Google earth engine（GEE）绘制沿山脉断面的海拔和温度（双轴坐标显示）

这次的案例是通过绘制一条直线，确定沿着这条直线的海拔和随着海拔的温度变化情况，用到的Landsat8数据和DEM数据。时间线主要是2013-2014年的夏天和冬天

温度采用的是L8的第十波段减去273.5得到的设置的时间是根据系统默认时间，获取影像的时间。

distance(right, maxError, proj)

Returns the minimum distance between the geometries of two features.

返回两个要素的几何之间的最小距离。

Arguments:

this:left (Element):

The feature containing the geometry used as the left operand of the operation.

right (Element):

The feature containing the geometry used as the right operand of the operation.

maxError (ErrorMargin, default: null):

The maximum amount of error tolerated when performing any necessary reprojection.

执行任何必要的重新投影时允许的最大错误量。

proj (Projection, default: null):

The projection in which to perform the operation. If not specified, the operation will be performed in a spherical coordinate system, and linear distances will be in meters on the sphere.

Returns: Float

//输入你的起点和重点的坐标，并且连接成线
var reno = [-119.821944, 39.527222];
var sf = [-122.416667, 37.783333];
var transect = ee.Geometry.LineString([reno, sf]);

// 选择影像按照你的线进行边界筛选，并且选出温度波段
var landsat8Toa = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');
var temperature = landsat8Toa.filterBounds(transect)
    .select(['B10'], ['temp'])
    .map(function(image) {
      // 温度值转化为摄氏度
      return image.subtract(273.15)
          .set('system:time_start', image.get('system:time_start'));
    });

// 波段平均值计算和日期筛选; 并且合成单一波段影像
var summer = temperature.filterDate('2014-06-21', '2014-09-23')
    .reduce(ee.Reducer.mean())
    .select([0], ['summer']);在0这个位置放入
var winter = temperature.filterDate('2013-12-21', '2014-03-20')
    .reduce(ee.Reducer.mean())
    .select([0], ['winter']);
//选择DEM影像
var elevation = ee.Image('USGS/NED');  
//将点放入矢量集合当中
var startingPoint = ee.FeatureCollection(ee.Geometry.Point(sf));
//容许的间隔误差
var distance = startingPoint.distance(500000);
//将三个值分别加入一个影像形成3个波段
var image = distance.addBands(elevation).addBands(winter).addBands(summer);

// 沿着抛面线提前三个值并且转化为数组提前将数据，采样的间隔是1000
var array = image.reduceRegion(ee.Reducer.toList(), transect, 1000)
                 .toArray(image.bandNames());

//沿横断面按点与起点的距离对点进行排序。
//先切片再排序这是X轴的
var distances = array.slice(0, 0, 1);
array = array.sort(distances);

//先切片再排序这是Y轴的
var elevationAndTemp = array.slice(0, 1);  // For the Y axis.
// 投影距离切片以创建 x 轴值的一维数组。
var distance = array.slice(0, 0, 1).project([1]);

// 画图ui.Chart.array.values（X轴数组，生成一维向量系列所沿的轴，沿图表 x 轴的刻度标签）
var chart = ui.Chart.array.values(elevationAndTemp, 1, distance)
    .setChartType('LineChart')
    .setSeriesNames(['Elevation', 'Winter 2014', 'Summer 2014'])
    .setOptions({
      title: 'Elevation and temperatures along SF-to-Reno transect',
      vAxes: {//分别色湖之Y轴的标题和颜色
        0: {
          title: 'Average seasonal temperature (Celsius)'
        },
        1: {
          title: 'Elevation (meters)',
          baselineColor: 'transparent'
        }
      },
      hAxis: {//设置横轴的标题
        title: 'Distance from SF (m)'
      },
      interpolateNulls: true,
      pointSize: 0,
      lineWidth: 1,
      // Our chart has two Y axes: one for temperature and one for elevation.
      // The Visualization API allows us to assign each series to a specific
      // Y axis, which we do here:
      series: {//谢列的三个位置，一个横轴两个数周
        0: {targetAxisIndex: 1},
        1: {targetAxisIndex: 0},
        2: {targetAxisIndex: 0}
      }
    });

print(chart);
Map.setCenter(-121, 38.5, 7);
Map.addLayer(elevation, {min: 4000, max: 0});
Map.addLayer(transect, {color: 'FF0000'});

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/119001011