| GEE：指数计算（NDVI、NBR、EVI、NDMI、NDSI、TC、NDFI、EBBI、VCI、BSI、NDBI、GRAY） | 您所在的位置：网站首页 › 骨质指数计算方法有哪些 › GEE：指数计算（NDVI、NBR、EVI、NDMI、NDSI、TC、NDFI、EBBI、VCI、BSI、NDBI、GRAY） |
GEE：指数计算（NDVI、NBR、EVI、NDMI、NDSI、TC、NDFI、EBBI、VCI、BSI、NDBI、GRAY）
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本文记录了，使用 Landsat 光学波段计算指数的方法和代码，包括NDVI、NBR、EVI、NDMI、NDSI、TC变化（绿度、亮度、湿度）、NDFI、EBBI、VCI等指数。 
文章目录
一、代码框架二、指数计算1.NDVI2.NBR3.EVI4.NDMI5.NDSI6.TC-Transform7.NDFI8.EBBI9.VCI10.BSI11.NDBI12.GRAY
一、代码框架
在GEE平台上使用Landsat影像计算指数需要以下几个步骤： 导入Landsat影像；定义指数计算函数；计算指数并将其添加到图层中；将图层添加到地图中。下面是一个简单的示例代码，演示如何使用GEE平台计算归一化植被指数（NDVI）： //导入研究区边界 var roi = table Map.centerObject(roi, 10); Map.addLayer(roi, {color:"black"}, "roi"); // Step 1: 导入Landsat影像 var l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') .filterDate('2019-01-01', '2019-12-31') .filterBounds(roi) .map(roiClip); //按研究区边界裁剪 function roiClip(image){ return image.clip(roi) } // Step 2: 定义NDVI计算函数 function addNDVI(image) { var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI'); return image.addBands(ndvi); } // Step 3: 计算NDVI并将其添加到图层中 var withNDVI = l8.map(addNDVI); // Step 4: 将图层添加到地图中 // 可视化参数 var viz = {min:-1, max:1, palette:'blue, white, green'}; Map.addLayer(withNDVI.select('NDVI'), viz, 'NDVI'); 二、指数计算以下指数计算中使用到的波段名，基于《GEE：时间序列分析2——将Landsat5、7、8所有影像合成一个影像集合，构建NDVI时间序列》一文中Landsat578波段统一后的名称。 1.NDVI归一化植被指数 // NDVI var ndviTransform = function(img){ var ndvi = img.normalizedDifference(['B4', 'B3']) // calculate normalized dif between band 4 and band 3 (B4-B3/B4_B3) .multiply(1000) // scale results by 1000 .select([0], ['NDVI']) // name the band .set('system:time_start', img.get('system:time_start')); return ndvi; }; 2.NBR // NBR var nbrTransform = function(img) { var nbr = img.normalizedDifference(['B4', 'B7']) // calculate normalized difference of B4 and B7. orig was flipped: ['B7', 'B4'] .multiply(1000) // scale results by 1000 .select([0], ['NBR']) // name the band .set('system:time_start', img.get('system:time_start')); return nbr; }; 3.EVI // EVI var eviTransform = function(img) { var evi = img.expression( '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', { 'NIR': img.select('B4'), 'RED': img.select('B3'), 'BLUE': img.select('B1') }) .multiply(1000) // scale results by 1000 .select([0], ['EVI']) // name the band .set('system:time_start', img.get('system:time_start')); return evi; }; 4.NDMI // NDMI var ndmiTransform = function(img) { var ndmi = img.normalizedDifference(['B4', 'B5']) // calculate normalized difference of B4 and B7. orig was flipped: ['B7', 'B4'] .multiply(1000) // scale results by 1000 .select([0], ['NDMI']) // name the band .set('system:time_start', img.get('system:time_start')); return ndmi; }; 5.NDSI // NDSI var ndsiTransform = function(img){ var ndsi = img.normalizedDifference(['B2', 'B5']) // calculate normalized dif between band 4 and band 3 (B4-B3/B4_B3) .multiply(1000) // scale results by 1000 .select([0], ['NDSI']) // name the band .set('system:time_start', img.get('system:time_start')); return ndsi; }; 6.TC-Transform // TASSELLED CAP var tcTransform = function(img){ var b = ee.Image(img).select(["B1", "B2", "B3", "B4", "B5", "B7"]); // select the image bands var brt_coeffs = ee.Image.constant([0.2043, 0.4158, 0.5524, 0.5741, 0.3124, 0.2303]); // set brt coeffs - make an image object from a list of values - each of list element represents a band var grn_coeffs = ee.Image.constant([-0.1603, -0.2819, -0.4934, 0.7940, -0.0002, -0.1446]); // set grn coeffs - make an image object from a list of values - each of list element represents a band var wet_coeffs = ee.Image.constant([0.0315, 0.2021, 0.3102, 0.1594, -0.6806, -0.6109]); // set wet coeffs - make an image object from a list of values - each of list element represents a band var sum = ee.Reducer.sum(); // create a sum reducer to be applyed in the next steps of summing the TC-coef-weighted bands var brightness = b.multiply(brt_coeffs).reduce(sum); // multiply the image bands by the brt coef and then sum the bands var greenness = b.multiply(grn_coeffs).reduce(sum); // multiply the image bands by the grn coef and then sum the bands var wetness = b.multiply(wet_coeffs).reduce(sum); // multiply the image bands by the wet coef and then sum the bands var angle = (greenness.divide(brightness)).atan().multiply(180/Math.PI).multiply(100); var tc = brightness.addBands(greenness) .addBands(wetness) .addBands(angle) .select([0,1,2,3], ['TCB','TCG','TCW','TCA']) //stack TCG and TCW behind TCB with .addBands, use select() to name the bands .set('system:time_start', img.get('system:time_start')); return tc; }; indexImg = tc.select(['TCB']); indexImg = tc.select(['TCG']); indexImg = tc.select(['TCW']); indexImg = tc.select(['TCA']); 7.NDFI //Ben added // NDFI - from CODED utility (original: users/bullocke/coded:coded/miscUtilities) var ndfiTransform = function(img) { // pre-defined endmembers var params = ee.Dictionary({ 'cfThreshold': 0.01, // CLOUD THRESHOLD 'soil': [2000, 3000, 3400, 5800, 6000, 5800], 'gv': [500, 900, 400, 6100, 3000, 1000], 'npv': [1400, 1700, 2200, 3000, 5500, 3000], 'shade': [0, 0, 0, 0, 0, 0], 'cloud': [9000, 9600, 8000, 7800, 7200, 6500] }); /* Utility function for calculating spectral indices */ var gv = params.get('gv'); var shade = params.get('shade'); var npv = params.get('npv'); var soil = params.get('soil'); var cloud = params.get('cloud'); //var cfThreshold = ee.Image.constant(params.get('cfThreshold')) /* Do spectral unmixing on a single image */ var unmixImage = ee.Image(img).unmix([gv, shade, npv, soil, cloud], true,true) .rename(['band_0', 'band_1', 'band_2','band_3','band_4']); var newImage = ee.Image(img).addBands(unmixImage); //var mask = newImage.select('band_4').lt(cfThreshold) var ndfi = unmixImage.expression( '((GV / (1 - SHADE)) - (NPV + SOIL)) / ((GV / (1 - SHADE)) + NPV + SOIL)', { 'GV': unmixImage.select('band_0'), 'SHADE': unmixImage.select('band_1'), 'NPV': unmixImage.select('band_2'), 'SOIL': unmixImage.select('band_3') }); var ndvi = ee.Image(img).normalizedDifference(['B4','B3']).rename('NDVI') var evi = ee.Image(img).expression( 'float(2.5*(((B4/10000) - (B3/10000)) / ((B4/10000) + (6 * (B3/10000)) - (7.5 * (B1/10000)) + 1)))', { 'B4': ee.Image(img).select(['B4']), 'B3': ee.Image(img).select(['B3']), 'B1': ee.Image(img).select(['B1']) }).rename('EVI'); var toExp = newImage .addBands([ndfi.rename(['NDFI']), ndvi, evi]) .select(['band_0','band_1','band_2','band_3','NDFI','NDVI','EVI','B1','B2','B3','B4','B5']) .rename(['GV','Shade','NPV','Soil','NDFI','NDVI','EVI','Blue','Green','Red','NIR','SWIR1']); //.updateMask(mask) toExp = toExp.select(['NDFI']) .multiply(1000) .set('system:time_start', img.get('system:time_start')); return toExp; }; 8.EBBI
计算多年来的植被状况指数（the vegetation condition index，VCI） （代码链接） var getvci = function(image){ // ((NDVI-NDVImin)/(NDVImax-MDVImin))*100 var vci = image.subtract(minImage).divide(maxImage.subtract(minImage)).rename('VCI') // return image.addBands(vci) // both output togther return vci // only the vci } 10.BSI var addBSI = function(image) {var bsi = image.expression( '((RED + SWIR) - (NIR + BLUE)) / ((RED + SWIR) + (NIR + BLUE)) ', { 'RED': image.select('B4'), 'BLUE': image.select('B2'), 'NIR': image.select('B8'), 'SWIR': image.select('B11'), } ) .rename('BSI') .copyProperties(image,['system:time_start']); return image.addBands(bsi); }; 11.NDBI归一化建筑指数：RNIR、RMIR分别为影像的近红外、中红外波段，OLI数据的5波段、6波段。 |
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