AI-generated Key Takeaways
-
Filtering a
FeatureCollectionis similar to filtering anImageCollectionand can be done usingfilterDate(),filterBounds(), or the generalfilter()method with anee.Filter. -
The provided code examples demonstrate filtering a
FeatureCollectionfirst by geographical boundaries (continental US) and then by a numerical property (areasqkm) to select larger watersheds. -
The examples show how to load a
FeatureCollection, define a geometry for spatial filtering, and applyfilterBounds()andfilter()methods in both JavaScript and Python.
Filtering a FeatureCollection
is analogous to filtering an ImageCollection
. (See the Filtering an
ImageCollection section
). There are the featureCollection.filterDate()
,
and featureCollection.filterBounds()
convenience methods and the featureCollection.filter()
method for use with any applicable ee.Filter
. For example:
Code Editor (JavaScript)
// Load watersheds from a data table. var sheds = ee . FeatureCollection ( 'USGS/WBD/2017/HUC06' ) // Convert 'areasqkm' property from string to number. . map ( function ( feature ){ var num = ee . Number . parse ( feature . get ( 'areasqkm' )); return feature . set ( 'areasqkm' , num ); }); // Define a region roughly covering the continental US. var continentalUS = ee . Geometry . Rectangle ( - 127.18 , 19.39 , - 62.75 , 51.29 ); // Filter the table geographically: only watersheds in the continental US. var filtered = sheds . filterBounds ( continentalUS ); // Check the number of watersheds after filtering for location. print ( 'Count after filter:' , filtered . size ()); // Filter to get only larger continental US watersheds. var largeSheds = filtered . filter ( ee . Filter . gt ( 'areasqkm' , 25000 )); // Check the number of watersheds after filtering for size and location. print ( 'Count after filtering by size:' , largeSheds . size ());
import ee import geemap.core as geemap
Colab (Python)
# Load watersheds from a data table. sheds = ( ee . FeatureCollection ( 'USGS/WBD/2017/HUC06' ) # Convert 'areasqkm' property from string to number. . map ( lambda feature : feature . set ( 'areasqkm' , ee . Number . parse ( feature . get ( 'areasqkm' )) ) ) ) # Define a region roughly covering the continental US. continental_us = ee . Geometry . Rectangle ( - 127.18 , 19.39 , - 62.75 , 51.29 ) # Filter the table geographically: only watersheds in the continental US. filtered = sheds . filterBounds ( continental_us ) # Check the number of watersheds after filtering for location. display ( 'Count after filter:' , filtered . size ()) # Filter to get only larger continental US watersheds. large_sheds = filtered . filter ( ee . Filter . gt ( 'areasqkm' , 25000 )) # Check the number of watersheds after filtering for size and location. display ( 'Count after filtering by size:' , large_sheds . size ())
