GEE学习笔记03（空间类型数据）

  • Ⅰ、数据类型
  • Ⅱ、Geometry
  • Ⅲ、Feature
  • Ⅳ、Feature collection
  • Ⅴ、Image
  • Ⅵ、Image Collection
  • Ⅵ、卫星数据
  • Ⅶ、矩阵操作

由于刚刚接触不久，如果有记录描述错误的地方，欢迎评论或私信指正，谢谢。

Ⅰ、数据类型

  1. Geometry
  2. Feature
  3. Feature collection
  4. Image
  5. Image Collection
  6. 卫星数据
  7. 矩阵操作

Ⅱ、Geometry

创建    ee.Geometry.Point()、ee.Geometry.Multipoint()
几何     Geometry.transform()、geometry.centroid ()、geometry.simplify ()、geometry.bounds()、geometry.buffer ()、geometry.union () ……
查询     geometry.geometries()、geometry.coordinates()、geometry.length ()、geometry.area ()、geometry.perimeter ()、geometry.distance ()
转换    geometry.toGeoJSON()

创建点

var Forbidden_City = ee.Geometry.Point(116.3968,39.9186)Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

创建多点

var Forbidden_City = ee.Geometry.MultiPoint([[116.3921, 39.9224],[116.4014, 39.9227],[116.4017, 39.9138],[116.3929, 39.9135]              ]);Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

创建线

var Forbidden_City = ee.Geometry.LineString([[116.39234040929296, 39.91321497621612],[116.39195417119481, 39.92276018488313],[116.40186761571385, 39.9230234821268],[116.40216802312352, 39.91354414349019]]);Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

创建多线

var Forbidden_City = ee.Geometry.MultiLineString([[[116.39367078496434, 39.92134494485143],[116.39899228764989, 39.92173989850053]],[[116.39401410771825, 39.91953471147776],[116.39693235112645, 39.92085124958646]],[[116.39628862096288, 39.91920557299557],[116.39873479558446, 39.91752694212849]]    ]);Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

创建单闭合线

var Forbidden_City = ee.Geometry.LinearRing([[116.39242623998143, 39.91321497621612],[116.39195417119481, 39.92269436041402],[116.40178178502538, 39.9230234821268],[116.40212510777928, 39.91357706013058],[116.39242623998143, 39.91321497621612]]);Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

创建多闭合线

var Forbidden_City =ee.Geometry.MultiLineString([[[116.39320681392098, 39.921786784372976],[116.39337847529794, 39.92040443758097],[116.39578173457528, 39.92070065709998],[116.39556715785409, 39.92188552236169],[116.39320681392098, 39.921786784372976]],[[116.39801333247567, 39.91918663276216],[116.39548132716561, 39.91777131887048],[116.4006311684742, 39.91747508667966],[116.39801333247567, 39.91918663276216]]]);Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

创建多边形

var Forbidden_City =ee.Geometry.MultiLineString([[[116.39320681392098, 39.921786784372976],[116.39337847529794, 39.92040443758097],[116.39578173457528, 39.92070065709998],[116.39556715785409, 39.92188552236169],[116.39320681392098, 39.921786784372976]],[[116.39801333247567, 39.91918663276216],[116.39548132716561, 39.91777131887048],[116.4006311684742, 39.91747508667966],[116.39801333247567, 39.91918663276216]]]);Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

创建嵌套多边形

var Forbidden_City =ee.Geometry.Polygon([[[116.39179060756112, 39.922576684295926],[116.39230559169198, 39.913064366936894],[116.40213320552255, 39.91352520169099],[116.40161822139169, 39.92297163084022]],[[116.39483759700204, 39.91961451259886],[116.39496634303475, 39.91648763678902],[116.39900038539315, 39.916553467224674],[116.39874289332772, 39.91977908105491]]    ]);Map.centerObject(Forbidden_City)
print(Forbidden_City)
Map.addLayer(Forbidden_City)

投影转换

var China_Geo   = ee.Geometry.Rectangle(65.9, 19.8,134.5, 50.9);
var China_Planr = ee.Geometry(China_Geo, null, false)
var China_Plnar_2 = China_Geo.transform('EPSG:4326', ee.ErrorMargin(100))Map.addLayer(China_Geo,  {color: 'FF0000'}, 'geodesic polygon')
Map.addLayer(China_Planr,{color: '000000'}, 'planar polygon')
Map.addLayer(China_Planr,{color: '0000CD'}, 'planar polygon')//https://epsg.io  投影编码对照网址

获取中心

var China_Geo    = ee.Geometry.Rectangle(65.9, 19.8,134.5, 50.9);
var China_Certer = China_Geo.centroid();Map.addLayer(China_Geo,  {color: 'FF0000'}, 'geodesic polygon')
Map.addLayer(China_Certer)

边界简化

var TGR = ee.FeatureCollection("users/wangjinzhulala/TGR").geometry();
var TGR_Simple = TGR.simplify(50000)Map.centerObject(TGR )
Map.addLayer(TGR)
Map.addLayer(TGR_Simple)

获取四至及外接多边形

var TGR       = ee.FeatureCollection("users/wangjinzhulala/TGR").geometry();
var TGR_Bound = TGR. bounds()
var TGR_Hall  = TGR.convexHull()Map.centerObject(TGR )
Map.addLayer(TGR)
Map.addLayer(TGR_Bound)
Map.addLayer(TGR_Hall)

获取缓冲区

var TGR       = ee.FeatureCollection("users/wangjinzhulala/TGR").geometry();
var TGR_Buffer = TGR.buffer(10000)Map.centerObject(TGR )
Map.addLayer(TGR)
Map.addLayer(TGR_Buffer)

多边形合并

var Polygon_1 = ee.Geometry.Polygon([[[152.71032334056088, 18.862610207551867],[-170.72717665943918, 19.69222816299581],[-171.07873915943918, 40.62061002589569],[153.41344834056088, 40.08478212000696]]]),Polygon_2 = ee.Geometry.Polygon([[[-176.52795790943912, 45.620172046005735],[-176.70373915943912, 32.2945468815855],[-142.60217665943918, 32.2945468815855],[-141.02014540943918, 45.49709377875777]]]);var Polygon_union = Polygon_1.union(Polygon_2);Map.addLayer(Polygon_1)
Map.addLayer(Polygon_2)
Map.addLayer(Polygon_union)
// .intersection  .symmetricDifference     .Difference 类似于ArcGIS中的其他操作也凭借类似方法实现

获取数据矢量信息

var Forbidden_City = ee.Geometry.MultiPolygon([[[[116.39393637477303, 39.921260179361866],[116.39350722133065, 39.91882457850439],[116.39676878749276, 39.918791664386056],[116.39711211024667, 39.920963962263336]]],[[[116.39887163936044, 39.91810046424626],[116.39595339595223, 39.91698136351405],[116.40080282985116, 39.91652055201477]]]]);
var Geometry_List = Forbidden_City.geometries()
Map.centerObject(Forbidden_City)
Map.addLayer(Forbidden_City)print(Geometry_List)

多边形面积周长计算

var Polygon_1 =ee.Geometry.Polygon([[[116.39179060756112, 39.922576684295926],[116.39230559169198, 39.913064366936894],[116.40213320552255, 39.91352520169099],[116.40161822139169, 39.92297163084022]],[[116.39483759700204, 39.91961451259886],[116.39496634303475, 39.91648763678902],[116.39900038539315, 39.916553467224674],[116.39874289332772, 39.91977908105491]]    ]);
var Polygon_2 =ee.Geometry.Polygon([[[116.39239142238046, 39.913146659084674],[116.40209029017831, 39.91354166001771],[116.40170405208016, 39.922988086896794],[116.39183352290536, 39.922593140447425]]]);var Area_1 = Polygon_1.area()
var Area_2 = Polygon_2.area()
var Perimeter_1 = Polygon_1.perimeter()
var Perimeter_2 = Polygon_2.perimeter()print(Area_1,Area_2,Perimeter_1,Perimeter_2)Map.centerObject(Polygon_1)
Map.addLayer(Polygon_1)
Map.addLayer(Polygon_2)

多个多变形之间距离量算

var geometry_1 = ee.Geometry.Polygon([[[116.39419386683858, 39.92130954875369],[116.39359305201924, 39.918873949652266],[116.3966829568044, 39.918709379020974],[116.39719794093526, 39.92101333186874]]]),geometry_2 = ee.Geometry.Polygon([[[116.39891455470479, 39.918116921473526],[116.3957817345754, 39.917063650955505],[116.40123198329366, 39.91647117917021]]]);
var Distance = geometry_1.distance(geometry_2)print(Distance)Map.centerObject(geometry_1)
Map.addLayer(geometry_1)
Map.addLayer(geometry_2)

数据格式转换

var geometry = ee.Geometry.Polygon([[[116.39419386683858, 39.92130954875369],[116.39359305201924, 39.918873949652266],[116.3966829568044, 39.918709379020974],[116.39719794093526, 39.92101333186874]]])var geometry_GeoJSON = geometry.toGeoJSON()
print(geometry)
print(geometry_GeoJSON)Map.centerObject(geometry)
Map.addLayer(geometry)
// 这里的目的主要是防止由GEE直接导出来的数据乱码，可以先转换为GeoJSON，再进一步进行转换到shp文件

Ⅲ、Feature

与Geometry相比，Feature除了包含空间信息外，又增加了属性信息，而Geometry仅仅包含空间信息。

创建   ee.Feature()、绘图工具
编辑      Feature.select()、Feature.transform()、Feature.set/setMulti()
几何      Feature.centroid/simplify/bounds/convexHull/buffer()、Feature.union/intersection/difference/symmetricDifference()
提取      Feature.geometry()、Feature.get()、Feature.Length/Area/Perimeter()

要素创建

var Forbidden_City =ee.Geometry.Polygon([[[116.39239142238046, 39.913146659084674],[116.40209029017831, 39.91354166001771],[116.40170405208016, 39.922988086896794],[116.39183352290536, 39.922593140447425]]]);
var Feature_Fobidden_City = ee.Feature(Forbidden_City, {name:'故宫', location:'北京'})Map.centerObject(Forbidden_City)
print(Feature_Fobidden_City)
Map.addLayer(Feature_Fobidden_City)

要素属性数据选取（复制属性到某一变量）

var Forbidden_City =ee.Geometry.Polygon([[[116.39239142238046, 39.913146659084674],[116.40209029017831, 39.91354166001771],[116.40170405208016, 39.922988086896794],[116.39183352290536, 39.922593140447425]]]);
var Feature_Fobidden_City = ee.Feature(Forbidden_City, {name:'故宫', location:'北京'})
var Feature_select = Feature_Fobidden_City.select(['name'])Map.centerObject(Forbidden_City)
print(Feature_Fobidden_City, Feature_select)
Map.addLayer(Feature_Fobidden_City)

// 与上面的不同，此段输出时会将name重命名
var Forbidden_City =ee.Geometry.Polygon([[[116.39239142238046, 39.913146659084674],[116.40209029017831, 39.91354166001771],[116.40170405208016, 39.922988086896794],[116.39183352290536, 39.922593140447425]]]);
var Feature_Fobidden_City = ee.Feature(Forbidden_City, {name:'故宫', location:'北京'})
var Feature_select = Feature_Fobidden_City.select(['name'],['名称'])Map.centerObject(Forbidden_City)
print(Feature_Fobidden_City, Feature_select)
Map.addLayer(Feature_Fobidden_City)

坐标转换

var China_Geo   = ee.Feature(ee.Geometry.Rectangle(65.9, 19.8,134.5, 50.9));
var China_Plnar = China_Geo.transform('EPSG:3857',ee.ErrorMargin(100));print(ee.Geometry(China_Geo.geometry()).projection(),ee.Geometry(China_Plnar.geometry()).projection())

要素属性重写

// .set
var Forbidden_City =ee.Geometry.Polygon([[[116.39239142238046, 39.913146659084674],[116.40209029017831, 39.91354166001771],[116.40170405208016, 39.922988086896794],[116.39183352290536, 39.922593140447425]]]);
var Feature_Fobidden_City = ee.Feature(Forbidden_City, {name:'故宫', location:'北京'})
var Feature_Set = Feature_Fobidden_City.set('name','Gugong', 'location','Beijing')
print(Feature_Fobidden_City,Feature_Set)Map.centerObject(Forbidden_City)
print(Feature_Fobidden_City)
Map.addLayer(Feature_Fobidden_City)

//.setMulti
var Forbidden_City =ee.Geometry.Polygon([[[116.39239142238046, 39.913146659084674],[116.40209029017831, 39.91354166001771],[116.40170405208016, 39.922988086896794],[116.39183352290536, 39.922593140447425]]]);
var Feature_Fobidden_City = ee.Feature(Forbidden_City, {name:'故宫', location:'北京'})
var Feature_Set = Feature_Fobidden_City.setMulti({'name':'Gugong', 'location':'Beijing'})
print(Feature_Fobidden_City,Feature_Set)Map.centerObject(Forbidden_City)
Map.addLayer(Feature_Fobidden_City)

Feature.centroid/simplify/bounds/convexHull/buffer()

var China = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var Chongqing = ee.Feature(China.filterBounds(ee.Geometry.Point([106,29])).first())
var CQ_Simple   = Chongqing.simplify(50000)
var CQ_Centroid = Chongqing.centroid()
var CQ_Hull     = Chongqing.convexHull()
var CQ_Bounds   = Chongqing.bounds()
var CQ_Buffer   = Chongqing.buffer(50000)Map.centerObject(Chongqing)Map.addLayer(Chongqing)
Map.addLayer(CQ_Simple)
Map.addLayer(CQ_Centroid)
Map.addLayer(CQ_Hull)
Map.addLayer(CQ_Bounds)
Map.addLayer(CQ_Buffer)

Feature. union/intersection/difference/symmetricDifference()

var China = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var Chongqing = ee.Feature(China.filterBounds(ee.Geometry.Point([106,29])).first())
var TGR = ee.Feature(ee.FeatureCollection("users/wangjinzhulala/TGR").first());var Union = Chongqing.union(TGR)
var Intersect = Chongqing.intersection(TGR)
var Difference = Chongqing.difference(TGR)
var Symmetric_Difference = Chongqing.symmetricDifference(TGR)Map.addLayer(Union,{color:'00ffff'},'Union')
Map.addLayer(Intersect,{color:'DC143C'},'Intersect')
Map.addLayer(Difference,{color:'7B68EE'},'Difference')
Map.addLayer(Symmetric_Difference,{color:'32CD32'},'Symmetric_Difference')

属性信息剥离，仅保留空间信息

var TGR = ee.Feature(ee.FeatureCollection("users/wangjinzhulala/TGR").first());
var TGR_geometry = TGR.geometry();
var TGR_get = TGR.get('BOUNT_ID') //通过get获得某个具体的信息print(TGR,TGR_geometry,TGR_get)

面积/距离量算

var TGR = ee.Feature(ee.FeatureCollection("users/wangjinzhulala/TGR").first());
var TGR_Area = TGR.area()
var TGR_Perimeter = TGR.perimeter()print(TGR_Area,TGR_Perimeter)

Ⅳ、Feature collection

创建       GEE自带、上传、FushionTable、ee.FeatureCollection()、.randomPoints()、手绘…
编辑    .filterMetadata()、.limit()、.filterDate()、.filterBounds()、.filter()、.select()、.distinct()、.union()      .merge()、.set()、.remap()、.sort()、.makeArray()
转换    .geometry()、.reduceToImage()
查询    .first()、.toList()、.aggregate_first()、.aggregate_array()
统计    .aggregate_stats/_histogram/_count/_count_distinct |.aggregate_max/_min/_sum/_mean/_product|.aggregate_sample_var/_total_var/_sample_sd/_total_sd/
其他    .map()

要素创建（点线面）

var Poly   = ee.Geometry.Polygon([[[116.39248531478768, 39.91319513792091],[116.40222709792977, 39.91378763804685],[116.40184085983162, 39.92303655876662],[116.39171283859139, 39.922674524864675]]]);
var Points = ee.Geometry.MultiPoint([[116.38823669570809, 39.92079850034943],[116.38677757400399, 39.91885659627606],[116.38669174331551, 39.916881722097074],[116.38767879623299, 39.91421555161684]]);
var Lines  = ee.Geometry.LineString([[116.37733619827156, 39.9167171466781],[116.38192814010506, 39.91754001981801],[116.37712162155037, 39.91365597179514],[116.38424556869393, 39.914478881715695]]);var Feature_Collection = ee.FeatureCollection( [Poly, Points, Lines] );Map.centerObject(Feature_Collection)
print (Feature_Collection)
Map.addLayer (Feature_Collection)

随机点

var Poly   = ee.Geometry.Polygon([[[116.39248531478768, 39.91319513792091],[116.40222709792977, 39.91378763804685],[116.40184085983162, 39.92303655876662],[116.39171283859139, 39.922674524864675]]]);
var Random_Points = ee.FeatureCollection.randomPoints(Poly,20)Map.centerObject(Poly)
Map.addLayer(Poly)
Map.addLayer(Random_Points)

数据筛选

// .filterMetadata
var Proction_Area = ee.FeatureCollection("WCMC/WDPA/current/polygons");
var Proction_Area_China = Proction_Area.filterMetadata('ISO3','equals','CHN')  //对其元数据进行筛选，条件是列标题=CHNMap.centerObject(Proction_Area_China)
Map.addLayer(Proction_Area_China, ,{color:'FF0000'})

// .limit(筛选数量、筛选字段（可选）、排序方式（可选，默认从小到大True）)
var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");Map.addLayer(China_Provinces)
Map.addLayer(China_Provinces.limit(5,'Shape_Area',false))

// .filterDate 根据时间筛选
var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT");
var Landsat_FilterDate = L8.filterDate('2019-03-01','2019-03-16').limit(50)
// 筛选出50个 减少信息量
print(Landsat_FilterDate)

// 根据空间位置筛选
var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var CQ_Point =ee.Geometry.Point([106.92371845031437, 29.430312117372274]);
var CQ = China_Provinces.filterBounds(CQ_Point)Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces)
Map.addLayer(CQ,{color:'FF0000'})

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var CQ_Point =ee.Geometry.Point([106.92371845031437, 29.430312117372274]);
var Filter_By_Chongqing = ee.Filter.bounds(CQ_Point)var CQ = China_Provinces.filter(Filter_By_Chongqing)Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces)
Map.addLayer(CQ,{color:'FF0000'})

要素信息提取（复制）

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var China_Area = China_Provinces.select(['NAME'])print(China_Provinces)
print(China_Area)Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces)
Map.addLayer(China_Area)

要素信息筛选（去除重复字段）

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var China_Area = China_Provinces.distinct(['NAME'])print(China_Provinces)
print(China_Area)Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces)
Map.addLayer(China_Area)

空间信息合并（合并后会丢失原有属性信息）

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var China_Union = China_Provinces.union()print(China_Provinces)
print(China_Union)Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces)
Map.addLayer(China_Union,{color:'ff0000'})

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var Chongqing = China_Provinces.filterMetadata('NAME','equals','Chong_Qing')
var Si_Chuan = China_Provinces.filterMetadata('NAME','equals','Si_Chuan')
var CQ_SC_Merge = Chongqing.merge(Si_Chuan)Map.centerObject(China_Provinces,4)
Map.addLayer(Chongqing)
Map.addLayer(Si_Chuan,{color:'0000ff'})
Map.addLayer(CQ_SC_Merge,{color:'ff0000'})

信息更改重置

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var Chongqing = China_Provinces.filterMetadata('NAME','equals','Chong_Qing')
var Si_Chuan = China_Provinces.filterMetadata('NAME','equals','Si_Chuan')
var CQ_SC_Merge = Chongqing.merge(Si_Chuan)
var CQ_SC_Merge_Set = Chongqing.merge(Si_Chuan).set('NAME','Chuan_Yu')print(CQ_SC_Merge)
print(CQ_SC_Merge_Set)
//.setMulti()参考前例

对FeatureCollection中某一个字段的每一条属性都进行相应的操作

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var Old_Provinces_ID = ee.List([1,2,3,4,5,6,7,8,9,12,13,14,15,16,18,19,20,21,22,23,24,27,28,29,30,31,32,33,34,35,36,37,38,39,1089])
var New_Provinces_ID = ee.List([1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,6,6,6,6,6,7,7,7,7,7])
var China_Remap = China_Provinces.remap(Old_Provinces_ID,New_Provinces_ID,'OBJECTID')
var China_Provinces_Map = China_Provinces.reduceToImage(['OBJECTID'], ee.Reducer.first( ) )
//类似于ArcGIS中的根据属性显色，这里以OBJECTID进行 颜色复制
var China_Remap_Map = China_Provinces.reduceToImage(['OBJECTID'], ee.Reducer.first( ) )Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces_Map,{min:1, max:40, palette:'16ff07,2901ff'},'China_Provinces_Map')
Map.addLayer(China_Remap_Map,{min:1, max:7, palette:'ff7248,fbff21,09ffe8'},'China_Remap_Map')

对属性表进行排序

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var China_Sort_Area = China_Provinces.sort('Shape_Area',false)
//根据Shape_Area进行排序，默认都是True，从小到大，最后得到面积前五大的省份
Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces.limit(5))
Map.addLayer(China_Sort_Area.limit(5),{color:'ff000000'})

将多个属性字段形成新的属性字段

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces").limit(3);
var China_Array = China_Provinces.makeArray(['OBJECTID','Shape_Area','Shape_Leng'],'An_Array')
//将'OBJECTID','Shape_Area','Shape_Leng'三个字段结合为'An_Array'的一个字段
print(China_Provinces)
print(China_Array)

获得所有feature的Geometry信息

// 执行操作后仅仅保留多边形的信息
var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces").limit(3);
var China_Geometry = China_Provinces.geometry()print(China_Provinces)
print(China_Geometry)

矢量转栅格

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")
var China_to_Image = China_Provinces.reduceToImage(['OBJECTID'],ee.Reducer.first())
// Reducer缩减器选择了first
Map.centerObject(China_Provinces,4)
Map.addLayer(China_to_Image,{"min":1,"max":40,"palette":["ff9c07","f0ff1b","1aff0b"]})

toList命令（更便于属性数据的提取）

// 将中国的面积信息进行筛选，排序并选取前十个
var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")
var China_List = China_Provinces.sort('Shape_Area',false).toList(10)
print(China_List)var Area_No_1 = ee.Feature(China_List.get(0))
var Area_No_3 = ee.Feature(China_List.get(2))
var Area_No_7 = ee.Feature(China_List.get(6))Map.centerObject(China_Provinces,4)
Map.addLayer(Area_No_1)
Map.addLayer(Area_No_3)
Map.addLayer(Area_No_7)

对属性表某一列进行操作
.aggregate操作这里仅列举四个，详细查看官方文档

//对面积信息进行筛选，选择其第一个
var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")
var Area_of_No_1 = China_Provinces.sort('Shape_Area',false).aggregate_first('Shape_Area')
print(Area_of_No_1)

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")
var Area_of_Top_3 = China_Provinces.sort('Shape_Area',false).limit(3).aggregate_array('Shape_Area')
print(Area_of_Top_3)

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")
var Area_Status = China_Provinces.aggregate_stats('Shape_Area')
print(Area_Status)

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")
var Area_Histogram = China_Provinces.aggregate_histogram('Shape_Area')
print(Area_Histogram)

对某一个数据集中每一个元素同时进行某一个操作

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")function Add_Center (feature){return feature.centroid()
}var China_Center = China_Provinces.map(Add_Center)
// 对省里面每一个命令都进行一个Add_Center的命令，进而得到每一个省的几何中心
print(China_Center)Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces)
Map.addLayer(China_Center,{color:'ff0000'})

学习的过程中不难发现String、Number等数据经常出现在Feature、FeatureCollection等中，即基本的数据类型是为了更好的反应空间信息的数据结构而存在的，加强理二者的关系对于日后复杂的操作具有重要的意义

Ⅴ、Image

创建   GEE自带   用户上传    ee.image()/constant()   ee.Image.pixelLonLat()
编辑   .mask()    .clip() .select()   .slice()    addBands()  .reproject()    .rbgtohsv()           .hsvtorbg()   .unit8()    .cast() .set()  .setMulti()     .remap()        .where()              .metadata()   .clamp()    .unitScale()    .interpolate()  .eq()……     .abs() ……         .sin() …… .bitWiseAnd() ……        .reduce()
地形   image.derivative() ee.Terrain.products()   ee.Algorithm.Terrain()  ee.Terrain.slope()          ee.Terrain.aspect() ee.Terrain.fillMinima() ee.Terrain.hillshade()  ee.Terrain.hillshadow()         ee.Algorithm.Hillshadow()
纹理   .entropy() .Texture()  .zeroCrossing() . ee.Algorithms.CannyEdgeDetector()         ee.Algorithms.HoughTransform()
邻域   .focal_max()   .focal_min()   .focal_median()     focal_mode() .convolve()   .reduceNeighborhood()
转换   .Image.ToVector()  Image.ToArray() Image.arrayFlatten()
其他   .CrossCorrelation()    .distance() Export.image()

GEE自带了许多实用的影像数据，如Landsat、Sentinel、Srtm、Land Cover
详情 查询以下网址：Datase of GEE developers

创建纯像素栅格图像

var Image_1 = ee.Image( 10 );   //创建的Image既没有空间分辨率，也没有边界，仅仅是数据
var Image_2 = ee.Image.constant( 20 ); //与创建Image_1类似，创建Image_2，像素为20
var Image_3 = Image_1.add(Image_2) //将Image_1+Image_2的值作为Image_3的值print(Image_1,Image_2,Image_3);

//由此创建的栅格图像任一点由两部分构成，经纬度
var Image_Lon_Lat = ee.Image.pixelLonLat( );
print( Image_Lon_Lat);  Map.addLayer( Image_Lon_Lat, {bands:['longitude'],min:-180,max:180},'Longitude' );
Map.addLayer( Image_Lon_Lat, {bands:['latitude'], min:- 60,max: 60},'Latitude'  );
Map.setCenter( 0, 0, 3 );

掩模操作（不删除原始数据，主要利用栅格图像进行操作）

var Image_DEM  = ee.Image( 'CGIAR/SRTM90_V4' );
var Image_Cropland = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover').eq(11);
var Image_Masked  = Image_DEM.mask( Image_Cropland );
print(Image_DEM,Image_Cropland,Image_Masked);Map.setCenter(106.4, 34.78, 4);
Map.addLayer( Image_DEM,    {"opacity":1,"bands":["elevation"],"min":0,"max":1400,"palette":["2dff07","ff0b0b"]}, ' Image_DEM ' );
Map.addLayer( Image_Cropland,   {"opacity":1,"bands":["landcover"],"palette":["ffffff","fbff2d"]}, ' Image_Cropland '     );
Map.addLayer( Image_Masked, {"opacity":1,"bands":["elevation"],"min":0,"max":1400,"palette":["2dff07","ff0b0b"]}, ' Image_Masked '   );

裁剪操作（删除原始数据，主要利用矢量数据操作）

var DEM = ee.Image("USGS/SRTMGL1_003");
var CQ = ee.FeatureCollection("users/wangjinzhulala/China_Provinces").filterBounds(ee.Geometry.Point([106.8, 29.3]))
var CQ_DEM = DEM.clip(CQ)Map.setCenter(106.8, 29.3,7)
Map.addLayer(CQ_DEM,{min:0,max:2000})

选择操作（挑选某一数据集中需要的波段信息）

var Glob_Cover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3");
var Land_cover = Glob_Cover.select('landcover')
var Land_cover_quality = Glob_Cover.select('qa')Map.setCenter(107.56, 34.79,4)
Map.addLayer(Land_cover)
Map.addLayer(Land_cover_quality)

利用波段序号选择提取想要的波段

var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT");
var L8_One = ee.Image(L8.filterBounds(ee.Geometry.Point(106.4958, 29.5856)).first())
var L8_Slice = L8_One.slice(2,5);print(L8_One,L8_Slice)

增加波段信息

var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT");
var L8_One = ee.Image(L8.filterBounds(ee.Geometry.Point(106.4958, 29.5856)).first())
var L8_B5 = L8_One.select('B5');
var L8_B4 = L8_One.select('B4');
var L8_B5_4 = L8_B5.addBands(L8_B4)print(L8_B5,L8_B4,L8_B5_4)

对栅格影像重投影

var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT");
var L8_One = ee.Image(L8.filterBounds(ee.Geometry.Point(106.4958, 29.5856)).first())
var L8_Reproject = L8_One.reproject('EPSG:3857',null,100)print(L8_One.select('B1').projection(),L8_Reproject.projection())Map.setCenter(106.58365, 29.56972,15)
Map.addLayer(L8_One,{"bands":["B5","B4","B3"],"min":10586,"max":18154},'30m')
Map.addLayer(L8_Reproject,{"bands":["B5","B4","B3"],"min":10586,"max":18154}, '100m')

图像显色方式变换，RGB与HSV互换的操作

var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT");
var L8_One = ee.Image(L8.filterBounds(ee.Geometry.Point(106.4958, 29.5856)).first()).slice(2,5).unitScale(0,32468)
var L8_HSV = L8_One.rgbToHsv()
var L8_RGB = L8_HSV.hsvToRgb()print(L8_One,L8_HSV,L8_RGB)Map.setCenter(106.5, 29.5,9)
Map.addLayer(L8_One,{},'Origin')
Map.addLayer(L8_HSV,{},'HSV')
Map.addLayer(L8_RGB,{},'RGB')

类型转换（数字格式变换，单波段）

.uint8   .uint16     .uint32     .uint64     .int8   .int16  .int32  .int64  .long   .float  .double
.toUnit8    toUint16    toUint32    toUint64    toInt8  toInt16 toInt32 toInt64 toLong  toFloat toDouble

类型转换（数字格式变换，多波段）

var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT");
var L8_One = ee.Image(L8.filterBounds(ee.Geometry.Point(106.4958,29.5856)).first()).slice(2,5).unitScale(0,32468)
var L8_Cast = L8_One.cast({'B5':'double','B4':'double','B3':'double'},['B5','B4','B3'])print(L8_One,L8_Cast)

属性修改，重置
这里与之前类似，单独的set操作不同于setMulti（需要以字典的形式进行对应的修改）

var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT");
var L8_One = ee.Image(L8.filterBounds(ee.Geometry.Point(106.4958, 29.5856)).first()).slice(2,5).unitScale(0,32468)var L8_HSV = L8_One.rgbToHsv().set('note',"This image was created by 'rgbToHsv()'").setMulti({'author':'Jinzhu Wang', 'Mood':'Not Bad'})print(L8_HSV)

将影像部分的数值重新赋值（遥感影像重分类）

// 有类别对应的代码 属性数据重分类
var Land_Cover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover');
var Land_Remap = Land_Cover.remap([11,14,20,30,40,50,60,70,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230],[0, 0, 0, 0, 1, 1, 1, 1, 1,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,  6,  7,  8],8,'landcover')Map.setCenter(102.919, 30.199,6)
Map.addLayer(Land_Cover)
Map.addLayer(Land_Remap,{"palette":["f1ff27","35ce48","07fff3","f017ff","e78845","250bff","ffffff","ffffff"]},'remaped')

//接用空间基础重分类，将高程小于 4000m的赋值为0
var DEM = ee.Image("USGS/SRTMGL1_003");
var Land_Cover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover');var High_Land = Land_Cover.where(DEM.lt(4000),0)Map.setCenter(88.96, 33.77,4)
Map.addLayer(DEM.lt(4000))
Map.addLayer(High_Land)

通过元数据修改属性信息

// 属性中是否云层覆盖均替换为云覆盖率
var L8_One = ee.Image(ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(106.5776, 29.5577)).first());
var Constat_Image_Cloud = L8_One.metadata('CLOUD_COVER','The Cloud Rate').clip(L8_One.geometry())print(Constat_Image_Cloud)
Map.setCenter(106.5776, 29.5577,7)
Map.addLayer(L8_One,{"bands":["B5","B4","B3"],"max":0.4,"gamma":1.03})
Map.addLayer(Constat_Image_Cloud,{"palette":["60ff0f"]})

掐断/过滤操作

// 小于450及大于795的数据均被删除
var DEM = ee.Image("USGS/SRTMGL1_003");
var DEM_Clamp = DEM.clamp(450,795)Map.setCenter(106.5662, 29.5589,10)
Map.addLayer(DEM_Clamp)

归一化

var L8_One = ee.Image(ee.ImageCollection("LANDSAT/LC08/C01/T1").filterBounds(ee.Geometry.Point(106.5776, 29.5577)).first());
var L8_Unitscale = L8_One.unitScale(0,32767)print(L8_One,L8_Unitscale)Map.addLayer(L8_One,{"bands":["B5","B4","B3"],"max":30000})
Map.addLayer(L8_Unitscale,{"bands":["B5","B4","B3"],"max":0.92})

属性数据拉伸

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");function Add_Center (feature){return ee.Geometry(feature.centroid())
}var Provinces_Centers = China_Provinces.map(Add_Center)
var Center_Distance = Provinces_Centers.distance(200000)
var Distance_Interpolate = Center_Distance.interpolate( [ 0, 50000, 100000, 150000, 200000 ],[ 0,    25,     50,    100,    75  ], 'extrapolate' );Map.centerObject(Provinces_Centers,4)
Map.addLayer(Center_Distance,{min:0,max:200000},'origin')
Map.addLayer(Distance_Interpolate,{min:0,max:100},'interpolated')
Map.addLayer(Provinces_Centers, {color:'0000ff'},'centers')

影像比较/利用阈值分割/或设定阈值进行过滤
.eq/neq /gt /gte/lt/lte
= / ≠ / > / ≥ /< / ≤

var China = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var night_Lights = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182013').select('stable_lights').clip(China);var urban = night_Lights.gte(20)Map.setCenter(115.218, 33.697, 8);
Map.addLayer(urban, {"opacity":0.33,"bands":["stable_lights"],"palette":["171801","fbff0f"]}, 'Nighttime Lights');

利用逻辑运算进行过滤
Image.and/or()

var China = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var night_Lights = ee.Image('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182013').select('stable_lights').clip(China);var urban = night_Lights.gte(20)
var Low_Area = ee.Image("USGS/SRTMGL1_003").lt(200).clip(China)
var Low_Urban = urban.and(Low_Area)
print(Low_Area,urban)Map.setCenter(115.218, 33.697, 4);
Map.addLayer(night_Lights)
Map.addLayer(Low_Area)
Map.addLayer(Low_Urban);

常用数学操作
Image.abs/abs/round/floor/ceil/sqrt/exp/log/log10 ()

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var CQ = China_Provinces.filterMetadata('NAME','equals','Chong_Qing')var CQ_Distance = CQ.distance(50000)
var Distance_Mod = CQ_Distance.mod(10000)Map.centerObject(CQ,6)
Map.addLayer(CQ_Distance,{"min":0,"max":50000,"palette":["0172ff","0dff09","05ffe8","fff707","ffa605","ff5909"]},'The Distance Effect')
Map.addLayer(Distance_Mod,{"min":0,"max":10000,"palette":["0172ff","0dff09","05ffe8","fff707","ffa605","ff5909"]},'The Distance.mod() Effect')

加减乘除
Image. add/subtract/multiply/divide()
.add/subtract/multiply/divide/max/min/mod/pow/hypot/first /and/first_nonzero

var L8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT").filterBounds(ee.Geometry.Point(106.5749, 29.5588)).first()
var B5 = ee.Image(L8.select('B5'))
var B4 = ee.Image(L8.select('B4'))
var B3 = ee.Image(L8.select('B3'))var NDVI = B5.subtract(B4).divide(B5.add(B3))Map.centerObject(NDVI)
Map.addLayer(NDVI,{min":-1,"max":1,"palette":["0f01e2","ff9d93","fff707","11ff2e","04ce14"]})

常见三角函数
.sin/cos/tan/sinh/cosh/tanh/acos/asin/atan ()

var CQ_Point = ee.FeatureCollection(ee.Geometry.Point(106.57, 29.55))
var CQ_Distance = CQ_Point.distance(50)
var Distance_Effect = CQ_Distance.sin()Map.centerObject(CQ_Point,19)
Map.addLayer(CQ_Distance,{"max":50,"palette":["2d07ff","ff430b"]},'distance Image')
Map.addLayer(Distance_Effect,{"min":-1,"palette":["3705ff","ff4209"]},'sin Image')

逻辑运算
bitwiseAnd/ bitwiseOr /bitwiseXor /bitwiseNot
bitwise_and /bitwise_or /bitwise_xor /bitwise_not
leftShift / rightShift
left_shift /right_shift

var Image_1 = ee.Image( 1 );
var Image_2 = ee.Image( 2 );
var Bit_And = Image_1.bitwiseAnd( Image_2 );
var Bit_Or  = Image_1.bitwiseOr( Image_2 );
print ( 'Image of bitwise 1 and 2 =', Bit_And   );
print ( 'Image of bitwise 1 or 2 =',  Bit_Or );Map.addLayer( Image_1, null, 'Image of 1s' );
Map.addLayer( Image_2, null, 'Image of 2s' );
Map.addLayer( Bit_And, null, 'Image of Bits from Both' );
Map.addLayer( Bit_Or , null, 'Image of Bits from Either' );

对像元的微分操作

// 输入的是DEM数据，这里为每一个像元的x方向和y方向的坡向
var OldIMAGE = ee.Image( 'CGIAR/SRTM90_V4' );
var NewIMAGE = OldIMAGE.derivative( );Map.setCenter(106.4448, 29.5684,10)
Map.addLayer( NewIMAGE, {bands:['elevation_x'], min:-90, max:900 }, 'Slope from Left to Right' );
Map.addLayer( NewIMAGE, {bands:['elevation_y'], min:-90, max:900 }, 'Slope from Top to Bottom' );

表面分析工具
ee.Terrain.products
ee.Algorithms.Terrain

var DEM = ee.Image( 'CGIAR/SRTM90_V4' );
var Terrain = ee.Terrain.products( DEM );Map.setCenter( 106.371, 29.6188 );
Map.addLayer( DEM,    { min:0,max:2000,palette:'000099,dddd00'}, 'Elevation');
Map.addLayer( Terrain,{bands:['slope'], min:0,max:90,palette:'000000,ff0000' }, 'Slope');
Map.addLayer( Terrain,{bands:['aspect'], palette:'00ff00,0000ff,00ff00' },'Aspect');
Map.addLayer( Terrain,{bands:['hillshade'],min:0,max:255,},  'Hillshading');

利用ee.Terrain.hillshadow()阴影生成
这里true和false的选择决定着生成的图像效果

var DEM  = ee.Image( 'CGIAR/SRTM90_V4' );
var DEM_Mercator = DEM.reproject( 'EPSG:3857', null, 30 ); //DEM图像在投影坐标系下才可以进行操作
var Shadow_1 = ee.Terrain.hillShadow( DEM_Mercator, 315.0, 75.0,0,true);
var Shadow_2 = ee.Terrain.hillShadow( DEM_Mercator, 315.0, 70.0,0,false);
Map.setCenter( 106.5568, 29.5527, 12 ); Map.addLayer( DEM, {min:0, max:1500, opacity:0.7, palette:'0000ff,ff0000'}, 'Elevation' );
Map.addLayer( Shadow_1, {min:0, max:1, opacity:0.3}, 'Longer Shadows' );
Map.addLayer( Shadow_2, {min:0, max:1, opacity:0.3}, 'Shorter Shadows' );

DEM洼地填充

var DEM  = ee.Image( 'CGIAR/SRTM90_V4' );
var DEM_Fill  = ee.Terrain.fillMinima( DEM, 0, 50 );
// 0代表被填充的值，50代表进行填洼，进行分析的栅格邻域的大小
Map.setCenter(107.8729, 29.2441, 12 );
Map.addLayer( DEM, {min:0, max:1500, palette:["2f5bff","0dff09","efff05","ff9931"]}, 'Unfilled' );
Map.addLayer( DEM_Fill,    {min:0, max:1500, palette:["2f5bff","0dff09","efff05","ff9931"]}, 'Filled'   );

熵值操作（对 DEM进行 熵值操作可以发现高程变化最快的一个往往具有最大的熵值）

var DEM  = ee.Image( 'CGIAR/SRTM90_V4' );
var DEM_Entropy  = DEM.entropy(  ee.Kernel.circle( 5, 'pixels', true ) );
Map.setCenter( 106.1201, 29.4977, 10 );
Map.addLayer( DEM,    {min:0, max:1500, palette:["050dff","ddd3cd","0bff34","e4ff09","ff9007","ff1c05"]}, 'Original' );
Map.addLayer( DEM_Entropy,    {min:3.31,max:4.55, palette:["050dff","ddd3cd","0bff34","e4ff09","ff9007","ff1c05"]}, 'Entropy'  );

影像纹理判断及操作

var DEM  = ee.Image( 'CGIAR/SRTM90_V4' );
var GLCM = DEM.glcmTexture(5, ee.Kernel.circle( 5, 'pixels', true ) );
var Texture = GLCM.select( 0 );
print( GLCM, Texture );Map.setCenter( 106.1201, 29.4977, 10 );
Map.addLayer( DEM,    {min:0, max:1500, palette:["050dff","ddd3cd","0bff34","e4ff09","ff9007","ff1c05"]}, 'Original' );
Map.addLayer( Texture,    {min:0.0067,max:0.0115, palette:["050dff","ddd3cd","0bff34","e4ff09","ff9007","ff1c05"]}, 'Texture'  );

判断高程起伏变化

var DEM = ee.Image( 'CGIAR/SRTM90_V4' ).subtract(465);
var Zero_Crossing = DEM.zeroCrossing( );
Map.setCenter( 106.1201, 29.4977, 7 );
Map.addLayer( DEM, { min:0, max:1500, palette:["050dff","ddd3cd","0bff34","e4ff09","ff9007","ff1c05"]}, 'DEM'    );
Map.addLayer( Zero_Crossing, { palette:'ffffff,ff0000'}, 'Zero Crossing' );

非0栅格值像元距离周围像元的最近距离量算
这里以欧氏距离为例

var DEM = ee.Image("USGS/SRTMGL1_003");
var Land_Cover = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover');var Zero_One = Land_Cover.where(DEM.gt(500),0)
var The_Distance = Zero_One.distance(ee.Kernel.euclidean( 1000, 'meters' ))Map.setCenter(107.7167, 29.3865,10)
Map.addLayer(Zero_One,{max:1})
Map.addLayer(The_Distance,{max:1500})

邻域操作（最大/最小/中间值/模）

var OldIMAGE    = ee.Image('MCD12Q1/MCD12Q1_005_2001_01_01').select('Land_Cover_Type_1');
var MaxIMAGE    = OldIMAGE.focal_max(    5, 'circle', 'pixels' );
var MinIMAGE    = OldIMAGE.focal_min(    5, 'circle', 'pixels' );
var MedianIMAGE = OldIMAGE.focal_median( 5, 'circle', 'pixels' );
var ModeIMAGE   = OldIMAGE.focal_mode(   5, 'circle', 'pixels' );
Map.setCenter( 106.0734, 29.4929, 10 );
Map.addLayer( OldIMAGE,    {min:0, max:17, palette:'000000,0000dd,ffffff'}, 'Original '     );
Map.addLayer( MaxIMAGE,    {min:0, max:17, palette:'000000,0000dd,ffffff'}, 'Focal Maximum' );
Map.addLayer( MinIMAGE,    {min:0, max:17, palette:'000000,0000dd,ffffff'}, 'Focal Minimum' );
Map.addLayer( MedianIMAGE, {min:0, max:17, palette:'000000,0000dd,ffffff'}, 'Focal Median'  );
Map.addLayer( ModeIMAGE,   {min:0, max:17, palette:'000000,0000dd,ffffff'}, 'Focal Mode'    );

利用矩阵对影像进行模糊/锐化处理

var DEM = ee.Image("USGS/SRTMGL1_003");
var KERNEL = ee.Kernel.fixed( 7,7,   [[ 4, 0, 0, 0, 0, 0, 1 ],[ 0, 0, 0, 0, 0, 0, 0 ],[ 0, 0, 0, 0, 0, 0, 0 ],[ 0, 0, 0, 0, 0, 0, 0 ],[ 0, 0, 0, 0, 0, 0, 0 ],[ 0, 0, 0, 0, 0, 0, 0 ],[ 1, 0, 0, 0, 0, 0, 3 ]] );
var Convelve_DEM  = DEM.convolve( KERNEL );Map.setCenter(105.8125, 29.4033,9)
Map.addLayer(DEM,{min:0,max:2000},'DEM')
Map.addLayer(Convelve_DEM,{max:12000},'Convelve_DEM')

栅格转矢量

var Water = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3").select('landcover').clamp(209,211);
var geometry = ee.Geometry.Polygon([[[106.32728668689242, 29.46329064748005],[106.76948639392367, 29.462692803378452],[106.77017303943148, 29.65740304071832],[106.31355377673617, 29.654419523039962]]]);var Water_Vecter      = Water.reduceToVectors( null, geometry,  null, 'polygon',  false);
var Water_BoundBox    = Water.reduceToVectors( null, geometry,  null, 'bb',       false);
var Water_Center_4    = Water.reduceToVectors( null, geometry,  null, 'centroid', false);
var Water_Center_8    = Water.reduceToVectors( null, geometry,  null, 'centroid', true );
//polygon/bb/centroid/为栅格转矢量的类型，分别是边，四至，点。若在转化的时候只考虑上下左右四个点就是false,若考虑八个方向就是true,考虑四个方向得到的栅格密度较大
Map.setCenter(106.5587, 29.5631,10)
Map.addLayer(Water,{"palette":["ffffff","1707ff"]},'Water')
Map.addLayer( Water_Vecter,   {color:'dddddd'}, 'The Polygons' );
Map.addLayer( Water_BoundBox, {color:'888888'}, 'The Bounding Boxes' );
Map.addLayer( Water_Center_4, {color:'ff0000'}, '4-way Centroids' );
Map.addLayer( Water_Center_8, {color:'ffffff'}, '8-way Centroids' );

生成栅格数据集

var L8_1 = ee.Image('LANDSAT/LC08/C01/T1_RT/LC08_127040_20130422')
var L8_2 = ee.Image('LANDSAT/LC08/C01/T1_RT/LC08_127039_20130422')
var Collection = ee.ImageCollection([L8_1, L8_2])Map.setCenter(106.4925, 29.574,7)
Map.addLayer(L8_1,{"bands":["B5","B4","B3"],"max":35000})
Map.addLayer(L8_2,{"bands":["B5","B4","B3"],"max":35000})
Map.addLayer(Collection,{"bands":["B5","B4","B3"],"max":35000})

将栅格图像的多波段值转化为Array模式存贮及将Array转回栅格图像
将空间信息转换为时间信息

var L8 = ee.Image('LANDSAT/LC08/C01/T1_RT/LC08_127040_20130422').select('B[1-7]')
var L8_Array = L8.toArray(0)//to array
var L8_Flatten = L8_Array.arrayFlatten([['a','b','c','d','e','f','g']])//to imageMap.setCenter(106.4949, 29.458,10)
Map.addLayer(L8_Array,{},'Array Image')
Map.addLayer(L8_Flatten,{"bands":["e","d","c"],"max":32000,"gamma":0.73})

栅格图像局域运算（对栅格图像某一区域进行拟合统计分析)

var DEM = ee.Image("USGS/SRTMGL1_003");
var ROI = ee.Geometry.Polygon([[[106.20650888671878, 29.570337903152346],[106.54159189453128, 29.56556010779314],[106.54159189453128, 29.725493070064537],[106.21612192382815, 29.71952998792036]]]);
var ROI_Mean = DEM.reduceRegion(ee.Reducer.mean(),ROI)
var ROI_Histo = DEM.reduceRegion(ee.Reducer.histogram(),ROI)print(ROI_Mean,ROI_Histo)Map.setCenter(106.4949, 29.458,10)
Map.addLayer(DEM,{min:0,max:2000},'DEM')
Map.addLayer(ROI)

导出做好的栅格影像

var DEM = ee.Image("USGS/SRTMGL1_003");
var ROI = ee.Geometry.Polygon([[[106.20650888671878, 29.570337903152346],[106.54159189453128, 29.56556010779314],[106.54159189453128, 29.725493070064537],[106.21612192382815, 29.71952998792036]]]);
var DEM_Clip = DEM.clip(ROI)Export.image.toDrive(DEM_Clip)

Ⅵ、Image Collection

创建    ee.ImageCollection()   ee.ImageCollection.load()
筛选    limit()    .filterMetadata()      .filterDate()   .filterBounds() .filter()   .select()       .distinct()          .comnibe() .unit8() …….    .set()  .setMulti()
求值    .mosaic() .and/or()       .sum/product/max/min/mean/mode/median/count()
转换    .first()  .toList()   .toArray()

数据提取

var China = ee.FeatureCollection("users/wangjinzhulala/China_Provinces")
var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(China.geometry()).filterDate('2018-01-01','2018-12-31').filterMetadata('CLOUD_COVER','less_than',0.1);Map.setCenter( 107.753, 34.238, 4);
Map.addLayer( L8, {bands:'B4,B3,B2', min:0, max:0.2}, 'Original Images' );

数据处理数量限制

var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(106.5487, 29.5444)).filterDate('2018-03-01','2019-03-01');  var L8_Limit  = L8.limit( 10 );
print( L8, L8_Limit );

信息提取（提取的信息需要有相同的数据结构，比如实例代码均为landsat数据）

var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(106.5487, 29.5444)).filterDate('2018-03-01','2019-03-01').limit(5);
var L8_Select = L8.select(['B5','B4','B3'], ['Near infrared','Red','Green']);
//提取波段并重命名
print( 'Original', L8 );
print( 'Selected', L8_Select );

去重复

var China_Provinces = ee.FeatureCollection("users/wangjinzhulala/China_Provinces");
var China_Area = China_Provinces.distinct(['NAME'])print(China_Provinces)
print(China_Area)Map.centerObject(China_Provinces,4)
Map.addLayer(China_Provinces)
Map.addLayer(China_Area)

数据合并

var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(106.5487, 29.5444)).filterDate('2018-03-01','2019-03-01').limit(1);
var Combine_1 = L8.select(['B1'],['Coastal aerosol'])
var Combine_2 = L8.select(['B2'],['Blue'])
var Combine   = Combine_1.combine(Combine_2)print(Combine_1,Combine_2,Combine)

修改属性信息

var L8_1  = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_127041_20180303');
var L8_2  = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_127040_20180303');
var L8_3  = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_127039_20180303');
var L8_Collection = ee.ImageCollection([L8_1,L8_2,L8_2])
var Set_Property = L8_Collection.set('Creater','Jinzhu Wang').setMulti({'Creat time':'2019','His Cat' : '烧白'})print(Set_Property)

影像镶嵌（重叠部分的像元值以函数后面的影像为准）

var L8_1 = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_127040_20180303').select('B5');
var L8_2 = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_128039_20130413').select('B5');
var L8  = ee.ImageCollection([L8_1,L8_2]);var L8_Mosaci = L8.mosaic()print(L8_1,L8_2,L8_Mosaci)
Map.addLayer(L8_1)
Map.addLayer(L8_2)
Map.addLayer(L8_Mosaci)

逻辑运算
这里以夜间灯光数据为例，仅筛选出亮过一次的地点就用or，筛选出一直亮的地点就用and

var Night_Light    = ee.ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS').select('stable_lights');
var Light_Or   = Night_Light.or();
var Light_And  = Night_Light.and();Map.setCenter(105.13, 35.68, 4);
Map.addLayer( Light_Or,  {min: 0, max: 1, palette: ['000000','ffff99']}, 'Sometimes Lit');
Map.addLayer( Light_And, {min: 0, max: 1, palette: ['333333','ffff55']}, 'Always Lit');

基础数学操作

var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(106.5206, 29.5618)).filterDate('2018-01-01','2018-12-31').select('B[3-5]');
var L8_Sum      = L8.sum()
var L8_Product  = L8.product()
var L8_Max      = L8.max()
var L8_Min      = L8.min()
var L8_Mean     = L8.mean()Map.addLayer(L8_Sum,{},'Sum')
Map.addLayer(L8_Product,{},'Product')
Map.addLayer(L8_Max,{},'Max')
Map.addLayer(L8_Min,{},'Min')
Map.addLayer(L8_Mean,{},'Mean')

to list & to array

var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(106.5206, 29.5618)).filterDate('2018-01-01','2018-12-31').select('B[3-5]').sort('DATE_ACQUIRED');
var L8_List = L8.toList(5)
var Time_No_1 = ee.Image(L8_List.get(0))
var Time_No_2 = ee.Image(L8_List.get(1))
var Time_No_3 = ee.Image(L8_List.get(2))print(Time_No_1,Time_No_2,Time_No_3)

var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(107.193, 29.1373)).filterDate('2018-01-01','2018-12-31').select('B[5]').sort('DATE_ACQUIRED');
var L8_Array = L8.toArray()print(L8,L8_Array)
Map.addLayer(L8)
Map.addLayer(L8_Array)

map操作，对影像每一个像元进行操作

var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(107.193, 29.1373)).filterDate('2018-01-01','2018-12-31').select('B[4,5]').limit(3);function add_NDVI (image){var NDVI = image.normalizedDifference(['B5','B4'])return image.addBands(NDVI)
}var L8_NDVI = L8.map(add_NDVI)print(L8,L8_NDVI)

Ⅵ、卫星数据

  • LansatCollection.qualityMosaic()
  • LansatCollection.unmixing()
  • LansatCollection.normalizedDifference()
  • ee.Algorithms.Landsat.simpleCloudScore()
  • ee.Algorithms.Landsat.simpleComposite()
    对像元操作
// 由于云层具有很低的NDVI，故这里相当于去云处理
var L8  = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(107.193, 29.1373)).filterDate('2018-01-01','2018-12-31').select('B[2-5]');function add_NDVI (image){var NDVI = image.normalizedDifference(['B5','B4'])return image.addBands(NDVI)
}
var L8_NDVI = L8.map(add_NDVI)
var NDVI_Quality = L8_NDVI.qualityMosaic('nd')Map.setCenter(106.618, 29.6653,9)
Map.addLayer(NDVI_Quality,{"bands":["B5","B4","B3"],"max":0.5,"gamma":1},'NDVI qualified')

解决同物异谱或者同谱异物

// 通过计算得到每一个像元属于其他像元的概率
var unmixImage = composite.select(['B1', 'B2', 'B3', 'B4', 'B5', 'B7']);var bareMean = unmixImage.reduceRegion(ee.Reducer.mean(), bare, 30).values();
var waterMean = unmixImage.reduceRegion(ee.Reducer.mean(), water, 30).values();
var vegMean = unmixImage.reduceRegion(ee.Reducer.mean(), veg, 30).values();
var endmembers = ee.Array.cat([bareMean, vegMean, waterMean], 1);var arrayImage = unmixImage.toArray().toArray(1);
var unmixed = ee.Image(endmembers).matrixSolve(arrayImage);
var unmixedImage = unmixed
.arrayProject([0])
.arrayFlatten([['bare', 'veg', 'water']]);

计算归一化指数

var l8 = ee.ImageCollection("LANDSAT/LC08/C01/T1");
var l8_one = l8.filterBounds(ee.Geometry.Point(106.705, 31.881)).filterDate('2016-01-01','2018-12-31').sort('CLOUD_COVER').first()
// 下面的两个计算等价，该指令相当于(a+b)/(a-b)
var NDVI_1 = l8_one.normalizedDifference(['B5','B4'])
var NDVI_2 = l8_one.expression('(B4+B5)/(B4-B5)',{'B5':  l8_one.select('B5'),'B4':  l8_one.select('B4')})
print(NDVI_1,NDVI_2)
Map.addLayer(NDVI_1,{"min":-0.1,"max":0.4,"palette":["0f0bff","ff3305","fbff1b","0bff1e"]})

判断影像云量相对多少，以[0-100]的得分为例

var l8_TOA = ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA").filterBounds(ee.Geometry.Point(106.2608, 29.5755)).first()
var Cloud_Rate = ee.Algorithms.Landsat.simpleCloudScore(l8_TOA)print(Cloud_Rate)Map.setCenter(106.14, 30.3488,8)
Map.addLayer(Cloud_Rate,{bands:['B5','B4','B3'],max:0.3})
Map.addLayer(Cloud_Rate.select('cloud'))

去云处理

// 这个操作仅限于RAW格式的数据，因为要求输入整形数据，而TOA数据不是
var L8_RAW = ee.ImageCollection("LANDSAT/LC08/C01/T1").filterBounds(ee.Geometry.Point(106.1125, 30.3772)).filterDate('2018-01-01','2018-12-31')var L8_Siple_Composite = ee.Algorithms.Landsat.simpleComposite({collection:L8_RAW,asFloat:true})print(L8_RAW,L8_Siple_Composite)
Map.setCenter(106.1125, 30.3772,8)
Map.addLayer(L8_Siple_Composite,{bands:['B5','B4','B3'],max:0.3})

Ⅶ、矩阵操作

GEE中，竖直方向代表真实像元数，横轴方向代表 分类像元数
创建 ee.ConfusionMatrix()
转换 ConfusionMatrix.array()
精度 .kappa() .accuracy() .producersAccuracy() .consumersAccuracy() .order()

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