An Introduction to Cloud-Based Geospatial Analysis with Earth Engine and Geemap
- Notebook: https://geemap.org/workshops/SciPy_2023
- Earth Engine: https://earthengine.google.com
- Geemap: https://geemap.org
Introduction (10 mins)¶
This notebook is for the tutorial presented at the SciPy 2023 Conference by Qiusheng Wu and Steve Greenberg. Check out this link for more information about the tutorial.
Abstract¶
This tutorial is an introduction to cloud-based geospatial analysis with Earth Engine and the geemap Python package. We will cover the basics of Earth Engine data types and how to visualize, analyze, and export Earth Engine data in a Jupyter environment using geemap. We will also demonstrate how to develop and deploy interactive Earth Engine web apps. Throughout the session, practical examples and hands-on exercises will be provided to enhance learning. The attendees should have a basic understanding of Python and Jupyter Notebooks. Familiarity with Earth science and geospatial datasets is not required, but will be useful.
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.
Prior Python Programming Level of Knowledge Expected¶
The attendees are expected to have a basic understanding of Python and Jupyter Notebook. Familiarity with Earth science and geospatial datasets is not necessary, but it will be helpful.
G4G Summit Tickets¶
We offer two Geo for Good (G4G) Summit tickets to attendees of the EE SciPy workshop. The G4G Summit will be held on October 11-12, 2023, at the Google campus in Mountain View, CA. The link to express your interest in the tickets will be provided during the workshop.
Introduction to Earth Engine and geemap (15 mins)¶
Earth Engine is free for noncommercial and research use. For more than a decade, Earth Engine has enabled planetary-scale Earth data science and analysis by nonprofit organizations, research scientists, and other impact users.
With the launch of Earth Engine for commercial use, commercial customers will be charged for Earth Engine services. However, Earth Engine will remain free of charge for noncommercial use and research projects. Nonprofit organizations, academic institutions, educators, news media, Indigenous governments, and government researchers are eligible to use Earth Engine free of charge, just as they have done for over a decade.
The geemap Python package is built upon the Earth Engine Python API and open-source mapping libraries. It allows Earth Engine users to interactively manipulate, analyze, and visualize geospatial big data in a Jupyter environment. Since its creation in April 2020, geemap has received over 2,700 GitHub stars and is being used by over 900 projects on GitHub.
Google Colab and Earth Engine Python API authentication (5 mins)¶
Install geemap¶
Uncomment the following line to install geemap if you are running this notebook in Google Colab.
# %pip install geemap[workshop]
Import libraries¶
import ee
import geemap
Authenticate and initialize Earth Engine¶
You will need to create a Google Cloud Project and enable the Earth Engine API for the project. You can find detailed instructions here.
geemap.ee_initialize()
Creating interactive maps¶
Let's create an interactive map using the ipyleaflet plotting backend. The geemap.Map class inherits the ipyleaflet.Map class. Therefore, you can use the same syntax to create an interactive map as you would with ipyleaflet.Map.
Map = geemap.Map()
To display it in a Jupyter notebook, simply ask for the object representation:
Map
To customize the map, you can specify various keyword arguments, such as center ([lat, lon]), zoom, width, and height. The default width is 100%, which takes up the entire cell width of the Jupyter notebook. The height argument accepts a number or a string. If a number is provided, it represents the height of the map in pixels. If a string is provided, the string must be in the format of a number followed by px, e.g., 600px.
Map = geemap.Map(center=[40, -100], zoom=4, height=600)
Map
To hide a control, set control_name to False, e.g., draw_ctrl=False.
Map = geemap.Map(data_ctrl=False, toolbar_ctrl=False, draw_ctrl=False)
Map
Adding basemaps¶
There are several ways to add basemaps to a map. You can specify the basemap to use in the basemap keyword argument when creating the map. Alternatively, you can add basemap layers to the map using the add_basemap method. Geemap has hundreds of built-in basemaps available that can be easily added to the map with only one line of code.
Create a map by specifying the basemap to use as follows. For example, the Esri.WorldImagery basemap represents the Esri world imagery basemap.
Map = geemap.Map(basemap="Esri.WorldImagery")
Map
You can add as many basemaps as you like to the map. For example, the following code adds the OpenTopoMap basemap to the map above:
Map.add_basemap("OpenTopoMap")
Print out the first 10 basemaps:
basemaps = list(geemap.basemaps.keys())
len(geemap.basemaps)
basemaps[:10]
Using Earth Engine data (30 mins)¶
Earth Engine data types (Image, ImageCollection, Geometry, Feature, FeatureCollection)¶
Earth Engine objects are server-side objects rather than client-side objects, which means that they are not stored locally on your computer. Similar to video streaming services (e.g., YouTube, Netflix, and Hulu), which store videos/movies on their servers, Earth Engine data are stored on the Earth Engine servers. We can stream geospatial data from Earth Engine on-the-fly without having to download the data just like we can watch videos from streaming services using a web browser without having to download the entire video to your computer.
- Image: the fundamental raster data type in Earth Engine.
- ImageCollection: a stack or time-series of images.
- Geometry: the fundamental vector data type in Earth Engine.
- Feature: a Geometry with attributes.
- FeatureCollection: a set of features.
Image¶
Raster data in Earth Engine are represented as Image objects. Images are composed of one or more bands and each band has its own name, data type, scale, mask and projection. Each image has metadata stored as a set of properties.
Loading Earth Engine images¶
image = ee.Image("USGS/SRTMGL1_003")
image
Visualizing Earth Engine images¶
Map = 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"],
}
Map.addLayer(image, vis_params, "SRTM")
Map
ImageCollection¶
An ImageCollection is a stack or sequence of images. An ImageCollection can be loaded by passing an Earth Engine asset ID into the ImageCollection constructor. You can find ImageCollection IDs in the Earth Engine Data Catalog.
Loading image collections¶
For example, to load the image collection of the Sentinel-2 surface reflectance:
collection = ee.ImageCollection("COPERNICUS/S2_SR")
Visualizing image collections¶
To visualize an Earth Engine ImageCollection, we need to convert an ImageCollection to an Image by compositing all the images in the collection to a single image representing, for example, the min, max, median, mean or standard deviation of the images. For example, to create a median value image from a collection, use the collection.median() method. Let's create a median image from the Sentinel-2 surface reflectance collection:
Map = geemap.Map()
collection = ee.ImageCollection("COPERNICUS/S2_SR")
image = collection.median()
vis = {
"min": 0.0,
"max": 3000,
"bands": ["B4", "B3", "B2"],
}
Map.setCenter(83.277, 17.7009, 12)
Map.addLayer(image, vis, "Sentinel-2")
Map
Filtering image collections¶
Map = geemap.Map()
collection = (
ee.ImageCollection("COPERNICUS/S2_SR")
.filterDate("2021-01-01", "2022-01-01")
.filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 5))
)
image = collection.median()
vis = {
"min": 0.0,
"max": 3000,
"bands": ["B4", "B3", "B2"],
}
Map.setCenter(83.277, 17.7009, 12)
Map.addLayer(image, vis, "Sentinel-2")
Map
FeatureCollection¶
A FeatureCollection is a collection of Features. A FeatureCollection is analogous to a GeoJSON FeatureCollection object, i.e., a collection of features with associated properties/attributes. Data contained in a shapefile can be represented as a FeatureCollection.
Loading feature collections¶
The Earth Engine Data Catalog hosts a variety of vector datasets (e.g,, US Census data, country boundaries, and more) as feature collections. You can find feature collection IDs by searching the data catalog. For example, to load the TIGER roads data by the U.S. Census Bureau:
Map = geemap.Map()
fc = ee.FeatureCollection("TIGER/2016/Roads")
Map.setCenter(-73.9596, 40.7688, 12)
Map.addLayer(fc, {}, "Census roads")
Map
Filtering feature collections¶
Map = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.eq("NAME", "Louisiana"))
Map.addLayer(fc, {}, "Louisiana")
Map.centerObject(fc, 7)
Map
feat = fc.first()
feat.toDictionary()
Map = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.inList("NAME", ["California", "Oregon", "Washington"]))
Map.addLayer(fc, {}, "West Coast")
Map.centerObject(fc, 5)
Map
region = Map.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)
Map.addLayer(fc, {}, "Southeastern U.S.")
Map.centerObject(fc, 6)
Visualizing feature collections¶
Map = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, "US States")
Map
Map = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
style = {"color": "0000ffff", "width": 2, "lineType": "solid", "fillColor": "FF000080"}
Map.addLayer(states.style(**style), {}, "US States")
Map
Map = 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"]
Map.add_styled_vector(
states, column="NAME", palette=palette, layer_name="Styled vector", **vis_params
)
Map
Earth Engine Data Catalog¶
The Earth Engine Data Catalog hosts a variety of geospatial datasets. As of March 2023, the catalog contains over 1,000 datasets with a total size of over 80 petabytes. Some notable datasets include: Landsat, Sentinel, MODIS, NAIP, etc. For a complete list of datasets in CSV or JSON formats, see the Earth Engine Datasets List.
Searching for datasets¶
The Earth Engine Data Catalog is searchable. You can search datasets by name, keyword, or tag. For example, enter "elevation" in the search box will filter the catalog to show only datasets containing "elevation" in their name, description, or tags. 52 datasets are returned for this search query. Scroll down the list to find the NASA SRTM Digital Elevation 30m dataset. On each dataset page, you can find the following information, including Dataset Availability, Dataset Provider, Earth Engine Snippet, Tags, Description, Code Example, and more (see {numref}ch03_gee_srtm). One important piece of information is the Image/ImageCollection/FeatureCollection ID of each dataset, which is essential for accessing the dataset through the Earth Engine JavaScript or Python APIs.
Map = geemap.Map()
Map
dataset_xyz = ee.Image("USGS/SRTMGL1_003")
Map.addLayer(dataset_xyz, {}, "USGS/SRTMGL1_003")
Map = geemap.Map()
dem = ee.Image("USGS/SRTMGL1_003")
vis_params = {
"min": 0,
"max": 4000,
"palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
Map.addLayer(dem, vis_params, "SRTM DEM")
Map
Using the datasets module¶
from geemap.datasets import DATA
Map = geemap.Map(center=[40, -100], zoom=4)
dataset = ee.Image(DATA.USGS_GAP_CONUS_2011)
Map.addLayer(dataset, {}, "GAP CONUS")
Map
from geemap.datasets import get_metadata
get_metadata(DATA.USGS_GAP_CONUS_2011)
Converting Earth Engine JavaScripts to Python¶
Find some Earth Engine JavaScript code that you want to convert to Python. For example, you can grab some sample code from the Earth Engine Documentation.
Map = geemap.Map()
Map
# Load an image.
image = ee.Image("LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318")
# Define the visualization parameters.
vizParams = {"bands": ["B5", "B4", "B3"], "min": 0, "max": 0.5, "gamma": [0.95, 1.1, 1]}
# Center the map and display the image.
Map.setCenter(-122.1899, 37.5010, 10)
# San Francisco Bay
Map.addLayer(image, vizParams, "False color composite")
Exercise 1 - Creating cloud-free imagery¶
Create a cloud-free imagery of Texas for the year of 2022. You can use either Landsat 9 or Sentinel-2 imagery. Relevant Earth Engine assets:
- ee.FeatureCollection("TIGER/2018/States")
- ee.ImageCollection("COPERNICUS/S2_SR")
- ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
Map = geemap.Map(center=(40, -100), zoom=4)
dem = ee.Image("USGS/SRTMGL1_003")
landsat7 = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003").select(
["B1", "B2", "B3", "B4", "B5", "B7"]
)
states = ee.FeatureCollection("TIGER/2018/States")
vis_params = {
"min": 0,
"max": 4000,
"palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
Map.addLayer(dem, vis_params, "SRTM DEM")
Map.addLayer(
landsat7,
{"bands": ["B4", "B3", "B2"], "min": 20, "max": 200, "gamma": 2.0},
"Landsat 7",
)
Map.addLayer(states, {}, "US States")
Map.add_inspector()
Map
Using the plotting tool¶
Map = 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}
Map.addLayer(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,
}
Map.addLayer(hyperion, hyperion_vis, "Hyperion")
Map.add_plot_gui()
Map
Map.set_plot_options(add_marker_cluster=True, overlay=True)
from geemap.legends import builtin_legends
for legend in builtin_legends:
print(legend)
Add NLCD WMS layer and legend to the map.
Map = geemap.Map(center=[40, -100], zoom=4)
Map.add_basemap("Google Hybrid")
Map.add_basemap("NLCD 2019 CONUS Land Cover")
Map.add_legend(builtin_legend="NLCD", max_width="100px")
Map
Add NLCD Earth Engine layer and legend to the map.
Map = geemap.Map(center=[40, -100], zoom=4)
Map.add_basemap("HYBRID")
nlcd = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019")
landcover = nlcd.select("landcover")
Map.addLayer(landcover, {}, "NLCD Land Cover 2019")
Map.add_legend(
title="NLCD Land Cover Classification", builtin_legend="NLCD", height="460px"
)
Map
Custom legends¶
Add a custom legend by specifying the colors and labels.
Map = geemap.Map(add_google_map=False)
labels = ["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)]
Map.add_legend(labels=labels, colors=colors, position="bottomright")
Map
Add a custom legend by specifying a dictionary of colors and labels.
Map = geemap.Map(center=[40, -100], zoom=4)
Map.add_basemap("Google Hybrid")
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/2019_REL/NLCD/2019")
landcover = nlcd.select("landcover")
Map.addLayer(landcover, {}, "NLCD Land Cover 2019")
Map.add_legend(title="NLCD Land Cover Classification", legend_dict=legend_dict)
Map
Creating color bars¶
Add a horizontal color bar.
Map = geemap.Map()
dem = ee.Image("USGS/SRTMGL1_003")
vis_params = {
"min": 0,
"max": 4000,
"palette": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
}
Map.addLayer(dem, vis_params, "SRTM DEM")
Map.add_colorbar(vis_params, label="Elevation (m)", layer_name="SRTM DEM")
Map
Add a vertical color bar.
Map.add_colorbar(
vis_params,
label="Elevation (m)",
layer_name="SRTM DEM",
orientation="vertical",
max_width="100px",
)
Map = geemap.Map()
Map.split_map(left_layer="Google Terrain", right_layer="OpenTopoMap")
Map
Create a split map with Earth Engine layers.
Map = geemap.Map(center=(40, -100), zoom=4, height=600)
nlcd_2001 = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2001").select("landcover")
nlcd_2019 = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select("landcover")
left_layer = geemap.ee_tile_layer(nlcd_2001, {}, "NLCD 2001")
right_layer = geemap.ee_tile_layer(nlcd_2019, {}, "NLCD 2019")
Map.split_map(left_layer, right_layer)
Map
Linked maps¶
Create a 2x2 linked map for visualizing Sentinel-2 imagery with different band combinations.
image = (
ee.ImageCollection("COPERNICUS/S2")
.filterDate("2018-09-01", "2018-09-30")
.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=[38.4151, 21.2712],
zoom=12,
ee_objects=[image],
vis_params=vis_params,
labels=labels,
label_position="topright",
)
Map = 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
Create a timeseries inspector for NLCD.
Map.ts_inspector(
left_ts=collection,
right_ts=collection,
left_names=years,
right_names=years,
left_vis=vis_params,
right_vis=vis_params,
width="80px",
)
Map
Time slider¶
Create a map for visualizing MODIS vegetation data.
Map = 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",
}
Map.add_time_slider(collection, vis_params, time_interval=2)
Map
Create a map for visualizing weather data.
Map = 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)]
Map.add_time_slider(collection, vis_params, labels=labels, time_interval=1, opacity=0.8)
Map
Visualizing Sentinel-2 imagery
Map = geemap.Map(center=[37.75, -122.45], zoom=12)
collection = (
ee.ImageCollection("COPERNICUS/S2_SR")
.filterBounds(ee.Geometry.Point([-122.45, 37.75]))
.filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 10)
)
vis_params = {"min": 0, "max": 4000, "bands": ["B8", "B4", "B3"]}
Map.add_time_slider(collection, vis_params)
Map
Exercise 2 - Creating land cover maps with a legend¶
Create a split map for visualizing NLCD land cover change in Texas between 2001 and 2019. Add the NLCD legend to the map. Relevant Earth Engine assets:
Map = geemap.Map()
centroid = ee.Geometry.Point([-122.4439, 37.7538])
image = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterBounds(centroid).first()
vis = {"min": 0, "max": 3000, "bands": ["B5", "B4", "B3"]}
Map.centerObject(centroid, 8)
Map.addLayer(image, vis, "Landsat-8")
Map.addLayer(centroid, {}, "Centroid")
Map
Check image properties.
image.propertyNames()
Check image property values.
image.toDictionary()
Get specific image properties.
image.get("CLOUD_COVER") # 0.05
Get image properties with easy-to-read time format.
props = geemap.image_props(image)
props
Compute image descriptive statistics.
stats = geemap.image_stats(image, scale=30)
stats
Map = 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"],
}
Map.addLayer(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}
Map.addLayer(landsat, landsat_vis, "Landsat", False)
# Add US Census States
states = ee.FeatureCollection("TIGER/2018/States")
style = {"fillColor": "00000000"}
Map.addLayer(states.style(**style), {}, "US States")
Map
Compute zonal statistics. In this case, we compute the mean elevation of each state and export the results to a CSV file.
out_dem_stats = "dem_stats.csv"
geemap.zonal_stats(
dem, states, out_dem_stats, stat_type="MEAN", scale=1000, return_fc=False
)
Display the csv file as a table.
geemap.csv_to_df(out_dem_stats).sort_values(by=["mean"], ascending=True)
Compute zonal statistics of mean spectral values of each state.
out_landsat_stats = "landsat_stats.csv"
geemap.zonal_stats(
landsat,
states,
out_landsat_stats,
stat_type="MEAN",
scale=1000,
return_fc=False,
)
geemap.csv_to_df(out_landsat_stats)
Zonal statistics by group¶
Compute zonal statistics by group. In this case, we compute the area of each land cover type in each state and export the results to a CSV file.
Map = geemap.Map(center=[40, -100], zoom=4)
# Add NLCD data
dataset = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019")
landcover = dataset.select("landcover")
Map.addLayer(landcover, {}, "NLCD 2019")
# Add US census states
states = ee.FeatureCollection("TIGER/2018/States")
style = {"fillColor": "00000000"}
Map.addLayer(states.style(**style), {}, "US States")
# Add NLCD legend
Map.add_legend(title="NLCD Land Cover", builtin_legend="NLCD")
Map
Compute zonal statistics by group and convert the area unit from square meters to square kilometers.
nlcd_stats = "nlcd_stats.csv"
geemap.zonal_stats_by_group(
landcover,
states,
nlcd_stats,
stat_type="SUM",
denominator=1e6,
decimal_places=2,
)
geemap.csv_to_df(nlcd_stats)
Calculate the percentage of each land cover type in each state.
nlcd_stats = "nlcd_stats_pct.csv"
geemap.zonal_stats_by_group(
landcover,
states,
nlcd_stats,
stat_type="PERCENTAGE",
denominator=1e6,
decimal_places=2,
)
Zonal statistics with two images¶
geemap.csv_to_df(nlcd_stats)
Zonal statistics by zone¶
The zonal statistics by zone algorithm is similar to the zonal statistics by group algorithm, but it takes an image as the zone input instead of a feature collection.
Map = geemap.Map(center=[40, -100], zoom=4)
dem = ee.Image("USGS/3DEP/10m")
vis = {"min": 0, "max": 4000, "palette": "terrain"}
Map.addLayer(dem, vis, "DEM")
Map
landcover = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select("landcover")
Map.addLayer(landcover, {}, "NLCD 2019")
Map.add_legend(title="NLCD Land Cover Classification", builtin_legend="NLCD")
Computer the mean elevation of each land cover type.
stats = geemap.image_stats_by_zone(dem, landcover, reducer="MEAN")
stats
stats.to_csv("mean.csv", index=False)
Compute the standard deviation of each land cover type.
geemap.image_stats_by_zone(dem, landcover, out_csv="std.csv", reducer="STD")
geemap.csv_to_df("std.csv")
Exercise 3 - Zonal statistics¶
Find out which state has the highest mean temperature on in the United States on June 28, 2023. Relevant Earth Engine assets:
lat_grid = geemap.latitude_grid(step=5.0, west=-180, east=180, south=-85, north=85)
Map = geemap.Map()
style = {"fillColor": "00000000"}
Map.addLayer(lat_grid.style(**style), {}, "Latitude Grid")
Map
df = geemap.ee_to_df(lat_grid)
df.head()
Create a longitudinal grid with a 5-degree interval.
lon_grid = geemap.longitude_grid(step=5.0, west=-180, east=180, south=-85, north=85)
Map = geemap.Map()
style = {"fillColor": "00000000"}
Map.addLayer(lon_grid.style(**style), {}, "Longitude Grid")
Map
Create a rectangular grid with a 10-degree interval.
grid = geemap.latlon_grid(
lat_step=10, lon_step=10, west=-180, east=180, south=-85, north=85
)
Map = geemap.Map()
style = {"fillColor": "00000000"}
Map.addLayer(grid.style(**style), {}, "Coordinate Grid")
Map
Creating fishnets¶
Create a fishnet based on an Earth Engine geometry.
Map = geemap.Map()
Map
Use the drawing tools to draw a polygon on the map above. If no polygon is drawn, the default polygon will be used.
roi = Map.user_roi
if roi is None:
roi = ee.Geometry.BBox(-112.8089, 33.7306, -88.5951, 46.6244)
Map.addLayer(roi, {}, "ROI")
Map.user_roi = None
Map.centerObject(roi)
Create a fishnet based on a user-drawn polygon with a 2-degree interval.
fishnet = geemap.fishnet(roi, h_interval=2.0, v_interval=2.0, delta=1)
style = {"color": "blue", "fillColor": "00000000"}
Map.addLayer(fishnet.style(**style), {}, "Fishnet")
Create a new map.
Map = geemap.Map()
Map
Draw a polygon on the map.
roi = Map.user_roi
if roi is None:
roi = ee.Geometry.Polygon(
[
[
[-64.602356, -1.127399],
[-68.821106, -12.625598],
[-60.647278, -22.498601],
[-47.815247, -21.111406],
[-43.860168, -8.913564],
[-54.582825, -0.775886],
[-60.823059, 0.454555],
[-64.602356, -1.127399],
]
]
)
Map.addLayer(roi, {}, "ROI")
Map.centerObject(roi)
Map
Create a fishnet based on a user-drawn polygon with specified number of rows and columns.
fishnet = geemap.fishnet(roi, rows=6, cols=8, delta=1)
style = {"color": "blue", "fillColor": "00000000"}
Map.addLayer(fishnet.style(**style), {}, "Fishnet")
Land use and land cover change analysis¶
Forest cover mapping¶
We will use the Hansen Global Forest Change v1.10 (2000-2022) dataset.
dataset = ee.Image("UMD/hansen/global_forest_change_2022_v1_10")
dataset.bandNames()
Select the imagery for 2000.
Map = geemap.Map()
first_bands = ["first_b50", "first_b40", "first_b30"]
first_image = dataset.select(first_bands)
Map.addLayer(first_image, {"bands": first_bands, "gamma": 1.5}, "Year 2000 Bands 5/4/3")
Select the imagery for 2022.
last_bands = ["last_b50", "last_b40", "last_b30"]
last_image = dataset.select(last_bands)
Map.addLayer(last_image, {"bands": last_bands, "gamma": 1.5}, "Year 2022 Bands 5/4/3")
Select the tree cover imagery for 2000.
treecover = dataset.select(["treecover2000"])
treeCoverVisParam = {"min": 0, "max": 100, "palette": ["black", "green"]}
name = "Tree cover (%)"
Map.addLayer(treecover, treeCoverVisParam, name)
Map.add_colorbar(treeCoverVisParam, label=name, layer_name=name)
Map.add_layer_manager()
Map
Extract tree cover 2000 by using the threshold of 10%.
threshold = 10
treecover_bin = treecover.gte(threshold).selfMask()
treeVisParam = {"palette": ["green"]}
Map.addLayer(treecover_bin, treeVisParam, "Tree cover bin")
Forest loss and gain mapping¶
Visualize forest loss.
Map = geemap.Map()
Map.add_basemap("Google Hybrid")
treeloss_year = dataset.select(["lossyear"])
treeLossVisParam = {"min": 0, "max": 22, "palette": ["yellow", "red"]}
layer_name = "Tree loss year since 2000"
Map.addLayer(treeloss_year, treeLossVisParam, layer_name)
Map.add_colorbar(treeLossVisParam, label=layer_name, layer_name=layer_name)
Map.add_layer_manager()
Map
Compare forest loss and gain.
Map = geemap.Map()
Map.add_basemap("Google Hybrid")
treeloss = dataset.select(["loss"]).selfMask()
treegain = dataset.select(["gain"]).selfMask()
Map.addLayer(treeloss, {"palette": "red"}, "Tree loss")
Map.addLayer(treegain, {"palette": "yellow"}, "Tree gain")
Map.add_layer_manager()
Map
Zonal statistics by country¶
Compute zonal statistics to find out which country has the largest forest area in 2000.
Add a country boundary layer to the map.
Map = geemap.Map()
countries = ee.FeatureCollection(geemap.examples.get_ee_path("countries"))
style = {"color": "#000000ff", "fillColor": "#00000000"}
Map.addLayer(countries.style(**style), {}, "Countries")
Map
Compute zonal statistics by country.
geemap.zonal_stats(
treecover_bin,
countries,
"forest_cover.csv",
stat_type="SUM",
denominator=1e6,
scale=1000,
)
Create a pie chart to visualize the forest area by country.
geemap.pie_chart(
"forest_cover.csv", names="NAME", values="sum", max_rows=20, height=400
)
Create a bar chart to visualize the forest area by country.
geemap.bar_chart(
"forest_cover.csv",
x="NAME",
y="sum",
max_rows=20,
x_label="Country",
y_label="Forest area (km2)",
)
Calculate the forest loss area by country.
geemap.zonal_stats(
treeloss.gt(0),
countries,
"treeloss.csv",
stat_type="SUM",
denominator=1e6,
scale=1000,
)
Create a bar chart to visualize the forest loss area by country.
geemap.pie_chart("treeloss.csv", names="NAME", values="sum", max_rows=20, height=600)
Create a bar chart to visualize the forest loss area by country.
geemap.bar_chart(
"treeloss.csv",
x="NAME",
y="sum",
max_rows=20,
x_label="Country",
y_label="Forest loss area (km2)",
)
Exercise 4 - Analyzing forest cover gain and loss¶
Find out which US state has the largest forest gain and loss between 2000 and 2022. Create pie charts and bar charts to show the results. Relevant Earth Engine assets:
Map = 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]}
Map.centerObject(image)
Map.addLayer(image, vis_params, "Landsat")
Map
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"}
Map.addLayer(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
)
Exporting image collections¶
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)
Exporting feature collections¶
Map = geemap.Map()
fc = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017").filter(
ee.Filter.eq("country_na", "Germany")
)
Map.addLayer(fc, {}, "Germany")
Map.centerObject(fc)
Map
To local drive
geemap.ee_to_shp(fc, filename="Germany.shp", selectors=None)
geemap.ee_export_vector(fc, filename="Germany2.shp")
geemap.ee_to_geojson(fc, filename="Germany.geojson")
geemap.ee_to_csv(fc, filename="Germany.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="germany", fileFormat="SHP", folder="export"
)
Exercise 5 - Exporting images by a fishnet¶
Create a fishnet with a 4-degree interval based on the extent of [-112.5439, 34.0891, -85.0342, 49.6858]. Use the fishnet to download the Landsat 7 image tiles by the fishnet using the geemap.download_ee_image_tiles() and geemap.download_ee_image_tiles_parallel() functions. Relevant Earth Engine assets:
ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')
Map = geemap.Map(center=[40, -100], zoom=4)
image = ee.Image("LANDSAT/LE7_TOA_5YEAR/1999_2003").select(["B4", "B3", "B2"])
Map.addLayer(image, {"min": 20, "max": 200, "gamma": 2.0}, "Landsat")
region = ee.Geometry.BBox(-112.5439, 34.0891, -85.0342, 49.6858)
Map.addLayer(region, {}, "ROI")
Map.centerObject(region)
Map
fishnet = geemap.fishnet(region, h_interval=4.0, v_interval=4.0, delta=0.5)
style = {"color": "ffff00ff", "fillColor": "00000000"}
Map.addLayer(fishnet.style(**style), {}, "Fishnet")
geemap.download_ee_image_tiles(
image, fishnet, out_dir="tiles", scale=1000, crs="EPSG:3857"
)
geemap.download_ee_image_tiles_parallel(
image, fishnet, out_dir="tiles", scale=1000, crs="EPSG:3857"
)
Creating Satellite timelapse animations (30 mins)¶
Creating satellite timeseries¶
Use the Harmonized Sentinel-2 MSI dataset to create a timeseries for a given location.
collection = ee.ImageCollection("COPERNICUS/S2_HARMONIZED").filterMetadata(
"CLOUDY_PIXEL_PERCENTAGE", "less_than", 10
)
Specify the location of interest and date range.
start_date = "2016-01-01"
end_date = "2022-12-31"
region = ee.Geometry.BBox(-122.5549, 37.6968, -122.3446, 37.8111)
Create an annual composite.
images = geemap.create_timeseries(
collection, start_date, end_date, region, frequency="year", reducer="median"
)
images
Display the timeseries.
Map = geemap.Map()
vis_params = {"min": 0, "max": 4000, "bands": ["B8", "B4", "B3"]}
labels = [str(y) for y in range(2016, 2023)]
Map.addLayer(images, vis_params, "Sentinel-2", False)
Map.add_time_slider(images, vis_params, time_interval=2, labels=labels)
Map.centerObject(region)
Map
Map = geemap.Map(center=[40, -100], zoom=4)
Map
roi = Map.user_roi
if roi is None:
roi = ee.Geometry.BBox(-99.1019, 47.1274, -99.0334, 47.1562)
Map.addLayer(roi, {}, "ROI")
Map.centerObject(roi)
collection = geemap.naip_timeseries(roi, start_year=2009, end_year=2022, RGBN=True)
years = geemap.image_dates(collection, date_format="YYYY").getInfo()
print(years)
size = len(years)
images = collection.toList(size)
for i in range(size):
image = ee.Image(images.get(i))
Map.addLayer(image, {"bands": ["N", "R", "G"]}, years[i])
Map
timelapse = geemap.naip_timelapse(
roi,
out_gif="naip.gif",
bands=["N", "R", "G"],
frames_per_second=3,
title="NAIP Timelapse",
)
geemap.show_image(timelapse)
Landsat timelapse¶
Map = geemap.Map()
Map
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 = Map.user_roi
if roi is None:
roi = ee.Geometry.BBox(-74.7222, -8.5867, -74.1596, -8.2824)
Map.addLayer(roi)
Map.centerObject(roi)
timelapse = geemap.landsat_timelapse(
roi,
out_gif="landsat.gif",
start_year=1984,
end_year=2022,
start_date="01-01",
end_date="12-31",
bands=["SWIR1", "NIR", "Red"],
frames_per_second=5,
title="Landsat Timelapse",
progress_bar_color="blue",
mp4=True,
)
geemap.show_image(timelapse)
Map = geemap.Map()
roi = ee.Geometry.BBox(-115.5541, 35.8044, -113.9035, 36.5581)
Map.addLayer(roi)
Map.centerObject(roi)
Map
timelapse = geemap.landsat_timelapse(
roi,
out_gif="las_vegas.gif",
start_year=1984,
end_year=2022,
bands=["NIR", "Red", "Green"],
frames_per_second=5,
title="Las Vegas, NV",
font_color="blue",
)
geemap.show_image(timelapse)
Map = geemap.Map()
roi = ee.Geometry.BBox(113.8252, 22.1988, 114.0851, 22.3497)
Map.addLayer(roi)
Map.centerObject(roi)
Map
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 timelapse¶
Map = geemap.Map()
Map
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 = Map.user_roi
if roi is None:
roi = ee.Geometry.BBox(-74.7222, -8.5867, -74.1596, -8.2824)
Map.addLayer(roi)
Map.centerObject(roi)
timelapse = geemap.sentinel2_timelapse(
roi,
out_gif="sentinel2.gif",
start_year=2016,
end_year=2021,
start_date="01-01",
end_date="12-31",
frequency="year",
bands=["SWIR1", "NIR", "Red"],
frames_per_second=3,
title="Sentinel-2 Timelapse",
)
geemap.show_image(timelapse)
MODIS timelapse¶
MODIS vegetation indices
Map = geemap.Map()
Map
roi = Map.user_roi
if roi is None:
roi = ee.Geometry.BBox(-18.6983, -36.1630, 52.2293, 38.1446)
Map.addLayer(roi)
Map.centerObject(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
Map = geemap.Map()
Map
roi = Map.user_roi
if roi is None:
roi = ee.Geometry.BBox(-171.21, -57.13, 177.53, 79.99)
Map.addLayer(roi)
Map.centerObject(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)
GOES timelapse¶
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)
Map = geemap.Map()
Map.add_gui("timelapse")
Map
Break 3 (10 mins)¶
Building interactive web apps (30 mins)¶
This section is optional. We might not have enough time to cover this section.
Follow the instructions here to build an interactive Earth Engine web app with Solara and geemap. You need to sign up for a free Hugging Face account to create the web app. It is free and easy to sign up.
Exercise 7 - Building an interactive web app for visualizing land cover change¶
After following the instructions above, you should have a web app that looks like this:
The web app URL should look like this: https://giswqs-solara-geemap.hf.space/split-map.