Interactive Cloud Computing with Google Earth Engine and Geemap
Introduction: This is a notebook prepared for the workshop at the AmericaView Annual Conference at Lafayette, Louisiana on March 13, 2023.
Overview: Google Earth Engine (GEE) is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. It enables scientists, researchers, and developers to analyze and visualize changes on the Earth’s surface. The geemap Python package provides GEE users with an intuitive interface to manipulate, analyze, and visualize geospatial big data interactively in a Jupyter-based environment. The topics will be covered in this workshop include: (1) introducing geemap and the Earth Engine Python API; (2) creating interactive maps; (3) searching GEE data catalog; (4) visualizing GEE datasets; (5) analyzing GEE datasets, and (6) exporting GEE datasets. More information about the geemap Python package can be found at https://geemap.org.
Requirement: Please sign up for a Google Earth Engine account if you don’t have one yet. No software installation is needed. You just need a browser with Internet access for this workshop.
%pip install geemap
import ee
import geemap
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()
Let's create an interactive map using the ipyleaflet
plotting backend. The geemap.Map
class inherits from 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
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 HYBRID
basemap represents the Google Satellite Hybrid basemap.
Map = geemap.Map(basemap="HYBRID")
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]
You can also add XYZ tile layers to the map using the Map.add_tile_layer()
method. For example, the following code creates an interactive map and adds the Google Terrain basemap to it:
Map = geemap.Map()
Map.add_tile_layer(
url="https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}",
name="Google Terrain",
attribution="Google",
)
Map
Similarly, you can add WMS tile layers to the map using the Map.add_wms_layer()
method. For example, the following code creates an interactive map and adds the National Land Cover Database (NLCD) 2019 basemap to it:
Map = geemap.Map(center=[40, -100], zoom=4)
url = "https://www.mrlc.gov/geoserver/mrlc_display/NLCD_2019_Land_Cover_L48/wms?"
Map.add_wms_layer(
url=url,
layers="NLCD_2019_Land_Cover_L48",
name="NLCD 2019",
format="image/png",
attribution="MRLC",
transparent=True,
)
Map
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.
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.
image = ee.Image("USGS/SRTMGL1_003")
image
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
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.
For example, to load the image collection of the Sentinel-2 surface reflectance:
collection = ee.ImageCollection("COPERNICUS/S2_SR")
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
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
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.
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
Map = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.eq("NAME", "Louisiana"))
Map.addLayer(fc, {}, "Louisiana")
Map.centerObject(fc)
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)
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)
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
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 40 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.
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.
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
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)
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
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")
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
Map.set_plot_options(add_marker_cluster=True, overlay=True)
legends = geemap.builtin_legends
for legend in legends:
print(legend)
Map.add_legend(builtin_legend="NLCD")
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="465px"
)
Map
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
Map = geemap.Map(center=[40, -100], zoom=4)
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
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
Map.add_colorbar(vis_params, label="Elevation (m)", layer_name="SRTM DEM")
Map.add_colorbar(
vis_params, label="Elevation (m)", layer_name="SRTM DEM", orientation="vertical"
)
Map.add_colorbar(
vis_params,
label="Elevation (m)",
layer_name="SRTM DEM",
orientation="vertical",
transparent_bg=True,
)
Map = geemap.Map()
Map.split_map(left_layer="HYBRID", right_layer="TERRAIN")
Map
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
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
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
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
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
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
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
out_dem_stats = "dem_stats.csv"
geemap.zonal_stats(
dem, states, out_dem_stats, stat_type="MEAN", scale=1000, return_fc=False
)
out_landsat_stats = "landsat_stats.csv"
geemap.zonal_stats(
landsat,
states,
out_landsat_stats,
stat_type="MEAN",
scale=1000,
return_fc=False,
)
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
nlcd_stats = "nlcd_stats.csv"
geemap.zonal_stats_by_group(
landcover,
states,
nlcd_stats,
stat_type="SUM",
denominator=1e6,
decimal_places=2,
)
nlcd_stats = "nlcd_stats_pct.csv"
geemap.zonal_stats_by_group(
landcover,
states,
nlcd_stats,
stat_type="PERCENTAGE",
denominator=1e6,
decimal_places=2,
)
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")
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")
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
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")
Map
geemap.ee_export_image(image, filename="landsat.tif", scale=30, region=region)
geemap.ee_export_image_to_drive(
image, description="landsat", folder="export", region=region, scale=30
)
assetId = "landsat_sfo"
geemap.ee_export_image_to_asset(
image, description="landsat", assetId=assetId, region=region, scale=30
)
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")
geemap.ee_export_image_collection(collection, out_dir=".", scale=10)
geemap.ee_export_image_collection_to_drive(collection, folder="export", scale=10)
Map = geemap.Map()
states = ee.FeatureCollection("TIGER/2018/States")
fc = states.filter(ee.Filter.eq("NAME", "Louisiana"))
Map.addLayer(fc, {}, "Louisiana")
Map.centerObject(fc)
Map
geemap.ee_to_shp(fc, filename="louisiana.shp", selectors=None)
geemap.ee_to_geojson(fc, filename="louisiana.geojson")
geemap.ee_to_csv(fc, filename="louisiana.csv")
df = geemap.ee_to_df(fc)
df
geemap.ee_export_vector_to_drive(
fc, description="louisiana", fileFormat="SHP", folder="export"
)
collection = ee.ImageCollection("COPERNICUS/S2_HARMONIZED").filterMetadata(
"CLOUDY_PIXEL_PERCENTAGE", "less_than", 10
)
start_date = "2016-01-01"
end_date = "2022-12-31"
region = ee.Geometry.BBox(-122.5549, 37.6968, -122.3446, 37.8111)
images = geemap.create_timeseries(
collection, start_date, end_date, region, frequency="year", reducer="median"
)
images
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()
Map
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)
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)
dataset = ee.Image("JRC/GSW1_4/GlobalSurfaceWater")
dataset.bandNames()
Map = geemap.Map()
Map.add_basemap("HYBRID")
Map
image = dataset.select(["occurrence"])
region = Map.user_roi # Draw a polygon on the map
if region is None:
region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531)
vis_params = {"min": 0.0, "max": 100.0, "palette": ["ffffff", "ffbbbb", "0000ff"]}
Map.addLayer(image, vis_params, "Occurrence")
Map.addLayer(region, {}, "ROI", True, 0.5)
Map.centerObject(region)
Map.add_colorbar(vis_params, label="Water occurrence (%)", layer_name="Occurrence")
df = geemap.image_histogram(
image,
region,
scale=30,
return_df=True,
)
df
hist = geemap.image_histogram(
image,
region,
scale=30,
x_label="Water Occurrence (%)",
y_label="Pixel Count",
title="Surface Water Occurrence",
layout_args={"title": dict(x=0.5)},
return_df=False,
)
hist
hist.update_layout(
autosize=False, width=800, height=400, margin=dict(l=30, r=20, b=10, t=50, pad=4)
)
dataset = ee.ImageCollection("JRC/GSW1_4/MonthlyHistory")
dataset.size()
dataset.aggregate_array("system:index")
Map = geemap.Map()
Map
image = dataset.filterDate("2020-08-01", "2020-09-01").first()
region = Map.user_roi # Draw a polygon on the map
if region is None:
region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531)
vis_params = {"min": 0.0, "max": 2.0, "palette": ["ffffff", "fffcb8", "0905ff"]}
Map.addLayer(image, vis_params, "Water")
Map.addLayer(region, {}, "ROI", True, 0.5)
Map.centerObject(region)
geemap.jrc_hist_monthly_history(
region=region, scale=30, frequency="month", denominator=1e4, y_label="Area (ha)"
)
geemap.jrc_hist_monthly_history(
region=region,
start_month=6,
end_month=9,
scale=30,
frequency="month",
denominator=1e4,
y_label="Area (ha)",
)
geemap.jrc_hist_monthly_history(
region=region,
start_month=6,
end_month=9,
scale=30,
frequency="year",
reducer="mean",
denominator=1e4,
y_label="Area (ha)",
)
geemap.jrc_hist_monthly_history(
region=region,
start_month=6,
end_month=9,
scale=30,
frequency="year",
reducer="max",
denominator=1e4,
y_label="Area (ha)",
)