.3. image_by_class#

Displays spectral signatures of ecoregions by wavelength, showing reflectance values across spectral bands for comparison.

ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")

image = (
    ee.ImageCollection("MODIS/061/MOD09A1")
    .filter(ee.Filter.date("2018-06-01", "2018-09-01"))
    .select("sur_refl_b0[0-7]")
    .mean()
    .select([2, 3, 0, 1, 4, 5, 6])
)

wavelengths = [469, 555, 655, 858, 1240, 1640, 2130]

fig = chart.image_by_class(
    image,
    class_band="label",
    region=ecoregions,
    reducer="MEAN",
    scale=500,
    x_labels=wavelengths,
    title="Ecoregion Spectral Signatures",
    x_label="Wavelength (nm)",
    y_label="Reflectance (x1e4)",
    colors=["#f0af07", "#0f8755", "#76b349"],
    legend_location="top-left",
    interpolation="basis",
)
fig

17.16.2.4. image_histogram#

Generates histograms to visualize MODIS surface reflectance distribution across red, NIR, and SWIR bands, providing insights into data distribution by band.

image = (
    ee.ImageCollection("MODIS/061/MOD09A1")
    .filter(ee.Filter.date("2018-06-01", "2018-09-01"))
    .select(["sur_refl_b01", "sur_refl_b02", "sur_refl_b06"])
    .mean()
)

region = ee.Geometry.Rectangle([-112.60, 40.60, -111.18, 41.22])

fig = chart.image_histogram(
    image,
    region,
    scale=500,
    max_buckets=200,
    min_bucket_width=1.0,
    max_raw=1000,
    max_pixels=int(1e6),
    title="MODIS SR Reflectance Histogram",
    labels=["Red", "NIR", "SWIR"],
    colors=["#cf513e", "#1d6b99", "#f0af07"],
)
fig

17.16.3. Charting Image Collections#

17.16.3.1. image_series#

Creates a time series chart of vegetation indices (NDVI and EVI) for a forest region, helping to track vegetation health over time.

# Define the forest feature collection.
forest = ee.FeatureCollection("projects/google/charts_feature_example").filter(
    ee.Filter.eq("label", "Forest")
)

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(ee.Filter.date("2010-01-01", "2020-01-01"))
    .select(["NDVI", "EVI"])
)

title = "Average Vegetation Index Value by Date for Forest"
x_label = "Year"
y_label = "Vegetation index (x1e4)"
colors = ["#e37d05", "#1d6b99"]

fig = chart.image_series(
    veg_indices,
    region=forest,
    reducer=ee.Reducer.mean(),
    scale=500,
    x_property="system:time_start",
    chart_type="LineChart",
    x_cols="date",
    y_cols=["NDVI", "EVI"],
    colors=colors,
    title=title,
    x_label=x_label,
    y_label=y_label,
    legend_location="right",
)
fig

17.16.3.2. image_series_by_region#

Shows NDVI time series by region, comparing desert, forest, and grassland areas. Each region has a unique series for easy comparison.

# Import the example feature collection.
ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(ee.Filter.date("2010-01-01", "2020-01-01"))
    .select(["NDVI"])
)

title = "Average NDVI Value by Date"
x_label = "Date"
y_label = "NDVI (x1e4)"
x_cols = "index"
y_cols = ["Desert", "Forest", "Grassland"]
colors = ["#f0af07", "#0f8755", "#76b349"]

fig = chart.image_series_by_region(
    veg_indices,
    regions=ecoregions,
    reducer=ee.Reducer.mean(),
    band="NDVI",
    scale=500,
    x_property="system:time_start",
    series_property="label",
    chart_type="LineChart",
    x_cols=x_cols,
    y_cols=y_cols,
    title=title,
    x_label=x_label,
    y_label=y_label,
    colors=colors,
    stroke_width=3,
    legend_location="bottom-left",
)
fig

17.16.3.3. image_doy_series#

Plots average vegetation index by day of year for a specific region, showing seasonal vegetation patterns over a decade.

# Import the example feature collection and subset the grassland feature.
grassland = ee.FeatureCollection("projects/google/charts_feature_example").filter(
    ee.Filter.eq("label", "Grassland")
)

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(ee.Filter.date("2010-01-01", "2020-01-01"))
    .select(["NDVI", "EVI"])
)

title = "Average Vegetation Index Value by Day of Year for Grassland"
x_label = "Day of Year"
y_label = "Vegetation Index (x1e4)"
colors = ["#f0af07", "#0f8755"]

fig = chart.image_doy_series(
    image_collection=veg_indices,
    region=grassland,
    scale=500,
    chart_type="LineChart",
    title=title,
    x_label=x_label,
    y_label=y_label,
    colors=colors,
    stroke_width=5,
)
fig

17.16.3.4. image_doy_series_by_year#

Compares NDVI by day of year across two different years, showing how vegetation patterns vary between 2012 and 2019.

# Import the example feature collection and subset the grassland feature.
grassland = ee.FeatureCollection("projects/google/charts_feature_example").filter(
    ee.Filter.eq("label", "Grassland")
)

# Load MODIS vegetation indices data and subset years 2012 and 2019.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(
        ee.Filter.Or(
            ee.Filter.date("2012-01-01", "2013-01-01"),
            ee.Filter.date("2019-01-01", "2020-01-01"),
        )
    )
    .select(["NDVI", "EVI"])
)

title = "Average Vegetation Index Value by Day of Year for Grassland"
x_label = "Day of Year"
y_label = "Vegetation Index (x1e4)"
colors = ["#e37d05", "#1d6b99"]

fig = chart.doy_series_by_year(
    veg_indices,
    band_name="NDVI",
    region=grassland,
    scale=500,
    chart_type="LineChart",
    colors=colors,
    title=title,
    x_label=x_label,
    y_label=y_label,
    stroke_width=5,
)
fig

17.16.3.5. image_doy_series_by_region#

Visualizes average NDVI by day of year across regions, showcasing seasonal changes for desert, forest, and grassland ecoregions.

# Import the example feature collection and subset the grassland feature.
ecoregions = ee.FeatureCollection("projects/google/charts_feature_example")

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(ee.Filter.date("2010-01-01", "2020-01-01"))
    .select(["NDVI"])
)

title = "Average Vegetation Index Value by Day of Year for Grassland"
x_label = "Day of Year"
y_label = "Vegetation Index (x1e4)"
colors = ["#f0af07", "#0f8755", "#76b349"]

fig = chart.image_doy_series_by_region(
    veg_indices,
    "NDVI",
    ecoregions,
    region_reducer="mean",
    scale=500,
    year_reducer=ee.Reducer.mean(),
    start_day=1,
    end_day=366,
    series_property="label",
    stroke_width=5,
    chart_type="LineChart",
    title=title,
    x_label=x_label,
    y_label=y_label,
    colors=colors,
    legend_location="right",
)
fig

17.16.4. Charting Array and List#

17.16.4.1. Scatter Plot#

Displays relationships between spectral bands, plotting red reflectance against NIR and SWIR reflectance to examine band correlations.

# Import the example feature collection and subset the forest feature.
forest = ee.FeatureCollection("projects/google/charts_feature_example").filter(
    ee.Filter.eq("label", "Forest")
)

# Define a MODIS surface reflectance composite.
modisSr = (
    ee.ImageCollection("MODIS/061/MOD09A1")
    .filter(ee.Filter.date("2018-06-01", "2018-09-01"))
    .select("sur_refl_b0[0-7]")
    .mean()
)

# Reduce MODIS reflectance bands by forest region; get a dictionary with
# band names as keys, pixel values as lists.
pixel_vals = modisSr.reduceRegion(
    **{"reducer": ee.Reducer.toList(), "geometry": forest.geometry(), "scale": 2000}
)

# Convert NIR and SWIR value lists to an array to be plotted along the y-axis.
y_values = pixel_vals.toArray(["sur_refl_b02", "sur_refl_b06"])


# Get the red band value list; to be plotted along the x-axis.
x_values = ee.List(pixel_vals.get("sur_refl_b01"))

title = "Relationship Among Spectral Bands for Forest Pixels"
colors = ["rgba(29,107,153,0.4)", "rgba(207,81,62,0.4)"]

fig = chart.array_values(
    y_values,
    axis=1,
    x_labels=x_values,
    series_names=["NIR", "SWIR"],
    chart_type="ScatterChart",
    colors=colors,
    title=title,
    x_label="Red reflectance (x1e4)",
    y_label="NIR & SWIR reflectance (x1e4)",
    default_size=15,
    xlim=(0, 800),
)
fig

x = ee.List(pixel_vals.get("sur_refl_b01"))
y = ee.List(pixel_vals.get("sur_refl_b06"))

fig = chart.array_values(
    y,
    x_labels=x,
    series_names=["SWIR"],
    chart_type="ScatterChart",
    colors=["rgba(207,81,62,0.4)"],
    title=title,
    x_label="Red reflectance (x1e4)",
    y_label="SWIR reflectance (x1e4)",
    default_size=15,
    xlim=(0, 800),
)
fig

17.16.4.2. Transect Line Plot#

Plots elevation along a transect line across a specific region, providing a profile view of terrain elevation.

# Define a line across the Olympic Peninsula, USA.
transect = ee.Geometry.LineString([[-122.8, 47.8], [-124.5, 47.8]])

# Define a pixel coordinate image.
lat_lon_img = ee.Image.pixelLonLat()

# Import a digital surface model and add latitude and longitude bands.
elev_img = ee.Image("USGS/SRTMGL1_003").select("elevation").addBands(lat_lon_img)

# Reduce elevation and coordinate bands by transect line; get a dictionary with
# band names as keys, pixel values as lists.
elev_transect = elev_img.reduceRegion(
    reducer=ee.Reducer.toList(),
    geometry=transect,
    scale=1000,
)

# Get longitude and elevation value lists from the reduction dictionary.
lon = ee.List(elev_transect.get("longitude"))
elev = ee.List(elev_transect.get("elevation"))

# Sort the longitude and elevation values by ascending longitude.
lon_sort = lon.sort(lon)
elev_sort = elev.sort(lon)

fig = chart.array_values(
    elev_sort,
    x_labels=lon_sort,
    series_names=["Elevation"],
    chart_type="AreaChart",
    colors=["#1d6b99"],
    title="Elevation Profile Across Longitude",
    x_label="Longitude",
    y_label="Elevation (m)",
    stroke_width=5,
    fill="bottom",
    fill_opacities=[0.4],
    ylim=(0, 2500),
)
fig

17.16.4.3. Metadata Scatter Plot#

Visualizes Landsat image metadata by plotting cloud cover against geometric RMSE, useful for quality assessment of image collections.

# Import a Landsat 8 collection and filter to a single path/row.
col = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filter(
    ee.Filter.expression("WRS_PATH ==  45 && WRS_ROW == 30")
)

# Reduce image properties to a series of lists; one for each selected property.
propVals = col.reduceColumns(
    reducer=ee.Reducer.toList().repeat(2),
    selectors=["CLOUD_COVER", "GEOMETRIC_RMSE_MODEL"],
).get("list")

# Get selected image property value lists; to be plotted along x and y axes.
x = ee.List(ee.List(propVals).get(0))
y = ee.List(ee.List(propVals).get(1))

colors = [geemap.hex_to_rgba("#96356f", 0.4)]
print(colors)

fig = chart.array_values(
    y,
    x_labels=x,
    series_names=["RMSE"],
    chart_type="ScatterChart",
    colors=colors,
    title="Landsat 8 Image Collection Metadata (045030)",
    x_label="Cloud cover (%)",
    y_label="Geometric RMSE (m)",
    default_size=15,
)
fig

17.16.4.4. Mapped Function Scatter & Line Plot#

Plots a sine function as a line chart, mapping mathematical functions onto chart axes for visualization.

import math

start = -2 * math.pi
end = 2 * math.pi
points = ee.List.sequence(start, end, None, 50)

def sin_func(val):
    return ee.Number(val).sin()

values = points.map(sin_func)

fig = chart.array_values(
    values,
    points,
    chart_type="LineChart",
    colors=["#39a8a7"],
    title="Sine Function",
    x_label="radians",
    y_label="sin(x)",
    marker="circle",
)
fig

17.16.5. Charting Data Table#

17.16.5.1. Manual DataTable chart#

Creates a chart from a manually created DataTable, such as US state populations.

import pandas as pd

data = {
    "State": ["CA", "NY", "IL", "MI", "OR"],
    "Population": [37253956, 19378102, 12830632, 9883640, 3831074],
}

df = pd.DataFrame(data)
df

fig = chart.Chart(
    df,
    x_cols=["State"],
    y_cols=["Population"],
    chart_type="ColumnChart",
    colors=["#1d6b99"],
    title="State Population (US census, 2010)",
    x_label="State",
    y_label="Population",
)
fig

17.16.5.2. Computed DataTable chart#

Computes a DataTable from MODIS vegetation indices data for a forest area, then charts NDVI and EVI time series.

# Import the example feature collection and subset the forest feature.
forest = ee.FeatureCollection("projects/google/charts_feature_example").filter(
    ee.Filter.eq("label", "Forest")
)

# Load MODIS vegetation indices data and subset a decade of images.
veg_indices = (
    ee.ImageCollection("MODIS/061/MOD13A1")
    .filter(ee.Filter.date("2010-01-01", "2020-01-01"))
    .select(["NDVI", "EVI"])
)

# Build a feature collection where each feature has a property that represents
# a DataFrame row.


def aggregate(img):
    # Reduce the image to the mean of pixels intersecting the forest ecoregion.
    stat = img.reduceRegion(
        **{"reducer": ee.Reducer.mean(), "geometry": forest, "scale": 500}
    )

    # Extract the reduction results along with the image date.
    date = geemap.image_date(img)
    evi = stat.get("EVI")
    ndvi = stat.get("NDVI")

    # Make a list of observation attributes to define a row in the DataTable.
    row = ee.List([date, evi, ndvi])

    # Return the row as a property of an ee.Feature.
    return ee.Feature(None, {"row": row})


reduction_table = veg_indices.map(aggregate)

# Aggregate the 'row' property from all features in the new feature collection
# to make a server-side 2-D list (DataTable).
data_table_server = reduction_table.aggregate_array("row")

# Define column names and properties for the DataTable. The order should
# correspond to the order in the construction of the 'row' property above.
column_header = ee.List([["Date", "EVI", "NDVI"]])

# Concatenate the column header to the table.
data_table_server = column_header.cat(data_table_server)

data_table = chart.DataTable(data_table_server, date_column="Date")
data_table.head()

fig = chart.Chart(
    data_table,
    chart_type="LineChart",
    x_cols="Date",
    y_cols=["EVI", "NDVI"],
    colors=["#e37d05", "#1d6b99"],
    title="Average Vegetation Index Value by Date for Forest",
    x_label="Date",
    y_label="Vegetation index (x1e4)",
    stroke_width=3,
    legend_location="right",
)
fig

17.16.5.3. Interval chart#

Displays annual NDVI time series with variance, showing inter-annual vegetation change and variability across the year.

# Define a point to extract an NDVI time series for.
geometry = ee.Geometry.Point([-121.679, 36.479])

# Define a band of interest (NDVI), import the MODIS vegetation index dataset,
# and select the band.
band = "NDVI"
ndvi_col = ee.ImageCollection("MODIS/061/MOD13Q1").select(band)

# Map over the collection to add a day of year (doy) property to each image.


def set_doy(img):
    doy = ee.Date(img.get("system:time_start")).getRelative("day", "year")
    # Add 8 to day of year number so that the doy label represents the middle of
    # the 16-day MODIS NDVI composite.
    return img.set("doy", ee.Number(doy).add(8))


ndvi_col = ndvi_col.map(set_doy)

# Join all coincident day of year observations into a set of image collections.
distinct_doy = ndvi_col.filterDate("2013-01-01", "2014-01-01")
filter = ee.Filter.equals(**{"leftField": "doy", "rightField": "doy"})
join = ee.Join.saveAll("doy_matches")
join_col = ee.ImageCollection(join.apply(distinct_doy, ndvi_col, filter))

# Calculate the absolute range, interquartile range, and median for the set
# of images composing each coincident doy observation group. The result is
# an image collection with an image representative per unique doy observation
# with bands that describe the 0, 25, 50, 75, 100 percentiles for the set of
# coincident doy images.


def cal_percentiles(img):
    doyCol = ee.ImageCollection.fromImages(img.get("doy_matches"))

    return doyCol.reduce(
        ee.Reducer.percentile([0, 25, 50, 75, 100], ["p0", "p25", "p50", "p75", "p100"])
    ).set({"doy": img.get("doy")})


comp = ee.ImageCollection(join_col.map(cal_percentiles))

# Extract the inter-annual NDVI doy percentile statistics for the
# point of interest per unique doy representative. The result is
# is a feature collection where each feature is a doy representative that
# contains a property (row) describing the respective inter-annual NDVI
# variance, formatted as a list of values.


def order_percentiles(img):
    stats = ee.Dictionary(
        img.reduceRegion(
            **{"reducer": ee.Reducer.first(), "geometry": geometry, "scale": 250}
        )
    )

    # Order the percentile reduction elements according to how you want columns
    # in the DataTable arranged (x-axis values need to be first).
    row = ee.List(
        [
            img.get("doy"),
            stats.get(band + "_p50"),
            stats.get(band + "_p0"),
            stats.get(band + "_p25"),
            stats.get(band + "_p75"),
            stats.get(band + "_p100"),
        ]
    )

    # Return the row as a property of an ee.Feature.
    return ee.Feature(None, {"row": row})


reduction_table = comp.map(order_percentiles)

# Aggregate the 'row' properties to make a server-side 2-D array (DataTable).
data_table_server = reduction_table.aggregate_array("row")

# Define column names and properties for the DataTable. The order should
# correspond to the order in the construction of the 'row' property above.
column_header = ee.List([["DOY", "median", "p0", "p25", "p75", "p100"]])

# Concatenate the column header to the table.
data_table_server = column_header.cat(data_table_server)

df = chart.DataTable(data_table_server)
df.head()

fig = chart.Chart(
    df,
    chart_type="IntervalChart",
    x_cols="DOY",
    y_cols=["p0", "p25", "median", "p75", "p100"],
    title="Annual NDVI Time Series with Inter-Annual Variance",
    x_label="Day of Year",
    y_label="Vegetation index (x1e4)",
    stroke_width=1,
    fill="between",
    fill_colors=["#b6d1c6", "#83b191", "#83b191", "#b6d1c6"],
    fill_opacities=[0.6] * 4,
    labels=["p0", "p25", "median", "p75", "p100"],
    display_legend=True,
    legend_location="top-right",
    ylim=(0, 10000),
)
fig

17.17. Exercises#

17.17.1. Exercise 1: Visualizing DEM Data#

Find a DEM dataset in the Earth Engine Data Catalog and clip it to a specific area (e.g., your country, state, or city). Display it with an appropriate color palette. For example, the sample map below shows the DEM of the state of Colorado.

17.17.2. Exercise 2: Cloud-Free Composite with Sentinel-2 or Landsat#

Use Sentinel-2 or Landsat-9 data to create a cloud-free composite for a specific year in a region of your choice.

Use Sentinel-2 or Landsat-9 data data to create a cloud-free composite for a specific year in a region of your choice. Display the imagery on the map with a proper band combination. For example, the sample map below shows a cloud-free false-color composite of Sentinel-2 imagery of the year 2021 for the state of Colorado.

17.17.3. Exercise 3: Visualizing NAIP Imagery#

Use NAIP imagery to create a cloud-free imagery for a U.S. county of your choice. For example, the sample map below shows a cloud-free true-color composite of NAIP imagery for Knox County, Tennessee. Keep in mind that there might be some counties with the same name in different states, so make sure to select the correct county for the selected state.

17.17.4. Exercise 4: Visualizing Watershed Boundaries#

Visualize the USGS Watershed Boundary Dataset with outline color only, no fill color.

17.17.5. Exercise 5: Visualizing Land Cover Change#

Use the USGS National Land Cover Database and US Census States to create a split-panel map for visualizing land cover change (2001-2019) for a US state of your choice. Make sure you add the NLCD legend to the map.

17.17.6. Exercise 6: Creating a Landsat Timelapse Animation#

Generate a timelapse animation using Landsat data to show changes over time for a selected region.

Spain

17.18. Summary#

This lecture introduced geemap, a Python library designed to streamline geospatial data visualization and analysis in the Google Earth Engine (GEE) environment. Geemap enables users to perform GIS and remote sensing tasks with GEE data interactively in Python notebooks. Key topics included setting up GEE with geemap, loading and manipulating data from the Earth Engine Data Catalog, and processing vector and raster data. Techniques for creating animated timelapses using data from Landsat, Sentinel, MODIS, and GOES illustrated dynamic landscape monitoring, while interactive charting tools, like scatter plots and time series, supported data exploration and trend analysis. The lecture also covered exporting images and features in multiple formats, such as GeoTIFF, Shapefile, and CSV, to facilitate data sharing. Overall, this session provided a foundational understanding of geemap’s capabilities, enabling users to efficiently visualize, analyze, and derive insights from Earth observation data in a Python setting.

Geemap

Contents

17. Geemap#

17.1. Overview#

17.2. Learning Objectives#

17.2.1. Prerequisites#

17.3. Introduction to Google Earth Engine#

17.3.1. Google Earth Engine Overview#

17.3.2. Installing Geemap#

17.3.3. Import Libraries#

17.3.4. Authenticate and Initialize Earth Engine#

17.3.5. Creating Interactive Maps#

17.3.5.1. Example Maps#

17.3.6. Adding Basemaps#

17.3.6.1. Adding Multiple Basemaps#

17.3.6.2. Listing Available Basemaps#

17.3.7. Google Basemaps#

17.4. Introduction to Interactive Maps and Tools#

17.4.1. Basemap Selector#

17.4.2. Layer Manager#

17.4.3. Inspector Tool#

17.4.4. Layer Editor#

17.4.4.1. Single-Band image#

17.4.4.2. Multi-Band image#

17.4.4.3. Feature Collection#

17.4.5. Draw Control#

17.5. The Earth Engine Data Catalog#

17.5.1. Searching Datasets on the Earth Engine Website#

17.5.2. Searching Datasets Within Geemap#

17.5.3. Using the Datasets Module in Geemap#

17.6. Earth Engine Data Types#

17.7. Earth Engine Raster Data#

17.7.1. Image#

17.7.1.1. Loading Earth Engine Images#

17.7.1.2. Visualizing Earth Engine Images#

17.7.2. ImageCollection#

17.7.2.1. Loading Image Collections#

17.7.2.2. Filtering Image Collections#

17.7.2.3. Visualizing Image Collections#

17.8. Earth Engine Vector Data#

17.8.1. Loading Feature Collections#

17.8.2. Filtering Feature Collections#

17.8.3. Visualizing Feature Collections#

17.9. More Tools for Visualizing Earth Engine Data#

17.9.1. Using the Plotting Tool#

17.9.2. Legends#

17.9.2.1. Built-in Legends#

17.9.2.2. Custom Legends#

17.9.3. Color Bars#

17.9.4. Split-panel Maps#

17.9.5. Linked Maps#

17.9.6. Timeseries Inspector#

17.9.7. Time Slider#

17.10. Processing of Vector Data#

17.10.1. From GeoJSON#

17.10.2. From Shapefile#

17.10.3. From GeoDataFrame#

17.10.4. To GeoJSON#

17.10.5. To Shapefile#

17.10.6. To GeoDataFrame#

17.10.7. To DataFrame#

17.10.8. To CSV#

17.11. Processing of Raster Data#

17.11.1. Extract Pixel Values#

17.11.1.1. Extracting Values to Points#

17.11.1.2. Extracting Pixel Values Along a Transect#

17.11.2. Zonal Statistics#

17.11.2.1. Zonal Statistics with an Image and a Feature Collection#

17.11.2.2. Zonal Statistics by Group#

17.11.2.3. Zonal Statistics with Two Images#

17.11.3. Map Algebra#

17.12. Working with Local Geospatial Data#

17.12.1. Raster Data#

17.12.1.1. Single-Band Raster Data#

17.12.1.2. Multi-Band Raster Data#

17.12.2. Vector Data#

17.12.2.1. GeoJSON#

17.12.2.2. Shapefile#

17.12.2.3. GeoDataFrame#

17.12.2.4. GeoPackage#