計算DMSP燈光數(shù)據(jù)的趨勢

主要功能

獲取1990年以來的燈光數(shù)據(jù)，

代碼

// 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');

步驟分析

1. 定義函數(shù)，生成時序波段
2. 生成數(shù)據(jù)集對象，選擇燈光數(shù)據(jù)
3. 計算燈光數(shù)據(jù)變化趨勢
4. 添加變化趨勢最大結果
5. 設置地圖中心，縮放等級
6. 添加圖層

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

以對象形式返回指定的影像數(shù)據(jù)。
輸入?yún)?shù)：輸入影像對象

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

返回兩個輸入時間間的差值，使用指定的單位，返回結果是浮點型的。
輸入?yún)?shù)：輸入時間對象，開始（時間對象），單位（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

在數(shù)據(jù)集內(nèi)的所有數(shù)據(jù)上執(zhí)行指定函數(shù)。
輸入?yún)?shù)：輸入數(shù)據(jù)集對象，算法（指定的函數(shù)），是否允許返回NULL（布爾型）

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

對數(shù)據(jù)集中的所有數(shù)據(jù)應用一個reducer。如果reducer只需要一個數(shù)據(jù)，則對每一個數(shù)據(jù)都執(zhí)行該操作；否則，必須與數(shù)據(jù)集中數(shù)據(jù)個數(shù)相同的輸入?yún)?shù)個數(shù)。
輸入?yún)?shù)：數(shù)據(jù)集對象，reducer，并行尺度（限制內(nèi)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

返回一個reduce對象，用于計算坡度和加權的偏移量