11 analysis

Descriptive statistics¶

Uncomment and execute the following code block to install geemap if needed.

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# !pip install geemap
# !pip install geemap

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import ee
import geemap
import ee
import geemap

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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
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

Out[3]:

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Using Earth Engine built-in functions to get image properties.

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image.propertyNames().getInfo()
image.propertyNames().getInfo()

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['IMAGE_QUALITY_TIRS',
 'CLOUD_COVER',
 'system:id',
 'EARTH_SUN_DISTANCE',
 'LANDSAT_ID',
 'system:footprint',
 'system:version',
 'CLOUD_COVER_LAND',
 'GEOMETRIC_RMSE_MODEL',
 'SR_APP_VERSION',
 'SATELLITE',
 'SOLAR_AZIMUTH_ANGLE',
 'IMAGE_QUALITY_OLI',
 'system:time_end',
 'WRS_PATH',
 'system:time_start',
 'SENSING_TIME',
 'ESPA_VERSION',
 'SOLAR_ZENITH_ANGLE',
 'WRS_ROW',
 'GEOMETRIC_RMSE_MODEL_Y',
 'LEVEL1_PRODUCTION_DATE',
 'GEOMETRIC_RMSE_MODEL_X',
 'system:asset_size',
 'PIXEL_QA_VERSION',
 'system:index',
 'system:bands',
 'system:band_names']

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image.get('CLOUD_COVER').getInfo()
image.get('CLOUD_COVER').getInfo()

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0.05

Using geemap.image_props() to get image properties.

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props = geemap.image_props(image)
props.getInfo()
props = geemap.image_props(image)
props.getInfo()

Out[6]:

{'CLOUD_COVER': 0.05,
 'CLOUD_COVER_LAND': 0.06,
 'EARTH_SUN_DISTANCE': 1.001791,
 'ESPA_VERSION': '2_23_0_1b',
 'GEOMETRIC_RMSE_MODEL': 6.678,
 'GEOMETRIC_RMSE_MODEL_X': 4.663,
 'GEOMETRIC_RMSE_MODEL_Y': 4.78,
 'IMAGE_DATE': '2013-04-09',
 'IMAGE_QUALITY_OLI': 9,
 'IMAGE_QUALITY_TIRS': 9,
 'LANDSAT_ID': 'LC08_L1TP_044034_20130409_20170310_01_T1',
 'LEVEL1_PRODUCTION_DATE': 1489126619000,
 'NOMINAL_SCALE': 30,
 'PIXEL_QA_VERSION': 'generate_pixel_qa_1.6.0',
 'SATELLITE': 'LANDSAT_8',
 'SENSING_TIME': '2013-04-09T18:46:34.7579070Z',
 'SOLAR_AZIMUTH_ANGLE': 142.742508,
 'SOLAR_ZENITH_ANGLE': 34.973495,
 'SR_APP_VERSION': 'LaSRC_1.3.0',
 'WRS_PATH': 44,
 'WRS_ROW': 34,
 'system:asset_size': '558.682087 MB',
 'system:band_names': ['B1',
  'B2',
  'B3',
  'B4',
  'B5',
  'B6',
  'B7',
  'B10',
  'B11',
  'sr_aerosol',
  'pixel_qa',
  'radsat_qa'],
 'system:id': 'LANDSAT/LC08/C01/T1_SR/LC08_044034_20130409',
 'system:index': 'LC08_044034_20130409',
 'system:time_end': '2013-04-09 18:46:34',
 'system:time_start': '2013-04-09 18:46:34',
 'system:version': 1581684730621653}

Get image summary statistics, such as max, min, mean, std, sum.

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stats = geemap.image_stats(image, scale=90)
stats.getInfo()
stats = geemap.image_stats(image, scale=90)
stats.getInfo()

Out[7]:

{'max': {'B1': 7624,
  'B10': 3155,
  'B11': 3127,
  'B2': 8471,
  'B3': 9778,
  'B4': 10679,
  'B5': 11606,
  'B6': 13054,
  'B7': 12327,
  'pixel_qa': 480,
  'radsat_qa': 254,
  'sr_aerosol': 228},
 'mean': {'B1': 373.1098468094287,
  'B10': 2933.4637979167583,
  'B11': 2916.278387909346,
  'B2': 427.4021313923417,
  'B3': 614.9744637045823,
  'B4': 591.3781773385491,
  'B5': 1954.134478186476,
  'B6': 1472.071307222312,
  'B7': 996.8232780577415,
  'pixel_qa': 322.8433703241347,
  'radsat_qa': 0.00019878780091270898,
  'sr_aerosol': 87.7488586248313},
 'min': {'B1': -1432,
  'B10': 2759,
  'B11': 2702,
  'B2': -1129,
  'B3': -345,
  'B4': -403,
  'B5': 35,
  'B6': 10,
  'B7': 5,
  'pixel_qa': 322,
  'radsat_qa': 0,
  'sr_aerosol': 8},
 'std': {'B1': 201.9355685609457,
  'B10': 67.57460806939051,
  'B11': 63.85549758560136,
  'B2': 230.81296623126636,
  'B3': 296.8097076885089,
  'B4': 393.63764571402265,
  'B5': 1202.7937141588027,
  'B6': 926.420271742664,
  'B7': 729.0233711578251,
  'pixel_qa': 4.9833065816273345,
  'radsat_qa': 0.1664709111036373,
  'sr_aerosol': 25.1855827242985},
 'sum': {'B1': 1557981451.9372544,
  'B10': 12249159935.51374,
  'B11': 12177399433.172562,
  'B2': 1784687804.2,
  'B3': 2567926888.180393,
  'B4': 2469396718.5607834,
  'B5': 8159809497.498039,
  'B6': 6146875544.007842,
  'B7': 4162399334.5490217,
  'pixel_qa': 1347970027.0274508,
  'radsat_qa': 830,
  'sr_aerosol': 366378380.98823535}}

Zonal statistics¶

Add Earth Engine datasets.

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Map = geemap.Map(center=[40, -100], zoom=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, "LE7_TOA_5YEAR/1999_2003")

states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, 'US States')
Map
Map = geemap.Map(center=[40, -100], zoom=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, "LE7_TOA_5YEAR/1999_2003")

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

Out[8]:

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Computer zonal statistics.

Allowed output formats: csv, shp, json, kml, kmz
Allowed statistics type: MEAN, MAXIMUM, MINIMUM, MEDIAN, STD, MIN_MAX, VARIANCE, SUM

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out_dem_stats = 'dem_stats.csv'
geemap.zonal_statistics(dem, states, out_dem_stats, statistics_type='MEAN', scale=1000)
out_dem_stats = 'dem_stats.csv'
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-b8cb040ec240655deac162c0a3d9269d:getFeatures
Please wait ...
Data downloaded to /home/runner/work/GEE-Courses/GEE-Courses/docs/geemap_intro/dem_stats.csv

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out_landsat_stats = 'landsat_stats.csv'
geemap.zonal_statistics(
    landsat, states, out_landsat_stats, statistics_type='SUM', scale=1000
)
out_landsat_stats = '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/ef7da058fa8cecffdece8d714da68ec8-4655e73f0e2f56812204f98d440b5861:getFeatures
Please wait ...
Data downloaded to /home/runner/work/GEE-Courses/GEE-Courses/docs/geemap_intro/landsat_stats.csv

Zonal statistics by group¶

Add Earth Engine datasets.

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

dataset = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019')
landcover = dataset.select('landcover')
Map.addLayer(landcover, {}, 'NLCD 2019')

states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, 'US States')
Map.add_legend(builtin_legend='NLCD')
Map
Map = geemap.Map(center=[40, -100], zoom=4)

dataset = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019')
landcover = dataset.select('landcover')
Map.addLayer(landcover, {}, 'NLCD 2019')

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

Out[11]:

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Computer zonal statistics by group.

statistics_type can be either 'SUM' or 'PERCENTAGE'
denominator can be used to convert square meters to other areal units, such as square kilimeters.

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nlcd_stats = 'nlcd_stats.csv'

geemap.zonal_statistics_by_group(
    landcover,
    states,
    nlcd_stats,
    statistics_type='SUM',
    denominator=1000000,
    decimal_places=2,
)
nlcd_stats = 'nlcd_stats.csv'

geemap.zonal_statistics_by_group(
    landcover,
    states,
    nlcd_stats,
    statistics_type='SUM',
    denominator=1000000,
    decimal_places=2,
)

Computing ... 
Generating URL ...
Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/133d101cfb013c1f804933a0d6c5b076-de75bc07554fc30e90bc157e52c6e1a0:getFeatures
Please wait ...
Data downloaded to /home/runner/work/GEE-Courses/GEE-Courses/docs/geemap_intro/nlcd_stats.csv

Last update: 2022-03-25