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ee.Classifier.amnhMaxent

  • Maxent is a Maximum Entropy classifier used for modeling species distribution probabilities based on environmental data from known presence and background locations.

  • The classifier outputs a 'probability' band representing the modeled probability, and optionally a 'clamp' band indicating the spatial distribution of clamping.

  • Theee.Classifier.amnhMaxent method takes numerous arguments to control various aspects of the model, including feature types, regularization, and output format.

 Creates a Maximum Entropy classifier. Maxent is used to model species distribution probabilities using environmental data for locations of known presence and for a large number of 'background' locations. For more information and to cite, see: https://biodiversityinformatics.amnh.org/open_source/maxent/ and the reference publication: Phillips, et. al., 2004 A maximum entropy approach to species distribution modeling, Proceedings of the Twenty-First International Conference on Machine Learning. The output is a single band named 'probability', containing the modeled probability, and an additional band named 'clamp' when the 'writeClampGrid' argument is true.

UsageReturns
ee.Classifier.amnhMaxent(categoricalNames,outputFormat,autoFeature,linear,quadratic,product,threshold,hinge,hingeThreshold,l2lqThreshold,lq2lqptThreshold,addSamplesToBackground,addAllSamplesToBackground,betaMultiplier,betaHinge,betaLqp,betaCategorical,betaThreshold,extrapolate,doClamp,writeClampGrid,randomTestPoints,seed)Classifier
ArgumentTypeDetails
categoricalNamesList, default: nullA list of the names of the categorical inputs. Any inputs not listed in this argument are considered to be continuous.
outputFormatString, default: "cloglog"Representation of probabilities in output.
autoFeatureBoolean, default: trueAutomatically select which feature classes to use, based on number of training samples.
linearBoolean, default: trueAllow linear features to be used. Ignored when autofeature is true.
quadraticBoolean, default: trueAllow quadratic features to be used. Ignored when autofeature is true.
productBoolean, default: trueAllow product features to be used. Ignored when autofeature is true.
thresholdBoolean, default: falseAllow threshold features to be used. Ignored when autofeature is true.
hingeBoolean, default: trueAllow hinge features to be used. Ignored when autofeature is true.
hingeThresholdInteger, default: 15Number of samples at which hinge features start being used. Ignored when autofeature is false.
l2lqThresholdInteger, default: 10Number of samples at which quadratic features start being used. Ignored when autofeature is false.
lq2lqptThresholdInteger, default: 80Number of samples at which product and threshold features start being used. Ignored when autofeature is false.
addSamplesToBackgroundBoolean, default: trueAdd to the background any sample for which has a combination of environmental values that isn't already present in the background.
addAllSamplesToBackgroundBoolean, default: falseAdd all samples to the background, even if they have combinations of environmental values that are already present in the background.
betaMultiplierFloat, default: 1Regularization multiplier. Multiply all automatic regularization parameters by this number. A higher number gives a more spread-out distribution.
betaHingeFloat, default: -1Regularization parameter to be applied to all hinge features; negative value enables automatic setting.
betaLqpFloat, default: -1Regularization parameter to be applied to all linear, quadratic and product features; negative value enables automatic setting.
betaCategoricalFloat, default: -1Regularization parameter to be applied to all categorical features; negative value enables automatic setting.
betaThresholdFloat, default: -1Regularization parameter to be applied to all threshold features; negative value enables automatic setting.
extrapolateBoolean, default: trueExtrapolate. Predict to regions of environmental space outside the limits encountered during training.
doClampBoolean, default: trueApply clamping to output.
writeClampGridBoolean, default: trueAdds a band to the output ('clamp') showing the spatial distribution of clamping. At each point, the value is the absolute difference between prediction values with and without clamping.
randomTestPointsInteger, default: 0Random test percentage. The percentage of training points to hold aside as test points, used to compute AUX, omission, etc.
seedLong, default: 0A seed used when generating random numbers.

Examples

Code Editor (JavaScript)

// Create some sample species presence/absence training data.vartrainingData=ee.FeatureCollection([// Species present points.ee.Feature(ee.Geometry.Point([-122.39567,38.02740]),{presence:1}),ee.Feature(ee.Geometry.Point([-122.68560,37.83690]),{presence:1}),// Species absent points.ee.Feature(ee.Geometry.Point([-122.59755,37.92402]),{presence:0}),ee.Feature(ee.Geometry.Point([-122.47137,37.99291]),{presence:0}),ee.Feature(ee.Geometry.Point([-122.52905,37.85642]),{presence:0}),ee.Feature(ee.Geometry.Point([-122.03010,37.66660]),{presence:0})]);// Import a Landsat 8 surface reflectance image.varimage=ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20200606')// Select the optical and thermal bands..select(['.._B.*']);// Sample the image at the location of the points.vartraining=image.sampleRegions({collection:trainingData,scale:30});// Define and train a Maxent classifier from the image-sampled points.varclassifier=ee.Classifier.amnhMaxent().train({features:training,classProperty:'presence',inputProperties:image.bandNames()});// Classify the image using the Maxent classifier.varimageClassified=image.classify(classifier);// Display the layers on the map.// Species presence probability [0, 1] grades from black to white.Map.centerObject(image,9);Map.addLayer(image.select(['SR_B4','SR_B3','SR_B2']).multiply(0.0000275).add(-0.2),{min:0,max:0.3},'Image');Map.addLayer(imageClassified,{bands:'probability',min:0,max:1},'Probability');Map.addLayer(trainingData.filter('presence == 0'),{color:'red'},'Training data (species absent)');Map.addLayer(trainingData.filter('presence == 1'),{color:'blue'},'Training data (species present)');

Python setup

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

importeeimportgeemap.coreasgeemap

Colab (Python)

"""Demonstrates the ee.Classifier.amnhMaxent method."""# Create some sample species presence/absence training data.training_data=ee.FeatureCollection([# Species present points.ee.Feature(ee.Geometry.Point([-122.39567,38.02740]),{'presence':1}),ee.Feature(ee.Geometry.Point([-122.68560,37.83690]),{'presence':1}),# Species absent points.ee.Feature(ee.Geometry.Point([-122.59755,37.92402]),{'presence':0}),ee.Feature(ee.Geometry.Point([-122.47137,37.99291]),{'presence':0}),ee.Feature(ee.Geometry.Point([-122.52905,37.85642]),{'presence':0}),ee.Feature(ee.Geometry.Point([-122.03010,37.66660]),{'presence':0})])# Import a Landsat 8 image and select the reflectance bands.image=(ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20200606').select(['SR_B[1-7]']).multiply(0.0000275).add(-0.2))# Apply scaling factors.# Sample the image at the location of the points.training=image.sampleRegions(**{'collection':training_data,'scale':30})# Define and train a Maxent classifier from the image-sampled points.classifier=ee.Classifier.amnhMaxent().train(**{'features':training,'classProperty':'presence','inputProperties':image.bandNames()})# Classify the image using the Maxent classifier.image_classified=image.classify(classifier)

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Last updated 2024-07-13 UTC.