GEE灯光数据趋势分析

计算DMSP灯光数据的趋势

主要功能

获取1990年以来的灯光数据，

代码

// Compute the trend of nighttime lights from DMSP.

// Add a band containing image date as years since 1990.
function createTimeBand(img) {
  var year = img.date().difference(ee.Date('1990-01-01'), 'year');
  return ee.Image(year).float().addBands(img);
}

// Fit a linear trend to the nighttime lights collection.
var collection = ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4')
    .select('avg_vis')
    .map(createTimeBand);
var fit = collection.reduce(ee.Reducer.linearFit());

// Display a single image
Map.addLayer(ee.Image(collection.select('avg_vis').first()),
         {min: 0, max: 63},
         'stable lights first asset');

// Display trend in red/blue, brightness in green.
Map.setCenter(30, 45, 4);
Map.addLayer(fit,
         {min: 0, max: [0.18, 20, -0.18], bands: ['scale', 'offset', 'scale']},
         'stable lights trend');

步骤分析

1. 定义函数，生成时序波段
2. 生成数据集对象，选择灯光数据
3. 计算灯光数据变化趋势
4. 添加变化趋势最大结果
5. 设置地图中心，缩放等级
6. 添加图层

主要方法

1. ee.Image.date()
   Returns the acquisition time of an image as a Date object. This helper function is equivalent to ee.Date(image.get('system:time_start')).
   Arguments:
   this:image (Image):
   The image whose acquisition time to return.
   Returns: Date

以对象形式返回指定的影像数据。
输入参数：输入影像对象

2. ee.Date.difference()
   Returns the difference between two Dates in the specified units; the result is floating-point and based on the average length of the unit.
   Arguments:
   this:date (Date)
   start (Date)
   unit (String):
   One of 'year', 'month' 'week', 'day', 'hour', 'minute', or 'second'.
   Returns: Float

返回两个输入时间间的差值，使用指定的单位，返回结果是浮点型的。
输入参数：输入时间对象，开始（时间对象），单位（字符串）

3. ee.ImageCollection.map()
   Maps an algorithm over a collection.
   Returns the mapped collection.
   Arguments:
   this:collection (Collection):
   The Collection instance.
   algorithm (Function):
   The operation to map over the images or features of the collection. A JavaScript function that receives an image or features and returns one. The function is called only once and the result is captured as a description, so it cannot perform imperative operations or rely on external state.
   dropNulls (Boolean, optional):
   If true, the mapped algorithm is allowed to return nulls, and the elements for which it returns nulls will be dropped.
   Returns: Collection

在数据集内的所有数据上执行指定函数。
输入参数：输入数据集对象，算法（指定的函数），是否允许返回NULL（布尔型）

4. ee.ImageCollection.reduce()
   Applies a reducer across all of the images in a collection.
   If the reducer has a single input, it will be applied separately to each band of the collection; otherwise it must have the same number of inputs as the collection has bands.
   The reducer output names determine the names of the output bands: reducers with multiple inputs will use the output names directly, while reducers with a single input will prefix the output name with the input band name (e.g. '10_mean', '20_mean', etc.).
   Arguments:
   this:collection (ImageCollection):
   The image collection to reduce.
   reducer (Reducer):
   The reducer to apply to the given collection.
   parallelScale (Float, default: 1):
   A scaling factor used to limit memory use; using a larger parallelScale (e.g. 2 or 4) may enable computations that run out of memory with the default.
   Returns: Image

对数据集中的所有数据应用一个reducer。如果reducer只需要一个数据，则对每一个数据都执行该操作；否则，必须与数据集中数据个数相同的输入参数个数。
输入参数：数据集对象，reducer，并行尺度（限制内存使用量）

5. ee.Reducer.linearFit()
   Returns a Reducer that computes the slope and offset for a (weighted) linear regression of 2 inputs.
   No arguments.
   Returns: Reducer

返回一个reduce对象，用于计算坡度和加权的偏移量