上一回分析了以下求如何进行获取地类面积转换的影像结果，这次我们进行地类转换后面积的统计计算，面积统计计算主要是两个，第一个首先要统计各地类影像面积，第二个就是要统计地类中的面积。期间会用到一些函数，如下：
上一篇文章的链接地址：
https://mp.csdn.net/mp_blog/creation/editor/131506639
函数：
ee.Reducer.frequencyHistogram()
Returns a Reducer that returns a (weighted) frequency table of its inputs.
返回一个Reducer，返回其输入的（加权）频率表。
No arguments.
Returns: Reducer
ee.Image.pixelArea()
Generate an image in which the value of each pixel is the area of that pixel in square meters. The returned image has a single band called "area."
返回一个带有面积area属性的部分
No arguments.
Returns: Image
ee.Reducer.sum()
Returns a Reducer that computes the (weighted) sum of its inputs.
No arguments.
Returns: Reducer
group(groupField, groupName)
Groups reducer records by the value of a given input, and reduces each group with the given reducer.
统计一个群组的数据，就需要使用group函数，他会对其每一个都进行统计分析。
Arguments:
this:reducer (Reducer):
The reducer to apply to each group, without the group field.
groupField (Integer, default: 0):
The field that contains record groups.
groupName (String, default: "group"):
The dictionary key that contains the group. Defaults to 'group'.
Returns: Reducer
代码：
var bangalore = ee.FeatureCollection('users/ujavalgandhi/public/bangalore_boundary');
var urban = ee.FeatureCollection('users/ujavalgandhi/e2e/urban_gcps');
var bare = ee.FeatureCollection('users/ujavalgandhi/e2e/bare_gcps');
var water = ee.FeatureCollection('users/ujavalgandhi/e2e/water_gcps');
var vegetation = ee.FeatureCollection('users/ujavalgandhi/e2e/vegetation_gcps');
var s2 = ee.ImageCollection('COPERNICUS/S2_SR');
var geometry = bangalore.geometry();
Map.centerObject(geometry);
var rgbVis = {
min: 0.0,
max: 3000,
bands: ['B4', 'B3', 'B2'],
};
// 2019 Jan
var filtered = s2
.filter(ee.Filter.date('2019-01-01', '2019-02-01'))
.filter(ee.Filter.bounds(geometry))
.select('B.*');
var before = filtered.median().clip(geometry);
// Display the input composite.
Map.addLayer(before, rgbVis, 'before');
var training = urban.merge(bare).merge(water).merge(vegetation);
// Overlay the point on the image to get training data.
var training = before.sampleRegions({
collection: training,
properties: ['landcover'],
scale: 10
});
// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50).train({
features: training,
classProperty: 'landcover',
inputProperties: before.bandNames()
});
// // Classify the image.
var beforeClassified = before.classify(classifier);
Map.addLayer(beforeClassified,
{min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, 'before_classified');
// 2020 Jan
var after = s2
.filter(ee.Filter.date('2020-01-01', '2020-02-01'))
.filter(ee.Filter.bounds(geometry))
.select('B.*')
.median()
.clip(geometry);
Map.addLayer(after, rgbVis, 'after');
// Classify the image.
var afterClassified= after.classify(classifier);
Map.addLayer(afterClassified,
{min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, 'after_classified');
// Reclassify from 0-3 to 1-4
var beforeClasses = beforeClassified.remap([0, 1, 2, 3], [1, 2, 3, 4]);
var afterClasses = afterClassified.remap([0, 1, 2, 3], [1, 2, 3, 4]);
// Show all changed areas
var changed = afterClasses.subtract(beforeClasses).neq(0);
Map.addLayer(changed, {min:0, max:1, palette: ['white', 'red']}, 'Change');
// 我们将之前的图像乘以 100，然后添加之后的图像
// 生成的像素值将是唯一的，并且代表每个唯一的过渡
// 即 102 是城市到裸露，103 是城市到水等。
var merged = beforeClasses.multiply(100).add(afterClasses).rename('transitions');
//
var transitionMatrix = merged.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: geometry,
maxPixels: 1e10,
scale:10,
tileScale: 16
});
// 使用频率直方图获取每个类别的像素计数
print(transitionMatrix.get('transitions'));
// 如果我们要计算每个类过渡的面积
// 我们可以使用分组reducer
// Divide by 1e6 to get the area in sq.km.
var areaImage = ee.Image.pixelArea().divide(1e6).addBands(merged);
// 按每个过渡类计算面积
// 使用分组Reducer
var areas = areaImage.reduceRegion({
reducer: ee.Reducer.sum().group({
groupField: 1,
groupName: 'transitions',
}),
geometry: geometry,
scale: 100,
tileScale: 4,
maxPixels: 1e10
});
// 对结果进行后处理以生成干净的输出
var classAreas = ee.List(areas.get('groups'));
var classAreaLists = classAreas.map(function(item) {
var areaDict = ee.Dictionary(item);
var classNumber = ee.Number(areaDict.get('transitions')).format();
var area = ee.Number(areaDict.get('sum')).round();//取整
return ee.List([classNumber, area]);
});
var classTransitionsAreaDict = ee.Dictionary(classAreaLists.flatten());
print(classTransitionsAreaDict);
最后统计的像素数量和各类转化的面积
最后的这个结果就是各类面积从一类转化为另一类的一个面积统计。