#冲刺创作新星#PIE-Engine:NDVI时序影像分析原创

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发布于 2022-9-24 20:19

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本次教程首先市选定我们合适的长时间序列影像，我们首先划定研究区，然后设定指定区域的NDVI计算这里用到的市mean合成的计算，设定我们的去云和可视化参数设定，我们定义一个佛如循环用来进行分别进行遍历，最后设定chart影像的格式，加载影像。我们这里需要用到一个函数：

split(regex)

按正则表达式拆分字符串，返回字符串列表。

方法参数：

string(String)

要拆分的字符串。

regex(String)

正则表达式，如果regex是空字符串，则将输入字符串拆分为单个字符。

返回值：String

当然这里我们还需要注意一点，当我们想要使用filterBounds来筛选我们所选择的影像时，我们首先要用一个函数，具体的转化的结果：

    [
        120.64386203675974,
        41.098996789722236
    ],
    [
        120.57825490076209,
        40.83858677068369
    ],
    [
        120.91186251168273,
        40.77854007464006
    ],
    [
        120.97742944943161,
        41.03986964756422
    ],
    [
        120.64386203675974,
        41.098996789722236
    ]
]], null);
var roi = pie.FeatureCollection(pie.Feature(geometry));

pie.Feature(geometry,properties)

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通过Geometry构造Feature对象。

方法参数：

feature(feature)

feature实例

geometry(Geometry)

几何形体对象

properties(Object)

属性信息JSON对象

返回值：Feature

pie.FeatureCollection()

通过矢量数据构造Feature集合。

方法参数：

args(String|Geometry|Feature|List)

矢量数据路径或单个PIEGeometry对象或单个PIEFeature对象或PIEFeature对象Array

返回值：FeatureCollection

代码：

var geometry = pie.Geometry.Polygon([[
    [
        120.64386203675974,
        41.098996789722236
    ],
    [
        120.57825490076209,
        40.83858677068369
    ],
    [
        120.91186251168273,
        40.77854007464006
    ],
    [
        120.97742944943161,
        41.03986964756422
    ],
    [
        120.64386203675974,
        41.098996789722236
    ]
]], null);
var roi = pie.FeatureCollection(pie.Feature(geometry));
//在地图上显示研究区域
Map.addLayer(roi, { color: "ff0000ff", fillColor: "00000000" }, "分析区域");
Map.centerObject(geometry, 9);

//第二步：筛选数据：调用Landsat8 TOA数据集，根据时间、空间、云量等筛选数据
var l8Col = pie.ImageCollection("LC08/01/T1")
    .filterBounds(geometry)
    .filterDate("2017-01-01", "2017-12-31")
    .filter(pie.Filter.lte('cloudCover', 5));
//查看符合筛选条件的影像个数               
print("2017年Landsat影像集合", l8Col.size(), l8Col);

//第三步：计算指定区域NDVI、NDVI均值并显示
//1、设置NDVI显示参数
var visParamNDVI = {
    min: -0.2,
    max: 0.8,
    palette: "FFFFFF, CE7E45, DF923D, F1B555, FCD163, "
        + "99B718, 74A901, 66A000, 529400, 3E8601, 207401,"
        + " 056201, 004C00, 023B01, 012E01, 011D01, 011301"
};
//2、定义指定区域NDVI计算方法
function imageNDVI(id) {
    var image = pie.Image(id);
    var b4 = image.select("B4");
    var b5 = image.select("B5");
    var ndvi = (b5.subtract(b4)).divide(b5.add(b4));
    var qa = image.select("BQA");
    var cloudMask = qa.bitwiseAnd(1 << 4).eq(0);
    ndvi = ndvi.updateMask(cloudMask);
    return ndvi;
}
//3、定义指定区域NDVI均值计算方法
function calcNDVIByRoi(ndvi, roi) {
    var result = ndvi.reduceRegion(pie.Reducer.mean(), roi, 300);
    return result;
}
//4、定义符合筛选条件的Landsat8 TOA数据ID集合
var imgIds = [
    "LC08/01/T1/LC08_121031_20170101",
    "LC08/01/T1/LC08_121031_20170117",
    "LC08/01/T1/LC08_121031_20170202",
    "LC08/01/T1/LC08_121031_20170218",
    "LC08/01/T1/LC08_121031_20170306",
    "LC08/01/T1/LC08_121031_20170322",
    "LC08/01/T1/LC08_121031_20170407",
    "LC08/01/T1/LC08_121031_20170423",
    "LC08/01/T1/LC08_121031_20170509",
    "LC08/01/T1/LC08_121031_20170525",
    "LC08/01/T1/LC08_121031_20170610",
    "LC08/01/T1/LC08_121031_20170626",
    "LC08/01/T1/LC08_121031_20170712",
    "LC08/01/T1/LC08_121031_20170728",
    "LC08/01/T1/LC08_121031_20170813",
    "LC08/01/T1/LC08_121031_20170829",
    "LC08/01/T1/LC08_121031_20170914",
    "LC08/01/T1/LC08_121031_20170930",
    "LC08/01/T1/LC08_121031_20171101",
    "LC08/01/T1/LC08_121031_20171117",
    "LC08/01/T1/LC08_121031_20171203",
    "LC08/01/T1/LC08_121031_20171219"
];


//5、计算指定区域NDVI、NDVI均值并显示
var xSeries = [];
var images = [];
for (var i = 0; i < imgIds.length; i++) {
    var key = imgIds[i].split("_")[2];
    var ndvi = imageNDVI(imgIds[i]).clip(geometry);
    Map.addLayer(ndvi, visParamNDVI, key);
    xSeries.push(key);
    var result = calcNDVIByRoi(ndvi, geometry);
    print(key + "的NDVI值为：", result);
    images.push(result);
};

//第四步：绘制指定区域NDVI动态变化折线图
//1、配置折线图的样式
var line_options = {
    title: '锦州周边植被变化',
    legend: ['NDVI均值'],
    xAxisName: "日期",
    yAxisName: "植被指数",
    chartType: "line",
    smooth: true //是否平滑
};
//2、调用影像绘图的方法绘制锦州周边NDVI变化折线图
ChartImage(images, xSeries, line_options);

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