108 image zonal stats

Uncomment the following line to install geemap if needed.

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# !pip install geemap

# !pip install geemap

Import libraries.

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import ee
import geemap
import geemap.colormaps as cm

import ee import geemap import geemap.colormaps as cm

Create an interactive map.

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

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

Add a DEM to the map.

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dem = ee.Image('USGS/3DEP/10m')
vis = {'min': 0, 'max': 4000, 'palette': cm.palettes.dem}

dem = ee.Image('USGS/3DEP/10m') vis = {'min': 0, 'max': 4000, 'palette': cm.palettes.dem}

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Map.addLayer(dem, vis, 'DEM')

Map.addLayer(dem, vis, 'DEM')

Add NLCD land cover data and legend to the map.

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landcover = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select('landcover')

landcover = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select('landcover')

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Map.addLayer(landcover, {}, 'NLCD 2019')
Map.add_legend(builtin_legend='NLCD')

Map.addLayer(landcover, {}, 'NLCD 2019') Map.add_legend(builtin_legend='NLCD')

Calculate image zonal statistics by zone. In this case, we are going to calculate the mean elevation by each land cover type. The result can be returned as a Panda DataFrame or saved as a CSV.

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

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

Save the resulting Pandas DataFrame as a CSV.

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

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

Calculate the standard deviation of elevation by each land cover type and save the result as a CSV.

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

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