Google Earth Engine（GEE）——如何加载ee.FeatureCollection中点数据在MAP中

遇到粉丝问这样的问题，如何加载矢量集合中的点数据在map地图中，其实这是一个非常简单的问题。首先我们要先有一个矢量集合，其次我们就直接用Map.addlayer就OK了。本次我asset中没有矢量集合点，所以我利用随机点生成一些点，作为我的矢量集合。

先来看两个函数：这里我不翻译了，大家应该都能看懂

ee.FeatureCollection(args, column)

FeatureCollections can be constructed from the following arguments:

- A string: assumed to be the name of a collection.

- A single geometry.

- A single feature.

- A list of features.

- A GeoJSON FeatureCollection

- A computed object: reinterpreted as a collection.

Arguments:

args (ComputedObject|Feature|FeatureCollection|Geometry|List<Object>|Number|String):

The constructor arguments.

column (String, optional):

The name of the geometry column to use. Only useful when working with a named collection.

Returns: FeatureCollection

Map.addLayer(eeObject, visParams, name, shown, opacity)

Adds a given EE object to the map as a layer.

Returns the new map layer.

Arguments:

eeObject (Collection|Feature|Image|RawMapId):

The object to add to the map.

visParams (FeatureVisualizationParameters|ImageVisualizationParameters, optional):

The visualization parameters. For Images and ImageCollection, see ee.data.getMapId for valid parameters. For Features and FeatureCollections, the only supported key is "color", as a CSS 3.0 color string or a hex string in "RRGGBB" format. Ignored when eeObject is a map ID.

name (String, optional):

The name of the layer. Defaults to "Layer N".

shown (Boolean, optional):

A flag indicating whether the layer should be on by default.

opacity (Number, optional):

The layer's opacity represented as a number between 0 and 1. Defaults to 1.

Returns: ui.Map.Layer

  

   

    

     

    
    

     

      var geometry = 
     
    
   

    

     

    
    

     
    /* color: #98ff00 */
     
    
   

    

     

    
    

     
    /* displayProperties: [
     
    
   

    

     

    
    

     

       {
     
    
   

    

     

    
    

     

       "type": "rectangle"
     
    
   

    

     

    
    

     

       }
     
    
   

    

     

    
    

     

       ] */
     
    
   

    

     

    
    

     

          ee.Geometry.Polygon(
     
    
   

    

     

    
    

     

              [[[-113.3658081828087, 41.45685505178655],
     
    
   

    

     

    
    

     

                [-113.3658081828087, 40.134503279488],
     
    
   

    

     

    
    

     

                [-110.0039917765587, 40.134503279488],
     
    
   

    

     

    
    

     

                [-110.0039917765587, 41.45685505178655]]], null, false);
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      // 设置数据时间
     
    
   

    

     

    
    

     

      var startDate = ee.Date.fromYMD(2018,1,1);
     
    
   

    

     

    
    

     

      var endDate = ee.Date.fromYMD(2019,1,1);
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      // 获取地表温度
     
    
   

    

     

    
    

     

      var temperature = ee.ImageCollection("MODIS/006/MOD11A2")
     
    
   

    

     

    
    

     

        .filterDate(startDate, endDate)
     
    
   

    

     

    
    

     

        .select('LST_Day_1km')
     
    
   

    

     

    
    

     

        .mean()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      // 抽取N个点
     
    
   

    

     

    
    

     

      var values = temperature.sample({ region: geometry, scale: 500, numPixels: 1000, geometries: true}) 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      //这里主要是为了让大家看清楚我展示确实是矢量集合
     
    
   

    

     

    
    

     

      var xx = ee.FeatureCollection(values)
     
    
   

    

     

    
    

     

      //加载点数据
     
    
   

    

     

    
    

     

      Map.addLayer(xx.style({ color: 'green', pointSize: 3 }), {}, 'samples1')
     
    
  
 

文章来源: blog.csdn.net，作者：此星光明2021年博客之星云计算Top3，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/qq_31988139/article/details/121412130