想通过长时序土地覆盖产品，调查某地土地覆盖类型变化，并通过GIF图查看。

加载数据集

var CLCDCol = ee.ImageCollection("users/GEEStudyRoom/CLCDImageCollection");
//可以通过slice来筛选需要的年份
//var LC_col = ee.ImageCollection(CLCDCol.toList(32).slice(12))

这里介绍一下数据集：基于Google Earth Engine上335,709景Landsat数据，武汉大学黄昕老师团队制作了中国年度土地覆盖数据集（annual China Land Cover Dataset, CLCD），包含1985—2019中国逐年土地覆盖信息。基于5,463个目视解译样本，CLCD的总体准确率达80%。

详细信息：武汉大学遥感院黄昕&李家艺团队

添加时间信息并绘制时序曲线

var add_time = function(image){var name = ee.String(image.get('system:index')).slice(9)var starttime = ee.Date.fromYMD(ee.Number.parse(name),1,1)var endtime = ee.Date.fromYMD(ee.Number.parse(name),12,31)var image = image.set('system:time_start',starttime.millis()).set('system:time_end',endtime.millis())return image}
var col = CLCDCol.map(add_time)//以下是绘图代码，一般只需要更改palette和names
function addLegend(palette, names) {//图例的底层Panelvar legend = ui.Panel({style: {position: 'bottom-left',padding: '5px 10px'}});//图例标题var title = ui.Label({value: '图例',style: {fontWeight: 'bold',color: "red",fontSize: '16px'}});legend.add(title);//添加每一列图例颜色以及说明var addLegendLabel = function(color, name) {var showColor = ui.Label({style: {backgroundColor: '#' + color,padding: '8px',margin: '0 0 4px 0'}});var desc = ui.Label({value: name,style: {margin: '0 0 4px 8px'}});//颜色和说明是水平放置return ui.Panel({widgets: [showColor, desc],layout: ui.Panel.Layout.Flow('horizontal')});};//添加所有的图例列表for (var i = 0; i < palette.length; i++) {var label = addLegendLabel(palette[i], names[i]);legend.add(label);}  Map.add(legend);
}var Landcover = col.select('b1');
//计算面积
var LandcoverArea = Landcover.map(function(image){var area = ee.Image.pixelArea().addBands(image).reduceRegion({reducer: ee.Reducer.sum().group({groupField: 1, groupName: "b1"}), geometry: table, scale: 30,maxPixels:10e15,});var image = image.set({"area":area});return image;
});
print('LandcoverArea',LandcoverArea)
// 把字典面积数据转换成feature
var area = LandcoverArea.reduceColumns(ee.Reducer.toList(), ['area']).get('list');var time = LandcoverArea.reduceColumns(ee.Reducer.toList(), ['system:time_start']).get('list');var feature = ee.List(time).zip(area).map(function(timeArea){var time = ee.Date(ee.List(timeArea).get(0)).get("year");var area = ee.List(timeArea).get(1);area = ee.Dictionary(area).get("groups");var feature = ee.List(area).map(function(dict){dict = ee.Dictionary(dict);var value = dict.values();return ee.Feature(null, { time: time,land:value.get(0),area:value.get(1)});});return feature;
});feature = feature.flatten();
var feature = ee.FeatureCollection(feature);
//print(feature,"feature");var palette = ['66CD00', '006400', '00FF7F', '7FFF00', '1C86EE', 'FFFFFF', '8B7500','8C8C8C', '698B69']
var names = [ 'cropland','forest','shrub','grassland','water','Snow/Ice','Barren','Impervious','Wetland',]// 面积统计（有时候也需要改一改）
var chart = ui.Chart.feature.groups(feature, "time", "area", "land")
.setSeriesNames({1:'cropland',2:'forest',3:'shrub',4:'grassland',5:'water',6:'Snow/Ice',7:'Barren',8:'Impervious',9:'Wetland',})//1Cropland、2Forest、3Shrub、4Grassland、5Water、6Sonw/Ice、7Barren、8Impervious、9Wetland.setOptions({title: 'Landcover area',hAxis: {'title': 'year'},vAxis: {'title': 'area (m^2)'},pointSize: 3,colors:['#66CD00', '#006400', '#7FFF00', '#1C86EE',  '#8B7500','#8C8C8C', '#698B69']})
print(chart)// 地图显示（最大最小值根据分类更改）
var landcoverVisualization = {palette: palette, min: 1, max: 9, format: 'png'};
//展示一下第一张（1985年的）
Map.addLayer(Landcover.first(), landcoverVisualization, 'landcover');//添加图例
addLegend(palette, names);//研究区的边界
/*var outline = ee.Image().byte().paint({featureCollection: table,color: 1,width: 3
});
Map.addLayer(outline, {palette: ['blue']}, 'ROI');*/
/*

其中绘图函数基本不用更改，一般只修改palette和names，下面addlayer也是。计算面积那一部分代码也是借鉴的，基本不用更改。

得到研究区（自行设置，代码部分的table是我设置的研究区）的土地利用类型变化。

绘制GIF图

// 可视化参数
var args = {crs: 'EPSG:3857',  // Maps Mercatordimensions: '500',region: rect,min: 1,max: 9,//2000palette: ['66CD00', '006400', '00FF7F', '7FFF00', '1C86EE', 'FFFFFF', '8B7500','8C8C8C', '698B69'],framesPerSecond: 2, //自行设置帧数
};// 制作动图，并且加载到地图上
var thumb = ui.Thumbnail({
//为“image”指定一个集合会使图像序列产生动画效果image: Landcover,params: args,style: {position: 'bottom-right', //位置width: '640px' //宽度}});
Map.add(thumb);print(Landcover.getVideoThumbURL(args)) //获得链接

 这里要注意，动图绘制一定是规则边界。所以要先画出一个矩形，也就是上面args里面的region，我命名为rect。

 分区绘制

这里我沿用了之前用到的笨笨的方法。得到不同分区的土地利用变化时序曲线。其实和前面代码是相同的，只是把绘图区域变成了roi（不同的子区域）。不多赘述。

var point = table.aggregate_array('NAME')for(var i = 0; i<5 ; i++){var roi = table.filterMetadata('NAME','equals',point.get(i));var LandcoverArea = Landcover.map(function(image){var area = ee.Image.pixelArea().addBands(image).reduceRegion({reducer: ee.Reducer.sum().group({groupField: 1, groupName: "b1"}), geometry:roi, scale: 30,maxPixels:10e15,});var image = image.set({"area":area});return image;});print('LandcoverArea',LandcoverArea)// 把字典面积数据转换成featurevar area = LandcoverArea.reduceColumns(ee.Reducer.toList(), ['area']).get('list');var time = LandcoverArea.reduceColumns(ee.Reducer.toList(), ['system:time_start']).get('list');var feature = ee.List(time).zip(area).map(function(timeArea){var time = ee.Date(ee.List(timeArea).get(0)).get("year");var area = ee.List(timeArea).get(1);area = ee.Dictionary(area).get("groups");var feature = ee.List(area).map(function(dict){dict = ee.Dictionary(dict);var value = dict.values();return ee.Feature(null, { time: time,land:value.get(0),area:value.get(1)});});return feature;});feature = feature.flatten();var feature = ee.FeatureCollection(feature);// 面积统计var chart = ui.Chart.feature.groups(feature, "time", "area", "land").setSeriesNames({1:'cropland',2:'forest',3:'shrub',4:'grassland',5:'water',6:'Snow/Ice',7:'Barren',8:'Impervious',9:'Wetland',})//1Cropland、2Forest、3Shrub、4Grassland、5Water、6Sonw/Ice、7Barren、8Impervious、9Wetland.setOptions({title: 'Landcover area',hAxis: {'title': 'year'},vAxis: {'title': 'area (m^2)'},pointSize: 3,colors:['#66CD00', '#006400',  '#7FFF00', '#1C86EE', '#8B7500','#8C8C8C', '#698B69']})print(chart)
}

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