Google Earth Engine（GEE）分析多个地区的植被覆盖趋势

数据介绍：

使用的是MODIS数据的NDVI和EVI来分析多个地区的植被覆盖趋势
在GEE调用的数据名称为“MODIS/061/MOD13A1”

本次利用MODIS数据中的两个数据分别是“NDVI”和"EVI"**

NDVI大家都很熟悉了，是归一化植被指数，其计算方式为：

NDVI=（NIR-R）/（NIR+R）

植物的蒸腾作用、太阳光的截取、光合作用、地表净初级生产力都是影响和作用NDVI的影响因素

NDVI值的范围在-1到1之间。

EVI是增强植被指数，EVI常用于LAI值高，即植被茂密区;

选择三个地区，地理位置如下：

今天年度最大合成NDVI和EVI为例，分析植被覆盖度的变化趋势

实现代码

//选择研究区域
var N = 
    ee.Geometry.Polygon(
        [[[105.9994962705556, 39.286379797139425],
           [105.9994962705556, 37.233502767057786],
           [108.8010099424306, 37.233502767057786],
           [108.8010099424306, 39.286379797139425]]], null, false),
    W = 
    ee.Geometry.Polygon(
        [[[99.47160302765269, 38.38663059828354],
           [99.47160302765269, 36.16644778705101],
           [102.48185693390269, 36.16644778705101],
           [102.48185693390269, 38.38663059828354]]], null, false),
    E = 
    ee.Geometry.Polygon(
        [[[107.93511351095901, 35.27607971065017],
           [107.93511351095901, 32.96623266509945],
           [110.87944944845901, 32.96623266509945],
           [110.87944944845901, 35.27607971065017]]], null, false);
//在MAP中显示
Map.centerObject(N,5)
Map.addLayer(N,{},"N")
Map.addLayer(W,{},"W")
Map.addLayer(E,{},"E")
//定义研究区collection
var roi_collection=ee.FeatureCollection([ee.Feature(N,{'label':'N'}),
                                     ee.Feature(W,{'label':'W'}),
                                      ee.Feature(E,{'label':'E'})]);
//变成list
var year_list=ee.List.sequence(2000,2021);

year_list=year_list.map(function(num){
  var time=ee.Date.fromYMD(num, 1, 1)
  var year_image=ee.ImageCollection('MODIS/061/MOD13A1')
                  .filterDate(time,ee.Date(time).advance(1,'year'))
                  .max();
  var year_ndvi=year_image.select('NDVI');
  year_ndvi=year_ndvi.set({'system:time_start':ee.Date.fromYMD(num,1,1)})
  return  year_ndvi;                 
}
  )

var img_collection=ee.ImageCollection.fromImages(year_list);
//绘制统计分析图
var ndviTimeSeries = ui.Chart.image.seriesByRegion(
  img_collection, roi_collection, ee.Reducer.mean(), 'NDVI',500,'system:time_start','label')
          .setOptions({
          vAxis: {title: 'NDVI*10000'},
          lineWidth: 3,
          pointSize: 4,
          series: {
            0: {color: 'FF0000'}, 
            1: {color: '00FF00'}, 
            2: {color: '0000FF'} 
}});
//输出print(ndviTimeSeries)

var year_list=ee.List.sequence(2000,2021);

year_list=year_list.map(function(num){
  var time=ee.Date.fromYMD(num, 1, 1)
  var year_image=ee.ImageCollection('MODIS/061/MOD13A1')
                  .filterDate(time,ee.Date(time).advance(1,'year'))
                  .max();
  var year_ndvi=year_image.select('EVI');
  year_ndvi=year_ndvi.set({'system:time_start':ee.Date.fromYMD(num,1,1)})
  return  year_ndvi;                 
}
  )

var img_collection=ee.ImageCollection.fromImages(year_list);

var EVITimeSeries = ui.Chart.image.seriesByRegion(
  img_collection, roi_collection, ee.Reducer.mean(), 'EVI',500,'system:time_start','label')
          .setOptions({
          vAxis: {title: 'EVI*10000'},
          lineWidth: 3,
          pointSize: 4,
          series: {
            0: {color: 'FF0000'}, 
            1: {color: '00FF00'}, 
            2: {color: '0000FF'} 
}});

print(EVITimeSeries)

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结果显示：