NDVI为归一化植被指数、EVI为增强植被指数,据GEE官方介绍NDTI为归一化热差指数，用于区分城市地区的人行道和屋顶、NDBI为归一化建筑物指数
用来分析城市建成区。今天来分享如何在GEE中利用landsat影像计算上述指数，并进行相关性分析。相关性分析用Pearson相关系数表示：

//首先，选择研究区，以山西省为研究区

var roi = ee.FeatureCollection("users/lilei655123/shanxi");

//去云函数

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));

return image.updateMask(mask)

.copyProperties(image)

.copyProperties(image, ["system:time_start",'system:time_end']);

}

var star_date = '2020-07-01'//定义起始时间

var end_date = '2020-09-30'//定义终止时间

var L8_ = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterBounds(roi)

.filterDate(star_date, end_date)

//.filter(ee.Filter.lt('CLOUD_COVER',10))//云量设//按云量排序//云量过滤

.map(rmL8Cloud)

.median()

.clip(roi)



print(L8_)



// 加载影像

Map.addLayer(L8_,{min:0.0,max:0.4, bands:'B5,B4,B3'},'Image');

Map.centerObject(roi)

var img = L8_.select(['B2','B3', 'B4', 'B5','B6', 'B7'])

//计算NDVI

var ndvi = img.expression(

'((NIR - RED) / (NIR + RED))', {

'NIR': img.select('B5'),

'RED': img.select('B4')

}).rename('NDVI');

//计算NDTI

var ndti = img.expression(

'((SWIR1-SWIR2)/(SWIR1 + SWIR2))',{

'SWIR1': img.select('B6'),

'SWIR2': img.select('B7')

}).rename('NDTI');

//计算EVI

var evi = img.expression(

'2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', {

'NIR': img.select('B5'),

'RED': img.select('B4'),

'BLUE': img.select('B2')

}).rename('EVI');

//计算NDBI

var ndbi = img.expression(

'(MIR - NIR)/(MIR + NIR)', {

'NIR': img.select('B5'),

'MIR': img.select('B6')

}).rename('NDBI');

//计算MNDWI

var mndwi = img.expression(

'(GREEN - MIR) / (GREEN + MIR)', {

'GREEN': img.select('B3'),

'MIR': img.select('B6')

}).rename('MNDWI')



//pearsonsCorrelation计算相关性系数

img = img.addBands(ndvi.rename('ndvi'))

img = img.addBands(ndti.rename('ndti'))

img = img.addBands(ndbi.rename('ndbi'))

img = img.addBands(evi.rename('evi'))

img = img.addBands(mndwi.rename('mndwi'))

var corra =img.select(["ndvi","ndti"])

// Here, sample the stack:

var sample = corra.sample({'region':roi,

'scale': 30,

'projection': 'EPSG:4326',

'numPixels':100})

// Call reducer correctly:

var correl = ee.Reducer.pearsonsCorrelation();

// Then choose two properties to see the correlation:

var reduced = sample.reduceColumns(correl, ['ndvi', 'ndti'])

print(reduced)

Map.addLayer(ndvi, {'min':0,'max':1,'palette':['#A9A9A9','00FF00']}, 'NDVI')

//导出函数

Export.image.toDrive({

image: ndvi,//

description: 'ndvi2020',//

scale: 30,//

region:roi,//

maxPixels:1e13,

fileFormat: 'GeoTIFF',

});

计算结果：

Pearson相关系数结果:

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声明：仅供学习使用！

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