Computes the distance to the nearest non-zero pixel in each band, using the specified distance kernel.
| Usage | Returns | Image.distance(kernel, skipMasked) | Image |
| Argument | Type | Details | this: image | Image | The input image. |
kernel | Kernel, default: null | The distance kernel. One of chebyshev, euclidean, or manhattan. |
skipMasked | Boolean, default: true | Mask output pixels if the corresponding input pixel is masked. |
Examples
Code Editor (JavaScript)
// The objective is to determine the per-pixel distance to a target
// feature (pixel value). In this example, the target feature is water in a
// land cover map.
// Import a Dynamic World land cover image and subset the 'label' band.
var lcImg = ee.Image(
'GOOGLE/DYNAMICWORLD/V1/20210726T171859_20210726T172345_T14TQS')
.select('label');
// Create a binary image where the target feature is value 1, all else 0.
// In the Dynamic World map, water is represented as value 0, so we use the
// ee.Image.eq() relational operator to set it to 1.
var targetImg = lcImg.eq(0);
// Set a max distance from target pixels to consider in the analysis. Pixels
// with distance greater than this value from target pixels will be masked out.
// Here, we are using units of meters, but the distance kernels also accept
// units of pixels.
var maxDistM = 10000; // 10 km
// Calculate distance to target pixels. Several distance kernels are provided.
// Euclidean distance.
var euclideanKernel = ee.Kernel.euclidean(maxDistM, 'meters');
var euclideanDist = targetImg.distance(euclideanKernel);
var vis = {min: 0, max: maxDistM};
Map.setCenter(-95.68, 46.46, 9);
Map.addLayer(euclideanDist, vis, 'Euclidean distance to target pixels');
// Manhattan distance.
var manhattanKernel = ee.Kernel.manhattan(maxDistM, 'meters');
var manhattanDist = targetImg.distance(manhattanKernel);
Map.addLayer(manhattanDist, vis, 'Manhattan distance to target pixels', false);
// Chebyshev distance.
var chebyshevKernel = ee.Kernel.chebyshev(maxDistM, 'meters');
var chebyshevDist = targetImg.distance(chebyshevKernel);
Map.addLayer(chebyshevDist, vis, 'Chebyshev distance to target pixels', false);
// Add the target layer to the map; water is blue, all else masked out.
Map.addLayer(targetImg.mask(targetImg), {palette: 'blue'}, 'Target pixels');
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)
# The objective is to determine the per-pixel distance to a target
# feature (pixel value). In this example, the target feature is water in a
# land cover map.
# Import a Dynamic World land cover image and subset the 'label' band.
lc_img = ee.Image(
'GOOGLE/DYNAMICWORLD/V1/20210726T171859_20210726T172345_T14TQS'
).select('label')
# Create a binary image where the target feature is value 1, all else 0.
# In the Dynamic World map, water is represented as value 0, so we use the
# ee.Image.eq() relational operator to set it to 1.
target_img = lc_img.eq(0)
# Set a max distance from target pixels to consider in the analysis. Pixels
# with distance greater than this value from target pixels will be masked out.
# Here, we are using units of meters, but the distance kernels also accept
# units of pixels.
max_dist_m = 10000 # 10 km
# Calculate distance to target pixels. Several distance kernels are provided.
# Euclidean distance.
euclidean_kernel = ee.Kernel.euclidean(max_dist_m, 'meters')
euclidean_dist = target_img.distance(euclidean_kernel)
vis = {'min': 0, 'max': max_dist_m}
m = geemap.Map()
m.set_center(-95.68, 46.46, 9)
m.add_layer(euclidean_dist, vis, 'Euclidean distance to target pixels')
# Manhattan distance.
manhattan_kernel = ee.Kernel.manhattan(max_dist_m, 'meters')
manhattan_dist = target_img.distance(manhattan_kernel)
m.add_layer(
manhattan_dist, vis, 'Manhattan distance to target pixels', False
)
# Chebyshev distance.
chebyshev_kernel = ee.Kernel.chebyshev(max_dist_m, 'meters')
chebyshev_dist = target_img.distance(chebyshev_kernel)
m.add_layer(
chebyshev_dist, vis, 'Chebyshev distance to target pixels', False
)
# Add the target layer to the map water is blue, all else masked out.
m.add_layer(
target_img.mask(target_img), {'palette': 'blue'}, 'Target pixels'
)
m