Google Earth Engine笔记-植被覆盖度计算

1 植被覆盖度遥感监测

根据$NDVI$数据进行像元二分模型计算植被覆盖度

像元二分模型将像元的植被覆盖结构分为纯像元与混合像元两类，纯像元植被完全覆盖覆盖度为1，混合想要由植被与非植被构成，其$NDVI$是植被覆盖部分的$NDVI$值与非植被覆盖部分$NDVI$值的线性加权：
$$NDVI=f_v\cdot NDV{I}_v+(1-f_v)\cdot NDV{I}_0$$
其中$NDVI$为像元$NDVI$值，$f_v$为像元植被覆盖度，$NDV{I}_v$与$NDV{I}_0$分布式植被覆盖部分和非植被覆盖部分的$NDVI$值，即为纯植被与纯裸地像元的$NDVI$值，可得
$$f_v=\frac{NDVI-NDV{I}_0}{NDV{I}_v-NDV{I}_0}$$
通常情况取直方图累计频率为5%与95%的$NDVI$值作为$NDV{I}_0$与$NDV{I}_v$，$NDVI\le NDV{I}_0$的像元植被覆盖度为0，$NDVI\ge NDV{I}_v$的像元植被覆盖都为1，在水体较多的区域为了避免水体面积过大对$NDV{I}_v$与$NDV{I}_0$取值产生影响，可将水体去除($NDVI<0$)后再统计$NDVI$直方图

• 准备数据，基于哨兵2号的SR数据来进行初始数据集筛选

var wuhan = ee.FeatureCollection("users/yp7454982/wuhan");
Map.addLayer(wuhan)
Map.centerObject(wuhan,8)
function maskS2clouds(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));

  return image.updateMask(mask).divide(10000);
}
var dataset_sentinel2=ee.ImageCollection("COPERNICUS/S2_SR")
                        .filterDate('2019-01-01','2019-12-31')
                        .filterBounds(wuhan)//
                        .filterMetadata('CLOUDY_PIXEL_PERCENTAGE',"less_than",20)
                        .map(maskS2clouds)
print(dataset_sentinel2)

筛选2019年在武汉市云量百分之20以下的所有数据，进行去云处理

• 中值合成并进行数据裁剪，真彩色合成

var mid_image=dataset_sentinel2.median().clip(wuhan.geometry());
var visualization = {
  min: 0.0,
  max: 0.3,
  bands: ['B4', 'B3', 'B2'],
};
Map.addLayer(mid_image,visualization,'RGB')

• 计算NDVI并将水体mask掉，并利用像元二分模型进行植被覆盖度反演

var NDVI=mid_image.normalizedDifference(["B8","B4"]).rename("NDVI")
print(NDVI)
Map.addLayer(NDVI.lt(0))//water
Map.addLayer(mid_image.updateMask(NDVI.gt(0)),visualization,'RGB1')
var mask_water_NDVI=NDVI.updateMask(NDVI.gt(0))//mask water
//var chart=ui.Chart.image.histogram(mask_water_NDVI,wuhan,100)
//print(chart)
function calFVC(BestVI,region,scale){
    var num = BestVI.reduceRegion({
      reducer:ee.Reducer.percentile([5,95]),
      geometry:region,
      scale:scale,
      maxPixels:1e13
    });
    var min = ee.Number(num.get("NDVI_p5"));
    var max = ee.Number(num.get("NDVI_p95"));
    //print(top_min);
    //print(top_max);
    //quantile and combine
    var greaterPart = BestVI.gt(max);
    var lessPart = BestVI.lt(min);
    var middlePart =ee.Image(1).subtract(greaterPart).subtract(lessPart);
    //calculate FVC
    var tempf1=BestVI.subtract(min).divide(max.subtract(min));
    var FVC=ee.Image(1).multiply(greaterPart).add(ee.Image(0).multiply(lessPart))
                      .add(tempf1.multiply(middlePart))
    return FVC.rename('FVC');
}
var FVC=calFVC(mask_water_NDVI,wuhan,10)
print(FVC)
var chart=ui.Chart.image.histogram({
  image:FVC,
  region:wuhan,
  scale:250,
  //maxPixels:1e13
})
print(chart)
Export.image.toDrive({
  image: FVC,
  description: "wuhan_fvc_2019",
  scale: 10,
  region: wuhan.geometry(),
  maxPixels: 1e13
});