问题描述
我在转换ee.Dictionary
中的sampleRegion
返回的某些Earth Engine
(具有空值键)时遇到麻烦。我正在尝试对多个区域的多波段图像进行采样,然后将生成的字典转换为ee.FeatureCollection
,其中(字典的)键/值对将成为具有null
几何形状的特征。我想保留所有键,包括具有null
值的键。具有null
值的键应重新编码为9或保留为null
,但我需要它们作为最终集合中的功能。我尝试使用ee.Algorithms.If
来处理这些具有null
值的键,但出现堆栈并出现以下错误:
FeatureCollection(错误) map(ID = 0)中的错误:Element.geometry,参数'feature':类型无效。预期类型:元素。实际类型:字符串。实际值:B3
以下是可复制的示例,也可以在here中找到。任何提示都会有很大帮助!
// Some features to use latter in sampleRegion
var roi1 =
/* color: #d63000 */
/* shown: false */
/* displayProperties: [
{
"type": "rectangle"
},{
"type": "rectangle"
},{
"type": "rectangle"
}
] */
ee.FeatureCollection(
[ee.Feature(
ee.Geometry.polygon(
[[[1.2850232278161755,14.924433184708537],[1.2850232278161755,14.741234323298656],[1.4882702981286755,14.924433184708537]]],null,false),{
"system:index": "0"
}),ee.Feature(
ee.Geometry.polygon(
[[[1.4772839700036755,14.04155518401385],[1.4772839700036755,13.86296344675159],[1.6393323098474255,14.04155518401385]]],{
"system:index": "1"
}),ee.Feature(
ee.Geometry.polygon(
[[[1.0817761575036755,14.478114793660426],[1.0817761575036755,14.313173466470698],[1.2767834817224255,14.478114793660426]]],{
"system:index": "2"
})]),roi2 =
/* color: #98ff00 */
/* displayProperties: [
{
"type": "rectangle"
}
] */
ee.FeatureCollection(
[ee.Feature(
ee.Geometry.polygon(
[[[1.6970105325036755,14.448859913271122],[1.6970105325036755,14.25994066279539],[1.9387097512536755,14.448859913271122]]],{
"system:index": "0"
})]),roi3 =
/* color: #0b4a8b */
/* displayProperties: [
{
"type": "rectangle"
}
] */
ee.FeatureCollection(
[ee.Feature(
ee.Geometry.polygon(
[[[1.7739148293786755,14.38501773168985],[1.7739148293786755,14.29188185649032],[1.8755383645349255,14.38501773168985]]],{
"rec": 3,"system:index": "0"
})]);
// Getting the image of the region of interest
var roi = ee.Geometry.Point([1.864578244475683,14.492292970253338]);
var image = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterDate('2019-01-01','2019-01-31')
.filterBounds(roi)
.select(['B5','B4','B3'])
.toBands()
.rename(['B5','B3']);
// Checking it out
print(image);
// Define the visualization parameters.
var vizParams = {
bands: ['B5','B3'],min: 0,max: 0.5,gamma: [0.95,1.1,1]
};
// Center the map and display the image.
Map.centerObject(image,9);
Map.addLayer(image,vizParams,'image');
// masking out some regions from the
// image,so that sampleRegion will return null in that region
var mask = ee.Image.constant(1).clip(roi2).mask().not()
var imageMasked = image.updateMask(mask);
// displaying the masked image
Map.addLayer(imageMasked,'imageMasked');
/////////// The actual problem start from here ///////////
// making a feature collection (masked + unmasked region)
var roi = roi1.merge(roi3);
var regionSamples = roi.map(function(x){
var out = imageMasked.reduceRegion({
reducer : ee.Reducer.mean().unweighted(),geometry : x.geometry(),scale : 30
})
// Getting the keys of the dictionary returned by sampleRegion
var keys = out.keys()
// mapping a function over the list of
// keys to latter extract their corresponding values
var keyvals = keys.map(function(y){
var proba = ee.Algorithms.If({
// test if the value corresponding to a key is null
condition: ee.Algorithms.IsEqual(out.get(y),null),// if it the case,return a feature with property prob set to 9
trueCase: ee.Feature(null,{prob: 9}),// if it not the case,return a feature with property prob
// set the value return by sampleRegion
falseCase: ee.Feature(null,{prob: out.get(y)})
})
return proba
})
return ee.FeatureCollection(keyvals)
})
print(regionSamples.flatten(),'regional samples')
解决方法
我终于弄清楚了,如果有人感兴趣,我会发布答案。我使用下面的函数解决了这些问题。
/////////// The actual problem start from here ///////////
/**
* Reduce multiple regions of an image to feature collection.
* @param {Image} The image to reduce.
* @param {FeatureCollection} roi The area/areas of interest.
* @param {Float} scale A nominal scale in meters of the projection to work in.
* @param {Float} nullKeyValue The value to use for keys where reduceRegion returns null.
* @return {FeatureCollection} A feature collection where dictionary keys returned by reduceRegion are converted to ee.Feature.
*/
function sampleFeatures(image,roi,nullKeyValue,scale){
var keyVals = roi.map(function(x){
var dictionary = image.reduceRegion({
reducer : ee.Reducer.mean().unweighted(),geometry : x.geometry(),scale : scale
})
var noNullDic = dictionary.map(function(key,val){
var dic = ee.Algorithms.If({
condition: ee.Algorithms.IsEqual(val,null),trueCase: nullKeyValue,falseCase: dictionary.get(key)
})
return dic;
});
var keys = noNullDic.keys()
var vals = keys.map(function(key){
var vl = ee.List([noNullDic.get(key)])
var ky = ee.List([key])
return ee.Feature(null,ee.Dictionary.fromLists(ky,vl))
})
return ee.FeatureCollection(vals)
})
return keyVals.flatten()
}
var test = sampleFeatures(imageMasked,999,30)
print(test,'test sampleFeatures')