145 chart image

Image Charts

Uncomment the following line to install geemap if needed.

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# %pip install -U geemap

# %pip install -U geemap

Import libraries¶

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import calendar
import ee
import geemap
from geemap import chart

import calendar import ee import geemap from geemap import chart

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geemap.ee_initialize()

geemap.ee_initialize()

image_by_region¶

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

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

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

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

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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

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

image_regions¶

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

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

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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', ...]

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', ...]

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

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

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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",
)

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", )

image_by_class¶

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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]

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]

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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

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

image_histogram¶

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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])

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])

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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

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