回复
#冲刺创作新星#PIE-engine APP 教程 ——太湖生态环境智能监测 原创
此星光明
发布于 2022-9-27 15:23
9815浏览
0收藏
本次开发教程是基于太湖生态环境智能监测系统，这个我们首先预加载我们所选的研究区，这次研究区是在太湖研究区，首秀按需要转化为几何，预加载持续时间，颜色图层预设，波段（MODIS、Landsat的QA波段去云函数）去云效果，然后UI界面的设定，这个界面非常长，所以设定了很多label标签、复选框、按钮和textbox，当然每一个部分都在一个面板，最后就是程序的嵌套和各个部分的，本此APP主要分为三个部分：第一部分就是太湖湖泊的监测、第二部分是基于Landsat数据月季年监测、第三部分太湖周围生态监测，最后是其它功能监测，这里每一个部分其实所用到的数据基本上上是相互独立的，这样有利于减少云计算的过程，减少运算压力。每一个部分都可以拿出来单独的使用，每一个部分都是一个单独的APP。
代码：
PIE-engine APP 教程 ——太湖生态环境智能监测系统/**
* @Name : 基于多源遥感的太湖生态环境智能监测系统
* @Time : 2020/7/21
* @Author : 中国矿业大学（北京）PIE小分队
* @Version : 1.0
* @E-mail : sun_yilin@yeah.net
* @Source
// 需要转换为geometry;
var lakes = pie.FeatureCollection("user/pieadmin/lakeDemo");
var XukouBay = lakes
.filter(pie.Filter.eq("NAME", "XukouBay"))
.first()
.geometry();
var EastLake = lakes
.filter(pie.Filter.eq("NAME", "EastLake"))
.first()
.geometry();
var ZhushanBay = lakes
.filter(pie.Filter.eq("NAME", "ZhushanBay"))
.first()
.geometry();
var WestLake = lakes
.filter(pie.Filter.eq("NAME", "WestLake"))
.first()
.geometry();
var SouthLake = lakes
.filter(pie.Filter.eq("NAME", "SouthLake"))
.first()
.geometry();
var MeiliangBay = lakes
.filter(pie.Filter.eq("NAME", "MeiliangBay"))
.first()
.geometry();
var GongBay = lakes.filter(pie.Filter.eq("NAME", "GongBay")).first().geometry();
var EntireLake = lakes
.filter(pie.Filter.eq("NAME", "EntireLake"))
.first()
.geometry();
var CentralLake = lakes
.filter(pie.Filter.eq("NAME", "CentralLake"))
.first()
.geometry();
var points = pie.FeatureCollection("user/pieadmin/lakeDemoPoints"); // 训练样本
var MOD11A2 = pie.ImageCollection("USGS/MOD11A2/006"); // 1km分辨率
Map.setCenter(120.22, 31.22, 10); // 太湖中心
var selectStartDate = "2020-08-01"; // 计算蓝藻水华初始时间
var selectEndDate = "2020-08-03";
var selectStartDate1 = "2016-05-01"; // 计算水域面积初始时间
var selectEndDate1 = "2020-09-30";
var selectStartDate2 = "2020-05-01"; // 影像显示初始时间
var selectEndDate2 = "2020-09-30";
var color1 = [
"#040274",
"#040281",
"#0502a3",
"#0502b8",
"#0502ce",
"#0502e6",
"#0602ff",
"#235cb1",
"#307ef3",
"#269db1",
"#30c8e2",
"#32d3ef",
"#3be285",
"#3ff38f",
"#86e26f",
"#3ae237",
"#b5e22e",
"#d6e21f",
"#fff705",
"#ffd611",
"#ffb613",
"#ff8b13",
"#ff6e08",
"#ff500d",
"#ff0000",
"#de0101",
"#c21301",
"#a71001",
"#911003",
];
var color2 = [
"#9AA5DF",
"#0602ff",
"#235cb1",
"#307ef3",
"#269db1",
"#30c8e2",
"#32d3ef",
"#3be285",
"#3ff38f",
"#86e26f",
];
var color3 = [
"#145999",
"#1b76cc",
"#1f86e6",
"#26aaea",
"#36b6eb",
"#4dc0eb",
"#60cfeb",
"#77daef",
"#b0dfef",
"#d1e5ee",
"#dce6f0",
"#e1e7f2",
"#dee6f1",
"#43c370",
"#44b772",
"#52c88e",
"#6fc993",
"#7ecb97",
"#acd2ad",
];
var color4 = [
"#145999",
"#1b76cc",
"#1f86e6",
"#26aaea",
"#36b6eb",
"#4dc0eb",
"#60cfeb",
"#77daef",
"#b0dfef",
"#d1e5ee",
"#dce6f0",
"#e1e7f2",
"#dee6f1",
"#43c370",
"#44b772",
"#52c88e",
"#6fc993",
"#7ecb97",
"#acd2ad",
"#d4c97a",
"#d0bc59",
"#d3b146",
"#c78f3c",
"#c3812d",
"#c5762b",
"#b95322",
"#ae4621",
"#ba6e78",
"#bc8fa9",
"#c7aac6",
"#dbcdde",
"#fffcff",
];
function generateVis(min, max, palette) {
var vis = {
min: min,
max: max,
palette: palette,
};
return vis;
}
function generateVisalg(min, max) {
return generateVis(min, max, color1);
}
function generateVisalgOccu(min, max) {
return generateVis(min, max, color2);
}
function addLegend(args) {
var title = args.title || "";
var labels = args.labels || [];
var step = args.step || 1;
var colors = args.colors || [];
// 图例
var data = {
title: title,
colors: colors,
labels: labels,
step: step,
};
var style = {
right: "100px",
bottom: "25px",
height: "70px",
width: "350px",
};
var legend = ui.Legend(data, style);
Map.addUI(legend);
}
function addLegendToMap(args) {
args["colors"] = color1;
addLegend(args);
}
function addOccuLegendToMap(args) {
args["colors"] = color2;
addLegend(args);
}
function addDem1LegendToMap(args) {
args["colors"] = color3;
addLegend(args);
}
function addDem2LegendToMap(args) {
args["colors"] = color4;
addLegend(args);
}
function addClassLegendToMap(args) {
var colors = ["#040274", "#269db1", "#3ae237", "#ff0000", "#911003"];
args["colors"] = colors;
addLegend(args);
}
var modisUtil = {
maskClouds: function (image) {
// 选择质量评估波段
var QA = image.select("state_1km");
// 1<<10表示二进制第10位，第10位表示有云
var bitMask = 1 << 10;
// 使得检测出含云像元置为0，进行掩膜去除含云
return image
.select([
"sur_refl_b01",
"sur_refl_b02",
"sur_refl_b05",
"sur_refl_b06",
"sur_refl_b07",
])
.updateMask(QA.bitwiseAnd(bitMask).eq(0));
},
getMOD09GA: function (startDate, endDate, roi) {
var MOD09GA = pie.ImageCollection("USGS/MOD09GA/006"); // 500米分辨率
var TerraGA = MOD09GA.filterDate(startDate, endDate) // 数据筛选
.filterBounds(roi)
.map(modisUtil.maskClouds);
return TerraGA;
},
// 大气校正步骤1
min567: function (image) {
var b05 = image.select("sur_refl_b05").multiply(0.0001); // 841-876nm, NIR(859)
var b06 = image.select("sur_refl_b06").multiply(0.0001); // 620-670nm, RED(645)
var b07 = image.select("sur_refl_b07").multiply(0.0001); // 1230-1250nm, SWIR1(1240)
var images = pie.ImageCollection().fromImages([b05, b06, b07]); // 重组影像
var min = images.min().rename("min"); // 获取最小值影像
return image.addBands(min);
},
// 大气校正步骤2
// multiply 0.0001 is needed because the data is scaled by 10000
jz: function (image) {
var b01 = image.select("sur_refl_b01").multiply(0.0001);
var b02 = image.select("sur_refl_b02").multiply(0.0001);
var b05 = image.select("sur_refl_b05").multiply(0.0001); // 841-876nm, NIR(859)
var min = image.select("min");
var b011 = b01.subtract(min); // 均减去min波段
var b021 = b02.subtract(min);
var b051 = b05.subtract(min);
return image.addBands(b011).addBands(b021).addBands(b051);
},
// 计算 FAI
funFAI: function (image) {
b02 = image.select("sur_refl_b02"); // 841-876nm, NIR(859)
b01 = image.select("sur_refl_b01"); // 620-670nm, RED(645)
b05 = image.select("sur_refl_b05"); // 1230-1250nm, SWIR1(1240)
var temp = b01.add(b05.subtract(b01).multiply(0.359663865546)); // 构建近红外波段
var Index = b02.subtract(temp).rename("FAI");
return Index;
},
// 建立 FAI 阈值掩膜.
mod_FAI: function (image) {
var FAI_threshold = image.select("FAI").rename("FAI_threshold");
FAI_threshold = FAI_threshold.updateMask(FAI_threshold.gte(-0.004));
return FAI_threshold;
},
// 统计发生频率
Binary: function (image) {
var algae = image.select("FAI").gte(-0.004).rename("alage"); // 大于等于-0.004为1，小于则为0
return image.addBands(algae);
},
//添加 IndexBands
addIndexBands: function (image) {
var FAI = modisUtil.funFAI(image);
return image.addBands(FAI);
},
};
var landsat8Util = {
// landsat8去云处理
removeCloud: function (image) {
// 数据去云处理
var qa = image.select("QA_PIXEL");
var cloudMask = qa
.bitwiseAnd(1 << 4)
.eq(0)
.and(qa.bitwiseAnd(1 << 3).eq(0));
return image.updateMask(cloudMask);
},
scaleImage: function (image) {
return image
.select(["B2", "B3", "B4", "B5", "B6", "B7"])
.multiply(0.0000275)
.subtract(0.2);
},
//加载Landsat 8 SR
alg: function (image) {
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
var b6 = image.select("B6"); // swir1
// 计算ndvi
var lswi = b5.subtract(b6).divide(b5.add(b6)).clip(TH_boundary);
var fai = b5
.subtract(b4.add(b6.subtract(b4)).multiply(0.219895287958))
.clip(TH_boundary);
var alg = fai
.updateMask(fai.gte(0.05))
.updateMask(lswi.gte(0.63))
.rename("alg"); // 判定太湖蓝藻水华
return image.addBands(alg); // 添加蓝藻波段
},
turbidity: function (image) {
var b4 = image.select("B4");
var b3 = image.select("B3");
var tbd = b4
.subtract(b3)
.divide(b4.add(b3))
.power(2)
.multiply(3117.4)
.add(b4.subtract(b3).divide(b4.add(b3)).power(2).multiply(1083.6))
.add(106.17)
.rename("turbidity");
return image.addBands(tbd);
},
MD: function (image) {
var b4 = image.select("B4");
var b5 = image.select("B5");
//计算蓝藻密度
var md = b5
.divide(b4)
.power(2)
.multiply(1352)
.subtract(b5.divide(b4).multiply(159.08))
.add(192.87)
.rename("md");
return image.addBands(md);
},
cha: function (image) {
var b4 = image.select("B4");
var b5 = image.select("B5");
//计算叶绿素
var cha = b5.multiply(100.49).divide(b4).subtract(15.776).rename("cha");
return image.addBands(cha);
},
//ndvi
ndvi: function (image) {
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
var ndvi = b5.subtract(b4).divide(b5.add(b4)).rename("ndvi");
return image.addBands(ndvi);
},
//fvc
fvc: function (image) {
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
var ndvi = b5.subtract(b4).divide(b5.add(b4)).rename("ndvi");
// var ndvi_per = ndvi.reduceRegion(pie.Reducer.percentile([2, 98]), roi, 1000).get("ndvi");
// var ndvi_min = pie.Number(pie.Dictionary(ndvi_per).get("p2"));
// var ndvi_max = pie.Number(pie.Dictionary(ndvi_per).get("p98"));
var ndvi_min = ndvi.reduceRegion(pie.Reducer.min(), roi, 1000).get("ndvi");
ndvi_min = pie.Number(ndvi_min);
var ndvi_max = ndvi.reduceRegion(pie.Reducer.max(), roi, 1000).get("ndvi");
ndvi_max = pie.Number(ndvi_max);
var fvc = ndvi
.subtract(ndvi_min)
.divide(ndvi_max.subtract(ndvi_min))
.rename("fvc");
return image.addBands(fvc);
},
// wet
wet: function (image) {
var b2 = image.select("B2"); // blue
var b3 = image.select("B3"); // green
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
var b6 = image.select("B6"); // swir1
var b7 = image.select("B7"); // swir1
var tem1 = b2.multiply(0.1511).add(b3.multiply(0.1973));
var tem2 = b4.multiply(0.3283).add(b5.multiply(0.3407));
var tem3 = b6.multiply(-0.7177).add(b7.multiply(-0.4559));
var wet_oli = tem1.add(tem2).subtract(tem3).rename("wet");
return image.addBands(wet_oli);
},
// ndbsi
ndbsi: function (image) {
var b2 = image.select("B2"); // blue
var b3 = image.select("B3"); // green
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
var b6 = image.select("B6"); // swir1
// var b7 = image.select("B7"); // swir1
var tem1 = b6
.divide(b6.add(b5))
.multiply(2)
.subtract(b5.divide(b6.add(b5)))
.add(b3.divide(b3.add(b6)));
var tem2 = b6
.divide(b6.add(b5))
.multiply(2)
.add(b5.divide(b6.add(b5)))
.add(b3.divide(b3.add(b6)));
var ibi = tem1.divide(tem2);
var tem3 = b6.multiply(1.0).add(b4).subtract(b5.add(b2));
var tem4 = b6.multiply(1.0).add(b4).add(b5.add(b2));
var si = tem3.divide(tem4);
var ndbsi = ibi.add(si).divide(2).rename("ndbsi"); // get the ndbsi
return image.addBands(ndbsi);
},
//ndbi
ndbi: function (image) {
var b6 = image.select("B6");
var b5 = image.select("B5");
var ndbi = b6.subtract(b5).divide(b6.add(b5)).rename("ndbi");
return image.addBands(ndbi);
},
// evi
evi: function (image) {
var b2 = image.select("B2"); // blue
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
var evi = b5
.subtract(b4)
.multiply(2.5)
.divide(b5.add(b4.multiply(6)).subtract(b2.multiply(7.5)).add(1))
.rename("evi");
return image.addBands(evi);
},
// mndwi
mndwi: function (image) {
var b6 = image.select("B6");
var b3 = image.select("B3");
var mndwi = b3.subtract(b6).divide(b3.add(b6)).rename("mndwi");
return image.addBands(mndwi);
},
// rvi
rvi: function (image) {
var b3 = image.select("B3"); // green
var b5 = image.select("B5"); // nir
var rvi = b3.divide(b5).rename("rvi");
return image.addBands(rvi);
},
// ndwi
ndwi: function (image) {
var b3 = image.select("B3"); // green
var b5 = image.select("B5"); // nir
var ndwi = b3.subtract(b5).divide(b3.add(b5)).rename("ndwi");
return image.addBands(ndwi);
},
trans: function (image) {
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
//计算叶绿素
//计算蓝藻密度
var alg = pie
.Image(-0.416)
.subtract(b5.log().multiply(0.587))
.subtract(b4.log().multiply(0.722));
var band = alg.exp().rename("trans"); // 计算指数并裁剪区域
return image.addBands(band); // 添加蓝藻波段
},
//加载Landsat 8 SR
ss: function (image) {
var b4 = image.select("B4"); // red
var b2 = image.select("B2"); // nir
//计算叶绿素
//计算蓝藻密度
var ss = b4.subtract(b2).divide(b4.add(b2)).rename("ss");
return image.addBands(ss); // 添加蓝藻波段
},
mndwiCa: function (image) {
var b4 = image.select("B4"); // red
var b5 = image.select("B5"); // nir
var b6 = image.select("B6"); // swir1
var b2 = image.select("B2"); // blue
var b3 = image.select("B3"); // green
var mndwi = b3.subtract(b6).divide(b3.add(b6)).rename("mndwi");
// var mask1 = mndwi.updateMask(mndwi.gt(ndvi).and(evi.lt(0.1))).rename('water');
return image.addBands(mndwi);
},
getLandsat8: function (startDate, endDate, roi, cloud = 30) {
var l8SR = pie.ImageCollection("LC08/02/SR");
var l8Col = l8SR
.filterDate(startDate, endDate)
.filterBounds(roi)
.filter(pie.Filter.lt("cloud_cover", cloud))
.map(landsat8Util.removeCloud)
.map(landsat8Util.scaleImage);
return l8Col;
},
};
function mergeMOD09GAData(startDate, endDate, roi, callback) {
var TerraGA = modisUtil.getMOD09GA(startDate, endDate, roi);
var dateList = TerraGA.reduceColumns(pie.Reducer.toList(), ["date"]);
dateList.getInfo(function (list) {
var dates = Array.from(new Set(list["list"]["date"]));
dates.sort();
callback(TerraGA, dates);
});
}
function globalTVDI(startDate, endDate, roi) {
// tvdi
var images = pie
.ImageCollection("BNU/GLOBAL_1KM_TVDI")
.filterBounds(roi)
.filterDate(startDate, endDate)
.select("B1")
.mean()
.clip(roi);
var tvdi = images.multiply(0.0001).rename("tvdi");
return tvdi;
}
function getDateList(startDate, endDate) {
var syear = parseInt(startDate.slice(0, 4)); // 获取起始日期前四位
var sother = startDate.slice(4, 10); // 获取起始日期后6位
var eyear = parseInt(endDate.slice(0, 4)); // 获取结束日期前四位
var eother = endDate.slice(4, 10); // 获取结束日期后6位
var dates = [];
if (syear > eyear) {
return dates;
}
for (var year = syear; year <= eyear; year++) {
dates.push({
start: year + sother,
end: year + eother,
});
}
return dates;
}
// UI界面
var panel = ui.Panel({
style: {
width: "400px"
},
}); // set the panel
var style1 = {"font-size": "20px", color: "green", "font-weight": "bold"}; // set the format
var style2 = {"font-size": "16px", color: "black", "font-weight": "bold"}; // set the format
var label1 = ui.Label("基于多源遥感的太湖生态环境智能监测系统 V1.0", style1); // edit the content
var label2 = ui.Label("制作人: PIE小分队.", style2);
var label3 = ui.Label("E-mail: sun_yilin@yeah.net.", style2);
panel.add(label1).add(label2).add(label3); // add the ui panel
// 设置目录
// 第一节，太湖湖泊监测
var labelA = ui.Label("一、太湖湖泊监测", {
"font-size": "20px",
"font-weight": "bold",
color: "red",
});
var labelA1 = ui.Label("1、基于MODIS数据逐日监测", {
"font-size": "18px",
"font-weight": "bold",
color: "red",
});
panel.add(labelA).add(labelA1);
// 1 选择区域
var label4 = ui.Label("1) 请选择一个区域", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
// 设置空的变量保存矢量.
var TH_boundary = [];
// Create a ui.Select widget for area selection.
var places = {
EntireLake: EntireLake,
CentralLake: CentralLake,
EastLake: EastLake,
SouthLake: SouthLake,
WestLake: WestLake,
GongBay: GongBay,
MeiliangBay: MeiliangBay,
ZhushanBay: ZhushanBay,
XukouBay: XukouBay,
};
// 设置选择区域
var selectArea = ui.Select({
items: Object.keys(places),
placeholder: "Click on the select",
value: null,
multiple: false,
onChange: function (key) {
// 存储选择的矢量边界
TH_boundary = places[key];
return TH_boundary;
},
});
panel.add(label4).add(selectArea);
// 显示研究区
function clickBtn() {
Map.addLayer(
TH_boundary, {color: "FF0000", fillColor: "00000000", width: 1},
"roi"
);
}
var btShow = ui.Button({
label: "显示研究区",
type: "success",
onClick: clickBtn,
});
var btNotShow = ui.Button({
label: "不显示研究区",
type: "success",
});
var btnPanelShow = ui.Panel({
widgets: [btShow, btNotShow],
layout: ui.Layout.flow("horizontal"),
});
panel.add(btnPanelShow);
// 2 选择时间
// 开始时间
var label5 = ui.Label("2) 请输入研究时间", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
function inputSDatea(value) {
selectStartDate = value;
}
var textBox1a = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectStartDate, //输入显示框显示数值
onChange: inputSDatea,
disabled: false,
});
var textboxName1a = ui.Label("开始时间：");
var textboxPanel1a = ui.Panel({
widgets: [textboxName1a, textBox1a],
layout: ui.Layout.flow("horizontal"),
});
panel.add(label5).add(textboxPanel1a);
// 结束时间
function inputEDatea(value) {
selectEndDate = value;
}
var textBox2a = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectEndDate, //输入显示框显示数值
onChange: inputEDatea,
disabled: false,
});
var textboxName2a = ui.Label("结束时间：");
var textboxPanel2a = ui.Panel({
widgets: [textboxName2a, textBox2a],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel2a);
// 水华面积计算
var label6 = ui.Label("3) 逐日水华监测", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btArea = ui.Button({
label: "计算每日水华面积",
type: "success",
onClick: function () {
print("开始蓝藻水华面积计算");
mergeMOD09GAData(
selectStartDate,
selectEndDate,
TH_boundary,
function (TerraGA, dates) {
var count_images = [];
for (var i = 0; i < dates.length; i++) {
var FAI_threshold = TerraGA.filter(pie.Filter.eq("date", dates[i]))
.map(modisUtil.min567)
.map(modisUtil.jz)
.map(modisUtil.addIndexBands)
.select("FAI")
.mosaic()
.clip(TH_boundary); // 镶嵌并裁剪
var areaPixel = FAI_threshold.gte(-0.004); // 统计方法2：生成二值影像
var countPixel = areaPixel.reduceRegion(
pie.Reducer.sum(),
TH_boundary,
500
); // 计算像素数之和
//设置图层显示属性
var visalg = generateVisalg(-0.004, 0.25);
Map.addLayer(
FAI_threshold.updateMask(areaPixel),
visalg,
dates[i],
false
); // 去云影像，绘制图层
count_images.push(countPixel); // 记录数量
print("日期：", dates[i]); // 输出日期
print("数量：", countPixel); // 打印结果
}
// Map.playLayersAnimation(dates, 0.5, 100); // 动画演示
// 显示图例
addLegendToMap({
title: "FAI",
labels: ["-0.004", "0.25"],
step: 1,
});
// 通过判断设置图表属性
if (TH_boundary == EntireLake) {
var line_a = {
title: "太湖水域蓝藻面积动态变化",
legend: ["蓝藻面积"],
xAxisName: "日期",
yAxisName: "蓝藻像元数量（500*500m）",
chartType: "line",
yMin: 0,
yMax: 25000, // 当显示为全部区域时，上限为25000
smooth: true,
};
} else {
var line_a = {
title: "太湖水域蓝藻面积动态变化",
legend: ["蓝藻像元数量"],
xAxisName: "日期",
yAxisName: "蓝藻像元数量（500*500m）",
chartType: "line",
yMin: 0,
yMax: 2000, // 当显示为部分区域时，上限为2000
smooth: true,
};
}
// 显示折线图
var countChart = ui.Chart.image(count_images, dates, line_a);
print(countChart);
print("结束蓝藻水华面积计算");
}
);
},
});
panel.add(label6).add(btArea);
// 时间范围内蓝藻频率图
var label7 = ui.Label("4) 水华发生频率", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btOccurency = ui.Button({
label: "计算频率影像",
type: "success",
onClick: function () {
print("开始蓝藻频率计算");
var TerraGA = modisUtil.getMOD09GA(
selectStartDate,
selectEndDate,
TH_boundary
);
var BinarySum = TerraGA.map(modisUtil.min567)
.map(modisUtil.jz)
.map(modisUtil.addIndexBands)
.map(modisUtil.Binary)
.select("alage")
.sum()
.clip(TH_boundary); //对影像集中的像素求和
//设置图层显示属性
var visalgOccu = generateVisalgOccu(1, 10);
Map.addLayer(BinarySum, visalgOccu, "Occurrence Frequency");
//地图上显示图例组件
addOccuLegendToMap({
title: "蓝藻出现频率",
labels: ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
step: 1,
});
print("结束蓝藻频率计算");
},
});
panel.add(label7).add(btOccurency);
// 蓝藻发生最早时间
function generateDateImageCollection(TerraGA, dates) {
var images = [];
for (var i = 0; i < dates.length; i++) {
var image = TerraGA.filter(pie.Filter.eq("date", dates[i]))
.mosaic()
.clip(TH_boundary);
image = modisUtil.funFAI(image);
image = modisUtil.mod_FAI(image);
var dateImage = image
.where(image.gte(-0.004), i + 1)
.rename("dateImage"); // 设置数据值
images.push(dateImage);
}
var imageCollection = pie.ImageCollection.fromImages(images);
return imageCollection;
}
var label71 = ui.Label("5) 水华发生起始时间", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btEarlyTime = ui.Button({
label: "计算起始时间影像",
type: "success",
onClick: function () {
print("开始计算蓝藻发生最早时间");
mergeMOD09GAData(
selectStartDate,
selectEndDate,
TH_boundary,
function (TerraGA, dates) {
var imageCollection = generateDateImageCollection(TerraGA, dates);
var earlyImage = imageCollection.select("dateImage").min(); // 获得最小值天数影像
//设置图层显示属性
var visalgOccu = generateVisalgOccu(1, 10);
Map.addLayer(earlyImage, visalgOccu, "earlyImage");
// 显示图例
addOccuLegendToMap({
title: "水华发生起始时间（天）",
labels: ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
step: 1,
});
print("结束计算蓝藻发生最早时间");
}
);
},
});
panel.add(label71).add(btEarlyTime);
// 蓝藻发生的截止时间
var label72 = ui.Label("6) 水华发生结束时间", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btLatestTime = ui.Button({
label: "计算结束时间影像",
type: "success",
onClick: function () {
print("开始计算蓝藻消失时间");
mergeMOD09GAData(
selectStartDate,
selectEndDate,
TH_boundary,
function (TerraGA, dates) {
var imageCollection = generateDateImageCollection(TerraGA, dates);
var latestImage = imageCollection.select("dateImage").max(); // 获得最小值天数影像
//设置图层显示属性
var visalgOccu = generateVisalgOccu(1, 10);
Map.addLayer(latestImage, visalgOccu, "latestImage");
// 显示图例
addOccuLegendToMap({
title: "水华发生结束时间（天）",
labels: ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
step: 1,
});
print("结束计算蓝藻消失时间");
}
);
},
});
panel.add(label72).add(btLatestTime);
// 蓝藻发生的持续时间
var label73 = ui.Label("7) 水华发生持续时间", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btDurationTime = ui.Button({
label: "计算水华持续时间影像",
type: "success",
onClick: function () {
print("开始计算蓝藻持续时间");
mergeMOD09GAData(
selectStartDate,
selectEndDate,
TH_boundary,
function (TerraGA, dates) {
var imageCollection = generateDateImageCollection(TerraGA, dates);
var earlyImage = imageCollection.select("dateImage").min(); // 获得最小值天数影像
var latesImage = imageCollection.select("dateImage").max(); // 获得最大值天数影像
var durationImage = latesImage.subtract(earlyImage); // 获得蓝藻持续时间
//设置图层显示属性
var visalgOccu = generateVisalgOccu(1, 10);
Map.addLayer(durationImage, visalgOccu, "durationImage");
// 显示图例
addOccuLegendToMap({
title: "水华发生持续时间（天）",
labels: ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
step: 1,
});
print("结束计算蓝藻持续时间");
}
);
},
});
panel.add(label73).add(btDurationTime);
// 等级显示及直方图计算
var label74 = ui.Label("8) 蓝藻水华等级显示及统计", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btHIS = ui.Button({
label: "显示水华等级",
type: "success",
onClick: function () {
print("开始水华等级显示");
mergeMOD09GAData(
selectStartDate,
selectEndDate,
TH_boundary,
function (TerraGA, dates) {
var image = TerraGA.filter(pie.Filter.eq("date", dates[0]))
.map(modisUtil.addIndexBands)
.select("FAI")
.mosaic()
.clip(TH_boundary);
print(dates[0] + "等级统计结果:");
// Create a VCI level (Lv.2 - Lv.6) classification function.
var VCIs = [
{name: "VCI_Lv1", min: -0.004, max: 0, palette: "#040274"},
{name: "VCI_Lv2", min: 0, max: 0.05, palette: "#269db1"},
{name: "VCI_Lv3", min: 0.05, max: 0.1, palette: "#3ae237"},
{name: "VCI_Lv4", min: 0.1, max: 0.15, palette: "#ff0000"},
{name: "VCI_Lv5", min: 0.15, max: null, palette: "#911003"},
];
var count_images = []; // 保存数量
print("统计结果输出：");
for (var i = 0; i < VCIs.length; i++) {
var vci_image = image.rename(VCIs[i].name);
if (VCIs[i].min !== null) {
vci_image = vci_image.updateMask(vci_image.gte(VCIs[i].min));
}
if (VCIs[i].max !== null) {
vci_image = vci_image.updateMask(vci_image.lt(VCIs[i].max));
}
var vci_image_count = vci_image.reduceRegion(
pie.Reducer.count(),
TH_boundary,
500
);
print(vci_image_count);
count_images.push(vci_image_count);
Map.addLayer(vci_image, {palette: VCIs[i].palette}, VCIs[i].name);
}
// 显示图例
addClassLegendToMap({
title: "VCI",
labels: ["VCI1", "VCI2", "VCI3", "VCI4", "VCI5"],
step: 1,
});
var labelui = ui.Label(dates[0] + "等级分类显示", {
"font-size": "22px",
"font-weight": "bold",
color: "black",
}); //ui界面添加标题
Map.addUI(labelui);
// 绘制表格
// 设置图表属性
if (TH_boundary == EntireLake) {
var line_a = {
title: "太湖水域蓝藻等级面积统计",
legend: ["蓝藻面积"],
xAxisName: "等级",
yAxisName: "蓝藻像元数量（500*500m）",
chartType: "line",
yMin: 0,
yMax: 5000, // 当显示为全部区域时，上限为40000
smooth: true,
};
} else {
var line_a = {
title: "太湖水域蓝藻等级面积统计",
legend: ["蓝藻面积"],
xAxisName: "等级",
yAxisName: "蓝藻像元数量（500*500m）",
chartType: "line",
yMin: 0,
yMax: 2000, // 当显示为部分区域时，上限为4000
// smooth: true
};
}
// 显示折线图
var labels = ["VCI1", "VCI2", "VCI3", "VCI4", "VCI5"];
var countChart = ui.Chart.image(count_images, labels, line_a); // 方法2
print(countChart);
print("结束水华等级显示");
}
);
},
});
panel.add(label74).add(btHIS);
// 基于Landsat系列数据进行月季年监测
var labelA2 = ui.Label("2、基于Landsat数据月季年监测", {
"font-size": "18px",
"font-weight": "bold",
color: "red",
});
panel.add(labelA2);
var label4A = ui.Label("1) 请选择一个区域", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var selectAreaA = ui.Select({
items: Object.keys(places),
placeholder: "点击选择",
value: null,
multiple: false,
onChange: function (key) {
// 存储选择的矢量边界
TH_boundary = places[key];
return TH_boundary;
},
});
panel.add(label4A).add(selectAreaA);
var btShowA = ui.Button({
label: "显示研究区",
type: "success",
onClick: clickBtn,
});
var btNotShowA = ui.Button({
label: "不显示研究区",
type: "success",
});
var btnPanelShowA = ui.Panel({
widgets: [btShowA, btNotShowA],
layout: ui.Layout.flow("horizontal"),
});
panel.add(btnPanelShowA);
// 2 选择时间
// 开始时间
var selectStartDateA = "2020-06-01"; // 设置初始值
var selectEndDateA = "2020-09-31";
var label5A = ui.Label("2) 请输入研究时间及云量", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
function inputSDateA(value) {
selectStartDateA = value;
}
var textBox1A = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectStartDateA, //输入显示框显示数值
onChange: inputSDateA,
disabled: false,
});
var textboxName1A = ui.Label("开始时间：");
var textboxPanel1A = ui.Panel({
widgets: [textboxName1A, textBox1A],
layout: ui.Layout.flow("horizontal"),
});
panel.add(label5A).add(textboxPanel1A);
// 结束时间
function inputEDateA(value) {
selectEndDateA = value;
}
var textBox2A = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectEndDateA, //输入显示框显示数值
onChange: inputEDateA,
disabled: false,
});
var textboxName2A = ui.Label("结束时间：");
var textboxPanel2A = ui.Panel({
widgets: [textboxName2A, textBox2A],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel2A);
// 筛选云量 输入数值
var textboxName2AA = ui.Label("输入云量：");
function funInputNumberA(value) {
selectNumA = value; // 设置云量值
}
var selectNumA = 20; // 设置起始输入数据
var inputNumberA = ui.InputNumber({
placeholder: "请输入云量",
value: selectNumA,
min: 0,
max: 100,
step: 1,
onChange: funInputNumberA,
disabled: false,
});
var textboxPanel2AA = ui.Panel({
widgets: [textboxName2AA, inputNumberA],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel2AA);
// 蓝藻水华计算
var label81 = ui.Label("3) 月季年蓝藻水华监测", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btALG = ui.Button({
label: "计算水华影像",
type: "success",
onClick: function () {
print("开始水华面积计算");
//设置图层显示属性
var visalg = generateVisalg(-0.5, 0.5);
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
var dateList = getDateList(selectStartDateA, selectEndDateA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start;
var endDate = dateList[i].end;
var year = new Date(startDate).getFullYear();
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
TH_boundary,
selectNumA
);
l8Col = l8Col.map(landsat8Util.alg);
var alg_mean = l8Col.select("alg").mean().clip(TH_boundary); // 求取平均值
var layerName = startDate + "_" + endDate + "alg"; // 设置图层名称
layerDate.push(year);
var count = alg_mean.reduceRegion(pie.Reducer.count(), TH_boundary, 30); // 统计数量
print(count);
counts.push(count);
Map.addLayer(alg_mean, visalg, layerName); // 加载图层
}
// 设置图表属性
var line_a = {
title: "太湖水域蓝藻动态变化",
legend: ["蓝藻"],
xAxisName: "日期",
yAxisName: "蓝藻数量",
chartType: "line",
yMin: 0,
yMax: 10000000,
smooth: true,
};
// 显示折线图
var chart = ui.Chart.image(counts, layerDate, line_a);
print(chart);
// 图例
addLegendToMap({
title: "FAI",
labels: ["0", "0.05", "0.1", "0.15", "0.20", "0.25"],
step: 30,
});
print("结束水华面积计算");
},
});
panel.add(label81).add(btALG);
// 蓝藻浑浊度计算
var label8 = ui.Label("4) 月季年蓝藻浑浊度监测", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btHZ = ui.Button({
label: "计算蓝藻浑浊度影像",
type: "success",
onClick: function () {
print("开始计算蓝藻浑浊度");
//加载Landsat 8 SR
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
var dateList = getDateList(selectStartDateA, selectEndDateA);
//设置图层显示属性
var visalg = generateVisalg(0, 1000);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
TH_boundary,
selectNumA
);
l8Col = l8Col.map(landsat8Util.turbidity);
var alg_mean = l8Col.select("turbidity").max().clip(TH_boundary); // 求取最大值
var layerName = startDate + "_" + endDate + "turbidity"; // 设置图层名称
layerDate.push(year);
var mean_count = alg_mean
.select("turbidity")
.reduceRegion(pie.Reducer.mean(), TH_boundary, 30); // 统计数量
counts.push(mean_count);
print(mean_count);
Map.addLayer(alg_mean, visalg, layerName); // 加载图层
}
// 显示图例
addLegendToMap({
title: "浑浊度",
labels: ["优", "差"],
step: 12,
});
// 设置图表属性
var line_a = {
title: "太湖水域蓝藻浑浊度动态变化",
legend: ["蓝藻浑浊度"],
xAxisName: "日期",
yAxisName: "浑浊度均值）",
chartType: "line",
yMin: 0,
yMax: 1000,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
print("结束计算蓝藻浑浊度");
},
});
panel.add(label8).add(btHZ);
// 蓝藻密度计算
var label9 = ui.Label("5) 月季年蓝藻密度监测", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btMD = ui.Button({
label: "计算蓝藻密度影像",
type: "success",
onClick: function () {
print("开始计算蓝藻密度");
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
var dateList = getDateList(selectStartDateA, selectEndDateA);
//设置图层显示属性
var visalg = generateVisalg(0, 1000);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
//加载Landsat 8 SR
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
TH_boundary,
selectNumA
);
l8Col = l8Col.map(landsat8Util.MD);
var alg_mean = l8Col.select("md").mean().clip(TH_boundary); // 求取平均值
var layerName = startDate + "_" + endDate + "md"; // 设置图层名称
layerDate.push(year);
var mean_count = alg_mean
.select("md")
.reduceRegion(pie.Reducer.mean(), TH_boundary, 30); // 统计数量
counts.push(mean_count);
print(mean_count);
Map.addLayer(alg_mean, visalg, layerName); // 加载图层
}
// 显示图例
addLegendToMap({
title: "密度",
labels: ["低", "高"],
step: 30,
});
// 绘制表格
// 设置图表属性
var line_a = {
title: "太湖水域蓝藻密度动态变化",
legend: ["蓝藻浑浊度"],
xAxisName: "日期",
yAxisName: "密度均值）",
chartType: "line",
yMin: 0,
yMax: 100000,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
print("结束计算蓝藻密度");
},
});
panel.add(label9).add(btMD);
// 计算叶绿素a
var label81 = ui.Label("6) 月季年叶绿素监测", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btChla = ui.Button({
label: "计算叶绿素影像",
type: "success",
onClick: function () {
print("开始计算叶绿素");
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
//设置图层显示属性
var visalg = generateVisalg(-50, 1000);
var dateList = getDateList(selectStartDateA, selectEndDateA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
//加载Landsat 8 SR
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
TH_boundary,
selectNumA
);
l8Col = l8Col.map(landsat8Util.cha);
var alg_mean = l8Col.select("cha").mean().clip(TH_boundary); // 求取平均值
var layerName = startDate + "_" + endDate + "cha"; // 设置图层名称
layerDate.push(year);
var mean_count = alg_mean
.select("cha")
.reduceRegion(pie.Reducer.mean(), TH_boundary, 30); // 统计数量
counts.push(mean_count);
print(mean_count);
Map.addLayer(alg_mean, visalg, layerName); // 加载图层
}
// 显示图例
addLegendToMap({
title: "叶绿素",
labels: ["低", "高"],
step: 30,
});
// 设置图表属性
var line_a = {
title: "太湖水域蓝藻叶绿素动态变化",
legend: ["蓝藻浑浊度"],
xAxisName: "日期",
yAxisName: "叶绿素均值）",
chartType: "line",
yMin: 0,
yMax: 1000,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
print("结束计算叶绿素");
},
});
panel.add(label81).add(btChla);
// 透明度反演
var label82 = ui.Label("7) 月季年透明度监测", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btParence = ui.Button({
label: "计算透明度影像",
type: "success",
onClick: function () {
print("开始计算透明度");
//加载Landsat 8 SR
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
//设置图层显示属性
var visalg = generateVisalg(0, 200);
var dateList = getDateList(selectStartDateA, selectEndDateA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
TH_boundary,
selectNumA
);
l8Col = l8Col.map(landsat8Util.trans);
var alg_mean = l8Col.select("trans").mean().clip(TH_boundary); // 求取平均值
var layerName = startDate + "_" + endDate + "trans"; // 设置图层名称
layerDate.push(year);
var mean_count = alg_mean
.select("trans")
.reduceRegion(pie.Reducer.mean(), TH_boundary, 30); // 统计数量
counts.push(mean_count);
print("均值：", mean_count);
Map.addLayer(alg_mean, visalg, layerName); // 加载图层
}
// 显示图例
addLegendToMap({title: "透明度", labels: ["低", "高"], step: 30});
// 绘制表格
// 设置图表属性
var line_a = {
title: "太湖水域蓝藻透明度动态变化",
legend: ["蓝藻透明度"],
xAxisName: "日期",
yAxisName: "透明度均值",
chartType: "line",
yMin: 0,
yMax: 1000,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
print("结束计算透明度");
},
});
panel.add(label82).add(btParence);
// 归一化悬浮物指数
var label83 = ui.Label("8) 月季年归一化悬浊物监测", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btSS = ui.Button({
label: "计算归一化悬浊物影像",
type: "success",
onClick: function () {
print("开始计算归一化悬浊物");
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
//设置图层显示属性
var visalg = generateVisalg(0, 1);
var dateList = getDateList(selectStartDateA, selectEndDateA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
TH_boundary,
selectNumA
);
l8Col = l8Col.map(landsat8Util.ss);
var alg_mean = l8Col.select("ss").mean().clip(TH_boundary); // 求取平均值
var layerName = startDate + "_" + endDate + "ss"; // 设置图层名称
layerDate.push(year);
var mean_count = alg_mean
.select("ss")
.reduceRegion(pie.Reducer.mean(), TH_boundary, 30); // 统计数量
counts.push(mean_count);
print(mean_count);
Map.addLayer(alg_mean, visalg, layerName); // 加载图层
}
// 显示图例
addLegendToMap({title: "归一化悬浊物", labels: ["低", "高"], step: 30});
// 绘制表格
// 设置图表属性
var line_a = {
title: "太湖水域蓝藻归一化悬浊物动态变化",
legend: ["归一化悬浊物"],
xAxisName: "日期",
yAxisName: "归一化悬浊物均值）",
chartType: "line",
yMin: 0,
yMax: 1,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
print("结束计算归一化悬浊物");
},
});
panel.add(label83).add(btSS);
// 计算水域面积变化
var label11 = ui.Label("8) 太湖水域面积", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btMJ = ui.Button({
label: "计算水域面积",
type: "success",
onClick: function () {
print("开始水华面积计算");
var waterArea = []; //存储面积
var yearDate = []; // 存储时间
var dateList = getDateList(selectStartDateA, selectEndDateA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
TH_boundary,
selectNumA
);
l8Col = l8Col.map(landsat8Util.mndwiCa);
var mndwi_max = l8Col.select("mndwi").max().clip(TH_boundary);
var l8_water = mndwi_max.gt(0.1);
Map.addLayer(l8_water, {palette: "#0602ff"}, year + "water");
//根据计算的水体数据计算面积
var areaImage = mndwi_max.pixelArea().multiply(l8_water.gt(0.1));
var water = areaImage.reduceRegion(pie.Reducer.sum(), TH_boundary, 30);
var water_area = pie.Number(water.get("constant")).divide(1000000);
waterArea.push(water_area.getInfo()); // 保存面积,需用getInfo
yearDate.push(year); // 存储年份
print(
year + "水体面积是（单位：平方千米）: ",
pie.Number(water.get("constant")).divide(1000000)
); //进行单位换算
}
// 设置图表属性
var line_options = {
title: "太湖水域面积动态变化",
legend: ["水域面积"],
xAxis: yearDate,
xAxisName: "年",
yAxisName: "平方千米",
series: [waterArea],
chartType: "line",
};
//调用绘制折线图的方法
var chart = ui.Chart.array(line_options);
print(chart);
//动画显示
Map.playLayersAnimation(dates, 0.5, -1);
},
});
panel.add(label11).add(btMJ);
var label111 = ui.Label("9) 太湖水域温度", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btLST = ui.Button({
label: "计算水域温度",
type: "success",
onClick: function () {
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
//设置图层显示属性
var visalg = generateVisalg(0, 40);
var dateList = getDateList(selectStartDateA, selectEndDateA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
// 筛选影像
var rawLST = MOD11A2.filterDate(startDate, endDate)
.filterBounds(TH_boundary)
.select("LST_Day_1km")
.mean()
.clip(TH_boundary);
var lst = rawLST.select("LST_Day_1km").multiply(0.02).subtract(273.15);
var layerName = startDate + "_" + endDate + "LSTmean";
Map.addLayer(lst, visalg, layerName);
var mean = lst.reduceRegion(pie.Reducer.mean(), TH_boundary, 1000); // 统计温度均值
counts.push(mean);
layerDate.push(year);
}
// 显示图例
addLegendToMap({
title: "湖面温度",
labels: ["0", "10 ", " 20", "30"],
step: 12,
});
// 绘制表格
// 设置图表属性
var line_a = {
title: "太湖水域温度均值变化",
legend: ["温度均值"],
xAxisName: "年份",
yAxisName: "温度均值(℃)",
chartType: "line",
yMin: 0,
yMax: 40,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
},
});
panel.add(label111).add(btLST);
// 第二节，太湖周围生态监测
// 基于Landsat系列数据计算太湖周围生态指数
var labelB = ui.Label("二、太湖周围生态监测", {
"font-size": "20px",
"font-weight": "bold",
color: "red",
});
panel.add(labelB);
var label1B1 = ui.Label("1、指数计算", {
"font-size": "18px",
"font-weight": "bold",
color: "red",
});
panel.add(label1B1);
// 2 选择时间
// 开始时间
var selectStartDateAA = "2014-03-01"; // 设置初始值
var selectEndDateAA = "2020-10-31";
var label5AA = ui.Label("1) 请输入研究时间及缓冲距离", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
function inputSDateAA(value) {
selectStartDateAA = value;
}
var textBox1AA = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectStartDateAA, //输入显示框显示数值
onChange: inputSDateAA,
disabled: false,
});
var textboxName1AA = ui.Label("开始时间：");
var textboxPanel1AA = ui.Panel({
widgets: [textboxName1AA, textBox1AA],
layout: ui.Layout.flow("horizontal"),
});
panel.add(label5AA).add(textboxPanel1AA);
// 结束时间
function inputEDateAA(value) {
selectEndDateAA = value;
}
var textBox2AA = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectEndDateAA, //输入显示框显示数值
onChange: inputEDateAA,
disabled: false,
});
var textboxName2AA = ui.Label("结束时间：");
var textboxPanel2AA = ui.Panel({
widgets: [textboxName2AA, textBox2AA],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel2AA);
//获取缓冲区
// 输入数值
var textboxName2AAA = ui.Label("请输入缓冲距离（km）：");
function funInputNumberAA(value) {
selectNumAA = value; // 设置值
}
var selectNumAA = 30; // 设置起始输入数据
var inputNumberAA = ui.InputNumber({
placeholder: "请输入缓冲距离",
value: selectNumAA,
min: 0,
max: 100,
step: 1,
onChange: funInputNumberAA,
disabled: false,
});
var textboxPanel2AA = ui.Panel({
widgets: [textboxName2AAA, inputNumberAA],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel2AA);
// 1缓冲区温度反演
var label11 = ui.Label("2) 温度显示", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btlst = ui.Button({
label: "计算温度",
type: "success",
onClick: function () {
print("开始计算温度");
var Th_buffer_1km = EntireLake.buffer(selectNumAA * 1000);
var Th_buffer_dif = Th_buffer_1km.difference(EntireLake);
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
var layers = [];
//设置图层显示属性
var visalg = generateVisalg(0, 40);
var dateList = getDateList(selectStartDateAA, selectEndDateAA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
// 筛选影像
var rawLST = MOD11A2.filterDate(startDate, endDate)
.filterBounds(Th_buffer_dif)
.select("LST_Day_1km")
.mean()
.clip(Th_buffer_dif);
var lst = rawLST.select("LST_Day_1km").multiply(0.02).subtract(273.15);
var layerName = startDate + "_" + endDate + "mean";
layers.push(layerName);
Map.addLayer(lst, visalg, layerName);
var mean = lst.reduceRegion(pie.Reducer.mean(), Th_buffer_dif, 1000); // 统计温度均值
print(mean);
counts.push(mean);
layerDate.push(year);
}
// 显示图例
addLegendToMap({
title: "湖面温度",
labels: ["0", "10 ", " 20", "30", "40"],
step: 12,
});
// 绘制表格
// 设置图表属性
var line_a = {
title: "太湖水域周围" + selectNumAA + "km温度均值变化",
legend: ["温度均值(℃)"],
xAxisName: "年份",
yAxisName: "温度均值(℃)",
chartType: "line",
yMin: 0,
yMax: 40,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
//动画显示
Map.playLayersAnimation(layers, 0.5, -1);
print("结束计算温度");
},
});
panel.add(label11).add(btlst);
// 缓冲区绿度反演
var label12 = ui.Label("3) NDVI显示", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btNdvi = ui.Button({
label: "计算NDVI",
type: "success",
onClick: function () {
print("开始计算NDVI");
var Th_buffer_1km = EntireLake.buffer(selectNumAA * 1000);
var Th_buffer_dif = Th_buffer_1km.difference(EntireLake);
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
var layers = [];
//设置图层显示属性
var visalg = generateVisalg(-1, 1);
var dateList = getDateList(selectStartDateAA, selectEndDateAA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
// 筛选影像
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
Th_buffer_dif,
20
);
l8Col = l8Col
.map(landsat8Util.ndvi)
.select("ndvi")
.max()
.clip(Th_buffer_dif);
var layerName = startDate + "_" + endDate + "mean";
layers.push(layerName);
Map.addLayer(l8Col, visalg, layerName);
var mean = l8Col
.select("ndvi")
.reduceRegion(pie.Reducer.mean(), Th_buffer_dif, 30); // 统计ndvi均值
print(mean);
counts.push(mean);
layerDate.push(year);
}
// 显示图例
addLegendToMap({
title: "NDVI",
labels: ["-1", "-0.5 ", " 0", "0.5", "1"],
step: 12,
});
// 设置图表属性
var line_a = {
title: "太湖水域周围" + selectNumAA + "kmNDVI均值变化",
legend: ["NDVI均值"],
xAxisName: "年份",
yAxisName: "NDVI均值",
chartType: "line",
yMin: 0,
yMax: 1,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
//动画显示
Map.playLayersAnimation(layers, 0.5, -1);
print("结束计算NDVI");
},
});
panel.add(label12).add(btNdvi);
// 计算植被覆盖度
var label121 = ui.Label("4) 植被覆盖度显示", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btfvc = ui.Button({
label: "计算FVC",
type: "success",
onClick: function () {
print("开始计算FVC");
var Th_buffer_1km = EntireLake.buffer(selectNumAA * 1000);
var Th_buffer_dif = Th_buffer_1km.difference(EntireLake);
var layers = [];
//设置图层显示属性
var visalg = generateVisalg(0, 1);
var dateList = getDateList(selectStartDateAA, selectEndDateAA);
var startDate = dateList[0].start; // 组合开始日期
var endDate = dateList[0].end; // 组合结束日期
// 筛选影像
var l8Col = landsat8Util.getLandsat8(startDate, endDate, Th_buffer_dif, 20);
var ndvi = l8Col
.map(landsat8Util.ndvi)
.select("ndvi")
.max()
.clip(Th_buffer_dif);
var result = pie.Dictionary(
ndvi.reduceRegion(pie.Reducer.percentile([5, 95]), Th_buffer_dif, 30)
);
var ndvi_p5 = pie.Dictionary(result.get("ndvi")).getNumber("p5").getInfo();
//反演区域植被覆盖度并加载
var fvc = ndvi.subtract(ndvi_p5).divide(1.2).rename("fvc");
var layerName = startDate + "_" + endDate + "mean";
layers.push(layerName);
Map.addLayer(fvc.select("fvc"), visalg, layerName);
var mean = fvc
.select("fvc")
.reduceRegion(pie.Reducer.mean(), Th_buffer_dif, 30); // 统计ndvi均值
print("fvc均值：", mean);
// 显示图例
addLegendToMap({
title: "FVC",
labels: ["0", "0.2 ", " 0.4", "0.6", "0.8", "1"],
step: 12,
});
print("结束计算FVC");
},
});
panel.add(label121).add(btfvc);
// 计算湿度
var label13 = ui.Label("5) Wet显示", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btWet = ui.Button({
label: "计算Wet",
type: "success",
onClick: function () {
print("开始计算wet");
var Th_buffer_1km = EntireLake.buffer(selectNumAA * 1000);
var Th_buffer_dif = Th_buffer_1km.difference(EntireLake);
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
var layers = [];
//设置图层显示属性
var visalg = generateVisalg(-0.5, 0.5);
var dateList = getDateList(selectStartDateAA, selectEndDateAA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
// 筛选影像
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
Th_buffer_dif,
20
);
var wet = landsat8Util.wet(
l8Col.select(["B2", "B3", "B4", "B5", "B6", "B7"]).mean()
);
wet = wet.select("wet").clip(Th_buffer_dif);
var layerName = startDate + "_" + endDate + "mean";
layers.push(layerName);
Map.addLayer(wet, visalg, layerName);
var mean = wet
.select("wet")
.reduceRegion(pie.Reducer.mean(), Th_buffer_dif, 30); // 统计WET均值
print("均值：", mean);
counts.push(mean);
layerDate.push(year);
}
// 显示图例
addLegendToMap({
title: "Wet",
labels: ["-0.5", "-0.25 ", " 0", "0.25", "0.5"],
step: 12,
});
// 设置图表属性
var line_a = {
title: "太湖水域周围" + selectNumAA + "kmWet均值变化",
legend: ["Wet均值"],
xAxisName: "年份",
yAxisName: "Wet均值",
chartType: "line",
yMin: -0.5,
yMax: 0.5,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
//动画显示
// Map.playLayersAnimation(layers, 0.5, -1);
print("结束计算Wet");
},
});
panel.add(label13).add(btWet);
// 城镇建筑指数
var label14 = ui.Label("6) NDBSI显示", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btNdbsi = ui.Button({
label: "计算NDBSI",
type: "success",
onClick: function () {
print("开始计算NDBSI");
var Th_buffer_1km = EntireLake.buffer(selectNumAA * 1000);
var Th_buffer_dif = Th_buffer_1km.difference(EntireLake);
var layerDate = []; //存储年份
var counts = []; // 存储像元数量
//设置图层显示属性
var visalg = generateVisalg(-0.1, 1);
var dateList = getDateList(selectStartDateAA, selectEndDateAA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
// 筛选影像
var l8Col = landsat8Util.getLandsat8(
startDate,
endDate,
Th_buffer_dif,
selectNumA
);
l8Col = l8Col.select(["B2", "B3", "B4", "B5", "B6"]).mean();
var ndbsi = landsat8Util.ndbsi(l8Col).select("ndbsi").clip(Th_buffer_dif);
var layerName = startDate + "_" + endDate + "mean";
Map.addLayer(ndbsi, visalg, layerName);
var mean = ndbsi.reduceRegion(pie.Reducer.mean(), Th_buffer_dif, 30); // 统计ndvi均值
print("均值：", mean);
counts.push(mean);
layerDate.push(year);
}
// 显示图例
addLegendToMap({
title: "NDBSI",
labels: ["0", "0.2 ", " 0.4", "0.6", "0.8", "1"],
step: 12,
});
// 设置图表属性
var line_a = {
title: "太湖水域周围" + selectNumAA + "kmNDBSI均值变化",
legend: ["Wet均值"],
xAxisName: "年份",
yAxisName: "NDBSI均值",
chartType: "line",
yMin: -0.1,
yMax: 1,
smooth: true,
};
// 显示折线图
var countChart = ui.Chart.image(counts, layerDate, line_a);
print(countChart);
print("结束计算NDBSI");
},
});
panel.add(label14).add(btNdbsi);
// 监督分类
// 第二节，土地利用监测
// 基于Landsat系列数据对太湖周边地区进行土地利用监测
var label1B2 = ui.Label("2、土地利用监测", {
"font-size": "18px",
"font-weight": "bold",
color: "red",
});
panel.add(label1B2);
function classifyLand(tag) {
Map.centerObject(EntireLake, 11);
var counts = []; // 存储像元数量
var Th_buffer_1km = EntireLake.buffer(selectNumAA * 1000);
var Th_buffer_dif = Th_buffer_1km.difference(EntireLake);
var dateList = getDateList(selectStartDateAA, selectEndDateAA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
var l8Col = landsat8Util.getLandsat8(startDate, endDate, Th_buffer_dif, 10);
var image = l8Col.select(["B2", "B3", "B4", "B5", "B6", "B7"]).median();
image = landsat8Util.ndvi(image);
image = landsat8Util.mndwi(image);
image = image.clip(Th_buffer_dif);
// 获得训练样本，并且按照8:2分成训练样本和验证样本
var sampleFeatureCollection = image.sampleRegions(
points,
["landcover"],
30
);
sampleFeatureCollection = sampleFeatureCollection.filter(pie.Filter.notNull(["B2",
"B3",
"B4",
"B5",
"B6",
"B7",
"ndvi",
"mndwi"
]))
sampleFeatureCollection = sampleFeatureCollection.randomColumn("random");
var sampleTrainingFeatures = sampleFeatureCollection.filter(
pie.Filter.lte("random", 0.8)
);
var sampleTestingFeatures = sampleFeatureCollection.filter(
pie.Filter.gt("random", 0.8)
);
var methods = {
rTrees: pie.Classifier.rTrees,
svm: pie.Classifier.svm,
normalBayes: pie.Classifier.normalBayes,
};
// 构建RT分类器，并训练数据
var classifer = methods[tag]().train(sampleTrainingFeatures, "landcover", [
"B2",
"B3",
"B4",
"B5",
"B6",
"B7",
"ndvi",
"mndwi",
]);
// 影像分类，并加载显示
var resultImage = image.classify(classifer, "classifyA");
// 形态学运算
resultImage = resultImage.focal_max(1); // 膨胀运算
resultImage = resultImage.focal_min(1); //腐蚀运算
var visParam = {
opacity: 1,
uniqueValue: "1,2,3,4,5",
palette: "#040274,#269db1,#3ae237,#ff0000,#911003",
};
Map.addLayer(resultImage, visParam, year + "-" + tag + "-Image");
Map.addLayer(
image, {min: 0, max: 0.3, bands: ["B4", "B3", "B2"]},
year + "-Original Image"
);
var counts = [];
for (var i = 1; i <= 5; i++) {
var areaImg = pie.Image().pixelArea();
var count = areaImg
.multiply(resultImage.eq(i))
.reduceRegion(pie.Reducer.sum(), Th_buffer_dif, 30); // 类别数量统计
counts.push(count);
}
// 设置图表属性
var line_a = {
title: dateList[i].start.slice(0, 4) + "年太湖水域周边地物面积统计",
legend: ["不同地物面积"],
xAxisName: "类别",
yAxisName: "面积（平方千米）",
chartType: "line",
yMin: 0,
yMax: 20000,
};
var labels = ["水体", "建设用地", "林地", "耕地", "裸地"];
var countChart = ui.Chart.image(counts, labels, line_a); // 方法2
print(countChart);
}
// 添加图例
addLegend({
title: "土地利用分类",
colors: ["#040274", "#269db1", "#3ae237", "#ff0000", "#911003"],
labels: ["水体", "建设用地", "林地", "耕地", "裸地"],
step: 1,
});
// 评估训练样本的精度
var checkM = classifer.confusionMatrix();
print("训练矩阵：", checkM);
print(
"训练矩阵-ACC系数：",
checkM.acc(),
"训练矩阵-Kappa系数：",
checkM.kappa()
);
// 评估验证样本的精度
var predictResult = sampleTestingFeatures.classify(
classifer,
"classification"
);
var errorM = predictResult.errorMatrix("landcover", "classification");
print("验证矩阵：", errorM);
print(
"验证矩阵-ACC系数：",
errorM.acc(),
"验证矩阵-Kappa系数：",
errorM.kappa()
);
}
// 城市建筑用地情况
var label15 = ui.Label("1) 随机森林分类", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btClass = ui.Button({
label: "开始分类",
type: "success",
onClick: function () {
// 获得样本点
print("开始随机森林分类");
classifyLand("rTrees");
print("结束随机森林分类");
},
});
panel.add(label15).add(btClass);
// 支持向量机分类
var label16 = ui.Label("2) 支持向量机分类", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btSVCClass = ui.Button({
label: "开始分类",
type: "success",
onClick: function () {
// 获得样本点
print("开始支持向量机分类");
classifyLand("svm");
print("结束支持向量机分类");
},
});
panel.add(label16).add(btSVCClass);
// 正太贝叶斯分类
var label17 = ui.Label("3) 正太贝叶斯分类", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
var btNBClass = ui.Button({
label: "开始分类",
type: "success",
onClick: function () {
// 获得样本点
print("开始正太贝叶斯分类");
classifyLand("normalBayes");
print("结束正太贝叶斯分类");
},
});
panel.add(label17).add(btNBClass);
// k-mean分类
var label18 = ui.Label("4) 非监督分类", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
panel.add(label18);
function clusterLand(tag) {
Map.centerObject(EntireLake, 11);
var Th_buffer_1km = EntireLake.buffer(selectNumAA * 1000);
var Th_buffer_dif = Th_buffer_1km.difference(EntireLake);
var dateList = getDateList(selectStartDateAA, selectEndDateAA);
// 筛选影像
for (var i = 0; i < dateList.length; i++) {
var startDate = dateList[i].start; // 组合开始日期
var endDate = dateList[i].end; // 组合结束日期
var year = new Date(startDate).getFullYear();
var l8Col = landsat8Util.getLandsat8(startDate, endDate, Th_buffer_dif, 10);
var image = l8Col.select(["B2", "B3", "B4", "B5", "B6", "B7"]).median();
var ndvi = landsat8Util.ndvi(image).select("ndvi");
var mndwi = landsat8Util.mndwi(image).select("mndwi");
image = image.addBands(ndvi).addBands(mndwi).clip(Th_buffer_dif);
// 随机采集样本，30米下采集200个点分类
var training = image.sample(Th_buffer_dif, 30, "", "", 200);
var methods = {
kMeans: pie.Clusterer.kMeans,
em: pie.Clusterer.em,
};
// 创建分类器，并进行训练
var cluster = methods[tag](selectNumAAA).train(training);
// 分类数据并显示
var resultImage = image.cluster(cluster, "clusterA");
// 形态学运算
resultImage = resultImage.focal_max(1); // 膨胀运算
resultImage = resultImage.focal_min(1); //腐蚀运算
var visParam = {
opacity: 1,
classify: "0,1,2,3,4,5,6",
palette: "FF0000,00FFFF,00FF00,FF00FF,0000FF,FFFF00,FF8000,00AAFF",
};
Map.addLayer(resultImage, visParam, year + "-" + tag + "-resultImage", false);
var vis = {
min: 0,
max: 0.3,
opacity: 0.9,
bands: ["B4", "B3", "B2"],
};
Map.addLayer(image.select(["B4", "B3", "B2"]), vis, year + "-Landsat8 RGB", false);
}
}
//获取类别
// 输入数值
var textboxName2AAAA = ui.Label("请输入k：");
function funInputNumberAAA(value) {
selectNumAAA = value; // 设置值
}
var selectNumAAA = 2; // 设置起始输入数据
var inputNumberAAA = ui.InputNumber({
placeholder: "请输入k",
value: selectNumAAA,
min: 0,
max: 5,
step: 1,
onChange: funInputNumberAAA,
disabled: false,
});
var textboxPanel2AAA = ui.Panel({
widgets: [textboxName2AAAA, inputNumberAAA],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel2AAA);
var btkmeanClass = ui.Button({
label: "k-means分类",
type: "success",
onClick: function () {
// 获得样本点
print("开始k-means分类");
clusterLand("kMeans");
print("结束k-means分类");
},
});
panel.add(btkmeanClass);
// 最大期望算法
var btemClass = ui.Button({
label: "最大期望算法",
type: "success",
onClick: function () {
// 获得样本点
print("开始最大期望分类");
clusterLand("em");
print("结束最大期望分类");
},
});
panel.add(btemClass);
// 第三节，其他功能
var labelC = ui.Label("三、其他功能", {
"font-size": "20px",
"font-weight": "bold",
color: "red",
});
panel.add(labelC);
var label510 = ui.Label("1) 请输入行政区划", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
panel.add(label510);
var label5101 = ui.Label(
"[注]：行政区划网址：http://www.mca.gov.cn/article/sj/xzqh/2020/", {
"font-size": "16px",
"font-weight": "bold",
color: "black"
}
);
panel.add(label5101);
// 单选按钮，选择省、城市、县
var selectDis = "县行政区划";
function funRadio(value) {
selectDis = value;
}
var radio = ui.Radio({
label: ["省行政区划", "县行政区划"],
value: selectDis,
onChange: funRadio,
disabled: false,
});
var textboxName31 = ui.Label("行政级别：");
var textboxPanel31 = ui.Panel({
widgets: [textboxName31, radio],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel31);
//定义常量参数
var layerKey = null;
var roiKey = null;
var selectCode = "110114";
// 输入行政区划
//获取指定的ROI数据
function getROI(code) {
if (selectDis == "县行政区划") {
var fCol = pie.FeatureCollection("NGCC/CHINA_COUNTY_BOUNDARY");
} else if (selectDis == "省行政区划") {
var fCol = pie.FeatureCollection("NGCC/CHINA_PROVINCE_BOUNDARY");
}
var roi = fCol
.filter(pie.Filter.eq("code", parseInt(code)))
.first()
.geometry();
if (roiKey != null) {
Map.removeLayer(roiKey);
}
var geometry = roi.centroid(); // 中心点
var roiPoint = pie.FeatureCollection(pie.Feature(geometry));
Map.addLayer(
roiPoint, {color: "00AAFF", fillColor: "00000000"},
"centroidPoint",
true
);
Map.centerObject(geometry, 10);
var label1a = ui.Label("行政区划代码:" + code, {
"font-size": "20px",
"font-weight": "bold",
color: "black",
});
Map.addUI(label1a);
roiKey = Map.addLayer(
roi, {color: "#ff0000", fillColor: "#00000000"},
"roi"
);
return roi;
}
function inputArea(value) {
if (value > 99999) {
selectCode = value;
var roi = getROI(selectCode);
print("行政区划的面积是（平方千米）：");
print(roi.area().divide(1000000)); // 计算面积
}
}
var textBox3 = ui.TextBox({
placeholder: "请输入行政区划编码",
value: selectCode,
onChange: inputArea,
disabled: false,
});
var textboxName3 = ui.Label("行政区划：");
var textboxPanel3 = ui.Panel({
widgets: [textboxName3, textBox3],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel3);
// 导出数据
function clickBtExpShp() {
var roi = getROI(selectCode);
Export.table({
collection: roi,
description: "ExportShp" + selectCode,
assetId: selectCode + "shp",
});
}
var btExpShp = ui.Button({
label: "导出行政边界",
type: "success",
onClick: clickBtExpShp,
});
panel.add(btExpShp);
// 输入时间
// 开始时间
var label511 = ui.Label("2) 请输入查询时间", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
function inputSDate(value) {
selectStartDate2 = value;
}
var textBox11 = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectStartDate2, //输入显示框显示数值
onChange: inputSDate,
disabled: false,
});
var textboxName11 = ui.Label("开始时间：");
var textboxPanel1 = ui.Panel({
widgets: [textboxName11, textBox11],
layout: ui.Layout.flow("horizontal"),
});
panel.add(label511).add(textboxPanel1);
// 结束时间
function inputEDate(value) {
selectEndDate2 = value;
}
var textBox21 = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectEndDate2, //输入显示框显示数值
onChange: inputEDate,
disabled: false,
});
var textboxName21 = ui.Label("结束时间：");
var textboxPanel21 = ui.Panel({
widgets: [textboxName21, textBox21],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel21);
// 查询影像
var label19 = ui.Label("3) 显示数据", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
function changeSelect(value) {
var roi = getROI(selectCode);
print("选择的数据:" + value);
if (value == "Landsat8") {
var imageCollection = landsat8Util
.getLandsat8(selectStartDate2, selectEndDate2, roi, 100)
.select(["B4", "B3", "B2"]);
print("影像集合", imageCollection.limit(1));
print("影像数量", imageCollection.size());
var image_median = imageCollection.median().clip(roi);
print(image_median);
var vis = {
min: [0, 0, 0],
max: [20000, 20000, 20000],
opacity: 0.9,
bands: ["B4", "B3", "B2"],
};
Map.addLayer(image_median, vis, "Landsat8");
} else if (value == "GF1") {
var imageCollection = pie
.ImageCollection("GF1/L1A/WFV_SR")
.filterBounds(roi)
.filterDate(selectStartDate2, selectEndDate2)
.select(["B3", "B2", "B1"]);
print("影像集合", imageCollection.limit(1));
print("影像数量", imageCollection.size());
var image_median = imageCollection.median().clip(roi);
var vis = {min: 0, max: 0.3, opacity: 0.9, bands: ["B3", "B2", "B1"]};
Map.addLayer(image_median, vis, "GF1");
} else {
// 哨兵二号去云2
function removeCloud2(image) {
var qa = image.select("QA60");
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
var mask = qa
.bitwiseAnd(cloudBitMask)
.eq(0)
.and(qa.bitwiseAnd(cirrusBitMask).eq(0));
return image.updateMask(mask);
}
var imageCollection = pie
.ImageCollection("S2/L1C")
.filterBounds(roi)
.filterDate(selectStartDate2, selectEndDate2)
.map(removeCloud2)
.select(["B4", "B3", "B2"]); // 真彩色
print("影像集合", imageCollection.limit(1));
print("影像数量", imageCollection.size());
var image_median = imageCollection.median().clip(roi);
var vis = {min: 0, max: 10000, opacity: 0.9, bands: ["B4", "B3", "B2"]};
Map.addLayer(image_median, vis, "Sentinel-2");
}
}
var selectId = ui.Select({
items: ["Landsat8", "Sentinel-2", "GF1"],
placeholder: "请选择",
value: "Landsat8",
multiple: false,
onChange: changeSelect,
});
panel.add(label19).add(selectId);
// 地形显示
var label20 = ui.Label("4) 地形显示", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
panel.add(label20);
var selectTag1A = "DEM"; // 设置初始值
// select
function changeSelect1A(value) {
selectTag1A = value;
var roi = getROI(selectCode);
var dem = pie
.ImageCollection("DEM/ASTGTM_003")
.filterBounds(roi)
.select("dem")
.mean()
.clip(roi);
var visParam;
if (value == "DEM") {
visParam = {
opacity: 0.8,
classify: "-100,0,100,200,300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000,2100,2200,2300,2400,2500,2600,2700,2800,2900,3000,3100",
palette: "145999,1b76cc,1f86e6,26aaea,36b6eb,4dc0eb,60cfeb,77daef,b0dfef,d1e5ee,dce6f0,e1e7f2,dee6f1,43c370,44b772,52c88e," +
"6fc993,7ecb97,acd2ad,d4c97a,d0bc59,d3b146,c78f3c,c3812d,c5762b,b95322,ae4621,ba6e78,bc8fa9,c7aac6,dbcdde,fffcff",
};
Map.addLayer(dem, visParam, "DEM");
var mean = dem.select("dem").reduceRegion(pie.Reducer.mean(), roi, 30);
print("DEM均值：" + mean.get("dem").getInfo() + "m");
// 添加图例
addDem2LegendToMap({
title: "DEM",
labels: ["-100", "500", "1000", "1500", "2000", "2500", "3000"],
step: 100,
});
} else if (value == "坡向") {
var aspect = pie.Terrain.aspect(dem); // 计算坡向
visParam = {
opacity: 0.8,
classify: "0,10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320",
palette: "145999,1b76cc,1f86e6,26aaea,36b6eb,4dc0eb,60cfeb,77daef,b0dfef,d1e5ee,dce6f0,e1e7f2,dee6f1,43c370,44b772,52c88e," +
"6fc993,7ecb97,acd2ad,d4c97a,d0bc59,d3b146,c78f3c,c3812d,c5762b,b95322,ae4621,ba6e78,bc8fa9,c7aac6,dbcdde,fffcff",
};
Map.addLayer(aspect, visParam, "Aspect");
// 添加图例
addDem2LegendToMap({
title: "坡向",
labels: ["0", "50", "100", "150", "200", "250", "300"],
step: 10,
});
} else if (value == "坡度") {
var slope = pie.Terrain.slope(dem).multiply(180 / 3.1415926); // 计算坡度
visParam = {
opacity: 0.8,
classify: "0,10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180",
palette: "145999,1b76cc,1f86e6,26aaea,36b6eb,4dc0eb,60cfeb,77daef,b0dfef,d1e5ee,dce6f0,e1e7f2,dee6f1,43c370,44b772,52c88e," +
"6fc993,7ecb97,acd2ad",
};
Map.addLayer(slope, visParam, "slope");
// 添加图例
addDem1LegendToMap({
title: "坡度",
labels: ["0", "30", "60", "90", "120", "150", "180"],
step: 10,
});
} else {
var imageShade = pie.Terrain.hillShade(dem, 45, 315, 8); // 山体阴影
visParam = {
opacity: 0.8,
classify: "-1100,-800,-700,-600,-500,-400,-300,-200,-100,-50,-20,-10,-5,0,5,10,25,50,75,100,125,150,175,200,250,300,350,400,450,500,550,600,880",
palette: "145999,1b76cc,1f86e6,26aaea,36b6eb,4dc0eb,60cfeb,77daef,b0dfef,d1e5ee,dce6f0,e1e7f2,dee6f1,43c370,44b772,52c88e," +
"6fc993,7ecb97,acd2ad,d4c97a,d0bc59,d3b146,c78f3c,c3812d,c5762b,b95322,ae4621,ba6e78,bc8fa9,c7aac6,dbcdde,fffcff",
};
Map.addLayer(imageShade, visParam, "imageShade");
// 添加图例
addDem1LegendToMap({
title: "山体阴影",
labels: ["0", "50", "100", "150", "200", "250", "300"],
step: 10,
});
}
}
var select1A = ui.Select({
items: ["DEM", "坡向", "坡度", "山体阴影"],
placeholder: "请选择",
value: selectTag1A,
multiple: false,
onChange: changeSelect1A,
});
var selectName1A = ui.Label("选择地形：");
var selectPanel1A = ui.Panel({
widgets: [selectName1A, select1A],
layout: ui.Layout.flow("horizontal"),
});
panel.add(selectPanel1A);
//导出数据
function clickbtExport() {
print(selectTag1A);
var roi = getROI(selectCode);
var dem = pie
.ImageCollection("DEM/ASTGTM_003")
.filterBounds(roi)
.select("dem")
.mean()
.clip(roi);
var aspect = pie.Terrain.aspect(dem); // 计算坡向
var slope = pie.Terrain.slope(dem).multiply(180 / 3.1415926); // 计算坡度
var imageShade = pie.Terrain.hillShade(dem, 45, 315, 8); // 山体阴影
var config = {
DEM: dem,
坡度: slope,
坡向: aspect,
山体阴影: imageShade,
};
Export.image({
image: config[selectTag1A],
description: "ExportImage:" + selectTag1A,
assetId: "test",
region: roi,
scale: 30,
});
}
var btExport = ui.Button({
label: "导出数据",
type: "success",
onClick: clickbtExport,
});
panel.add(btExport);
// 指数计算
var label21 = ui.Label("5) 指数计算", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
panel.add(label21);
function changeSelect1B(value) {
selectTag1B = value;
var roi = getROI(selectCode);
var l8Col = landsat8Util.getLandsat8(
selectStartDate2,
selectEndDate2,
roi,
20
);
l8Col = l8Col.select(["B2", "B3", "B4", "B5", "B6", "B7"]).median().clip(roi);
if (value == "ndvi") {
var ndvi = landsat8Util.ndvi(l8Col).select("ndvi");
//设置图层显示属性
var visalg = generateVisalg(-1, 1);
Map.addLayer(ndvi, visalg, "NDVI");
var mean = ndvi.select("ndvi").reduceRegion(pie.Reducer.mean(), roi, 30);
print("ndvi均值：" + mean.get("ndvi").getInfo());
// 显示图例
addLegendToMap({
title: "NDVI",
labels: ["-1", "-0.5 ", " 0", "0.5", "1"],
step: 30,
});
} else if (value == "fvc") {
var fvc = landsat8Util.fvc(l8Col).select("fvc");
var visalg = generateVisalg(0, 1);
Map.addLayer(fvc, visalg, "fvc");
var mean = fvc.select("fvc").reduceRegion(pie.Reducer.mean(), roi, 30);
print("fvc均值：" + mean.get("fvc").getInfo());
// 显示图例
addLegendToMap({
title: "fvc",
labels: ["0", "0.2 ", " 0.4", "0.6", "0.8", "1"],
step: 30,
});
} else if (value == "rvi") {
var rvi = landsat8Util.rvi(l8Col).select("rvi");
var visalg = generateVisalg(0, 1);
Map.addLayer(rvi, visalg, "rvi");
var mean = rvi.select("rvi").reduceRegion(pie.Reducer.mean(), roi, 30);
print("rvi均值：" + mean.get("rvi").getInfo());
// 显示图例
addLegendToMap({
title: "rvi",
labels: ["0", "0.2 ", " 0.4", "0.6", "0.8", "1"],
step: 30,
});
} else if (value == "evi") {
var evi = landsat8Util.evi(l8Col).select("evi");
//设置图层显示属性
var visalg = generateVisalg(-1, 1);
Map.addLayer(evi, visalg, "evi");
var mean = evi.select("evi").reduceRegion(pie.Reducer.mean(), roi, 30);
print("evi均值：" + mean.get("evi").getInfo());
// 显示图例
addLegendToMap({
title: "evi",
labels: ["-1", "-0.5 ", " 0", "0.5", "1"],
step: 30,
});
} else if (value == "wet") {
var wet = landsat8Util.wet(l8Col).select("wet");
var visalg = generateVisalg(-1, 1);
Map.addLayer(wet, visalg, "wet_oli");
var mean = wet_oli.select("wet").reduceRegion(pie.Reducer.mean(), roi, 30);
print("wet均值：" + mean.get("wet").getInfo());
// 显示图例
addLegendToMap({
title: "wet",
labels: ["-1", "-0.5 ", " 0", "0.5", "1"],
step: 30,
});
} else if (value == "ndbsi") {
var ndbsi = landsat8Util.ndbsi(l8Col).select("ndbsi");
var visalg = generateVisalg(0, 0.5);
Map.addLayer(ndbsi, visalg, "ndbsi");
var mean = ndbsi.select("ndbsi").reduceRegion(pie.Reducer.mean(), roi, 30);
print("ndbsi均值：" + mean.get("ndbsi").getInfo());
// 显示图例
addLegendToMap({
title: "ndbsi",
labels: ["0", "0.1", " 0.2", "0.3", "0.4"],
step: 30,
});
} else if (value == "ndbi") {
var ndbi = landsat8Util.ndbi(l8Col).select("ndbi");
var visalg = generateVisalg(-0.5, 0.5);
var ndbi = b6.subtract(b5).divide(b6.add(b5)).rename("ndbi");
Map.addLayer(ndbi, visalg, "ndbi");
var mean = ndbi.select("ndbi").reduceRegion(pie.Reducer.mean(), roi, 30);
print("ndbi均值：" + mean.get("ndbi").getInfo());
// 显示图例
addLegendToMap({
title: "ndbi",
labels: ["-0.5", "-0.25", " 0", "0.25", "0.5"],
step: 30,
});
} else if (value == "lai") {
//加载Terra星全球500m叶面积指数/FPAR 8天合成产品(MOD15A2H V6)
var images = pie
.ImageCollection("USGS/MOD15A2H/006")
.filterDate(selectStartDate2, selectEndDate2);
//选择LAI波段,计算LAI均值
var lai = images
.select("Lai_500m")
.map(function (image) {
return image.updateMask(image.lt(200));
})
.mean()
.clip(roi)
.rename("lai");
//设置图层显示属性
var visalg = generateVisalg(0, 100);
Map.addLayer(lai, visalg, "lai");
var mean = lai.select("lai").reduceRegion(pie.Reducer.mean(), roi, 500);
print("lai均值：" + mean.get("lai").getInfo());
// 显示图例
addLegendToMap({
title: "lai",
labels: ["0", "30 ", " 60", "90"],
step: 30,
});
} else if (value == "fpar") {
//加载Terra星全球500m叶面积指数/FPAR 8天合成产品(MOD15A2H V6)
var images = pie
.ImageCollection("USGS/MOD15A2H/006")
.filterDate(selectStartDate2, selectEndDate2);
//选择FPAR波段,计算FPAR均值
var fpar = images.select("Fpar_500m").mean().clip(roi).rename("fpar");
//设置图层显示属性
var visalg = generateVisalg(0, 100);
Map.addLayer(fpar, visalg, "fpar");
var mean = fpar.select("fpar").reduceRegion(pie.Reducer.mean(), roi, 500);
print("fpar均值：" + mean.get("fpar").getInfo());
// 显示图例
addLegendToMap({
title: "fpar",
labels: ["0", "30 ", " 60", "90"],
step: 30,
});
} else if (value == "lst") {
var lst = MOD11A2.filterDate(selectStartDate2, selectEndDate2)
.filterBounds(roi)
.select("LST_Day_1km")
.mean()
.multiply(0.02)
.subtract(273.15)
.clip(roi)
.rename("lst");
//设置图层显示属性
var visalg = generateVisalg(0, 30);
Map.addLayer(lst, visalg, "lst");
var mean = lst.select("lst").reduceRegion(pie.Reducer.mean(), roi, 1000);
print("lst均值：" + mean.get("lst").getInfo());
// 显示图例
addLegendToMap({
title: "温度",
labels: ["0", "10 ", " 20", "30"],
step: 30,
});
} else if (value == "tvdi") {
var image = globalTVDI(selectStartDate2, selectEndDate2, roi);
//设置图层显示属性
var visalg = generateVisalg(0, 1);
Map.addLayer(image, visalg, "tvdi");
var mean = image.reduceRegion(pie.Reducer.mean(), roi, 1000);
print("tvdi均值：", mean.get("tvdi"));
// 显示图例
addLegendToMap({
title: "tdvi",
labels: ["0", "0.2 ", " 0.4", "0.6", "0.8", "1"],
step: 30,
});
} else if (value == "ndwi") {
var ndwi = landsat8Util.ndwi(l8Col).select("ndwi");
var visalg = generateVisalg(-0.5, 0.5);
Map.addLayer(ndwi, visalg, "ndwi");
// 显示图例
addLegendToMap({
title: "ndwi",
labels: ["-0.5", "-0.25 ", " 0", "0.25", "0.5"],
step: 30,
});
} else if (value == "mndwi") {
var mndwi = landsat8Util.mndwi(l8Col).select("mndwi");
var visalg = generateVisalg(-0.5, 0.5);
Map.addLayer(mndwi, visalg, "mndwi");
// 显示图例
addLegendToMap({
title: "mndwi",
labels: ["-0.5", "-0.25 ", " 0", "0.25", "0.5"],
step: 30,
});
//根据计算的水体数据计算面积
var areaImage = mndwi
.select("water")
.pixelArea()
.multiply(mndwi.select("water").gt(0.1));
var water = areaImage.reduceRegion(pie.Reducer.sum(), roi, 30);
print(water);
print(
"水体面积是（单位：平方千米）: ",
pie.Number(water.get("constant").getInfo()).divide(1000000)
); //进行单位换算
}
}
var selectTag1B = "ndvi"; // 设置初值
var select1B = ui.Select({
items: [
"ndvi",
"rvi",
"evi",
"fvc",
"lai",
"fpar",
"wet",
"lst",
"ndbsi",
"ndbi",
"tvdi",
"mndwi",
"ndwi",
],
placeholder: "请选择",
value: selectTag1B,
multiple: false,
onChange: changeSelect1B,
});
var selectName1B = ui.Label("选择指数：");
var selectPanel1B = ui.Panel({
widgets: [selectName1B, select1B],
layout: ui.Layout.flow("horizontal"),
});
panel.add(selectPanel1B);
function clickbtExport1() {
print(selectTag1B);
var roi = getROI(selectCode);
var l8Col = landsat8Util.getLandsat8(
selectStartDate2,
selectEndDate2,
roi,
20
);
l8Col = l8Col.select(["B2", "B3", "B4", "B5", "B6", "B7"]).median().clip(roi);
// tvdi
var tvdi = globalTVDI(selectStartDate2, selectEndDate2, roi);
// lst
var lst = MOD11A2.filterDate(selectStartDate2, selectEndDate2)
.filterBounds(roi)
.select("LST_Day_1km")
.mean()
.multiply(0.02)
.subtract(273.15)
.rename("lst")
.clip(roi);
// lai
//加载Terra星全球500m叶面积指数/FPAR 8天合成产品(MOD15A2H V6)
var images1 = pie
.ImageCollection("USGS/MOD15A2H/006")
.filterDate(selectStartDate2, selectEndDate2);
//选择LAI波段,计算LAI均值
var lai = images1
.select("Lai_500m")
.map(function (image) {
return image.updateMask(image.lt(200));
})
.mean()
.clip(roi)
.rename("lai");
// fpar
//选择FPAR波段,计算FPAR均值
var fpar = images1.select("Fpar_500m").mean().clip(roi).rename("fpar");
l8Col = landsat8Util.ndvi(l8Col);
l8Col = landsat8Util.wet(l8Col);
l8Col = landsat8Util.ndbsi(l8Col);
l8Col = landsat8Util.evi(l8Col);
l8Col = landsat8Util.mndwi(l8Col);
l8Col = landsat8Util.fvc(l8Col);
l8Col = landsat8Util.rvi(l8Col);
l8Col = landsat8Util.ndbi(l8Col);
l8Col = l8Col
.addBands(ndwi.rename("ndwi"))
.addBands(lst.rename("lst"))
.addBands(tvdi.rename("tvdi"))
.addBands(lai.rename("lai"))
.addBands(fpar.rename("fpar"));
//导出影像
Export.image({
image: l8Col.select(selectTag1B),
description: "ExportImage:" + selectTag1B,
assetId: "test",
region: roi,
scale: 30,
});
}
//导出数据
var btExport1 = ui.Button({
label: "导出数据",
type: "success",
onClick: clickbtExport1,
});
panel.add(btExport1);
// 回归分析
var label22 = ui.Label("6) 回归分析", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
panel.add(label22);
function changeCheckbox(value) {
var roi = getROI(selectCode);
print("开始回归分析");
// 裁剪
function clip(image) {
image = image.clip(roi);
return image;
}
var l8Col = landsat8Util.getLandsat8(
selectStartDate2,
selectEndDate2,
roi,
20
);
l8Col = l8Col
.select(["B2", "B3", "B4", "B5", "B6", "B7"])
.map(clip)
.map(landsat8Util.ndvi)
.map(landsat8Util.wet)
.map(landsat8Util.evi);
var visParam1 = {
min: [0, 0.5, 0],
max: [0.3, 2, 0.3],
opacity: 1,
bands: ["offset", "scale", "offset"],
};
var image1 = l8Col.select(["ndvi", "evi"]).reduce(pie.Reducer.linearFit()); // 回归算法
Map.addLayer(image1, visParam1, "linearFit");
print("结束回归分析");
}
var checkbox = ui.Checkbox({
label: ["ndvi", "evi", "wet"],
value: [],
disabled: [],
onChange: changeCheckbox,
});
panel.add(checkbox);
// 土地堵盖类型显示
var label23 = ui.Label("7) 土地覆盖类型显示", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
panel.add(label23);
function clickbtLandShow() {
var roi = getROI(selectCode);
//加载全球地表覆盖GlobeLand30数据
var sdate = new Date(selectStartDate2).getFullYear() + "-01-01"; // 获取起始日期前四位
var edate = new Date(selectEndDate2).getFullYear() + "-12-31"; // 获取结束日期前四位
var img = pie
.ImageCollection("NGCC/GLOBELAND30")
.filterBounds(roi)
.filterDate(sdate, edate)
.select("B1")
.mosaic()
.clip(roi);
//按不同地表覆盖类型值设定预览参数
var colors = [
"#00FF00",
"#00e900",
"#00dF00",
"#00ce00",
"#00ba00",
"#0002FF",
"#97FF00",
"#FF0000",
"#FFce00",
"#FFFFFF",
];
var visGL = {
uniqueValue: "10, 20, 30, 40, 50, 60, 70, 80, 90, 100",
palette: colors,
};
//加载显示影像
Map.addLayer(img, visGL, "img");
// 图例
addLegend({
title: "地表覆盖",
colors: colors,
labels: [
"耕地",
"林地",
"草地",
"灌木地",
"湿地",
"水体",
"苔原",
"人造地表",
"裸地",
"积雪",
],
step: 1,
});
}
var btLandShow = ui.Button({
label: "显示",
type: "success",
onClick: clickbtLandShow,
});
panel.add(btLandShow);
// 时间转换
var label24 = ui.Label("8) 时间转换", {
"font-size": "16px",
"font-weight": "bold",
color: "red",
});
panel.add(label24);
var selectStartDateAAA = "2021-06-30";
function inputSDateAAA(value) {
selectStartDateAAA = value;
}
var textBox1AAA = ui.DateSelect({
type: "date",
placeholder: "请输入数值",
value: selectStartDateAAA, //输入显示框显示数值
onChange: inputSDateAAA,
disabled: false,
});
var textboxName1AAA = ui.Label("输入转换日期：");
var textboxPanel1AAA = ui.Panel({
widgets: [textboxName1AAA, textBox1AAA],
layout: ui.Layout.flow("horizontal"),
});
panel.add(textboxPanel1AAA);
// 转换天数,输入数据为pieNumber类型
function convertYearToDay(dateStr) {
var now = new Date(dateStr);
var start = new Date(now.getFullYear(), 0, 0);
var diff = (now - start) + ((start.getTimezoneOffset() - now.getTimezoneOffset()) * 60 * 1000);
var oneDay = 1000 * 60 * 60 * 24;
var day = Math.floor(diff / oneDay);
return day;
}
function clickbtCon() {
var dateNum = convertYearToDay(selectStartDateAAA); //转换为1年中的某一天
print("转换后的天数：" + dateNum);
}
var btCon = ui.Button({
label: "转换",
type: "success",
onClick: clickbtCon,
});
panel.add(btCon);
ui.root.add(panel);- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18.
- 19.
- 20.
- 21.
- 22.
- 23.
- 24.
- 25.
- 26.
- 27.
- 28.
- 29.
- 30.
- 31.
- 32.
- 33.
- 34.
- 35.
- 36.
- 37.
- 38.
- 39.
- 40.
- 41.
- 42.
- 43.
- 44.
- 45.
- 46.
- 47.
- 48.
- 49.
- 50.
- 51.
- 52.
- 53.
- 54.
- 55.
- 56.
- 57.
- 58.
- 59.
- 60.
- 61.
- 62.
- 63.
- 64.
- 65.
- 66.
- 67.
- 68.
- 69.
- 70.
- 71.
- 72.
- 73.
- 74.
- 75.
- 76.
- 77.
- 78.
- 79.
- 80.
- 81.
- 82.
- 83.
- 84.
- 85.
- 86.
- 87.
- 88.
- 89.
- 90.
- 91.
- 92.
- 93.
- 94.
- 95.
- 96.
- 97.
- 98.
- 99.
- 100.
- 101.
- 102.
- 103.
- 104.
- 105.
- 106.
- 107.
- 108.
- 109.
- 110.
- 111.
- 112.
- 113.
- 114.
- 115.
- 116.
- 117.
- 118.
- 119.
- 120.
- 121.
- 122.
- 123.
- 124.
- 125.
- 126.
- 127.
- 128.
- 129.
- 130.
- 131.
- 132.
- 133.
- 134.
- 135.
- 136.
- 137.
- 138.
- 139.
- 140.
- 141.
- 142.
- 143.
- 144.
- 145.
- 146.
- 147.
- 148.
- 149.
- 150.
- 151.
- 152.
- 153.
- 154.
- 155.
- 156.
- 157.
- 158.
- 159.
- 160.
- 161.
- 162.
- 163.
- 164.
- 165.
- 166.
- 167.
- 168.
- 169.
- 170.
- 171.
- 172.
- 173.
- 174.
- 175.
- 176.
- 177.
- 178.
- 179.
- 180.
- 181.
- 182.
- 183.
- 184.
- 185.
- 186.
- 187.
- 188.
- 189.
- 190.
- 191.
- 192.
- 193.
- 194.
- 195.
- 196.
- 197.
- 198.
- 199.
- 200.
- 201.
- 202.
- 203.
- 204.
- 205.
- 206.
- 207.
- 208.
- 209.
- 210.
- 211.
- 212.
- 213.
- 214.
- 215.
- 216.
- 217.
- 218.
- 219.
- 220.
- 221.
- 222.
- 223.
- 224.
- 225.
- 226.
- 227.
- 228.
- 229.
- 230.
- 231.
- 232.
- 233.
- 234.
- 235.
- 236.
- 237.
- 238.
- 239.
- 240.
- 241.
- 242.
- 243.
- 244.
- 245.
- 246.
- 247.
- 248.
- 249.
- 250.
- 251.
- 252.
- 253.
- 254.
- 255.
- 256.
- 257.
- 258.
- 259.
- 260.
- 261.
- 262.
- 263.
- 264.
- 265.
- 266.
- 267.
- 268.
- 269.
- 270.
- 271.
- 272.
- 273.
- 274.
- 275.
- 276.
- 277.
- 278.
- 279.
- 280.
- 281.
- 282.
- 283.
- 284.
- 285.
- 286.
- 287.
- 288.
- 289.
- 290.
- 291.
- 292.
- 293.
- 294.
- 295.
- 296.
- 297.
- 298.
- 299.
- 300.
- 301.
- 302.
- 303.
- 304.
- 305.
- 306.
- 307.
- 308.
- 309.
- 310.
- 311.
- 312.
- 313.
- 314.
- 315.
- 316.
- 317.
- 318.
- 319.
- 320.
- 321.
- 322.
- 323.
- 324.
- 325.
- 326.
- 327.
- 328.
- 329.
- 330.
- 331.
- 332.
- 333.
- 334.
- 335.
- 336.
- 337.
- 338.
- 339.
- 340.
- 341.
- 342.
- 343.
- 344.
- 345.
- 346.
- 347.
- 348.
- 349.
- 350.
- 351.
- 352.
- 353.
- 354.
- 355.
- 356.
- 357.
- 358.
- 359.
- 360.
- 361.
- 362.
- 363.
- 364.
- 365.
- 366.
- 367.
- 368.
- 369.
- 370.
- 371.
- 372.
- 373.
- 374.
- 375.
- 376.
- 377.
- 378.
- 379.
- 380.
- 381.
- 382.
- 383.
- 384.
- 385.
- 386.
- 387.
- 388.
- 389.
- 390.
- 391.
- 392.
- 393.
- 394.
- 395.
- 396.
- 397.
- 398.
- 399.
- 400.
- 401.
- 402.
- 403.
- 404.
- 405.
- 406.
- 407.
- 408.
- 409.
- 410.
- 411.
- 412.
- 413.
- 414.
- 415.
- 416.
- 417.
- 418.
- 419.
- 420.
- 421.
- 422.
- 423.
- 424.
- 425.
- 426.
- 427.
- 428.
- 429.
- 430.
- 431.
- 432.
- 433.
- 434.
- 435.
- 436.
- 437.
- 438.
- 439.
- 440.
- 441.
- 442.
- 443.
- 444.
- 445.
- 446.
- 447.
- 448.
- 449.
- 450.
- 451.
- 452.
- 453.
- 454.
- 455.
- 456.
- 457.
- 458.
- 459.
- 460.
- 461.
- 462.
- 463.
- 464.
- 465.
- 466.
- 467.
- 468.
- 469.
- 470.
- 471.
- 472.
- 473.
- 474.
- 475.
- 476.
- 477.
- 478.
- 479.
- 480.
- 481.
- 482.
- 483.
- 484.
- 485.
- 486.
- 487.
- 488.
- 489.
- 490.
- 491.
- 492.
- 493.
- 494.
- 495.
- 496.
- 497.
- 498.
- 499.
- 500.
- 501.
- 502.
- 503.
- 504.
- 505.
- 506.
- 507.
- 508.
- 509.
- 510.
- 511.
- 512.
- 513.
- 514.
- 515.
- 516.
- 517.
- 518.
- 519.
- 520.
- 521.
- 522.
- 523.
- 524.
- 525.
- 526.
- 527.
- 528.
- 529.
- 530.
- 531.
- 532.
- 533.
- 534.
- 535.
- 536.
- 537.
- 538.
- 539.
- 540.
- 541.
- 542.
- 543.
- 544.
- 545.
- 546.
- 547.
- 548.
- 549.
- 550.
- 551.
- 552.
- 553.
- 554.
- 555.
- 556.
- 557.
- 558.
- 559.
- 560.
- 561.
- 562.
- 563.
- 564.
- 565.
- 566.
- 567.
- 568.
- 569.
- 570.
- 571.
- 572.
- 573.
- 574.
- 575.
- 576.
- 577.
- 578.
- 579.
- 580.
- 581.
- 582.
- 583.
- 584.
- 585.
- 586.
- 587.
- 588.
- 589.
- 590.
- 591.
- 592.
- 593.
- 594.
- 595.
- 596.
- 597.
- 598.
- 599.
- 600.
- 601.
- 602.
- 603.
- 604.
- 605.
- 606.
- 607.
- 608.
- 609.
- 610.
- 611.
- 612.
- 613.
- 614.
- 615.
- 616.
- 617.
- 618.
- 619.
- 620.
- 621.
- 622.
- 623.
- 624.
- 625.
- 626.
- 627.
- 628.
- 629.
- 630.
- 631.
- 632.
- 633.
- 634.
- 635.
- 636.
- 637.
- 638.
- 639.
- 640.
- 641.
- 642.
- 643.
- 644.
- 645.
- 646.
- 647.
- 648.
- 649.
- 650.
- 651.
- 652.
- 653.
- 654.
- 655.
- 656.
- 657.
- 658.
- 659.
- 660.
- 661.
- 662.
- 663.
- 664.
- 665.
- 666.
- 667.
- 668.
- 669.
- 670.
- 671.
- 672.
- 673.
- 674.
- 675.
- 676.
- 677.
- 678.
- 679.
- 680.
- 681.
- 682.
- 683.
- 684.
- 685.
- 686.
- 687.
- 688.
- 689.
- 690.
- 691.
- 692.
- 693.
- 694.
- 695.
- 696.
- 697.
- 698.
- 699.
- 700.
- 701.
- 702.
- 703.
- 704.
- 705.
- 706.
- 707.
- 708.
- 709.
- 710.
- 711.
- 712.
- 713.
- 714.
- 715.
- 716.
- 717.
- 718.
- 719.
- 720.
- 721.
- 722.
- 723.
- 724.
- 725.
- 726.
- 727.
- 728.
- 729.
- 730.
- 731.
- 732.
- 733.
- 734.
- 735.
- 736.
- 737.
- 738.
- 739.
- 740.
- 741.
- 742.
- 743.
- 744.
- 745.
- 746.
- 747.
- 748.
- 749.
- 750.
- 751.
- 752.
- 753.
- 754.
- 755.
- 756.
- 757.
- 758.
- 759.
- 760.
- 761.
- 762.
- 763.
- 764.
- 765.
- 766.
- 767.
- 768.
- 769.
- 770.
- 771.
- 772.
- 773.
- 774.
- 775.
- 776.
- 777.
- 778.
- 779.
- 780.
- 781.
- 782.
- 783.
- 784.
- 785.
- 786.
- 787.
- 788.
- 789.
- 790.
- 791.
- 792.
- 793.
- 794.
- 795.
- 796.
- 797.
- 798.
- 799.
- 800.
- 801.
- 802.
- 803.
- 804.
- 805.
- 806.
- 807.
- 808.
- 809.
- 810.
- 811.
- 812.
- 813.
- 814.
- 815.
- 816.
- 817.
- 818.
- 819.
- 820.
- 821.
- 822.
- 823.
- 824.
- 825.
- 826.
- 827.
- 828.
- 829.
- 830.
- 831.
- 832.
- 833.
- 834.
- 835.
- 836.
- 837.
- 838.
- 839.
- 840.
- 841.
- 842.
- 843.
- 844.
- 845.
- 846.
- 847.
- 848.
- 849.
- 850.
- 851.
- 852.
- 853.
- 854.
- 855.
- 856.
- 857.
- 858.
- 859.
- 860.
- 861.
- 862.
- 863.
- 864.
- 865.
- 866.
- 867.
- 868.
- 869.
- 870.
- 871.
- 872.
- 873.
- 874.
- 875.
- 876.
- 877.
- 878.
- 879.
- 880.
- 881.
- 882.
- 883.
- 884.
- 885.
- 886.
- 887.
- 888.
- 889.
- 890.
- 891.
- 892.
- 893.
- 894.
- 895.
- 896.
- 897.
- 898.
- 899.
- 900.
- 901.
- 902.
- 903.
- 904.
- 905.
- 906.
- 907.
- 908.
- 909.
- 910.
- 911.
- 912.
- 913.
- 914.
- 915.
- 916.
- 917.
- 918.
- 919.
- 920.
- 921.
- 922.
- 923.
- 924.
- 925.
- 926.
- 927.
- 928.
- 929.
- 930.
- 931.
- 932.
- 933.
- 934.
- 935.
- 936.
- 937.
- 938.
- 939.
- 940.
- 941.
- 942.
- 943.
- 944.
- 945.
- 946.
- 947.
- 948.
- 949.
- 950.
- 951.
- 952.
- 953.
- 954.
- 955.
- 956.
- 957.
- 958.
- 959.
- 960.
- 961.
- 962.
- 963.
- 964.
- 965.
- 966.
- 967.
- 968.
- 969.
- 970.
- 971.
- 972.
- 973.
- 974.
- 975.
- 976.
- 977.
- 978.
- 979.
- 980.
- 981.
- 982.
- 983.
- 984.
- 985.
- 986.
- 987.
- 988.
- 989.
- 990.
- 991.
- 992.
- 993.
- 994.
- 995.
- 996.
- 997.
- 998.
- 999.
- 1000.
- 1001.
- 1002.
- 1003.
- 1004.
- 1005.
- 1006.
- 1007.
- 1008.
- 1009.
- 1010.
- 1011.
- 1012.
- 1013.
- 1014.
- 1015.
- 1016.
- 1017.
- 1018.
- 1019.
- 1020.
- 1021.
- 1022.
- 1023.
- 1024.
- 1025.
- 1026.
- 1027.
- 1028.
- 1029.
- 1030.
- 1031.
- 1032.
- 1033.
- 1034.
- 1035.
- 1036.
- 1037.
- 1038.
- 1039.
- 1040.
- 1041.
- 1042.
- 1043.
- 1044.
- 1045.
- 1046.
- 1047.
- 1048.
- 1049.
- 1050.
- 1051.
- 1052.
- 1053.
- 1054.
- 1055.
- 1056.
- 1057.
- 1058.
- 1059.
- 1060.
- 1061.
- 1062.
- 1063.
- 1064.
- 1065.
- 1066.
- 1067.
- 1068.
- 1069.
- 1070.
- 1071.
- 1072.
- 1073.
- 1074.
- 1075.
- 1076.
- 1077.
- 1078.
- 1079.
- 1080.
- 1081.
- 1082.
- 1083.
- 1084.
- 1085.
- 1086.
- 1087.
- 1088.
- 1089.
- 1090.
- 1091.
- 1092.
- 1093.
- 1094.
- 1095.
- 1096.
- 1097.
- 1098.
- 1099.
- 1100.
- 1101.
- 1102.
- 1103.
- 1104.
- 1105.
- 1106.
- 1107.
- 1108.
- 1109.
- 1110.
- 1111.
- 1112.
- 1113.
- 1114.
- 1115.
- 1116.
- 1117.
- 1118.
- 1119.
- 1120.
- 1121.
- 1122.
- 1123.
- 1124.
- 1125.
- 1126.
- 1127.
- 1128.
- 1129.
- 1130.
- 1131.
- 1132.
- 1133.
- 1134.
- 1135.
- 1136.
- 1137.
- 1138.
- 1139.
- 1140.
- 1141.
- 1142.
- 1143.
- 1144.
- 1145.
- 1146.
- 1147.
- 1148.
- 1149.
- 1150.
- 1151.
- 1152.
- 1153.
- 1154.
- 1155.
- 1156.
- 1157.
- 1158.
- 1159.
- 1160.
- 1161.
- 1162.
- 1163.
- 1164.
- 1165.
- 1166.
- 1167.
- 1168.
- 1169.
- 1170.
- 1171.
- 1172.
- 1173.
- 1174.
- 1175.
- 1176.
- 1177.
- 1178.
- 1179.
- 1180.
- 1181.
- 1182.
- 1183.
- 1184.
- 1185.
- 1186.
- 1187.
- 1188.
- 1189.
- 1190.
- 1191.
- 1192.
- 1193.
- 1194.
- 1195.
- 1196.
- 1197.
- 1198.
- 1199.
- 1200.
- 1201.
- 1202.
- 1203.
- 1204.
- 1205.
- 1206.
- 1207.
- 1208.
- 1209.
- 1210.
- 1211.
- 1212.
- 1213.
- 1214.
- 1215.
- 1216.
- 1217.
- 1218.
- 1219.
- 1220.
- 1221.
- 1222.
- 1223.
- 1224.
- 1225.
- 1226.
- 1227.
- 1228.
- 1229.
- 1230.
- 1231.
- 1232.
- 1233.
- 1234.
- 1235.
- 1236.
- 1237.
- 1238.
- 1239.
- 1240.
- 1241.
- 1242.
- 1243.
- 1244.
- 1245.
- 1246.
- 1247.
- 1248.
- 1249.
- 1250.
- 1251.
- 1252.
- 1253.
- 1254.
- 1255.
- 1256.
- 1257.
- 1258.
- 1259.
- 1260.
- 1261.
- 1262.
- 1263.
- 1264.
- 1265.
- 1266.
- 1267.
- 1268.
- 1269.
- 1270.
- 1271.
- 1272.
- 1273.
- 1274.
- 1275.
- 1276.
- 1277.
- 1278.
- 1279.
- 1280.
- 1281.
- 1282.
- 1283.
- 1284.
- 1285.
- 1286.
- 1287.
- 1288.
- 1289.
- 1290.
- 1291.
- 1292.
- 1293.
- 1294.
- 1295.
- 1296.
- 1297.
- 1298.
- 1299.
- 1300.
- 1301.
- 1302.
- 1303.
- 1304.
- 1305.
- 1306.
- 1307.
- 1308.
- 1309.
- 1310.
- 1311.
- 1312.
- 1313.
- 1314.
- 1315.
- 1316.
- 1317.
- 1318.
- 1319.
- 1320.
- 1321.
- 1322.
- 1323.
- 1324.
- 1325.
- 1326.
- 1327.
- 1328.
- 1329.
- 1330.
- 1331.
- 1332.
- 1333.
- 1334.
- 1335.
- 1336.
- 1337.
- 1338.
- 1339.
- 1340.
- 1341.
- 1342.
- 1343.
- 1344.
- 1345.
- 1346.
- 1347.
- 1348.
- 1349.
- 1350.
- 1351.
- 1352.
- 1353.
- 1354.
- 1355.
- 1356.
- 1357.
- 1358.
- 1359.
- 1360.
- 1361.
- 1362.
- 1363.
- 1364.
- 1365.
- 1366.
- 1367.
- 1368.
- 1369.
- 1370.
- 1371.
- 1372.
- 1373.
- 1374.
- 1375.
- 1376.
- 1377.
- 1378.
- 1379.
- 1380.
- 1381.
- 1382.
- 1383.
- 1384.
- 1385.
- 1386.
- 1387.
- 1388.
- 1389.
- 1390.
- 1391.
- 1392.
- 1393.
- 1394.
- 1395.
- 1396.
- 1397.
- 1398.
- 1399.
- 1400.
- 1401.
- 1402.
- 1403.
- 1404.
- 1405.
- 1406.
- 1407.
- 1408.
- 1409.
- 1410.
- 1411.
- 1412.
- 1413.
- 1414.
- 1415.
- 1416.
- 1417.
- 1418.
- 1419.
- 1420.
- 1421.
- 1422.
- 1423.
- 1424.
- 1425.
- 1426.
- 1427.
- 1428.
- 1429.
- 1430.
- 1431.
- 1432.
- 1433.
- 1434.
- 1435.
- 1436.
- 1437.
- 1438.
- 1439.
- 1440.
- 1441.
- 1442.
- 1443.
- 1444.
- 1445.
- 1446.
- 1447.
- 1448.
- 1449.
- 1450.
- 1451.
- 1452.
- 1453.
- 1454.
- 1455.
- 1456.
- 1457.
- 1458.
- 1459.
- 1460.
- 1461.
- 1462.
- 1463.
- 1464.
- 1465.
- 1466.
- 1467.
- 1468.
- 1469.
- 1470.
- 1471.
- 1472.
- 1473.
- 1474.
- 1475.
- 1476.
- 1477.
- 1478.
- 1479.
- 1480.
- 1481.
- 1482.
- 1483.
- 1484.
- 1485.
- 1486.
- 1487.
- 1488.
- 1489.
- 1490.
- 1491.
- 1492.
- 1493.
- 1494.
- 1495.
- 1496.
- 1497.
- 1498.
- 1499.
- 1500.
- 1501.
- 1502.
- 1503.
- 1504.
- 1505.
- 1506.
- 1507.
- 1508.
- 1509.
- 1510.
- 1511.
- 1512.
- 1513.
- 1514.
- 1515.
- 1516.
- 1517.
- 1518.
- 1519.
- 1520.
- 1521.
- 1522.
- 1523.
- 1524.
- 1525.
- 1526.
- 1527.
- 1528.
- 1529.
- 1530.
- 1531.
- 1532.
- 1533.
- 1534.
- 1535.
- 1536.
- 1537.
- 1538.
- 1539.
- 1540.
- 1541.
- 1542.
- 1543.
- 1544.
- 1545.
- 1546.
- 1547.
- 1548.
- 1549.
- 1550.
- 1551.
- 1552.
- 1553.
- 1554.
- 1555.
- 1556.
- 1557.
- 1558.
- 1559.
- 1560.
- 1561.
- 1562.
- 1563.
- 1564.
- 1565.
- 1566.
- 1567.
- 1568.
- 1569.
- 1570.
- 1571.
- 1572.
- 1573.
- 1574.
- 1575.
- 1576.
- 1577.
- 1578.
- 1579.
- 1580.
- 1581.
- 1582.
- 1583.
- 1584.
- 1585.
- 1586.
- 1587.
- 1588.
- 1589.
- 1590.
- 1591.
- 1592.
- 1593.
- 1594.
- 1595.
- 1596.
- 1597.
- 1598.
- 1599.
- 1600.
- 1601.
- 1602.
- 1603.
- 1604.
- 1605.
- 1606.
- 1607.
- 1608.
- 1609.
- 1610.
- 1611.
- 1612.
- 1613.
- 1614.
- 1615.
- 1616.
- 1617.
- 1618.
- 1619.
- 1620.
- 1621.
- 1622.
- 1623.
- 1624.
- 1625.
- 1626.
- 1627.
- 1628.
- 1629.
- 1630.
- 1631.
- 1632.
- 1633.
- 1634.
- 1635.
- 1636.
- 1637.
- 1638.
- 1639.
- 1640.
- 1641.
- 1642.
- 1643.
- 1644.
- 1645.
- 1646.
- 1647.
- 1648.
- 1649.
- 1650.
- 1651.
- 1652.
- 1653.
- 1654.
- 1655.
- 1656.
- 1657.
- 1658.
- 1659.
- 1660.
- 1661.
- 1662.
- 1663.
- 1664.
- 1665.
- 1666.
- 1667.
- 1668.
- 1669.
- 1670.
- 1671.
- 1672.
- 1673.
- 1674.
- 1675.
- 1676.
- 1677.
- 1678.
- 1679.
- 1680.
- 1681.
- 1682.
- 1683.
- 1684.
- 1685.
- 1686.
- 1687.
- 1688.
- 1689.
- 1690.
- 1691.
- 1692.
- 1693.
- 1694.
- 1695.
- 1696.
- 1697.
- 1698.
- 1699.
- 1700.
- 1701.
- 1702.
- 1703.
- 1704.
- 1705.
- 1706.
- 1707.
- 1708.
- 1709.
- 1710.
- 1711.
- 1712.
- 1713.
- 1714.
- 1715.
- 1716.
- 1717.
- 1718.
- 1719.
- 1720.
- 1721.
- 1722.
- 1723.
- 1724.
- 1725.
- 1726.
- 1727.
- 1728.
- 1729.
- 1730.
- 1731.
- 1732.
- 1733.
- 1734.
- 1735.
- 1736.
- 1737.
- 1738.
- 1739.
- 1740.
- 1741.
- 1742.
- 1743.
- 1744.
- 1745.
- 1746.
- 1747.
- 1748.
- 1749.
- 1750.
- 1751.
- 1752.
- 1753.
- 1754.
- 1755.
- 1756.
- 1757.
- 1758.
- 1759.
- 1760.
- 1761.
- 1762.
- 1763.
- 1764.
- 1765.
- 1766.
- 1767.
- 1768.
- 1769.
- 1770.
- 1771.
- 1772.
- 1773.
- 1774.
- 1775.
- 1776.
- 1777.
- 1778.
- 1779.
- 1780.
- 1781.
- 1782.
- 1783.
- 1784.
- 1785.
- 1786.
- 1787.
- 1788.
- 1789.
- 1790.
- 1791.
- 1792.
- 1793.
- 1794.
- 1795.
- 1796.
- 1797.
- 1798.
- 1799.
- 1800.
- 1801.
- 1802.
- 1803.
- 1804.
- 1805.
- 1806.
- 1807.
- 1808.
- 1809.
- 1810.
- 1811.
- 1812.
- 1813.
- 1814.
- 1815.
- 1816.
- 1817.
- 1818.
- 1819.
- 1820.
- 1821.
- 1822.
- 1823.
- 1824.
- 1825.
- 1826.
- 1827.
- 1828.
- 1829.
- 1830.
- 1831.
- 1832.
- 1833.
- 1834.
- 1835.
- 1836.
- 1837.
- 1838.
- 1839.
- 1840.
- 1841.
- 1842.
- 1843.
- 1844.
- 1845.
- 1846.
- 1847.
- 1848.
- 1849.
- 1850.
- 1851.
- 1852.
- 1853.
- 1854.
- 1855.
- 1856.
- 1857.
- 1858.
- 1859.
- 1860.
- 1861.
- 1862.
- 1863.
- 1864.
- 1865.
- 1866.
- 1867.
- 1868.
- 1869.
- 1870.
- 1871.
- 1872.
- 1873.
- 1874.
- 1875.
- 1876.
- 1877.
- 1878.
- 1879.
- 1880.
- 1881.
- 1882.
- 1883.
- 1884.
- 1885.
- 1886.
- 1887.
- 1888.
- 1889.
- 1890.
- 1891.
- 1892.
- 1893.
- 1894.
- 1895.
- 1896.
- 1897.
- 1898.
- 1899.
- 1900.
- 1901.
- 1902.
- 1903.
- 1904.
- 1905.
- 1906.
- 1907.
- 1908.
- 1909.
- 1910.
- 1911.
- 1912.
- 1913.
- 1914.
- 1915.
- 1916.
- 1917.
- 1918.
- 1919.
- 1920.
- 1921.
- 1922.
- 1923.
- 1924.
- 1925.
- 1926.
- 1927.
- 1928.
- 1929.
- 1930.
- 1931.
- 1932.
- 1933.
- 1934.
- 1935.
- 1936.
- 1937.
- 1938.
- 1939.
- 1940.
- 1941.
- 1942.
- 1943.
- 1944.
- 1945.
- 1946.
- 1947.
- 1948.
- 1949.
- 1950.
- 1951.
- 1952.
- 1953.
- 1954.
- 1955.
- 1956.
- 1957.
- 1958.
- 1959.
- 1960.
- 1961.
- 1962.
- 1963.
- 1964.
- 1965.
- 1966.
- 1967.
- 1968.
- 1969.
- 1970.
- 1971.
- 1972.
- 1973.
- 1974.
- 1975.
- 1976.
- 1977.
- 1978.
- 1979.
- 1980.
- 1981.
- 1982.
- 1983.
- 1984.
- 1985.
- 1986.
- 1987.
- 1988.
- 1989.
- 1990.
- 1991.
- 1992.
- 1993.
- 1994.
- 1995.
- 1996.
- 1997.
- 1998.
- 1999.
- 2000.
- 2001.
- 2002.
- 2003.
- 2004.
- 2005.
- 2006.
- 2007.
- 2008.
- 2009.
- 2010.
- 2011.
- 2012.
- 2013.
- 2014.
- 2015.
- 2016.
- 2017.
- 2018.
- 2019.
- 2020.
- 2021.
- 2022.
- 2023.
- 2024.
- 2025.
- 2026.
- 2027.
- 2028.
- 2029.
- 2030.
- 2031.
- 2032.
- 2033.
- 2034.
- 2035.
- 2036.
- 2037.
- 2038.
- 2039.
- 2040.
- 2041.
- 2042.
- 2043.
- 2044.
- 2045.
- 2046.
- 2047.
- 2048.
- 2049.
- 2050.
- 2051.
- 2052.
- 2053.
- 2054.
- 2055.
- 2056.
- 2057.
- 2058.
- 2059.
- 2060.
- 2061.
- 2062.
- 2063.
- 2064.
- 2065.
- 2066.
- 2067.
- 2068.
- 2069.
- 2070.
- 2071.
- 2072.
- 2073.
- 2074.
- 2075.
- 2076.
- 2077.
- 2078.
- 2079.
- 2080.
- 2081.
- 2082.
- 2083.
- 2084.
- 2085.
- 2086.
- 2087.
- 2088.
- 2089.
- 2090.
- 2091.
- 2092.
- 2093.
- 2094.
- 2095.
- 2096.
- 2097.
- 2098.
- 2099.
- 2100.
- 2101.
- 2102.
- 2103.
- 2104.
- 2105.
- 2106.
- 2107.
- 2108.
- 2109.
- 2110.
- 2111.
- 2112.
- 2113.
- 2114.
- 2115.
- 2116.
- 2117.
- 2118.
- 2119.
- 2120.
- 2121.
- 2122.
- 2123.
- 2124.
- 2125.
- 2126.
- 2127.
- 2128.
- 2129.
- 2130.
- 2131.
- 2132.
- 2133.
- 2134.
- 2135.
- 2136.
- 2137.
- 2138.
- 2139.
- 2140.
- 2141.
- 2142.
- 2143.
- 2144.
- 2145.
- 2146.
- 2147.
- 2148.
- 2149.
- 2150.
- 2151.
- 2152.
- 2153.
- 2154.
- 2155.
- 2156.
- 2157.
- 2158.
- 2159.
- 2160.
- 2161.
- 2162.
- 2163.
- 2164.
- 2165.
- 2166.
- 2167.
- 2168.
- 2169.
- 2170.
- 2171.
- 2172.
- 2173.
- 2174.
- 2175.
- 2176.
- 2177.
- 2178.
- 2179.
- 2180.
- 2181.
- 2182.
- 2183.
- 2184.
- 2185.
- 2186.
- 2187.
- 2188.
- 2189.
- 2190.
- 2191.
- 2192.
- 2193.
- 2194.
- 2195.
- 2196.
- 2197.
- 2198.
- 2199.
- 2200.
- 2201.
- 2202.
- 2203.
- 2204.
- 2205.
- 2206.
- 2207.
- 2208.
- 2209.
- 2210.
- 2211.
- 2212.
- 2213.
- 2214.
- 2215.
- 2216.
- 2217.
- 2218.
- 2219.
- 2220.
- 2221.
- 2222.
- 2223.
- 2224.
- 2225.
- 2226.
- 2227.
- 2228.
- 2229.
- 2230.
- 2231.
- 2232.
- 2233.
- 2234.
- 2235.
- 2236.
- 2237.
- 2238.
- 2239.
- 2240.
- 2241.
- 2242.
- 2243.
- 2244.
- 2245.
- 2246.
- 2247.
- 2248.
- 2249.
- 2250.
- 2251.
- 2252.
- 2253.
- 2254.
- 2255.
- 2256.
- 2257.
- 2258.
- 2259.
- 2260.
- 2261.
- 2262.
- 2263.
- 2264.
- 2265.
- 2266.
- 2267.
- 2268.
- 2269.
- 2270.
- 2271.
- 2272.
- 2273.
- 2274.
- 2275.
- 2276.
- 2277.
- 2278.
- 2279.
- 2280.
- 2281.
- 2282.
- 2283.
- 2284.
- 2285.
- 2286.
- 2287.
- 2288.
- 2289.
- 2290.
- 2291.
- 2292.
- 2293.
- 2294.
- 2295.
- 2296.
- 2297.
- 2298.
- 2299.
- 2300.
- 2301.
- 2302.
- 2303.
- 2304.
- 2305.
- 2306.
- 2307.
- 2308.
- 2309.
- 2310.
- 2311.
- 2312.
- 2313.
- 2314.
- 2315.
- 2316.
- 2317.
- 2318.
- 2319.
- 2320.
- 2321.
- 2322.
- 2323.
- 2324.
- 2325.
- 2326.
- 2327.
- 2328.
- 2329.
- 2330.
- 2331.
- 2332.
- 2333.
- 2334.
- 2335.
- 2336.
- 2337.
- 2338.
- 2339.
- 2340.
- 2341.
- 2342.
- 2343.
- 2344.
- 2345.
- 2346.
- 2347.
- 2348.
- 2349.
- 2350.
- 2351.
- 2352.
- 2353.
- 2354.
- 2355.
- 2356.
- 2357.
- 2358.
- 2359.
- 2360.
- 2361.
- 2362.
- 2363.
- 2364.
- 2365.
- 2366.
- 2367.
- 2368.
- 2369.
- 2370.
- 2371.
- 2372.
- 2373.
- 2374.
- 2375.
- 2376.
- 2377.
- 2378.
- 2379.
- 2380.
- 2381.
- 2382.
- 2383.
- 2384.
- 2385.
- 2386.
- 2387.
- 2388.
- 2389.
- 2390.
- 2391.
- 2392.
- 2393.
- 2394.
- 2395.
- 2396.
- 2397.
- 2398.
- 2399.
- 2400.
- 2401.
- 2402.
- 2403.
- 2404.
- 2405.
- 2406.
- 2407.
- 2408.
- 2409.
- 2410.
- 2411.
- 2412.
- 2413.
- 2414.
- 2415.
- 2416.
- 2417.
- 2418.
- 2419.
- 2420.
- 2421.
- 2422.
- 2423.
- 2424.
- 2425.
- 2426.
- 2427.
- 2428.
- 2429.
- 2430.
- 2431.
- 2432.
- 2433.
- 2434.
- 2435.
- 2436.
- 2437.
- 2438.
- 2439.
- 2440.
- 2441.
- 2442.
- 2443.
- 2444.
- 2445.
- 2446.
- 2447.
- 2448.
- 2449.
- 2450.
- 2451.
- 2452.
- 2453.
- 2454.
- 2455.
- 2456.
- 2457.
- 2458.
- 2459.
- 2460.
- 2461.
- 2462.
- 2463.
- 2464.
- 2465.
- 2466.
- 2467.
- 2468.
- 2469.
- 2470.
- 2471.
- 2472.
- 2473.
- 2474.
- 2475.
- 2476.
- 2477.
- 2478.
- 2479.
- 2480.
- 2481.
- 2482.
- 2483.
- 2484.
- 2485.
- 2486.
- 2487.
- 2488.
- 2489.
- 2490.
- 2491.
- 2492.
- 2493.
- 2494.
- 2495.
- 2496.
- 2497.
- 2498.
- 2499.
- 2500.
- 2501.
- 2502.
- 2503.
- 2504.
- 2505.
- 2506.
- 2507.
- 2508.
- 2509.
- 2510.
- 2511.
- 2512.
- 2513.
- 2514.
- 2515.
- 2516.
- 2517.
- 2518.
- 2519.
- 2520.
- 2521.
- 2522.
- 2523.
- 2524.
- 2525.
- 2526.
- 2527.
- 2528.
- 2529.
- 2530.
- 2531.
- 2532.
- 2533.
- 2534.
- 2535.
- 2536.
- 2537.
- 2538.
- 2539.
- 2540.
- 2541.
- 2542.
- 2543.
- 2544.
- 2545.
- 2546.
- 2547.
- 2548.
- 2549.
- 2550.
- 2551.
- 2552.
- 2553.
- 2554.
- 2555.
- 2556.
- 2557.
- 2558.
- 2559.
- 2560.
- 2561.
- 2562.
- 2563.
- 2564.
- 2565.
- 2566.
- 2567.
- 2568.
- 2569.
- 2570.
- 2571.
- 2572.
- 2573.
- 2574.
- 2575.
- 2576.
- 2577.
- 2578.
- 2579.
- 2580.
- 2581.
- 2582.
- 2583.
- 2584.
- 2585.
- 2586.
- 2587.
- 2588.
- 2589.
- 2590.
- 2591.
- 2592.
- 2593.
- 2594.
- 2595.
- 2596.
- 2597.
- 2598.
- 2599.
- 2600.
- 2601.
- 2602.
- 2603.
- 2604.
- 2605.
- 2606.
- 2607.
- 2608.
- 2609.
- 2610.
- 2611.
- 2612.
- 2613.
- 2614.
- 2615.
- 2616.
- 2617.
- 2618.
- 2619.
- 2620.
- 2621.
- 2622.
- 2623.
- 2624.
- 2625.
- 2626.
- 2627.
- 2628.
- 2629.
- 2630.
- 2631.
- 2632.
- 2633.
- 2634.
- 2635.
- 2636.
- 2637.
- 2638.
- 2639.
- 2640.
- 2641.
- 2642.
- 2643.
- 2644.
- 2645.
- 2646.
- 2647.
- 2648.
- 2649.
- 2650.
- 2651.
- 2652.
- 2653.
- 2654.
- 2655.
- 2656.
- 2657.
- 2658.
- 2659.
- 2660.
- 2661.
- 2662.
- 2663.
- 2664.
- 2665.
- 2666.
- 2667.
- 2668.
- 2669.
- 2670.
- 2671.
- 2672.
- 2673.
- 2674.
- 2675.
- 2676.
- 2677.
- 2678.
- 2679.
- 2680.
- 2681.
- 2682.
- 2683.
- 2684.
- 2685.
- 2686.
- 2687.
- 2688.
- 2689.
- 2690.
- 2691.
- 2692.
- 2693.
- 2694.
- 2695.
- 2696.
- 2697.
- 2698.
- 2699.
- 2700.
- 2701.
- 2702.
- 2703.
- 2704.
- 2705.
- 2706.
- 2707.
- 2708.
- 2709.
- 2710.
- 2711.
- 2712.
- 2713.
- 2714.
- 2715.
- 2716.
- 2717.
- 2718.
- 2719.
- 2720.
- 2721.
- 2722.
- 2723.
- 2724.
- 2725.
- 2726.
- 2727.
- 2728.
- 2729.
- 2730.
- 2731.
- 2732.
- 2733.
- 2734.
- 2735.
- 2736.
- 2737.
- 2738.
- 2739.
- 2740.
- 2741.
- 2742.
- 2743.
- 2744.
- 2745.
- 2746.
- 2747.
- 2748.
- 2749.
- 2750.
- 2751.
- 2752.
- 2753.
- 2754.
- 2755.
- 2756.
- 2757.
- 2758.
- 2759.
- 2760.
- 2761.
- 2762.
- 2763.
- 2764.
- 2765.
- 2766.
- 2767.
- 2768.
- 2769.
- 2770.
- 2771.
- 2772.
- 2773.
- 2774.
- 2775.
- 2776.
- 2777.
- 2778.
- 2779.
- 2780.
- 2781.
- 2782.
- 2783.
- 2784.
- 2785.
- 2786.
- 2787.
- 2788.
- 2789.
- 2790.
- 2791.
- 2792.
- 2793.
- 2794.
- 2795.
- 2796.
- 2797.
- 2798.
- 2799.
- 2800.
- 2801.
- 2802.
- 2803.
- 2804.
- 2805.
- 2806.
- 2807.
- 2808.
- 2809.
- 2810.
- 2811.
- 2812.
- 2813.
- 2814.
- 2815.
- 2816.
- 2817.
- 2818.
- 2819.
- 2820.
- 2821.
- 2822.
- 2823.
- 2824.
- 2825.
- 2826.
- 2827.
- 2828.
- 2829.
- 2830.
- 2831.
- 2832.
- 2833.
- 2834.
- 2835.
- 2836.
- 2837.
- 2838.
- 2839.
- 2840.
- 2841.
- 2842.
- 2843.
- 2844.
- 2845.
- 2846.
- 2847.
- 2848.
- 2849.
- 2850.
- 2851.
- 2852.
- 2853.
- 2854.
- 2855.
- 2856.
- 2857.
- 2858.
- 2859.
- 2860.
- 2861.
- 2862.
- 2863.
- 2864.
- 2865.
- 2866.
- 2867.
- 2868.
- 2869.
- 2870.
- 2871.
- 2872.
- 2873.
- 2874.
- 2875.
- 2876.
- 2877.
- 2878.
- 2879.
- 2880.
- 2881.
- 2882.
- 2883.
- 2884.
- 2885.
- 2886.
- 2887.
- 2888.
- 2889.
- 2890.
- 2891.
- 2892.
- 2893.
- 2894.
- 2895.
- 2896.
- 2897.
- 2898.
- 2899.
- 2900.
- 2901.
- 2902.
- 2903.
- 2904.
- 2905.
- 2906.
- 2907.
- 2908.
- 2909.
- 2910.
- 2911.
- 2912.
- 2913.
- 2914.
- 2915.
- 2916.
- 2917.
- 2918.
- 2919.
- 2920.
- 2921.
- 2922.
- 2923.
- 2924.
- 2925.
- 2926.
- 2927.
- 2928.
- 2929.
- 2930.
- 2931.
- 2932.
- 2933.
- 2934.
- 2935.
- 2936.
- 2937.
- 2938.
- 2939.
- 2940.
- 2941.
- 2942.
- 2943.
- 2944.
- 2945.
- 2946.
- 2947.
- 2948.
- 2949.
- 2950.
- 2951.
- 2952.
- 2953.
- 2954.
- 2955.
- 2956.
- 2957.
- 2958.
- 2959.
- 2960.
- 2961.
- 2962.
- 2963.
- 2964.
- 2965.
- 2966.
- 2967.
- 2968.
- 2969.
- 2970.
- 2971.
- 2972.
- 2973.
- 2974.
- 2975.
- 2976.
- 2977.
- 2978.
- 2979.
- 2980.
- 2981.
- 2982.
- 2983.
- 2984.
- 2985.
- 2986.
- 2987.
- 2988.
- 2989.
- 2990.
- 2991.
- 2992.
- 2993.
- 2994.
- 2995.
- 2996.
- 2997.
- 2998.
- 2999.
- 3000.
- 3001.
- 3002.
- 3003.
- 3004.
- 3005.
- 3006.
- 3007.
- 3008.
- 3009.
- 3010.
- 3011.
- 3012.
- 3013.
- 3014.
- 3015.
- 3016.
- 3017.
- 3018.
- 3019.
- 3020.
- 3021.
- 3022.
- 3023.
- 3024.
- 3025.
- 3026.
- 3027.
- 3028.
- 3029.
- 3030.
- 3031.
- 3032.
- 3033.
- 3034.
- 3035.
- 3036.
- 3037.
- 3038.
- 3039.
- 3040.
- 3041.
- 3042.
- 3043.
- 3044.
- 3045.
- 3046.
- 3047.
- 3048.
- 3049.
- 3050.
- 3051.
- 3052.
- 3053.
- 3054.
- 3055.
- 3056.
- 3057.
- 3058.
- 3059.
- 3060.
- 3061.
- 3062.
- 3063.
- 3064.
- 3065.
- 3066.
- 3067.
- 3068.
- 3069.
- 3070.
- 3071.
- 3072.
- 3073.
- 3074.
- 3075.
- 3076.
- 3077.
- 3078.
- 3079.
- 3080.
- 3081.
- 3082.
- 3083.
- 3084.
- 3085.
- 3086.
- 3087.
- 3088.
- 3089.
- 3090.
- 3091.
- 3092.
- 3093.
- 3094.
- 3095.
- 3096.
- 3097.
- 3098.
- 3099.
- 3100.
- 3101.
- 3102.
- 3103.
- 3104.
- 3105.
- 3106.
- 3107.
- 3108.
- 3109.
- 3110.
- 3111.
- 3112.
- 3113.
- 3114.
- 3115.
- 3116.
- 3117.
- 3118.
- 3119.
- 3120.
- 3121.
- 3122.
- 3123.
- 3124.
- 3125.
- 3126.
- 3127.
- 3128.
- 3129.
- 3130.
- 3131.
- 3132.
- 3133.
- 3134.
- 3135.
- 3136.
- 3137.
- 3138.
- 3139.
- 3140.
- 3141.
- 3142.
- 3143.
- 3144.
- 3145.
- 3146.
- 3147.
- 3148.
- 3149.
- 3150.
- 3151.
- 3152.
- 3153.
- 3154.
- 3155.
- 3156.
- 3157.
- 3158.
- 3159.
- 3160.
- 3161.
- 3162.
- 3163.
- 3164.
- 3165.
- 3166.
- 3167.
- 3168.
- 3169.
- 3170.
- 3171.
- 3172.
- 3173.
- 3174.
- 3175.
- 3176.
- 3177.
- 3178.
- 3179.
- 3180.
- 3181.
- 3182.
- 3183.
- 3184.
- 3185.
- 3186.
- 3187.
- 3188.
- 3189.
- 3190.
- 3191.
- 3192.
- 3193.
- 3194.
- 3195.
- 3196.
- 3197.
- 3198.
- 3199.
- 3200.
- 3201.
- 3202.
- 3203.
- 3204.
- 3205.
- 3206.
- 3207.
- 3208.
- 3209.
- 3210.
- 3211.
- 3212.
- 3213.
- 3214.
- 3215.
- 3216.
- 3217.
- 3218.
- 3219.
- 3220.
- 3221.
- 3222.
- 3223.
- 3224.
- 3225.
- 3226.
- 3227.
- 3228.
- 3229.
- 3230.
- 3231.
- 3232.
- 3233.
- 3234.
- 3235.
- 3236.
- 3237.
- 3238.
- 3239.
- 3240.
- 3241.
- 3242.
- 3243.
- 3244.
- 3245.
- 3246.
- 3247.
- 3248.
- 3249.
- 3250.
- 3251.
- 3252.
- 3253.
- 3254.
- 3255.
- 3256.
- 3257.
- 3258.
- 3259.
- 3260.
- 3261.
- 3262.
- 3263.
- 3264.
- 3265.
- 3266.
- 3267.
- 3268.
- 3269.
- 3270.
- 3271.
- 3272.
- 3273.
- 3274.
- 3275.
- 3276.
- 3277.
- 3278.
- 3279.
- 3280.
- 3281.
- 3282.
- 3283.
- 3284.
- 3285.
- 3286.
- 3287.
- 3288.
- 3289.
- 3290.
- 3291.
- 3292.
- 3293.
- 3294.
- 3295.
- 3296.
- 3297.
- 3298.
- 3299.
- 3300.
- 3301.
- 3302.
- 3303.
- 3304.
- 3305.
- 3306.
- 3307.
- 3308.
- 3309.
- 3310.
- 3311.
- 3312.
- 3313.
- 3314.
- 3315.
- 3316.
- 3317.
- 3318.
- 3319.
- 3320.
- 3321.
- 3322.
- 3323.
- 3324.
- 3325.
- 3326.
- 3327.
- 3328.
结果：
©著作权归作者所有，如需转载，请注明出处，否则将追究法律责任
已于2022-9-27 15:24:29修改
赞
2
收藏
回复
分享
微博
QQ
微信
举报
举报
2
微信扫码分享
删除帖子
删除 取消
相关推荐
