地理可视化/菲律宾/遥感 (GEOVISUALIZATION / PHILIPPINES / REMOTE-SENSING)

Big data is incredible! The way Big Data manages to bring sciences and business domains to new levels is almost sort of magical. It allows us to tap into a variety of avenues to access the information we normally do not access in order to gain fresh insights.

大数据令人难以置信！ 大数据设法将科学和商业领域提升到新的水平几乎是一种神奇的方法。 它使我们能够利用各种途径来访问我们通常不访问的信息，以获得新的见解。

Satellite images are an amazing unconventional source to tap into for these fresh insights. Urban planners, environmentalists, and geospatial data scientists usually turn to satellite images for a big picture perspective and find insights from this exercise that brings their intended solutions to a whole new level.

卫星图像是令人惊叹的非常规资源，可用于获取这些新见解。 城市规划人员，环境保护主义者和地理空间数据科学家通常将卫星图像用于全局视角，并从此练习中找到见解，从而将其预期的解决方案提高到一个全新的水平。

Crash Course in Satellite Image Analysis a.k.a. Remote Sensing

卫星图像分析又名遥感速成班

If you ever came across the term “remote-sensing” in online articles, more likely this refers to the process of observing the earth using satellites. They’re called as such because they have the ability to make measurements remotely.

如果您在在线文章中遇到过“遥感”一词，则更可能是指使用卫星观测地球的过程。 之所以这样称呼它们是因为它们具有远程进行测量的能力。

What do these remote-sensing instruments sense? The short answer is radiation. To be more technical, these instruments sense electromagnetic radiation.

这些遥感仪器有什么意义？ 简短的答案是辐射。 从技术上讲，这些仪器可感应电磁辐射。

These electromagnetic radiations vary in wavelength and frequencies and along this spectrum, there is a tiny portion our eyes can see known as the “visible spectrum”.

这些电磁辐射的波长和频率各不相同，并且沿着该光谱，我们的眼睛可以看到的一小部分称为“可见光谱”。

This is where satellites come into play. Satellites that are orbiting the earth have lenses that can not only detect visible light but infrared wavelengths (nearest one to the visible spectrum) and microwaves.

这就是卫星发挥作用的地方。 环绕地球运行的卫星具有不仅可以检测可见光，而且可以检测红外波长( 最接近可见光谱的波长 )和微波的透镜。

When the light coming from the sun reaches the surface of the earth, every object reflects, absorbs, or transmits this energy differently. For example, green plants absorb all colors but reflect the green color. This is why green is the one visible to us when looking at plants. In some cases, the same type of plant may not be equally hydrated, and while this tiny difference will not be seen by the naked eye, the way these two plants reflect light will be different.

当来自太阳的光到达地球表面时，每个物体都会以不同的方式反射，吸收或传输该能量。 例如，绿色植物吸收所有颜色但反射绿色。 这就是为什么绿色是我们看植物时可见的一种原因。 在某些情况下，同一种植物的水合程度可能不同，虽然肉眼看不到这种微小差异，但两种植物反射光的方式却有所不同。

And as you know it, not all light reflected is visible.

如您所知，并不是所有反射的光都是可见的。

Because surfaces reflect light differently, satellites have been used in studying vegetation, flood-prone areas and even analyzing the built-up areas of a place across the years.

由于表面反射光线的方式不同，多年来，人造卫星已用于研究植被，易发生洪水的地区，甚至分析某个地方的建筑面积。

While anyone can go and look up and download satellite images, they may not contain the data coming from the reflection which produces so many insights at once.

尽管任何人都可以查找并下载卫星图像，但它们可能不包含来自反射的数据，而反射会立即产生很多洞察力。

This is the data that we’re after. In the visualization that we will be doing later, the difference in color represents the difference in radiation being reflected. Since this is a multitemporal visualization (multiple time periods), the colors will represent surface of the built-up area across different time series for a place.

这是我们需要的数据。 在我们稍后将要进行的可视化中，颜色的差异表示所反射的辐射的差异。 由于这是一个多时间可视化( 多个时间段 )，因此颜色将代表一个地点在不同时间序列上的建筑区域的表面。

Preliminaries

初赛

To continue with this tutorial, make sure the following is done first:

要继续本教程，请确保首先完成以下操作：

1. Create an account with Google Earth Engine. This is free and will be authenticated in your Jupyter Notebook.

   使用Google Earth Engine创建一个帐户。 这是免费的，将在您的Jupyter Notebook中进行身份验证。

2. Install Google Maps (pip install geemap) and Earth Engine (pip install ee).

   安装Google Maps(pip install geemap )和Earth Engine(pip install ee )。

3. Initialize earth engine using ee.Initialize()
   使用ee.Initialize()初始化地球引擎

Dataset

数据集

For this exercise, we will access the “GHSL: Global Human Settlement Layers” dataset from the Google Earth Engine database.

在本练习中，我们将从Google Earth Engine数据库访问“ GHSL：全球人类住区图层”数据集。

The Global Human Settlement (GHS) framework produces global spatial information about the human presence on the planet over time. This includes physical settlement so this captures built-up area over time.

全球人类住区 (GHS)框架会随着时间的推移生成有关地球上人类活动的全球空间信息。 这包括物理沉降，以便随着时间的推移捕获建筑面积。

Let’s Code!

让我们编码！

#Preliminaries
import geemap
import ee#After importing, it is important to initialize this.
ee.Initialize()

Initalize the folium map:

初始化大叶贴图：

Map = geemap.Map(center=[15.222871,120.574440], zoom=7)
Map

Load the dataset and store it as an object.

加载数据集并将其存储为对象。

#Note that ee.Image and ee.ImageCollection can both access datasets but you need to check which one is applicable for your intended datasetimage = ee.Image('JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1')

Select “built” band as we want the built-up area.

选择“内置”频段，因为我们需要内置区域。

builtUp = image.select('built')

Define the Vis Parameters.

定义可见参数。

visParams = {
  'min': 1.0,
  'max': 6.0,
  'palette': ['0c1d60', '000000', '448564', '70daa4', '83ffbf', 'ffffff'],
}

For this to make sense, let’s refer to what these min and max refer to:

为了使这有意义，让我们参考这些最小和最大所指：

The min and max refer to the value column in the built Class Table above. For best viewing, you should choose min=1 and max=6. You do not want to leave out the water and land values as your visualization may not have a legible form for some readers that are not experts in reading maps.

最小值和最大值是指上面构建的类表中的value列。 为了获得最佳观看效果，您应该选择min = 1和max = 6。 您不希望遗漏水和土地的价值，因为对于某些不是阅读地图专家的读者来说，可视化可能没有清晰的形式。

The palette simply assigns a color to the class value. The suggested colors are seen in the built Class table. You can use hex codes but leave the “#’ out.

调色板只是将颜色分配给类值。 建议的颜色显示在内置的“类”表中。 您可以使用十六进制代码，但不要使用“＃”。

#.addLayer method adds this on top of your initialized Map above
Map.addLayer(builtUp, visParams, 'Built-Up Multitemporal')

This is how it will appear for the suggested colors.

这就是建议颜色的显示方式。

Alternatively, you can use color names directly for the palette. Make sure to arrange it so the first color matches Value 1 for the built Class table.

或者，您可以直接将颜色名称用于调色板。 确保对其进行排列，以使第一种颜色与构建的Class表的Value 1相匹配。

visParams = {
  'min': 1.0,
  'max': 6.0,
  'palette': ['0c1d60', 'black', 'yellow', 'green', 'blue', 'white'],
}

Notice you can use the inspector function to check the value of a point:

注意，您可以使用检查器功能检查点的值：

So from this exercise, you can see that most built-up areas are found on the capital of the Philippines and areas near it like Region III and Region IV-A. And you can likewise trace a pattern and answer the question “Where does development occur for a particular period?”.

因此，通过此练习，您可以看到在菲律宾首都及其附近地区，如第三区和第四区-A区，发现了大多数建成区。 同样，您可以跟踪模式并回答“在特定时期内哪里发生发展？”的问题。

There are other modifications that we can do for this such as limiting the visualization to simply one city or province.

我们可以为此进行其他修改，例如将可视化范围限制为一个城市或省份。

With Google Earth Engine, you can access other datasets as well.

使用Google Earth Engine，您还可以访问其他数据集。

Feel free to explore the other datasets. Happy coding!

随意探索其他数据集。 编码愉快！

Click here to view the Jupyter Notebook for this one on Github.

单击此处 ，在Github上查看此书的Jupyter笔记本。