ee.Filter.greaterThanOrEquals

创建一元或二元过滤器，除非左操作数小于右操作数，否则会通过。

用法	返回
ee.Filter.greaterThanOrEquals(leftField, rightValue, rightField, leftValue)	过滤

参数	类型	详细信息
leftField	字符串，默认值：null	左操作数的选择器。如果指定了 leftValue，则不应指定此参数。
rightValue	对象，默认值：null	右操作数的值。如果指定了 rightField，则不应指定此字段。
rightField	字符串，默认值：null	用于选择右操作数的选择器。如果指定了 rightValue，则不应指定此参数。
leftValue	对象，默认值：null	左操作数的值。如果指定了 leftField，则不应指定此字段。

示例

代码编辑器 (JavaScript)

// Field site vegetation characteristics from projects in western USA.
var fc = ee.FeatureCollection('BLM/AIM/v1/TerrADat/TerrestrialAIM')
  .filter('ProjectName == "Colorado NWDO Kremmling FO 2016"');

// Display field plots on the map.
Map.setCenter(-107.792, 39.871, 7);
Map.addLayer(fc);

// Compare the per-feature values of two properties and filter the collection
// based on the results of various relational expressions. The two properties
// to compare are invasive and non-invasive annual forb cover at each plot.
var leftProperty = 'InvAnnForbCover_AH';
var rightProperty = 'NonInvAnnForbCover_AH';

print('Plots where invasive forb cover is…');

print('…EQUAL to non-invasive cover',
      fc.filter(ee.Filter.equals(
        {leftField: leftProperty, rightField: rightProperty})));

print('…NOT EQUAL to non-invasive cover',
      fc.filter(ee.Filter.notEquals(
        {leftField: leftProperty, rightField: rightProperty})));

print('…LESS THAN non-invasive cover',
      fc.filter(ee.Filter.lessThan(
        {leftField: leftProperty, rightField: rightProperty})));

print('…LESS THAN OR EQUAL to non-invasive cover',
      fc.filter(ee.Filter.lessThanOrEquals(
        {leftField: leftProperty, rightField: rightProperty})));

print('…GREATER THAN non-invasive cover',
      fc.filter(ee.Filter.greaterThan(
        {leftField: leftProperty, rightField: rightProperty})));

print('…GREATER THAN OR EQUAL to non-invasive cover',
      fc.filter(ee.Filter.greaterThanOrEquals(
        {leftField: leftProperty, rightField: rightProperty})));

print('…is not greater than 10 percent different than non-invasive cover',
      fc.filter(ee.Filter.maxDifference(
        {difference: 10, leftField: leftProperty, rightField: rightProperty})));

// Instead of comparing values of two feature properties using the leftField
// and rightField parameters, you can compare a property value (leftProperty)
// against a constant value (rightValue).
print('Plots where invasive forb cover is greater than 20%',
      fc.filter(ee.Filter.greaterThan(
        {leftField: leftProperty, rightValue: 20})));

// You can also swap the operands to assign the constant to the left side of
// the relational expression (leftValue) and the feature property on the right
// (rightField). Here, we get the complement of the previous example.
print('Plots where 20% is greater than invasive forb cover.',
      fc.filter(ee.Filter.greaterThan(
        {leftValue: 20, rightField: leftProperty})));

// Binary filters are useful in joins. For example, group all same-site plots
// together using a saveAll join.
var groupingProp = 'SiteID';
var sitesFc = fc.distinct(groupingProp);

var joinFilter = ee.Filter.equals(
  {leftField: groupingProp, rightField: groupingProp});

var groupedPlots = ee.Join.saveAll('site_plots').apply(sitesFc, fc, joinFilter);
print('List of plots in first site', groupedPlots.first().get('site_plots'));

Python 设置

如需了解 Python API 和如何使用 geemap 进行交互式开发，请参阅 Python 环境页面。

import ee
import geemap.core as geemap

Colab (Python)

# Field site vegetation characteristics from projects in western USA.
fc = ee.FeatureCollection('BLM/AIM/v1/TerrADat/TerrestrialAIM').filter(
    'ProjectName == "Colorado NWDO Kremmling FO 2016"'
)

# Display field plots on the map.
m = geemap.Map()
m.set_center(-107.792, 39.871, 7)
m.add_layer(fc)
display(m)

# Compare the per-feature values of two properties and filter the collection
# based on the results of various relational expressions. The two properties
# to compare are invasive and non-invasive annual forb cover at each plot.
left_property = 'InvAnnForbCover_AH'
right_property = 'NonInvAnnForbCover_AH'

display('Plots where invasive forb cover is…')

display(
    '…EQUAL to non-invasive cover',
    fc.filter(
        ee.Filter.equals(leftField=left_property, rightField=right_property)
    ),
)

display(
    '…NOT EQUAL to non-invasive cover',
    fc.filter(
        ee.Filter.notEquals(leftField=left_property, rightField=right_property)
    ),
)

display(
    '…LESS THAN non-invasive cover',
    fc.filter(
        ee.Filter.lessThan(leftField=left_property, rightField=right_property)
    ),
)

display(
    '…LESS THAN OR EQUAL to non-invasive cover',
    fc.filter(
        ee.Filter.lessThanOrEquals(
            leftField=left_property, rightField=right_property
        )
    ),
)

display(
    '…GREATER THAN non-invasive cover',
    fc.filter(
        ee.Filter.greaterThan(
            leftField=left_property, rightField=right_property
        )
    ),
)

display(
    '…GREATER THAN OR EQUAL to non-invasive cover',
    fc.filter(
        ee.Filter.greaterThanOrEquals(
            leftField=left_property, rightField=right_property
        )
    ),
)

display(
    '…is not greater than 10 percent different than non-invasive cover',
    fc.filter(
        ee.Filter.maxDifference(
            difference=10, leftField=left_property, rightField=right_property
        )
    ),
)

# Instead of comparing values of two feature properties using the leftField
# and rightField parameters, you can compare a property value (left_property)
# against a constant value (rightValue).
display(
    'Plots where invasive forb cover is greater than 20%',
    fc.filter(ee.Filter.greaterThan(leftField=left_property, rightValue=20)),
)

# You can also swap the operands to assign the constant to the left side of
# the relational expression (leftValue) and the feature property on the right
# (rightField). Here, we get the complement of the previous example.
display(
    'Plots where 20% is greater than invasive forb cover.',
    fc.filter(ee.Filter.greaterThan(leftValue=20, rightField=left_property)),
)

# Binary filters are useful in joins. For example, group all same-site plots
# together using a saveAll join.
grouping_prop = 'SiteID'
sites_fc = fc.distinct(grouping_prop)

join_filter = ee.Filter.equals(
    leftField=grouping_prop, rightField=grouping_prop
)

grouped_plots = ee.Join.saveAll('site_plots').apply(sites_fc, fc, join_filter)
display('List of plots in first site', grouped_plots.first().get('site_plots'))