CCDCNEW2.txt

var utils = require('users/parevalo_bu/gee-ccdc-tools:ccdcUtilities/api')


// Define parameters

// Change detection parameters
//设置CCDC连续变化检测的参数
var changeDetection = {
  breakpointBands: ['GREEN','RED','NIR','SWIR1','SWIR2'],
  tmaskBands: ['GREEN','SWIR1'],
  minObservations: 6,
  chiSquareProbability: .99,
  minNumOfYearsScaler: 1.33,
  dateFormat: 2,
  lambda: 20/10000,
  maxIterations: 25000
}


// Classification parameters
var classification = {
  bandNames: ['GREEN','RED','NIR','SWIR1','SWIR2'],
  inputFeatures: ["INTP", "SLP","PHASE","RMSE"],
  coefs: ["INTP", "SLP","COS", "SIN","RMSE","COS2","SIN2","COS3","SIN3"],
  ancillaryFeatures: ["ELEVATION","ASPECT","DEM_SLOPE","RAINFALL","TEMPERATURE"],
  resultFormat: 'SegCollection',
  classProperty: 'landcover',
  yearProperty: 'year',
  classifier: ee.Classifier.smileRandomForest,
  classifierParams: {
    numberOfTrees: 500,
    variablesPerSplit: null,
    minLeafPopulation: 1,
    bagFraction: 0.5,
    maxNodes: null
  },
  outPath: 'projects/GLANCE/RESULTS/CLASSIFICATION/VERSION_1',
  segs: ["S1", "S2", "S3", "S4", "S5", "S6"],
  trainingPath: 'users/2404182135/SingleTrainingPoint2010',
  trainingPathPredictors: 'users/2404182135/TrainingPointGEENew2',
}

var studyRegion = ee.FeatureCollection('users/2404182135/TaiYuanNew')
  

var params = {
  start: '2000-01-01',
  end: '2024-01-01',
  ChangeDetection: changeDetection,
  Classification: classification,
  StudyRegion: studyRegion
}

// Filter by date and a location in Brazil
var filteredLandsat = utils.Inputs.getLandsat()
    .filterBounds(studyRegion)
    .filterDate(params.start, params.end)
    .select(params.Classification.bandNames)

print(filteredLandsat.size())






params.ChangeDetection['collection'] = filteredLandsat

var results = ee.Algorithms.TemporalSegmentation.Ccdc(params.ChangeDetection)
print(results,'result')




//这段代码可能有问题，用下面那一段
// //第 2 部分：准备训练数据
// // Define bands to use in classification
// var bands = params.Classification.bandNames

// // Define coefficients to use in classification
// var coefs = params.Classification.coefs

// // Segment ids
// var segs = params.Classification.segs

// // Property corresponding to year of training data
// var yearProperty = params.Classification.yearProperty

// // Define path to change detection results
// params.Classification.changeResults = results

// // Load ccd image stack with coefficients and change information
// var ccdImage = utils.CCDC.buildCcdImage(params.Classification.changeResults, params.Classification.segs.length, params.Classification.bandNames)

// print('CCD Image:', ccdImage)

// // Finally, get ancillary topographic and climate data
// var ancillary = utils.Inputs.getAncillary()





// var exportResults=true
// if (exportResults){
//   // Create a metadata dictionary with the parameters and arguments used.
//   //使用使用的参数和参数创建元数据字典。创建元数据字典以跟踪用于生成 ccdResults 的参数和设置。
//   //这对于可重复性和理解数据的上下文至关重要。
//   var metadata=changeDetection;
//   metadata['breakpointBands']=metadata['breakpointBands'].toString();//检测断点的带。
//   metadata['tmaskBands']=metadata['tmaskBands'].toString();//创建掩码的带
//   metadata['startDate']='2000-01-01';
//   metadata['endDate']='2024-01-01';
//   metadata['bands']=params.Classification.bandNames.toString();//用于分析的条带。
    
     
//   //Export results, assigning the metadata as image properties.
//   //导出结果，将元数据指定为图像属性。
//   Export.image.toAsset({
//       image: ccdImage.set(metadata),
//       region: studyRegion,
//       pyramidingPolicy:{
//           ".default": 'sample'//金字塔。“样本”意味着聚合将获取数据样本，这通常适用于分类或离散数据，确保即使在较低分辨率下查看更改检测数据也能保持完整性。
//       },
//       scale: 30,//空间分辨率
//       priority: 10,
//   });
// }
// //将 pyramidingPolicy 的值设置为“sample”可确保输出中的所有波段都具有正确的策略。







// // trainingData = utils.Classification.remapLC(
// //     trainingData, 'Class', 'Class',['Bare','Developed','Forest','Herbaceous','Shrub','Snow/Ice','Water'],[1,2,3,4,5,6,7],[1,2,3])
// // print(trainingData.aggregate_histogram('Class'))




// // trainingData  = trainingData.map(function(feat) {
// //   return feat.set('year',2014)})
  
  
  
  
  
// Define bands to use in classification
var bands = params.Classification.bandNames

// Define coefficients to use in classification
var coefs = params.Classification.coefs

// Segment ids
var segs = params.Classification.segs

// Property corresponding to year of training data
var yearProperty = params.Classification.yearProperty

// Define path to change detection resultsCCDC只是ccdImage计算的一部分
 params.Classification.changeResults = ee.Image('users/2404182135/CCDCNew2_5_2')
 Map.centerObject(params.Classification.changeResults, 10);
// Load ccd image stack with coefficients and change information
//这里要将CCDC，Segs，BandNames这三个结合
var ccdImage = utils.CCDC.buildCcdImage(params.Classification.changeResults, params.Classification.segs.length, params.Classification.bandNames)
//print(ccdImage,'ccdImage')





  //     Export.image.toAsset({  
  //     image: ccdImage.clip(studyRegion),  
  //     description: 'CCDImage5_6', 
  //     assetId: 'CCDImage5_6',  // 确保使用正确的参数名称
  //     scale: 30,  // 分辨率为30米
  //     region: studyRegion.geometry(),  // 指定研究区
  //     maxPixels: 1e13,  // 最大像素数
       
  //     crs: "EPSG:4326"  // 设置投影为WGS84
  // });













// Finally, get ancillary topographic and climate data
var ancillary = utils.Inputs.getAncillary()

// // var resultsPath=
// //   'users/2404182135/TaiYuanCCDC';
// // var ccdImage=ee.Image(resultsPath);//将CCDC结果加载为图像：此行从指定路径处的数据创建图像对象
// // Map.centerObject(ccdImage, 10);//将地图居中在图像上：缩放级别为 10
// // print('CCD Image:',ccdImage);//打印图像对象：此命令将 ccdResults 图像对象的属性输出到 Earth Engine 代码编辑器中的控制台。
// // //它可用于检查图像的属性，例如波段数、波段名称、属性、投影等，这有助于了解数据结构和元数据。

// print('CCD Image:', ccdImage)



var trainingData = ee.FeatureCollection(params.Classification.trainingPath)
print(trainingData.first())

var trainingData = utils.Classification.getTrainingCoefsAtDate(
  trainingData, coefs, bands, yearProperty, ancillary, ccdImage, segs)


// Filter points with no data
var testBand = params.Classification.bandNames[0] + '_' + params.Classification.coefs[0]
trainingData = trainingData.filter(ee.Filter.notNull([testBand]))

print('First training point with predictors:', trainingData.first())

Export.table.toAsset({
  collection: trainingData,
  description: 'trainingDataProcessed',
  assetId: params.Classification.trainingPathPredictors})
  
  
  
  
  
  
var trainingData=ee.FeatureCollection('users/2404182135/SingeTraining2010Prediction')
print(trainingData)
// Now do the actual classification add the first segments classification to the map

// Get training data as FC
//var trainingData = ee.FeatureCollection(params.Classification.trainingPath)

// Optionally filter by study area
trainingData = trainingData.filterBounds(params.StudyRegion)

// Next, turn array image into image
//var imageToClassify = utils.CCDC.buildCcdImage(params.Classification.changeResults, params.Classification.segs.length, params.Classification.bandNames)
//var imageToClassify = ee.Image('users/2404182135/CCDCNew2_5_2')
var imageToClassify =ccdImage
// Now get ancillary data
var demImage = ee.Image('USGS/SRTMGL1_003').rename('ELEVATION')
var slope = ee.Terrain.slope(demImage).rename('DEM_SLOPE')
var aspect = ee.Terrain.aspect(demImage).rename('ASPECT')
//温度受影响变化大，会影响分类精度
var bio = ee.Image('WORLDCLIM/V1/BIO')
    .select(['bio01','bio12'])
    .rename(['TEMPERATURE','RAINFALL'])
var ancillary = ee.Image.cat([demImage, slope, aspect, bio])

// Get classifier with params
var classifier = params.Classification.classifier(params.Classification.classifierParams)

var results = utils.Classification.classifySegments(
    imageToClassify, params.Classification.segs.length, params.Classification.bandNames,
    ancillary, params.Classification.ancillaryFeatures,
    trainingData, classifier, params.StudyRegion, params.Classification.classProperty,
    params.Classification.inputFeatures)
    .clip(params.StudyRegion)
Map.centerObject(params.StudyRegion, 10);


var glanceViz = {min: 1, max: 8, palette: ['#386cb0', 'white', 'red', 'black', '#33a02c', '#b2df8a', '#ffff99','#654321']}
Map.addLayer(results.select(0), glanceViz, 'Seg1 Classification')
//Map.addLayer(results.select(0).randomVisualizer(), {}, 'Seg1 Classification')




// Export to asset!    
  Export.image.toAsset({
    image: ee.Image(results).int8(),//.clip(outGeo),
    scale: 30,
    description: 'classification_segments',
    assetId: 'classification_segments',
    maxPixels: 1e13,
    region: params.StudyRegion,
    pyramidingPolicy: {
      '.default': 'mode'
    }
  })