GEE 教程——基于sentinel-2数据的NDVI和EVI指数的对比

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发布于 2025-01-21 12:45:21

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文章被收录于专栏：GEE数据专栏，GEE学习专栏，GEE错误集等专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏

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简介

GEE 教程——基于sentinel-2数据的NDVI和EVI指数的对比

NDVI（Normalized Difference Vegetation Index）和EVI（Enhanced Vegetation Index）是两种常用的植被指数，用于评估植被生长状况和活力。

NDVI是最常见的植被指数之一，通过计算红外波段和可见光波段的差异来评估植被的绿度。NDVI的计算公式为：(NIR - Red) / (NIR + Red)，其中NIR为近红外波段反射值，Red为红光波段反射值。NDVI的取值范围为-1到1之间，数值越高表示植被覆盖度越高。

EVI是一种对NDVI的改进，主要解决NDVI在饱和和大气效应方面存在的问题。EVI的计算公式为：2.5 * (NIR - Red) / (NIR + 6 * Red - 7.5 * Blue + 1)，其中NIR、Red和Blue分别为对应波段的反射值。EVI的取值范围没有限制，通常为负数到正数之间，数值越高表示植被覆盖度越高。

NDVI和EVI可以用于监测植被生长的季节变化、评估植被健康状况、监测干旱程度等。它们在农业、林业、生态学等领域有广泛的应用。

函数

ui.Chart.array.values(array, axis, xLabels)

Generates a Chart from an array. Plots separate series for each 1-D vector along the given axis.

X-axis = Array index along axis, optionally labeled by xLabels.
Y-axis = Value.
Series = Vector, described by indices of the non-axis array axes.

Returns a chart.

Arguments:

array (Array|List<Object>):

Array to chart.

axis (Number):

The axis along which to generate the 1-D vector series.

xLabels (Array|List<Object>, optional):

Labels for ticks along the x-axis of the chart.

Returns: ui.Chart

代码解释

### 1. 区域定义 - **AOI (Area of Interest)**: 定义了一个感兴趣区域，使用多边形来指定地理坐标。 - **Silviculture**: 定义了一个林业区域，包含多个多边形，表示特定的林业区域。

### 2. 时间范围 - **startDate 和 endDate**: 设置了数据筛选的时间范围，从2020年1月15日到1月30日。

### 3. 数据获取 - **S2 (Sentinel-2 Image Collection)**: 从 Sentinel-2 数据集中获取影像，使用 `filterDate` 方法筛选指定时间段内的影像，并使用 `filterBounds` 方法确保影像覆盖 AOI 区域。 - **img**: 计算所选影像集合的中位数，并使用 `clip` 方法裁剪到 AOI 区域。

### 4. 可视化设置 - **viz**: 定义了影像的可视化参数，包括最小值、最大值和所选波段（B4, B3, B2），用于生成真实颜色图层。

### 5. 指数计算 - **EVI (Enhanced Vegetation Index)**: 通过公式计算 EVI，使用 NIR（近红外）、红光和蓝光波段的数据。计算后重命名为 'EVI' 并添加到地图上。 - **NDVI (Normalized Difference Vegetation Index)**: 通过公式计算 NDVI，使用 NIR 和红光波段的数据。计算后重命名为 'NDVI' 并添加到地图上。

### 6. 数据提取 - **reduceRegion**: 对 NDVI 和 EVI 使用 `reduceRegion` 方法，提取在 Silviculture 区域内的指数值，并将结果存储为列表。

### 7. 图表生成 - **ui.Chart.array.values**: 创建一个散点图，X 轴为 NDVI，Y 轴为 EVI。设置图表的标题、坐标轴标签、点大小和颜色等选项。

### 8. 显示图表 - **print(chart)**: 将生成的图表打印到控制台，以便查看 NDVI 和 EVI 之间的关系。

代码

var AOI = 
    /* color: #d63000 */
    /* shown: false */
    /* displayProperties: [
      {
        "type": "rectangle"
      }
    ] */
    ee.FeatureCollection(
        [ee.Feature(
            ee.Geometry.Polygon(
                [[[-51.92002134176155, -20.867693855608483],
                  [-51.92002134176155, -21.076711250815702],
                  [-51.66733579488655, -21.076711250815702],
                  [-51.66733579488655, -20.867693855608483]]], null, false),
            {
              "system:index": "0"
            })]),
    Silviculture = /* color: #d63000 */ee.FeatureCollection(
        [ee.Feature(
            ee.Geometry.Polygon(
                [[[-51.919489641815915, -21.070400386207087],
                  [-51.91914631906201, -21.07608680920586],
                  [-51.86970784249951, -21.075926631253044],
                  [-51.86884953561474, -21.062230778235893],
                  [-51.880780001312985, -21.055262228676774],
                  [-51.89142300668408, -21.049174493073686],
                  [-51.91408230844189, -21.06431326993577],
                  [-51.91365315499951, -21.065114220519593],
                  [-51.91459729257275, -21.065915166790838],
                  [-51.91528393808056, -21.065354504853783],
                  [-51.91837384286572, -21.06759713992241]]]),
            {
              "system:index": "0"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
                [[[-51.89446721090192, -21.03403128224377],
                  [-51.89060482992048, -21.0283432524203],
                  [-51.88047680868024, -21.0342716167512],
                  [-51.883395052088446, -21.04003952862024],
                  [-51.86648640645856, -21.04997263103133],
                  [-51.862709856165594, -21.04404489157035],
                  [-51.86785969747419, -21.04060028591779],
                  [-51.874897813929266, -21.032028479606254],
                  [-51.885884142054266, -21.02113276177408],
                  [-51.89257893575544, -21.026580720248674],
                  [-51.893866396082586, -21.025859678353108],
                  [-51.899617052210516, -21.030506331598072]]]),
            {
              "system:index": "1"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
                [[[-51.82752095358001, -21.001431265393105],
                  [-51.81516133443939, -21.009844581869093],
                  [-51.813702212735286, -21.0084824580534],
                  [-51.80958233968841, -21.011206693248024],
                  [-51.80168591634857, -21.001912039112916],
                  [-51.817993747159115, -20.989571690123878],
                  [-51.827692614956966, -21.000870360762605]]]),
            {
              "system:index": "2"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
                [[[-51.91164435698424, -20.972607294147554],
                  [-51.909756081837756, -20.96723751303933],
                  [-51.908554452199084, -20.967558102399774],
                  [-51.90984191252623, -20.97308816065749],
                  [-51.90314711882506, -20.97429032016416],
                  [-51.90271796538268, -20.972687438673304],
                  [-51.9011730129901, -20.972607294147554],
                  [-51.89396323515807, -20.94279055042917],
                  [-51.90743865324889, -20.938541982407138],
                  [-51.91542090727721, -20.971805846526593]]]),
            {
              "system:index": "3"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
                [[[-51.88027011563257, -20.933849466828253],
                  [-51.87168704678491, -20.933689136122553],
                  [-51.859670750398195, -20.93801800493194],
                  [-51.85795413662866, -20.935292435456148],
                  [-51.86636554409937, -20.925512042578937],
                  [-51.870657078523195, -20.928237790018102],
                  [-51.87271701504663, -20.923587955709245],
                  [-51.86756717373804, -20.914127502638163],
                  [-51.871172062654054, -20.9053078985724],
                  [-51.86979877163843, -20.897289626668368],
                  [-51.87271701504663, -20.892959581678944],
                  [-51.86722385098413, -20.88093102356683],
                  [-51.867395512361085, -20.87788363587991],
                  [-51.870657078523195, -20.878044026246894],
                  [-51.88679324795679, -20.919419016077246],
                  [-51.88679324795679, -20.931765154267616]]]),
            {
              "system:index": "4"
            })]);
            
            
var startDate = ee.Date.fromYMD(2020,1,15);
var endDate = ee.Date.fromYMD(2020,1,30);

var S2 = ee.ImageCollection("COPERNICUS/S2").filterDate(startDate,endDate).filterBounds(AOI)
var img = ee.Image(S2.median()).clip(AOI);

var viz = {min:0,max:3000,bands:"B4,B3,B2"};
Map.addLayer(img,viz,"True Color");

var evi = img.expression(
    '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', {
      'NIR': img.select('B8').divide(10000),
      'RED': img.select('B4').divide(10000),
      'BLUE': img.select('B2').divide(10000)
}).rename('EVI');

var eviParams = {min: -0.2, max: 0.8, palette: ['red', 'yellow', 'green']};
Map.addLayer(evi, eviParams, 'EVI');

var ndvi = img.expression(
    '((NIR - RED) / (NIR + RED))', {
      'NIR': img.select('B8').divide(10000),
      'RED': img.select('B4').divide(10000)
}).rename('NDVI');

var ndviParams = {min: -0.2, max: 0.8, palette: ['red', 'yellow', 'green']};
Map.addLayer(ndvi, ndviParams, 'NDVI');

var ndvi_silv_list = ndvi.reduceRegion(ee.Reducer.toList(), Silviculture, 120);
var evi_silv_list = evi.reduceRegion(ee.Reducer.toList(), Silviculture, 120);

var y = ee.Array(evi_silv_list.get('EVI'));
var x = ee.Array(ndvi_silv_list.get('NDVI'));

var chart = ui.Chart.array.values(y, 0, x)
      .setOptions({
      title: 'NDVI vs. EVI',
      hAxis: {'title': 'NDVI'},
      vAxis: {'title': 'EVI'},
      pointSize: 1,
});

print(chart);