PIE-Engine：生态遥感指数RSEI研究

/**
 * @Name    :   广东省遥感生态指数研究
 * @Author  :   广州大学张三的组
 * @Source  :   航天宏图第四届 “航天宏图杯”PIE软件二次开发大赛云开发组三等奖获奖作品
 * @Description :  2、计算展示分析RSEI
 */
var province = pie.FeatureCollection('NGCC/CHINA_PROVINCE_BOUNDARY');
var gd = province.filter(pie.Filter.eq("name","广东省")).first().geometry();

//导入归一化后且PCA处理完成影像
var RSEI = pie.Image('user/pieadmin/saas/07/rsei_guangdong').select('B1').clip(gd);
var aveRSEI = pie.Number(RSEI.reduceRegion(pie.Reducer.mean(),gd,75).get('B1'))
var rseiVis = {
    min: 0,
    max: 1,
    palette: [
        'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
        '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
        '012E01', '011D01', '011301'
    ],
};
//RSEI可视化渲染
Map.addLayer(RSEI,rseiVis,'RSEI');

//导入市级行政区矢量
var city = pie.FeatureCollection('NGCC/CHINA_CITY_BOUNDARY')
    .filter(pie.Filter.eq("pname", "广东省"));

//构建函数输出广东市级RSEI
function rsei(region){
    var aveRSEI = pie.Number(RSEI.reduceRegion(pie.Reducer.mean(),region,1000).get('B1'));
    return aveRSEI;
}
city = city.map(function(feature) {
    feature = pie.Feature(feature);
    var data = rsei(feature.geometry());
    feature = feature.set("rsei", data);
    return feature;
});

var chart = ui.Chart.PIEFeature.byFeature({
    features: city,
    xProperty: "name",
    yProperties: ["rsei"]
}).setChartType("bar")
    .setSeriesNames(['2020年12月'])
    .setOptions({
        title: {
            name: "广东省市级区域RSEI归一化平均值"
        },
        xAxis: {
            name: "RSEI指数"
        },
        yAxis: {
            name: "市级地区"
        }
    })
print(chart);