Urban ecology is closely related to human life, and it has become a research focus in the field of ecology to quickly, accurately and objectively understand urban ecological conditions. Based on remote sensing technology, a Remote Sensing Ecological Index (RSEI) based entirely on remote sensing technology and mainly based on natural factors is proposed to quickly monitor and evaluate the ecological status of cities. The index uses principal component analysis technology to integrate four evaluation indicators, namely vegetation index, humidity component, surface temperature and building index, which respectively represent the four ecological elements of greenness, humidity, heat and dryness.
This paper implements the RSEI algorithm based on the GEE platform.
operation result:
Step 1: Define the research area and change your own research area
Step 2: Load the data set and define the cloud function
Step 3: Main function, calculate the ecological index
Step 4: PCA fusion, extract the first principal component
Step 5: Use PC1 to calculate RSEI and normalize
full code
The code is as follows (example):
// 第一步:定义研究区，自行更换自己的研究区
var roi =
/* color: #98ff00 */
/* displayProperties: [
{
"type": "rectangle"
}
] */
ee.Geometry.Polygon(
[[[120.1210075098537, 35.975189051414006],
[120.1210075098537, 35.886229778229115],
[120.25764996590839, 35.886229778229115],
[120.25764996590839, 35.975189051414006]]], null, false);
Map.centerObject(roi);
// 第二步：加载数据集,定义去云函数
function removeCloud(image){
var qa = image.select('BQA')
var cloudMask = qa.bitwiseAnd(1 << 4).eq(0)
var cloudShadowMask = qa.bitwiseAnd(1 << 8).eq(0)
var valid = cloudMask.and(cloudShadowMask)
return image.updateMask(valid)
}
// 数据集去云处理
var L8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterBounds(roi)
.filterDate('2018-01-01', '2019-12-31')
.filterMetadata('CLOUD_COVER', 'less_than',50)
.map(function(image){
return image.set('year', ee.Image(image).date().get('year'))
})
.map(removeCloud)
// 影像合成
var L8imgList = ee.List([])
for(var a = 2018; a < 2020; a++){
var img = L8.filterMetadata('year', 'equals', a).median().clip(roi)
var L8img = img.set('year', a)
L8imgList = L8imgList.add(L8img)
}
// 第三步：主函数,计算生态指标
var L8imgCol = ee.ImageCollection(L8imgList)
.map(function(img){
return img.clip(roi)
})
L8imgCol = L8imgCol.map(function(img){
// 湿度函数:Wet
var Wet = img.expression('B*(0.1509) + G*(0.1973) + R*(0.3279) + NIR*(0.3406) + SWIR1*(-0.7112) + SWIR2*(-0.4572)',{
'B': img.select(['B2']),
'G': img.select(['B3']),
'R': img.select(['B4']),
'NIR': img.select(['B5']),
'SWIR1': img.select(['B6']),
'SWIR2': img.select(['B7'])
})
img = img.addBands(Wet.rename('WET'))
// 绿度函数:NDVI
var ndvi = img.normalizedDifference(['B5', 'B4']);
img = img.addBands(ndvi.rename('NDVI'))
// 热度函数:lst 直接采用MODIS产品
var lst = ee.ImageCollection('MODIS/006/MOD11A1').map(function(img){
return img.clip(roi)
})
.filterDate('2014-01-01', '2019-12-31')
var year = img.get('year')
lst=lst.filterDate(ee.String(year).cat('-01-01'),ee.String(year).cat('-12-31')).select(['LST_Day_1km', 'LST_Night_1km']);
// reproject主要是为了确保分辨率为1000
var img_mean=lst.mean().reproject('EPSG:4326',null,1000);
//print(img_mean.projection().nominalScale())
img_mean = img_mean.expression('((Day + Night) / 2)',{
'Day': img_mean.select(['LST_Day_1km']),
'Night': img_mean.select(['LST_Night_1km']),
})
img = img.addBands(img_mean.rename('LST'))
// 干度函数:ndbsi = ( ibi + si ) / 2
var ibi = img.expression('(2 * SWIR1 / (SWIR1 + NIR) - (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1))) / (2 * SWIR1 / (SWIR1 + NIR) + (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1)))', {
'SWIR1': img.select('B6'),
'NIR': img.select('B5'),
'RED': img.select('B4'),
'GREEN': img.select('B3')
})
var si = img.expression('((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))', {
'SWIR1': img.select('B6'),
'NIR': img.select('B5'),
'RED': img.select('B4'),
'BLUE': img.select('B2')
})
var ndbsi = (ibi.add(si)).divide(2)
return img.addBands(ndbsi.rename('NDBSI'))
})
var bandNames = ['NDVI', "NDBSI", "WET", "LST"]
L8imgCol = L8imgCol.select(bandNames)
//定义归一化函数:归一化
var img_normalize = function(img){
var minMax = img.reduceRegion({
reducer:ee.Reducer.minMax(),
geometry: roi,
scale: 1000,
maxPixels: 10e13,
})
var year = img.get('year')
var normalize = ee.ImageCollection.fromImages(
img.bandNames().map(function(name){
name = ee.String(name);
var band = img.select(name);
return band.unitScale(ee.Number(minMax.get(name.cat('_min'))), ee.Number(minMax.get(name.cat('_max'))));
})
).toBands().rename(img.bandNames()).set('year', year);
return normalize;
}
var imgNorcol = L8imgCol.map(img_normalize);
// 第四步:PCA融合，提取第一主成分
var pca = function(img){
var bandNames = img.bandNames();
var region = roi;
var year = img.get('year')
// Mean center the data to enable a faster covariance reducer
// and an SD stretch of the principal components.
var meanDict = img.reduceRegion({
reducer: ee.Reducer.mean(),
geometry: region,
scale: 1000,
maxPixels: 10e13
});
var means = ee.Image.constant(meanDict.values(bandNames));
var centered = img.subtract(means).set('year', year);
// This helper function returns a list of new band names.
var getNewBandNames = function(prefix, bandNames){
var seq = ee.List.sequence(1, 4);
//var seq = ee.List.sequence(1, bandNames.length());
return seq.map(function(n){
return ee.String(prefix).cat(ee.Number(n).int());
});
};
// This function accepts mean centered imagery, a scale and
// a region in which to perform the analysis. It returns the
// Principal Components (PC) in the region as a new image.
var getPrincipalComponents = function(centered, scale, region){
var year = centered.get('year')
var arrays = centered.toArray();
// Compute the covariance of the bands within the region.
var covar = arrays.reduceRegion({
reducer: ee.Reducer.centeredCovariance(),
geometry: region,
scale: scale,
bestEffort:true,
maxPixels: 10e13
});
// Get the 'array' covariance result and cast to an array.
// This represents the band-to-band covariance within the region.
var covarArray = ee.Array(covar.get('array'));
// Perform an eigen analysis and slice apart the values and vectors.
var eigens = covarArray.eigen();
// This is a P-length vector of Eigenvalues.
var eigenValues = eigens.slice(1, 0, 1);
// This is a PxP matrix with eigenvectors in rows.
var eigenVectors = eigens.slice(1, 1);
// Convert the array image to 2D arrays for matrix computations.
var arrayImage = arrays.toArray(1)
// Left multiply the image array by the matrix of eigenvectors.
var principalComponents = ee.Image(eigenVectors).matrixMultiply(arrayImage);
// Turn the square roots of the Eigenvalues into a P-band image.
var sdImage = ee.Image(eigenValues.sqrt())
.arrayProject([0]).arrayFlatten([getNewBandNames('SD',bandNames)]);
// Turn the PCs into a P-band image, normalized by SD.
return principalComponents
// Throw out an an unneeded dimension, [[]] -> [].
.arrayProject([0])
// Make the one band array image a multi-band image, [] -> image.
.arrayFlatten([getNewBandNames('PC', bandNames)])
// Normalize the PCs by their SDs.
.divide(sdImage)
.set('year', year);
}
// Get the PCs at the specified scale and in the specified region
img = getPrincipalComponents(centered, 1000, region);
return img;
};
var PCA_imgcol = imgNorcol.map(pca)
Map.addLayer(PCA_imgcol.first(), {"bands":["PC1"]}, 'pc1')
// 第五步：利用PC1，计算RSEI，并归一化
var RSEI_imgcol = PCA_imgcol.map(function(img){
img = img.addBands(ee.Image(1).rename('constant'))
var rsei = img.expression('constant - pc1' , {
constant: img.select('constant'),
pc1: img.select('PC1')
})
rsei = img_normalize(rsei)
return img.addBands(rsei.rename('rsei'))
})
print(RSEI_imgcol)
var visParam = {
palette: '040274, 040281, 0502a3, 0502b8, 0502ce, 0502e6, 0602ff, 235cb1, 307ef3, 269db1, 30c8e2, 32d3ef, 3be285, 3ff38f, 86e26f, 3ae237, b5e22e, d6e21f, fff705, ffd611, ffb613, ff8b13, ff6e08, ff500d, ff0000, de0101, c21301, a71001, 911003'
};
Map.addLayer(RSEI_imgcol.first().select('rsei'), visParam, 'rsei')