【GEE】​3、 栅格遥感影像波段特征及渲染可视化

本文主要是介绍【GEE】​3、 栅格遥感影像波段特征及渲染可视化，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1、简介

在本单元中，将学习以下内容：

1. 使用遥感传感器捕获的不同类型的能量。
2. 如何构建 JavaScript 字典和列表以选择单个栅格波段。
3. 如何可视化多波段和单波段栅格的不同组合。

// This is a dictionary object. It has a key and an item.
var dictionary_one = {key: 'item', term: 'value'};
var dictionary_two = {first_key: 1.1, second_key: 2.2, third_key: 3.3};
print(dictionary_one,"dict1");
print(dictionary_two, "dict2");// Occasionally, dictionary items can be lists. Lists are simply containers for multiple items.
var dictionary_with_list = {key: ['item1', 'item2', 'item3']};
print(dictionary_with_list, "dictList");

3.1真彩色 (TCI)

在本单元中，我们将使用许多数据集。在本节中，我们将导入 Landsat 8 表面反射率集合。我们将首先使用真彩色图像 (TCI) 来调查和解释加拿大蒙特利尔的景观。使用下面的代码应该会产生如下所示的图像。我们在新闻和网上遇到的许多基于空间的图像都是 TCI。通过使用 Google Earth Engine 中的 TCI，我们可以利用自己的经验和常识识别来对物体进行识别和分类。使用以下代码启动一个新脚本以生成如下所示的 TCI。

// Load the Landsat image collection.
var ls8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR");// Filter the collection by date and cloud cover.
var ls8 = ls8
.filterDate('2017-05-01','2017-09-30')
.filterMetadata('CLOUD_COVER','less_than', 3.0);// There are a number of keys that you can use in a visParams dictionary.
// Here, we are going to start by defining our 'bands', 'min', 'max', and 'gamma' values.
// You can think of 'gamma' essentially as a brightness level.
var ls_tci = {bands: ['B4','B3','B2'], min: 0, max: 3000, gamma: 1.4};Map.setCenter(-73.755, 45.5453, 11);
Map.addLayer(ls8, ls_tci, 'True Color');

可视化 2017 年加拿大蒙特利尔上空 Landsat 8 表面反射率数据的真彩色图像。
 

// Add the Color Infrared visualization.
var ls_ci = {bands: ['B5','B4','B3'], min: 0, max: 3000, gamma: [0.7, 1, 1]};
Map.addLayer(ls8, ls_ci, 'Color Infrared');

用彩色红外图像替换我们的真彩色图像，以识别加拿大蒙特利尔和周边陆地区域的活跃植被和水道区域。
 

// Add the False Color 1 visualization.
var ls_fc1 = {bands: ['B6','B5','B4'], min: 0, max: 4000, gamma: 0.9};
Map.addLayer(ls8, ls_fc1, 'False Color 1');// Add the False Color 2 visualization.
var ls_fc2 = {bands: ['B7','B6','B4'], min: 0, max: 3000, gamma: 0.9};
Map.addLayer(ls8, ls_fc2, 'False Color 2');

我们使用两个不同（但相关）的假彩色图像对加拿大蒙特利尔的最终描绘，突出了农业和城市地区以及水道之间的差异。

// Load and select the Gross Primary Production (GPP) image collection.
var dataset = ee.ImageCollection('UMT/NTSG/v2/LANDSAT/GPP').filter(ee.Filter.date('2018-05-01', '2018-10-31'));
var gpp = dataset.select('GPP');// Build a set of visualization parameters and add the GPP layer to the map.
var gppVis = {min: 0.0,max: 500.0,palette: ['ffffe5','f7fcb9','d9f0a3','addd8e','78c679','41ab5d','238443','005a32']};Map.setCenter(-120.9893, 47.2208, 10);
Map.addLayer(gpp, gppVis, 'GPP');

可视化美国华盛顿州喀斯喀特山脉东坡的总初级生产力（来自 Landsat 数据），其中多种土地用途以红色突出显示。

// Load the NLCD image collection and select the land cover layer.
var dataset = ee.ImageCollection('USGS/NLCD');
var landcover = dataset.select('landcover');// Values from 41-43 represent explicitly defined forest types.
var landcoverVis = {min: 41.0,max: 43.0,palette: ['black','green', 'green','green','black'],opacity: 0.75
};Map.setCenter(-120.9893, 47.2208, 10);
Map.addLayer(landcover, landcoverVis, 'Landcover');

森林与非森林区域的简单二元可视化，同样位于喀斯喀特山脉的东坡，使用国家土地覆盖数据集类。

我们将在第9单元中再次处理分类数据。

// This is a dictionary object. It has a key and an item.
var dictionary_one = {key: 'item', term: 'value'}
var dictionary_two = {first_key: 1.1, second_key: 2.2, third_key: 3.3}
print(dictionary_one,"dict1");
print(dictionary_two, "dict2");// Occasionally, dictionary items can be lists. Lists are simply containers for multiple items.
var dictionary_with_list = {key: ['item1', 'item2', 'item3']};
print(dictionary_with_list, "dictList");// Load the Landsat image collection.
var ls8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR");// Filter the collection by date and cloud cover.
var ls8 = ls8
.filterDate('2017-05-01','2017-09-30')
.filterMetadata('CLOUD_COVER','less_than', 3.0);// There are a number of keys that you can use in a visParams dictionary.
// Here, we are going to start by defining our 'bands', 'min', 'max', and 'gamma' values.
// You can think of 'gamma' essentially as a brightness level.
var ls_tci = {bands: ['B4','B3','B2'], min: 0, max: 3000, gamma: 1.4};Map.setCenter(-73.755, 45.5453, 11);
Map.addLayer(ls8, ls_tci, 'True Color');// Add the Color Infrared visualization.
var ls_ci = {bands: ['B5','B4','B3'], min: 0, max: 3000, gamma: [0.7, 1, 1]};
Map.addLayer(ls8, ls_ci, 'Color Infrared');// Add the False Color 1 visualization.
var ls_fc1 = {bands: ['B6','B5','B4'], min: 0, max: 4000, gamma: 0.9};
Map.addLayer(ls8, ls_fc1, 'False Color 1');// Add the False Color 2 visualization.
var ls_fc2 = {bands: ['B7','B6','B4'], min: 0, max: 3000, gamma: 0.9};
Map.addLayer(ls8, ls_fc2, 'False Color 2');// Load and select the Gross Primary Production (GPP) image collection.
var dataset = ee.ImageCollection('UMT/NTSG/v2/LANDSAT/GPP').filter(ee.Filter.date('2018-05-01', '2018-10-31'));
var gpp = dataset.select('GPP');// Build a set of visualization parameters and add the GPP layer to the map.
var gppVis = {min: 0.0,max: 500.0,palette: ['ffffe5','f7fcb9','d9f0a3','addd8e','78c679','41ab5d','238443','005a32']};Map.setCenter(-120.9893, 47.2208, 10);
Map.addLayer(gpp, gppVis, 'GPP');// Load the NLCD image collection and select the land cover layer.
var dataset = ee.ImageCollection('USGS/NLCD');
var landcover = dataset.select('landcover');// Values from 41-43 represent explicitly defined forest types.
var landcoverVis = {min: 41.0,max: 43.0,palette: ['black','green', 'green','green','black'],opacity: 0.75
};Map.setCenter(-120.9893, 47.2208, 10);
Map.addLayer(landcover, landcoverVis, 'Landcover');

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