Zonal statistics

Uncomment the following line to install geemap if needed.

In [1]:

# !pip install geemap

# !pip install geemap

In [2]:

import ee
import geemap
import os

import ee import geemap import os

Create an interactive map¶

In [3]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

Out[3]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Add Earth Engine data¶

In [4]:

# Add Earth Engine dataset
dem = ee.Image('USGS/SRTMGL1_003')

# Set visualization parameters.
dem_vis = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

# Add Earth Engine DEM to map
Map.addLayer(dem, dem_vis, 'SRTM DEM')

# Add Landsat data to map
landsat = ee.Image('LE7_TOA_5YEAR/1999_2003')

landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(landsat, landsat_vis, "Landsat", False)

states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, 'US States')

# Add Earth Engine dataset dem = ee.Image('USGS/SRTMGL1_003') # Set visualization parameters. dem_vis = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } # Add Earth Engine DEM to map Map.addLayer(dem, dem_vis, 'SRTM DEM') # Add Landsat data to map landsat = ee.Image('LE7_TOA_5YEAR/1999_2003') landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4} Map.addLayer(landsat, landsat_vis, "Landsat", False) states = ee.FeatureCollection("TIGER/2018/States") Map.addLayer(states, {}, 'US States')

Compute zonal statistics for one image¶

In [5]:

out_dir = os.path.expanduser('~/Downloads')
if not os.path.exists(out_dir):
    os.makedirs(out_dir)

out_dir = os.path.expanduser('~/Downloads') if not os.path.exists(out_dir): os.makedirs(out_dir)

In [6]:

out_dem_stats = os.path.join(out_dir, 'dem_stats.csv')

# Allowed output formats: csv, shp, json, kml, kmz
# Allowed statistics type: MEAN, MAXIMUM, MINIMUM, MEDIAN, STD, MIN_MAX, VARIANCE, SUM
geemap.zonal_statistics(dem, states, out_dem_stats, statistics_type='MEAN', scale=1000)

out_dem_stats = os.path.join(out_dir, 'dem_stats.csv') # Allowed output formats: csv, shp, json, kml, kmz # Allowed statistics type: MEAN, MAXIMUM, MINIMUM, MEDIAN, STD, MIN_MAX, VARIANCE, SUM geemap.zonal_statistics(dem, states, out_dem_stats, statistics_type='MEAN', scale=1000)

Computing statistics ...
Generating URL ...
Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/04e8c7b138ea4c98a9a99871c8737598-ec04c9f55156370ce0aad7df702cbd2b:getFeatures
Please wait ...
Data downloaded to /home/runner/Downloads/dem_stats.csv

In [7]:

out_landsat_stats = os.path.join(out_dir, 'landsat_stats.csv')
geemap.zonal_statistics(
    landsat, states, out_landsat_stats, statistics_type='SUM', scale=1000
)

out_landsat_stats = os.path.join(out_dir, 'landsat_stats.csv') geemap.zonal_statistics( landsat, states, out_landsat_stats, statistics_type='SUM', scale=1000 )

Computing statistics ...
Generating URL ...
Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/ef7da058fa8cecffdece8d714da68ec8-ad6297d0a66e53701510771001283d7b:getFeatures
Please wait ...
Data downloaded to /home/runner/Downloads/landsat_stats.csv

Compute zonal statistics for time-series images¶

In [8]:

Map = geemap.Map()

collection = (
    ee.ImageCollection('MODIS/MCD43A4_006_NDVI')
    .filter(ee.Filter.date('2018-04-01', '2018-05-01'))
    .select("NDVI")
)
vis_params = {
    'min': 0.0,
    'max': 1.0,
    'palette': [
        'FFFFFF',
        'CE7E45',
        'DF923D',
        'F1B555',
        'FCD163',
        '99B718',
        '74A901',
        '66A000',
        '529400',
        '3E8601',
        '207401',
        '056201',
        '004C00',
        '023B01',
        '012E01',
        '011D01',
        '011301',
    ],
}

first_image = collection.first()

Map.addLayer(first_image, vis_params, "First image")
Map.setCenter(-7.03125, 31.0529339857, 2)
Map

Map = geemap.Map() collection = ( ee.ImageCollection('MODIS/MCD43A4_006_NDVI') .filter(ee.Filter.date('2018-04-01', '2018-05-01')) .select("NDVI") ) vis_params = { 'min': 0.0, 'max': 1.0, 'palette': [ 'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901', '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01', '012E01', '011D01', '011301', ], } first_image = collection.first() Map.addLayer(first_image, vis_params, "First image") Map.setCenter(-7.03125, 31.0529339857, 2) Map

Out[8]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [9]:

modis = collection.toBands()
Map.addLayer(modis, {}, "MODIS Time series", False)

modis = collection.toBands() Map.addLayer(modis, {}, "MODIS Time series", False)

In [10]:

out_landsat_stats = os.path.join(out_dir, 'ndvi.csv')
geemap.zonal_statistics(
    modis, states, out_landsat_stats, statistics_type='MEAN', scale=1000
)

out_landsat_stats = os.path.join(out_dir, 'ndvi.csv') geemap.zonal_statistics( modis, states, out_landsat_stats, statistics_type='MEAN', scale=1000 )

Computing statistics ...
Generating URL ...
Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/0dd2ec712cd35a166923cfa585323a23-7b4167ae807df8c38dbc18347a42fb60:getFeatures
Please wait ...
Data downloaded to /home/runner/Downloads/ndvi.csv

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Last update: 2022-03-25