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PIE-engine 教程 ——长时间序列NDVI影像计算和chart时序图加载(2017年锦州全年植被指数展示)


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:

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));

pie.Feature(geometry,properties)

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:

//第一步:划定研究区域
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);

结果:

PIE-engine 教程 ——长时间序列NDVI影像计算和chart时序图加载(2017年锦州全年植被指数展示)_python

 

 

 

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