Relational, Conditional, and Boolean Operations

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    ee.Image objects have a set of relational, conditional, and boolean methods for constructing decision-making expressions. The results of these methods are useful for limiting analysis to certain pixels or regions through masking, developing classified maps, and value reassignment.

    Relational and boolean operators

    Relationalmethods include:

    eq() , gt() , gte() , lt() , and lte()

    Booleanmethods include:

    Code Editor (JavaScript)

    and() , or() , and not()

    Colab (Python)

    And() , Or() , and Not()

    To perform per-pixel comparisons between images, use relational operators. To extract urbanized areas in an image, this example uses relational operators to threshold spectral indices, combining the thresholds with the and operator:

    Code Editor (JavaScript) 

     // Load a Landsat 8 image. 
 var 
  
 image 
  
 = 
  
 ee 
 . 
 Image 
 ( 
 'LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318' 
 ); 
 // Create NDVI and NDWI spectral indices. 
 var 
  
 ndvi 
  
 = 
  
 image 
 . 
 normalizedDifference 
 ([ 
 'B5' 
 , 
  
 'B4' 
 ]); 
 var 
  
 ndwi 
  
 = 
  
 image 
 . 
 normalizedDifference 
 ([ 
 'B3' 
 , 
  
 'B5' 
 ]); 
 // Create a binary layer using logical operations. 
 var 
  
 bare 
  
 = 
  
 ndvi 
 . 
 lt 
 ( 
 0.2 
 ). 
 and 
 ( 
 ndwi 
 . 
 lt 
 ( 
 0 
 )); 
 // Mask and display the binary layer. 
 Map 
 . 
 setCenter 
 ( 
 - 
 122.3578 
 , 
  
 37.7726 
 , 
  
 12 
 ); 
 Map 
 . 
 setOptions 
 ( 
 'satellite' 
 ); 
 Map 
 . 
 addLayer 
 ( 
 bare 
 . 
 selfMask 
 (), 
  
 {}, 
  
 'bare' 
 );

    Python setup

    See the Python Environment page for information on the Python API and using geemap for interactive development.

     import 
  
 ee 
 import 
  
 geemap.core 
  
 as 
  
 geemap

    Colab (Python) 

     # Load a Landsat 8 image. 
 image 
 = 
 ee 
 . 
 Image 
 ( 
 'LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318' 
 ) 
 # Create NDVI and NDWI spectral indices. 
 ndvi 
 = 
 image 
 . 
 normalizedDifference 
 ([ 
 'B5' 
 , 
 'B4' 
 ]) 
 ndwi 
 = 
 image 
 . 
 normalizedDifference 
 ([ 
 'B3' 
 , 
 'B5' 
 ]) 
 # Create a binary layer using logical operations. 
 bare 
 = 
 ndvi 
 . 
 lt 
 ( 
 0.2 
 ) 
 . 
 And 
 ( 
 ndwi 
 . 
 lt 
 ( 
 0 
 )) 
 # Define a map centered on San Francisco Bay. 
 map_bare 
 = 
 geemap 
 . 
 Map 
 ( 
 center 
 = 
 [ 
 37.7726 
 , 
 - 
 122.3578 
 ], 
 zoom 
 = 
 12 
 ) 
 # Add the masked image layer to the map and display it. 
 map_bare 
 . 
 add_layer 
 ( 
 bare 
 . 
 selfMask 
 (), 
 None 
 , 
 'bare' 
 ) 
 display 
 ( 
 map_bare 
 )

    As illustrated by this example, the output of relational and boolean operators is either true (1) or false (0). To mask the 0's, you can mask the resultant binary image with itself using selfMask() .

    relational_sf
    Low NDVI and low NDWI (white) from Landsat 8, San Francisco, California, USA.

    The binary images that are returned by relational and boolean operators can be used with mathematical operators. This example creates zones of urbanization in a nighttime lights image using relational operators and add() :

    Code Editor (JavaScript) 

     // Load a 2012 nightlights image. 
 var 
  
 nl2012 
  
 = 
  
 ee 
 . 
 Image 
 ( 
 'NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012' 
 ); 
 var 
  
 lights 
  
 = 
  
 nl2012 
 . 
 select 
 ( 
 'stable_lights' 
 ); 
 // Define arbitrary thresholds on the 6-bit stable lights band. 
 var 
  
 zones 
  
 = 
  
 lights 
 . 
 gt 
 ( 
 30 
 ). 
 add 
 ( 
 lights 
 . 
 gt 
 ( 
 55 
 )). 
 add 
 ( 
 lights 
 . 
 gt 
 ( 
 62 
 )); 
 // Display the thresholded image as three distinct zones near Paris. 
 var 
  
 palette 
  
 = 
  
 [ 
 '000000' 
 , 
  
 '0000FF' 
 , 
  
 '00FF00' 
 , 
  
 'FF0000' 
 ]; 
 Map 
 . 
 setCenter 
 ( 
 2.373 
 , 
  
 48.8683 
 , 
  
 8 
 ); 
 Map 
 . 
 addLayer 
 ( 
 zones 
 , 
  
 { 
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 3 
 , 
  
 palette 
 : 
  
 palette 
 }, 
  
 'development zones' 
 );

    Python setup

    See the Python Environment page for information on the Python API and using geemap for interactive development.

     import 
  
 ee 
 import 
  
 geemap.core 
  
 as 
  
 geemap

    Colab (Python) 

     # Load a 2012 nightlights image. 
 nl_2012 
 = 
 ee 
 . 
 Image 
 ( 
 'NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012' 
 ) 
 lights 
 = 
 nl_2012 
 . 
 select 
 ( 
 'stable_lights' 
 ) 
 # Define arbitrary thresholds on the 6-bit stable lights band. 
 zones 
 = 
 lights 
 . 
 gt 
 ( 
 30 
 ) 
 . 
 add 
 ( 
 lights 
 . 
 gt 
 ( 
 55 
 )) 
 . 
 add 
 ( 
 lights 
 . 
 gt 
 ( 
 62 
 )) 
 # Define a map centered on Paris, France. 
 map_zones 
 = 
 geemap 
 . 
 Map 
 ( 
 center 
 = 
 [ 
 48.8683 
 , 
 2.373 
 ], 
 zoom 
 = 
 8 
 ) 
 # Display the thresholded image as three distinct zones near Paris. 
 palette 
 = 
 [ 
 '000000' 
 , 
 '0000FF' 
 , 
 '00FF00' 
 , 
 'FF0000' 
 ] 
 map_zones 
 . 
 add_layer 
 ( 
 zones 
 , 
 { 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 3 
 , 
 'palette' 
 : 
 palette 
 }, 
 'development zones' 
 ) 
 display 
 ( 
 map_zones 
 )

    Conditional operators

    Note that the code in the previous example is equivalent to using a ternary operator implemented by expression() :

    Code Editor (JavaScript) 

     // Create zones using an expression, display. 
 var 
  
 zonesExp 
  
 = 
  
 nl2012 
 . 
 expression 
 ( 
  
 "(b('stable_lights') > 62) ? 3" 
  
 + 
  
 ": (b('stable_lights') > 55) ? 2" 
  
 + 
  
 ": (b('stable_lights') > 30) ? 1" 
  
 + 
  
 ": 0" 
 ); 
 Map 
 . 
 addLayer 
 ( 
 zonesExp 
 , 
  
 { 
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 3 
 , 
  
 palette 
 : 
  
 palette 
 }, 
  
 'development zones (ternary)' 
 );

    Python setup

    See the Python Environment page for information on the Python API and using geemap for interactive development.

     import 
  
 ee 
 import 
  
 geemap.core 
  
 as 
  
 geemap

    Colab (Python) 

     # Create zones using an expression, display. 
 zones_exp 
 = 
 nl_2012 
 . 
 expression 
 ( 
 "(b('stable_lights') > 62) ? 3 " 
 ": (b('stable_lights') > 55) ? 2 " 
 ": (b('stable_lights') > 30) ? 1 " 
 ': 0' 
 ) 
 # Define a map centered on Paris, France. 
 map_zones_exp 
 = 
 geemap 
 . 
 Map 
 ( 
 center 
 = 
 [ 
 48.8683 
 , 
 2.373 
 ], 
 zoom 
 = 
 8 
 ) 
 # Add the image layer to the map and display it. 
 map_zones_exp 
 . 
 add_layer 
 ( 
 zones_exp 
 , 
 { 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 3 
 , 
 'palette' 
 : 
 palette 
 }, 
 'zones exp' 
 ) 
 display 
 ( 
 map_zones_exp 
 )

    Observe that in the previous expression example, the band of interest is referenced using the b() function, rather than a dictionary of variable names. Learn more about image expressions on this page . Using either mathematical operators or an expression will produce the same result.

    conditional_paris
    Arbitrary zones of 2012 nightlights imagery for Paris, France.

    Another way to implement conditional operations on images is with the where() operator. Consider the need to replace masked pixels with some other data. In the following example, cloudy pixels are replaced by pixels from a cloud-free image using where() :

    Code Editor (JavaScript) 

     // Load a cloudy Sentinel-2 image. 
 var 
  
 image 
  
 = 
  
 ee 
 . 
 Image 
 ( 
  
 'COPERNICUS/S2_SR/20210114T185729_20210114T185730_T10SEG' 
 ); 
 Map 
 . 
 addLayer 
 ( 
 image 
 , 
  
 { 
 bands 
 : 
  
 [ 
 'B4' 
 , 
  
 'B3' 
 , 
  
 'B2' 
 ], 
  
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 2000 
 }, 
  
 'original image' 
 ); 
 // Load another image to replace the cloudy pixels. 
 var 
  
 replacement 
  
 = 
  
 ee 
 . 
 Image 
 ( 
  
 'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG' 
 ); 
 // Set cloudy pixels (greater than 5% probability) to the other image. 
 var 
  
 replaced 
  
 = 
  
 image 
 . 
 where 
 ( 
 image 
 . 
 select 
 ( 
 'MSK_CLDPRB' 
 ). 
 gt 
 ( 
 5 
 ), 
  
 replacement 
 ); 
 // Display the result. 
 Map 
 . 
 setCenter 
 ( 
 - 
 122.3769 
 , 
  
 37.7349 
 , 
  
 11 
 ); 
 Map 
 . 
 addLayer 
 ( 
 replaced 
 , 
  
 { 
 bands 
 : 
  
 [ 
 'B4' 
 , 
  
 'B3' 
 , 
  
 'B2' 
 ], 
  
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 2000 
 }, 
  
 'clouds replaced' 
 );

    Python setup

    See the Python Environment page for information on the Python API and using geemap for interactive development.

     import 
  
 ee 
 import 
  
 geemap.core 
  
 as 
  
 geemap

    Colab (Python) 

     # Load a cloudy Sentinel-2 image. 
 image 
 = 
 ee 
 . 
 Image 
 ( 
 'COPERNICUS/S2_SR/20210114T185729_20210114T185730_T10SEG' 
 ) 
 # Load another image to replace the cloudy pixels. 
 replacement 
 = 
 ee 
 . 
 Image 
 ( 
 'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG' 
 ) 
 # Set cloudy pixels (greater than 5% probability) to the other image. 
 replaced 
 = 
 image 
 . 
 where 
 ( 
 image 
 . 
 select 
 ( 
 'MSK_CLDPRB' 
 ) 
 . 
 gt 
 ( 
 5 
 ), 
 replacement 
 ) 
 # Define a map centered on San Francisco Bay. 
 map_replaced 
 = 
 geemap 
 . 
 Map 
 ( 
 center 
 = 
 [ 
 37.7349 
 , 
 - 
 122.3769 
 ], 
 zoom 
 = 
 11 
 ) 
 # Display the images on a map. 
 vis_params 
 = 
 { 
 'bands' 
 : 
 [ 
 'B4' 
 , 
 'B3' 
 , 
 'B2' 
 ], 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 2000 
 } 
 map_replaced 
 . 
 add_layer 
 ( 
 image 
 , 
 vis_params 
 , 
 'original image' 
 ) 
 map_replaced 
 . 
 add_layer 
 ( 
 replaced 
 , 
 vis_params 
 , 
 'clouds replaced' 
 ) 
 display 
 ( 
 map_replaced 
 )
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