• PIE-Engine：房山区洪涝灾害风险评价

    房山区洪涝灾害风险评价这个可以说不是一个APP，要改进的话可以通过UI控件设定来在MAP上完成，剩余的其它部分都可以实现在，这里灾害风险评价时暴露性结果统计与展示、脆弱性结果统计与展示、危险性结果统计与展示以及洪涝灾害风险结果统计与展示。代码的整个流程主要是受埃希纳加载研究区和数据，然后对DEM、slope、降水强度等进行归一化处理，在这里的每一个归一化处理中，都会通过获取min和max然后通过归一化公式来获取。

    处理完DEM然后处理影像数据，这里用的是Landsat 8数据，并计算FVC，地表产流能力 ，但后进行监督分类，并进行河网密度，加载土壤类型数据集，紧接着脆弱性计算，GDP的计算，到道路网的距离的计算，人口密度在计算晚以上的内容后就可以直接进行4个评价分析。

    函数：

    minMax()得到的结果时"B1_min","B1_max"的波段名称，这里就可以利用这个参数进行归一化处理

    得到一个返回最大、最小值的统计器。

    方法参数：

    返回值：Reducer

    reduceRegion(reducer,geometry,scale)

    对特定区域的所有像素进行统计，返回结果为一个JSON对象；目前可完成最大、最小和求和统计计算。

    方法参数：

    - image(Image)

    Image实例。

    - reducer(Reducer)

    统计类型，包括最大值、最小值和求和。

    - geometry(Geometry)

    统计区域范围。默认是影像第一个波段的范围。

    - scale(Number)

    统计采样比例。

    返回值：Dictionary

    代码：

    1. /**
    2.  * @Name    :   房山区洪涝灾害风险评价
    3.  * @Author  :   首都师范大学云开发——“图”以致用组
    4.  * @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二等奖获奖作品
    5.  */
    6.  
    7. var studyArea = pie.FeatureCollection('user/pieadmin/CNUelites')
    8.     .first()
    9.     .geometry();
    10. Map.addLayer(studyArea, { color: 'black', fillColor: '00000000' }, "roi");
    11.  
    12. //定位地图中心
    13. Map.centerObject(studyArea, 11);
    14.  
    15. //加载SRTM30m数据
    16. var SRTM = pie.ImageCollection('DEM/SRTM_30')
    17.     .filterBounds(studyArea)
    18.     .select("elevation")
    19.     .mean()
    20.     .clip(studyArea);
    21. //DEM数据归一化
    22. var srtm_band = SRTM.select('elevation')
    23. var srtm_minmax = SRTM.reduceRegion(pie.Reducer.minMax(), studyArea, 1000);
    24. srtm_minmax = pie.Dictionary(srtm_minmax);
    25. var elevation_min = pie.Number(srtm_minmax.get("elevation_min"));
    26. var elevation_max = pie.Number(srtm_minmax.get("elevation_max"));
    27. var dem_result = pie.Image(elevation_max).subtract(srtm_band).divide(elevation_max.subtract(elevation_min));
    28. var vis_DEM = {
    29.     min:0,
    30.     max:1,
    31.     palette: ['#BF812D', '#DFC27D', '#F6E8C3', '#A1D99B', '#41AB5D']
    32. };
    33. Map.addLayer(dem_result, vis_DEM, "dem", false);
    34.  
    35. /***********************************************************************/
    36. //Slope归一化//计算坡度
    37. var Slope = pie.Terrain.slope(SRTM).rename("slope");
    38. var slope_band = Slope.select('slope')
    39. var slope_minmax = Slope.reduceRegion(pie.Reducer.minMax(), studyArea, 1000);
    40. slope_minmax = pie.Dictionary(slope_minmax);
    41. var slope_min = pie.Number(slope_minmax.get("slope_min"));
    42. var slope_max = pie.Number(slope_minmax.get("slope_max"));
    43. var slope_result = pie.Image(slope_max).subtract(slope_band).divide(slope_max.subtract(slope_min));
    44. var vis_slope = {
    45.     min:0,
    46.     max:1,
    47.     palette: ['#C7E9C0', '#F6E8C3', '#DFC27D', '#CD853F', '#BF812D']
    48. };
    49. Map.addLayer(slope_result, vis_slope, "slope", false);
    50.  
    51. /***********************************************************************/
    52. // 降水强度
    53. var precipitation = pie.ImageCollection('TPDC/CHINA_1KM_PRE_MONTH')
    54.     .filterDate("2017-06-01", "2017-08-31")
    55.     .select("B1")
    56.     .mean()
    57.     .clip(studyArea);
    58. var pre_band = precipitation.select('B1')
    59. var pre_value = precipitation.reduceRegion(pie.Reducer.minMax(), studyArea, 1000)
    60. pre_value = pie.Dictionary(pre_value);
    61. var pre_min = pie.Number(pre_value.get("B1_min"));
    62. var pre_max = pie.Number(pre_value.get("B1_max"));
    63. //计算公式 result = x-min/max-min
    64. var pre_result = pre_band.subtract(pre_min).divide(pre_max.subtract(pre_min));
    65. var vis_pre = {
    66.     min:0,
    67.     max:1,
    68.     palette: ["#ADD8E6", "#87CEEB", "#87CEFA", "#6495ED", "#4169E1"]
    69. };
    70. Map.addLayer(pre_result, vis_pre, "pre", false);
    71.  
    72. /***********************************************************************/
    73. //危险性计算
    74. var danger = ((dem_result.multiply(0.332)).add(slope_result.multiply(0.308)))
    75.     .add(pre_result.multiply(0.360))
    76.  
    77. //计算植被覆盖度，加载landsat8影像数据
    78. var L8 = pie.ImageCollection("LC08/01/T1/LC08_123032_20170710")
    79.     .filterBounds(studyArea)
    80.     .select(["B5", "B4"])
    81.     .filterDate("2017-6-1", "2017-10-1")
    82.     .filter(pie.Filter.lt("cloudCover", 5))
    83.     .first()
    84.     .clip(studyArea);
    85. var B4 = L8.select("B4");
    86. var B5 = L8.select("B5");
    87. var NDVI = (B5.subtract(B4)).divide(B5.add(B4)).rename("NDVI");
    88.  
    89. //计算FVC
    90. var info = NDVI.reduceRegion(pie.Reducer.minMax(), studyArea, 1000)
    91. info = pie.Dictionary(info);
    92. var NDVI_min = pie.Number(info.get("NDVI_min"));
    93. var NDVI_max = pie.Number(info.get("NDVI_max"));
    94. var FVC = pie.Image(NDVI_max).subtract(NDVI).divide(NDVI_max.subtract(NDVI_min));
    95.  
    96. //加载FVC影像
    97. var vis_fvc = {
    98.     min:0, 
    99.     max:1,
    100.     palette: ['#006837', '#66BD63', '#FFCC33', '#D6604D', '#A50026']
    101. };
    102. Map.addLayer(FVC, vis_fvc, "FVC", false);
    103.  
    104. /***********************************************************************/
    105. //地表产流能力
    106. //加载landsat影像数据
    107. var L8 = pie.ImageCollection("LC08/01/T1")
    108.     .filterBounds(studyArea)
    109.     .select(["B9", "B8", "B7", "B6", "B5", "B4", "B3", "B2", "B1"])
    110.     .filterDate("2017-6-1", "2017-11-1")
    111.     .filter(pie.Filter.lt("cloudCover", 10))
    112.     .median()
    113.     .clip(studyArea);
    114. //分类样本             
    115. var featureCollection = pie.FeatureCollection('user/pieadmin/ROIpoint');
    116. featureCollection = featureCollection.randomColumn('random');
    117. //SVM监督分类
    118. var sampleFeatureCollection = L8.sampleRegions(featureCollection, ["type", "random"], 50);
    119. var sampleTrainingFeatures = sampleFeatureCollection.filter(pie.Filter.lte("random", 0.8));
    120. var sampleTestingFeatures = sampleFeatureCollection.filter(pie.Filter.gt("random", 0.2));
    121. var classifer = pie.Classifier.svm()
    122.     .train(sampleTrainingFeatures, "type", ["B9", "B8", "B7", "B6", "B5", "B4", "B3", "B2", "B1"]);
    123. var resultImage = L8.classify(classifer, "GFclassify");
    124. var classifer_result = resultImage.divide(1000);
    125.  
    126. /***********************************************************************/
    127. //河网密度
    128. var Rivernetwork_density = pie.Image('user/pieadmin/Den_river100')
    129.     .select('B1')
    130.     .clip(studyArea);
    131. var vis_RND = {
    132.     min:0,
    133.     max:1,
    134.     palette: ["#ADD8E6", "#87CEEB", "#87CEFA", "#6495ED", "#4169E1"]
    135. };
    136. var RND_band = Rivernetwork_density.select('B1');
    137. var RND_minmax = Rivernetwork_density.reduceRegion(pie.Reducer.minMax(), studyArea, 30);
    138. RND_minmax = pie.Dictionary(RND_minmax);
    139. var RND_min = pie.Number(RND_minmax.get("B1_min"));
    140. var RND_max = pie.Number(RND_minmax.get("B1_max"));
    141. var RND_result = RND_band.subtract(RND_min).divide(RND_max.subtract(RND_min));
    142. Map.addLayer(RND_result, vis_RND, "rnd", false);
    143. /***********************************************************************/
    144. //土壤类型
    145. var Soiltype = pie.Image('user/pieadmin/soiltype')
    146.     .select('B1')
    147.     .clip(studyArea);
    148.  
    149. var vis_soil = {
    150.     min:0,
    151.     max:1,
    152.     palette: ["#1A9850", "#66BD63", "#A6D96A", "#FFFFBF", "#FDAE61"]
    153. };
    154. var soiltype_band = Soiltype.select('B1');
    155. //计算公式 result = (x/1000)-0.1
    156. var soil_result = (soiltype_band.divide(1000)).subtract(0.1)
    157. Map.addLayer(soil_result, vis_soil, "soil", false);
    158. /***********************************************************************/
    159. //脆弱性计算
    160. var Vulnerability = FVC.multiply(0.207)
    161.     .add(classifer_result.multiply(0.256))
    162.     .add(RND_result.multiply(0.354))
    163.     .add(soil_result.multiply(0.183))
    164.  
    165. //GDP的计算
    166. var GDP2015 = pie.Image('user/pieadmin/GDP2015_100')
    167.     .select("B1")
    168.     .clip(studyArea);
    169. var vis_GDP = {
    170.     min:0,
    171.     max:1,
    172.     palette: ['#FCC5C0', '#FA9FB5', '#F768A1', '#DD3497', '#AE017E']
    173. };
    174. var gdp_band = GDP2015.select('B1');
    175. var GDP2017 = gdp_band.multiply((681.7 - 568.4) / 568.4 + 1)
    176. var gdp_minmax = GDP2017.reduceRegion(pie.Reducer.minMax(), studyArea, 30);
    177. gdp_minmax = pie.Dictionary(gdp_minmax);
    178. var GDP_min = pie.Number(gdp_minmax.get("B1_min"));
    179. var GDP_max = pie.Number(gdp_minmax.get("B1_max"));
    180. //计算公式 result =(x-min)/(max-min)
    181. var GDP_result = (gdp_band.subtract(GDP_min)).divide(GDP_max.subtract(GDP_min));
    182. Map.addLayer(GDP_result, vis_GDP, "gdp", false);
    183.  
    184. /***********************************************************************/
    185. //到道路网的距离的计算
    186. var road = pie.Image('user/pieadmin/Dist_road100')
    187.     .select('B1')
    188.     .clip(studyArea);
    189. var road_band = road.select('B1');
    190. var road_minmax = road_band.reduceRegion(pie.Reducer.minMax(), studyArea, 100);
    191. road_minmax = pie.Dictionary(road_minmax);
    192. var road_min = pie.Number(road_minmax.get("B1_min"));
    193. var road_max = pie.Number(road_minmax.get("B1_max"));
    194. var road_result = pie.Image(road_max).subtract(road_band).divide(road_max.subtract(road_min));
    195. var vis_road = {
    196.     min:0,
    197.     max:1,
    198.     palette: ['#D9D9D9', '#BDBDBD', '#969696', '#737373', '#525252']
    199. };
    200. Map.addLayer(road_result, vis_road, "road", false);
    201.  
    202. /***********************************************************************/
    203. //人口密度
    204. var pop_des = pie.ImageCollection("WorldPop/Global_100m_UNadj")
    205.     .filterDate("2017-1-1", "2017-12-30")
    206.     .first()
    207.     .select('population')
    208.     .clip(studyArea);
    209. var pop_band = pop_des.select('population');
    210. var pop_minmax = pop_des.reduceRegion(pie.Reducer.minMax(), studyArea, 100);
    211. pop_minmax = pie.Dictionary(pop_minmax);
    212. var pop_min = pie.Number(pop_minmax.get("population_min"));
    213. var pop_max = pie.Number(pop_minmax.get("population_max"));
    214. //计算公式 result = (x-min)/(max-min)
    215. var pop_result = (pop_band.subtract(pop_min)).divide(pop_max.subtract(pop_min));
    216.  
    217. /***********************************************************************/
    218. //暴露性计算
    219. var Exposure = ((pop_result.multiply(0.411)).add(GDP_result.multiply(0.327)))
    220.     .add(road_result.multiply(0.262))
    221.  
    222. /***********************************************************************/
    223. //洪涝灾害风险计算
    224. var FloodDisasterrisk_cal = ((danger.multiply(0.416)).add(Vulnerability.multiply(0.362)))
    225.     .add(Exposure.multiply(0.222))
    226.  
    227. /**button模块 */
    228. function generatePIEChart(image, chartOption) {
    229.     var conditions = [
    230.         {min:null, max:0.2},
    231.         {min:0.2, max:0.4},
    232.         {min:0.4, max:0.6},
    233.         {min:0.6, max:0.8},
    234.         {min:0.8, max:null},
    235.     ];
    236.     for (let i=0; ilength; i++) {
    237.         var newImg;
    238.         if (conditions[i].min == null) {
    239.             newImg = image.lte(conditions[i].max);
    240.         } else if (conditions[i].max == null) {
    241.             newImg = image.gt(conditions[i].min);
    242.         } else {
    243.             newImg = image.gt(conditions[i].min).and(image.lte(conditions[i].max));
    244.         }
    245.         var areaImage = pie.Image().pixelArea().multiply(newImg);
    246.         var data = areaImage.reduceRegion(pie.Reducer.sum(), studyArea, 1000);
    247.         image = image.set("class_"+i, data.get("constant"));
    248.     }
    249.     image.getInfo(function(data) {
    250.         let properties = data.properties;
    251.         let xValues = [];
    252.         let yValues = [];
    253.         let labels = chartOption.legend
    254.         for (let i=0; ilength; i++) {
    255.             xValues.push(labels[i]);
    256.             yValues.push(parseFloat((properties["class_"+i]/1000000).toFixed(2)));
    257.         }
    258.         let chart = ui.Chart.PIEArray.values({
    259.             array: yValues,
    260.             axis: 0,
    261.             xLabels: xValues
    262.         });
    263.         chart = chart.setChartType("pie")
    264.             .setSeriesNames(xValues)
    265.             .setOptions({
    266.                 title: {
    267.                     name: chartOption.title
    268.                 },
    269.                 xAxis: {
    270.                     name: chartOption.seriesName
    271.                 }
    272.             });
    273.         print(chart);
    274.     });
    275. }
    276.  
    277. var productLayer = null;
    278. var productLegend = null;
    279.  
    280. function addProductLegend(title, labels, colors) {
    281.     if (productLegend != null) {
    282.         Map.removeUI(productLegend);
    283.     }
    284.     // 图例
    285.     var data = {
    286.         title: title,
    287.         colors: colors,
    288.         labels: labels,
    289.         step: 30
    290.     };
    291.     var style = {
    292.         right: "150px",
    293.         bottom: "10px",
    294.         height: "70px",
    295.         width: "350px"
    296.     };
    297.     productLegend = ui.Legend(data, style);
    298.     Map.addUI(productLegend);
    299. }
    300.  
    301. function addProductLayer(layer, vis, layerName) {
    302.     if (productLayer != null) {
    303.         Map.removeLayer(productLayer);
    304.     }
    305.     productLayer = Map.addLayer(layer, vis, layerName);
    306. }
    307.  
    308. /**暴露性 */
    309. var ExposureShow = ui.Button({
    310.     label: '暴露性等级分布图',
    311.     onClick: showExposureShow
    312. })
    313.  
    314. function showExposureShow() {
    315.     var vis_exposure = {
    316.         min:0,
    317.         max:1,
    318.         palette: ["1A9850", "A6D96A", "FFFFBF", 'EF6548', 'D7301F']
    319.     };
    320.     addProductLayer(Exposure, vis_exposure, "Exposure");
    321.     addProductLegend('暴露性等级',
    322.         ['very low', 'low', 'medium', 'high', 'very high'],
    323.         ["#1A9850", "#A6D96A", "#FFFFBF", '#EF6548', '#D7301F']);
    324.     print("暴露性结果统计与展示");
    325.     //暴露性结果统计与展示
    326.     var pie_options_Exposure = {
    327.         title: '暴露性结果统计图',
    328.         legend: ["very low", "low", "medium", "high", "very high"],
    329.         seriesName: "暴露性等级",
    330.     }
    331.     generatePIEChart(Exposure, pie_options_Exposure);
    332. }
    333. print(ExposureShow);
    334.  
    335. /**脆弱性 */
    336. var VulnerabilityShow = ui.Button({
    337.     label: '脆弱性等级分布图',
    338.     onClick: showVulnerability
    339. })
    340.  
    341. function showVulnerability() {
    342.     var vis_Vulnerability = {
    343.         min:0,
    344.         max:1,
    345.         palette: ["#1A9850", "#66BD63", "#A6D96A", "#F46D43", "#D73027"]
    346.     };
    347.     addProductLayer(Vulnerability, vis_Vulnerability, 'Vulnerability');
    348.     addProductLegend('脆弱性等级',
    349.         ['very low', 'low', 'medium', 'high', 'very high'],
    350.         ["#1A9850", "#66BD63", "#A6D96A", "#F46D43", "#D73027"]);
    351.  
    352.     print("脆弱性结果统计与展示");
    353.     //脆弱性结果统计与展示
    354.     var pie_options_Vulnerability = {
    355.         title: '脆弱性结果统计图',
    356.         legend: ["very low", "low", "medium", "high", "very high"],
    357.         seriesName: "脆弱性等级",
    358.     }
    359.     generatePIEChart(Vulnerability, pie_options_Vulnerability);
    360. }
    361. print(VulnerabilityShow);
    362.  
    363. /**危险性 */
    364. var dangerShow = ui.Button({
    365.     label: '危险性等级分布图',
    366.     onClick: showdanger
    367. })
    368.  
    369. function showdanger() {
    370.     var vis_danger = {
    371.         min:0,
    372.         max:1,
    373.         palette: ["#35978F", "#80CDC1", "#C7EAE5", "#FF7F50", "#D73027"]
    374.     };
    375.     addProductLayer(danger, vis_danger, 'Hazard');
    376.     addProductLegend('危险性等级',
    377.         ['very low', 'low', 'medium', 'high', 'very high'],
    378.         ["#35978F", "#80CDC1", "#C7EAE5", "#FF7F50", "#D73027"]);
    379.  
    380.     print("危险性结果统计与展示");
    381.     //危险性结果统计与展示
    382.     var pie_options_danger = {
    383.         title: '危险性结果统计图',
    384.         legend: ["very low", "low", "medium", "high", "very high"],
    385.         seriesName: "危险性等级",
    386.     }
    387.     generatePIEChart(danger, pie_options_danger);
    388. }
    389. print(dangerShow);
    390. /**总指标 */
    391. var FDRCShow = ui.Button({
    392.     label: '洪涝灾害风险分布图',
    393.     onClick: showfdrc
    394. })
    395.  
    396. function showfdrc() {
    397.     var FDRC = {
    398.         min:0,
    399.         max:1,
    400.         palette: ['#2166AC', '#4393C3', '#92C5DE', '#D6604D', '#AE182B']
    401.     };
    402.     addProductLayer(FloodDisasterrisk_cal, FDRC, 'FloodDisasterrisk_cal');
    403.     addProductLegend('风险性等级',
    404.         ['very low', 'low', 'medium', 'high', 'very high'],
    405.         ['#2166AC', '#4393C3', '#92C5DE', '#D6604D', '#AE182B']);
    406.  
    407.     print("洪涝灾害风险结果统计与展示");
    408.     //洪涝灾害风险结果统计与展示
    409.     var pie_options_FloodDisasterrisk = {
    410.         title: '风险性结果统计图',
    411.         legend: ["very low", "low", "medium", "high", "very high"],
    412.         seriesName: "风险等级",
    413.     }
    414.     generatePIEChart(FloodDisasterrisk_cal, pie_options_FloodDisasterrisk);
    415. }
    416. print(FDRCShow);

    洪涝灾害风险分布图

     危险性等级分布图

     脆弱性等级分布图

     暴露性等级分布图

     相应的饼图结果：

     

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  • 原文地址：https://blog.csdn.net/qq_31988139/article/details/127112794