Notebook Example

from gee_zonal import Catalog, ZonalStats
import ee
import geopandas as gpd

Input Features

Input target features can be referenced directly as a GEE asset, or can be supplied as a geopandas.GeoDataFrame, or a path to a shapefile/GeoJSON (will be automatically converted to ee.FeatureCollection).

AOIs = ee.FeatureCollection('users/afche18/Ethiopia_AOI')
AOIs.getInfo()

{'type': 'FeatureCollection',
 'columns': {'Area': 'Float', 'Id': 'Integer', 'system:index': 'String'},
 'version': 1570633589922603,
 'id': 'users/afche18/Ethiopia_AOI',
 'properties': {'system:asset_size': 11288},
 'features': [{'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': [[[42.06158510035763, 9.397823252053993],
      [42.06119314725495, 9.20601121183654],
      [42.18901118834056, 9.20584119448768],
      [42.316813505733144, 9.205625705724506],
      [42.3125304107434, 9.301530485292622],
      [42.30825425836041, 9.397435608019856],
      [42.184926941544006, 9.39765098188209],
      [42.06158510035763, 9.397823252053993]]]},
   'id': '00000000000000000000',
   'properties': {'Area': 586.656, 'Id': 0}},
  {'type': 'Feature',
   'geometry': {'type': 'Polygon',
    'coordinates': [[[35.164007676333824, 7.315648092803201],
      [35.164482803968816, 7.270435726027228],
      [35.25634053363444, 7.210965732212082],
      [35.348179623989004, 7.151464739986416],
      [35.50236858461292, 7.092454680579472],
      [35.51569123843248, 7.11196715143379],
      [35.461916853024185, 7.22665425180959],
      [35.41052043644675, 7.307558148286968],
      [35.23872404563077, 7.330402310785946],
      [35.164007676333824, 7.315648092803201]]]},
   'id': '00000000000000000001',
   'properties': {'Area': 523.326, 'Id': 1}}]}

gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

gdf = gdf.head().copy()
gdf

	pop_est	continent	name	iso_a3	gdp_md_est	geometry
0	889953.0	Oceania	Fiji	FJI	5496	MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1	58005463.0	Africa	Tanzania	TZA	63177	POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2	603253.0	Africa	W. Sahara	ESH	907	POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3	37589262.0	North America	Canada	CAN	1736425	MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4	328239523.0	North America	United States of America	USA	21433226	MULTIPOLYGON (((-122.84000 49.00000, -120.0000...

Initialize Zonal Stats

Option A: Retrieve results directly

Output: DataFrame with statistics
Recommended for small areas / low n

zs = ZonalStats(
    collection_id='LANDSAT/LC08/C01/T1_8DAY_NDVI', 
    target_features=AOIs, 
    statistic_type="all",
    frequency="annual",
    temporal_stat="mean"
)

res = zs.runZonalStats()
res

	2013_NDVI_mean_max	2013_NDVI_mean_mean	2013_NDVI_mean_min	2013_NDVI_mean_stdDev	2014_NDVI_mean_max	2014_NDVI_mean_mean	2014_NDVI_mean_min	2014_NDVI_mean_stdDev	2015_NDVI_mean_max	2015_NDVI_mean_mean	...	2021_NDVI_mean_max	2021_NDVI_mean_mean	2021_NDVI_mean_min	2021_NDVI_mean_stdDev	2022_NDVI_mean_max	2022_NDVI_mean_mean	2022_NDVI_mean_min	2022_NDVI_mean_stdDev	Area	Id
00000000000000000000	0.478839	0.312925	0.125459	0.041014	0.536693	0.328255	0.133519	0.039759	0.547818	0.325812	...	0.435856	0.292027	0.107515	0.037247	NaN	NaN	NaN	NaN	586.656	0.0
00000000000000000001	0.669967	0.561163	0.058939	0.056626	0.549813	0.449039	0.028017	0.044816	0.600975	0.491076	...	0.630799	0.478778	0.107342	0.057189	NaN	NaN	NaN	NaN	523.326	1.0

2 rows × 42 columns

Option B: Submit a task

Output: csv table on Google Drive
Recommended for big areas / high n

zs = ZonalStats(
    collection_id='LANDSAT/LC08/C01/T1_8DAY_NDVI', 
    target_features=AOIs, 
    statistic_type="mean",
    frequency="annual",
    temporal_stat="mean",
    scale=1000,
    output_dir="pretty_folder",
    output_name="pretty_file"
)

zs.runZonalStats()

zs.reportRunTime()

Completed
Runtime: 0 minutes and 7 seconds