网站开发net,wordpress nginx php,wordpress api 自定义,二手车网站模版地物分类 1. 写在前面2. 北京作物分类 1. 写在前面 今天分享一个有意思的文章#xff0c;用于进行农作物分类。文章提出了一个灵活的物候辅助监督水稻(PSPR)制图框架。主要是通过提取植被物候#xff0c;并自动对物候数据进行采样#xff0c;获得足够多的样本点#xff0c;… 地物分类 1. 写在前面2. 北京作物分类 1. 写在前面 今天分享一个有意思的文章用于进行农作物分类。文章提出了一个灵活的物候辅助监督水稻(PSPR)制图框架。主要是通过提取植被物候并自动对物候数据进行采样获得足够多的样本点再使用随机森林等机器学习方法进行分类。这种方法有效解决了样本量不足或者样本位置不够精确的问题并且分类结构相较于之前的方法更高。我认为这是一种比较有意思的文章当然这种方法还可以用到其他植被类型分类中。
灵活的物候辅助监督水稻(PSPR)制图框架
2. 北京作物分类 首先使用Landsat5、7、8数据获取植被物候信息再提取随机采样点。 各植被类型的物候
/**************************使用PSPR方法生成随机采样点*************************/
// PSPR: 灵活的物候辅助监督水稻制图框架
//设置研究区位置: 北京
var table ee.FeatureCollection(users/cduthes1991/boundry/China_province_2019);
var BJGrid4 table.filter(ee.Filter.eq(provinces,beijing));
var roi BJGrid4;
Map.addLayer(roi,{color:grey},roi,false);
Map.centerObject(roi,7);var GridTest GridRegion(BJGrid4.geometry(),6,6).filterBounds(roi);
print(GridTest size:,GridTest.size());
var color {color:0000FF,fillColor:FF000000};
Map.addLayer(GridTest.style(color),null,GridTest);/********* cropland mask**************************************************/
//这个数据为中科院的30LUCC数据其中11和12分别表示水田和旱地
var CAS_LULC_2018 ee.Image(users/chengkangmk/Global-LULC-China/LULC_CAS/CAS_LULC_30m_2018);
var cropland CAS_LULC_2018.eq(11).or(CAS_LULC_2018.eq(12)).clip(roi);
Map.addLayer(cropland.randomVisualizer(),null,cropland,false);/*******************************自定义函数*********************************/
// Landsat 4, 5 and 7 去云
function rmL457Cloud(image) {var qa image.select(pixel_qa);// If the cloud bit (5) is set and the cloud confidence (7) is high// or the cloud shadow bit is set (3), then its a bad pixel.var cloud qa.bitwiseAnd(1 5).and(qa.bitwiseAnd(1 7)).or(qa.bitwiseAnd(1 3));// Remove edge pixels that dont occur in all bandsvar mask2 image.mask().reduce(ee.Reducer.min());var mask3 image.select(B1).gt(2000);return image.updateMask(cloud.not()).updateMask(mask2).updateMask(mask3.not()).toDouble().divide(1e4).copyProperties(image).copyProperties(image, [system:time_start,system:time_end]);
}// Landsat-8 去云
function rmL8Cloud(image) { var cloudShadowBitMask (1 3); var cloudsBitMask (1 5); var qa image.select(pixel_qa); var mask qa.bitwiseAnd(cloudShadowBitMask).eq(0) .and(qa.bitwiseAnd(cloudsBitMask).eq(0)); var mask2 image.select(B2).gt(2000); return image.updateMask(mask).updateMask(mask2.not()).toDouble().divide(1e4).copyProperties(image).copyProperties(image, [system:time_start,system:time_end]);
}// Sentinel-2 去云
function rmS2cloud(image) {var qa image.select(QA60);// Bits 10 and 11 are clouds and cirrus, respectively.var cloudBitMask 1 10;var cirrusBitMask 1 11;// Both flags should be set to zero, indicating clear conditions.var mask qa.bitwiseAnd(cloudBitMask).eq(0).and(qa.bitwiseAnd(cirrusBitMask).eq(0));var mask2 image.select(B2).lte(2000);return image.updateMask(mask).updateMask(mask2).toDouble().divide(1e4).copyProperties(image).copyProperties(image, [system:time_start, system:time_end]);
}// 计算相关指数包括NDVI、LSWI植被水分含量指数、EVI
function addIndex(image){// original bandsvar blue image.select(blue); var red image.select(red);var green image.select(green);var nir image.select(nir);var swir1 image.select(swir1);var ndvi image.normalizedDifference([nir, red]).rename(NDVI).toDouble();var lswi image.normalizedDifference([nir, swir1]).rename(LSWI).toDouble();var lswi2ndvi lswi.subtract(ndvi).rename(LSWI2NDVI).toDouble();var evi image.expression(2.5 * ((NIR - RED) / (NIR 6 * RED - 7.5 * BLUE 1)), {NIR: nir,RED: red,BLUE:blue}).rename(EVI);return image.addBands(ndvi).addBands(lswi).addBands(lswi2ndvi).addBands(evi);
}// 为影像的特定波段指定名称
var LC8_BANDS [B2, B3, B4, B5, B6, B7]; //Landsat 8
var LC7_BANDS [B1, B2, B3, B4, B5, B7]; //Landsat 7
var LC5_BANDS [B1, B2, B3, B4, B5, B7]; //Llandsat 5
var S2_BANDS [B2, B3, B4, B8, B11, B12]; // Sentinel-2
var STD_NAMES [blue, green, red, nir, swir1, swir2];// 定义时间变量、LSWI阈值、采样点数量
var year 2015;
var th_lswi2NDVI 0;
var pointNum 1000;// 导入数据
// landsat 8
var l8Col ee.ImageCollection(LANDSAT/LC08/C01/T1_SR).map(rmL8Cloud).filterBounds(roi).filterDate(year-04-01,year-11-01)// .filter(ee.Filter.lte(CLOUD_COVER,10)).select(LC8_BANDS, STD_NAMES);
// landsat 7
var l7Col ee.ImageCollection(LANDSAT/LE07/C01/T1_SR).map(rmL457Cloud).filterBounds(roi).filterDate(year-04-01,year-11-01).select(LC7_BANDS, STD_NAMES);
// landsat 5
var l5Col ee.ImageCollection(LANDSAT/LT05/C01/T1_SR).map(rmL457Cloud).filterBounds(roi).filterDate(year-04-01,year-11-01).select(LC5_BANDS, STD_NAMES); var L578COl ee.ImageCollection(l8Col.merge(l7Col).merge(l5Col)).sort(system:time_start).map(addIndex);
print(L578COl: , L578COl);//****************************************************************************
//***************************** 基于LSWI的水稻制图****************************
//****************************************************************************
var palette1 {min: 0, max: 1.0, palette: [000000, 00FF00]};// 1.水淹阶段定义植被的时间窗口
var imgCol_flood L578COl.filterDate(year-05-01,year-06-30) .filterBounds(roi);// 使用 LSWI-NDVI 提取水稻
// rice_0_lswi 表示初步产生水稻的时间
var rice_0_lswi imgCol_flood.select(LSWI2NDVI).map(function(image){return image.select(LSWI2NDVI).gt(th_lswi2NDVI);
});// 从rice_0_lswi图像集合中获取每个像素位置上的LSWI指数最大值(找到最大的 LSWI 值)。
// LSWI是一种用于检测土地表面水体的指数其数值通常与水体的存在程度成正比。在稻田中LSWI 的最大值对应于稻田灌溉时水体充足的情况。因此这样可以最大程度的保证水稻位置的获取。
var rice_0_lswi rice_0_lswi.reduce(ee.Reducer.max()).clip(roi).updateMask(cropland);
Map.addLayer(rice_0_lswi, palette1, rice_0_lswi candidates,false);//2.生长阶段水稻幼苗被移植到稻田中直到水稻成熟
var imgCol_growth L578COl.filterDate(year-07-01,year-10-31).filterBounds(roi);// 不同阶段图像
var PalettePanel {bands:[swir1,nir,red],min:0,max:0.3};var imgCol_flood_qmosaic imgCol_flood.qualityMosaic(LSWI2NDVI).clip(roi);
Map.addLayer(imgCol_flood_qmosaic, PalettePanel, imgCol_flood_qmosaic);var imgCol_growth_qmosaic imgCol_growth.qualityMosaic(NDVI).clip(roi);
Map.addLayer(imgCol_growth_qmosaic, PalettePanel, imgCol_growth_qmosaic, false);// 使用 0 值来填充rice_0_lswi的区域
var rice_0_map rice_0_lswi.unmask(0).clip(roi);
Map.addLayer(rice_0_map.selfMask(), {palette:#FF0000}, rice_0_map,false);//****************************************************************************
//*********************基于CCVS 方法提取水稻物候******************************
//****************************************************************************
var visParam {min: -0.2,max: 0.8,palette: FFFFFF, CE7E45, DF923D, F1B555, FCD163, 99B718, 74A901, 66A000, 529400, 3E8601, 207401, 056201, 004C00, 023B01, 012E01, 011D01, 011301
};// 使用LSWI_max, LSWI_min, NDVI_max, NDVI_min来推导RCLN其中RCLN表示LSWI相对于与NDVI的变化幅度
var RCLE imgCol_growth_qmosaic.select(LSWI).subtract(imgCol_flood_qmosaic.select(LSWI)).abs().divide(imgCol_growth_qmosaic.select(EVI).subtract(imgCol_flood_qmosaic.select(EVI)).abs()).rename(RCLE);
Map.addLayer(RCLE, visParam,RCLE, false);// 创建一个布尔类型的图像其中大于 0.1 的像素被设置为 true小于或等于 0.1 的像素被设置为 false
var LSIW_min imgCol_flood.select(LSWI).min().gt(0.1).clip(roi).updateMask(cropland);
Map.addLayer(LSIW_min.selfMask(), {palette:#FF0000}, LSIW_min,false);//
var RCLE_rice RCLE.updateMask(RCLE.gt(0)).updateMask(LSIW_min).lt(0.6).unmask(0).clip(roi);
Map.addLayer(RCLE_rice.selfMask(), {palette: green}, CCVS_rice,false);var riceCombine RCLE_rice.add(rice_0_map);// 将以上两种方法得到的数据图像进行合并求交
var stableMask riceCombine.eq(0).or(riceCombine.eq(2));
var LULU_mutual riceCombine.updateMask(stableMask).where(riceCombine.eq(2),1).rename(RiceMap).updateMask(cropland);
Map.addLayer(LULU_mutual,palette1,LULU_mutual,false);var riceMapCol GridTest.toList(GridTest.size()).map(function(ROIFea){// set study areavar roi ee.FeatureCollection([ROIFea]);var ricemap riceTrainData(roi);return ee.FeatureCollection(ricemap);
});
var samplePoint ee.FeatureCollection(riceMapCol).flatten();
Map.addLayer(samplePoint,null,samplePoint,false);// check the generated sample data
print(samplePoint:,samplePoint.limit(10));var ricePoint_1 samplePoint.filter(ee.Filter.eq(landcover,1));
Map.addLayer(ricePoint_1,{color:#FFA500},ricePoint_1);
print(ricePoint_1 size,ricePoint_1.size());var NonricePoint_1 samplePoint.filter(ee.Filter.eq(landcover,0));
print(NonricePoint_1 size,NonricePoint_1.size());Export.table.toAsset(samplePoint,PSPRSampleGenerationyear,PSPRSampleGenerationyear)function riceTrainData(roiRegion){var roi roiRegion;var LULU_mutual2 LULU_mutual.clip(roi);/***********************************************************************Define neighboor function* and generate the samples***********************************************************************/function neighFun(img,kernalRadius,roi){var kernel ee.Kernel.square(kernalRadius,pixels,false);var kernelArea (ee.Number(kernalRadius).multiply(2).add(1)).pow(2);var imgNeibor ee.Image(img).convolve(kernel).eq(kernelArea).set(system:footprint,roi.geometry());return img.updateMask(imgNeibor);}var samplePoint ee.List([]);for(var i0;i1;i){var class_Num i;var class_i_mask neighFun(LULU_mutual2.eq(class_Num),1,roi);var class_i LULU_mutual2.updateMask(class_i_mask);samplePoint samplePoint.add(class_i);}var samplePoint ee.ImageCollection(samplePoint).mosaic().rename(landcover).updateMask(cropland);var pointSample samplePoint.stratifiedSample({numPoints:pointNum,classBand:landcover,region:roi.geometry(),scale:30,seed:0,// tileScale:8,geometries:true});return pointSample;
}/**************************************************************************
generate the grid
***************************************************************************/
function generateGrid(xmin, ymin, xmax, ymax, dx, dy) {var xx ee.List.sequence(xmin, ee.Number(xmax).subtract(0.0001), dx);var yy ee.List.sequence(ymin, ee.Number(ymax).subtract(0.0001), dy);var cells xx.map(function(x) {return yy.map(function(y) {var x1 ee.Number(x);var x2 ee.Number(x).add(ee.Number(dx));var y1 ee.Number(y);var y2 ee.Number(y).add(ee.Number(dy));var coords ee.List([x1, y1, x2, y2]);var rect ee.Algorithms.GeometryConstructors.Rectangle(coords); return ee.Feature(rect);});}).flatten(); return ee.FeatureCollection(cells);
}function GridRegion(roiRegion,xBlock,yBlock){//roiRegion: area of interest in the form of geometry// compute the coordinatesvar bounds roiRegion.bounds();var coords ee.List(bounds.coordinates().get(0));var xmin ee.List(coords.get(0)).get(0);var ymin ee.List(coords.get(0)).get(1);var xmax ee.List(coords.get(2)).get(0);var ymax ee.List(coords.get(2)).get(1);var dx (ee.Number(xmax).subtract(xmin)).divide(xBlock); //4var dy (ee.Number(ymax).subtract(ymin)).divide(yBlock);var grid generateGrid(xmin, ymin, xmax, ymax, dx, dy); grid grid.filterBounds(roiRegion); return grid;
}
植被水分含量指数LSWI 在其公式中使用了NIR和SWIR通道SWIR波段对植被含水量的变化较敏感
结果展示
