[{"value":"undefined","content":{}},{"value":"water","content":{"interpret":["

How to read the colors:
Yellow to green colored areas correspond to non-water content like rocks, vegetation, buildings or dry soil. Blue areas indicate water.

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Dive deeper into this topic:
You should never blindly rely on data. They know no context. That means the correct reading and interpretation of the data is up to you. Therefore, you should familiarize yourself with the NDWI, its strengths and weaknesses, before publishing your findings or making decisions based on them.

Here we have some further resources for you:

Factsheet: Normalized Difference Water Index
Article: Applications and limitations (Bikesh Bade)

\n"],"introduction":["

In the next 20-30 minutes we will analyze water bodies on a satellite image showing the region you have chosen. For this we will use a scientific indicator - the Normalized Difference Water Index. Or NDWI for short. Don't worry, it sounds more complicated than it is. We will guide you step-by-step through this process.

At the end you will get a picture where water bodies are colored vibrant blue and non-water content, i.e. soil, vegetation or buildings, are colored yellow to green. This gives you a quick overview of the water in your study area and helps you to monitor changes in water bodies.

Don't worry, if you get stuck, just use our forum. We and other users are happy to help you.

Sounds good? Then go ahead...

\n"],"process":["

Step 1: Download and install QGis

QGis is a free geographic information system application that supports viewing, editing, and analysis of geospatial data. Although it is free, it is a very professional software that is also used by satellite imagary professionals.

QGis can be installed on all major operating systems. These include Mac OS, Microsoft Windows and also Linux.

Installation instructions and the download for the software itself can be found here: https://www.qgis.org/en/site/forusers/download.html

The installation requires 15-25 minutes. After completion, you can skip to step two.

Step 2: Open QGis and start a new project

Great, you've cleared the first hurdle. Now the fun begins, because we are going to edit the satellite data!

The first thing to do is to start the QGis software you just installed. Now go to Project in the menu and then select New.

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For the next steps: Depending on which operating system you are using, the display of QGis may be slightly different. However, the corresponding options and fields should still be available.

And one more thing: Of course, I don't use the same data on the screenshots that you do. In other words, my data may look different. Don't let this confuse you.

Step 3: Open the data

It sounds complicated to open satellite data, doesn't it? But it's not so bad, QGis does most of the work for us.

It’s pretty easy to open the files: Just go to the directory where your satellite data is saved, probably in the downloads directory, select the files and drag them into the layers panel in QGis. Just take a look on my screen to know exactly where to drag them:

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Note: If you have downloaded the files as a ZIP file, unpack them first.

No you can see your data in the layers panel and on the right, big panel you can see your data visualized. A bit too grey, isn't it? We'll take care of that in a moment...

Make sure that you save your project. Do this by clicking on Project and then on Save as. A new window opens and you can choose the right directory on your PC and give your project a suitable name. Then just click on Save.

Step 4: Add a map for better orientation

So that we can orient ourselves better on the data, we put a map on top of the data. You can imagine it like Google Maps. So we can see better where we are. In technical language, such maps are called basemaps. That’s, too, super simple. So, let's add a basemap.

Go to the browser panel (on top of the layers panel) and look for the entry XYZ Tiles. Click on the small arrow on the left. Now just double click on OpenStreetMap.

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What is OpenStreetMap?

OpenStreetMap, or OSM in short, is an alternative to Google Maps. The cool thing is it is completely free to use and open source. That means everybody can contribute to the maps and you can do whatever you want with the data. But that's not important for us now. If you are interested in OSM, take a look at their website.

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Congratulations, you’ve added a basemap to your project.

But, what happened there! Sometimes the basemap lays on top of your satellite data and you can't see it anymore, other times the basemap lays between or under the data and you cant see the basemap anymore. No problem, we will fix this.

Depending on what you want to do, you can change the opacity of the layers, the order or hide specific layers. For now, we will just put the basemap behind our data. To do that, we just can rearrange the order of the layers with simple drag and drop. Select the OpenStreetMap layer and drag it to the bottom of the list.

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Step 5: Learn about your data and how to delineate open water features from it

Well, brace yourself: now comes the magic. What happens now? We will process the data so that we can see from the image where water is blue, non-water content is yellow.

How do we do that?
We use a method that calculates the different data with a certain formula so that the result is a value between -1 and +1 for each point on the image. -1 stands for \"no water\" and +1 for, surprise, \"water\".

This method is called calculating an index. The index is the result of the formula and the formula we use is called NDWI, the Normalized Difference Water Index.

This all sounds very complicated, doesn't it? Let's start from the beginning. What exactly is our satellite data?

Note: This content is optional. If you are not interested in the theoretical background, just jump to Step 6.

So, what does 'raster data' mean?

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Raster data is like a picture that you would take with a digital camera: at the lowest level of abstraction, it is a list of pixels with values. When you ‘zoom in’ and look closer at raster data, at some point you’ll see these discrete pixels, and it will look pixelated.

Raster data is used in pictures of the Earth, like those taken by satellites - but that is just the beginning. Pixels don’t need to have colors - instead, each pixel can have a number that represents height and the raster data as a whole stores elevation data. Or pixels can store temperature or reflection data and be useful for environmental work.

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Cool, we have raster data. But why do you have multiple files of the same location and why do they look different? That’s are different bands. On this project we use Band 3 and Band 8 of the satellite.

What are satellite bands?

The pixels in raster data are not necessarily just filled color: we call its contents ‘bands’. A normal image has three familiar bands: Red, Green, and Blue. Combined, they make a picture we’re familiar with. Some raster data can have fewer bands, like just one for elevation, or some can have a lot more - not just visible colors, but wavelengths we can’t see, like infrared and ultraviolet. When raster data is analyzed and displayed, you can combine and pick different bands to use to suit what you’re looking for.

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Sure, raster data, bands, but what the heck is the NDWI? Great question, my friend. With the specific formula of the NDWI we will combine the Bands 3 and 8. Why? Read on:

How does the NDWI indicator work?

The Normalized Difference Water Index (NDWI) is derived from the Near-Infrared and Green bands. This formula highlights the amount of water in water bodies.

The NDWI is efficient to detect surface water because these surfaces have a very low reflectance in the NIR region of the spectra - in contrast to the vegetation which is characterized by a high reflectance in the NIR.

The values of the NDWI can lie between -1 and +1. Negative values indicate no water, like buildings or dry soil. The higher the positive NDWI value, more water content is present.

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Enough dry theory. Now it's down to the nitty-gritty.

Step 6: Calculating the NDWI

Let’s get ready to rumble. What we will do now, is calculate the NDWI from the data you’ve downloaded.

The formula for the NDWI is the following: (B03 - B08) / (B03 + B08)

Band 8, or shorter B08, represents the near infrared radiation and Band 3, or B03, the visible green.

To use this formula in QGis and with your data, you have to go the following steps:

Click on Raster in the top menu.
Choose the raster calculator. A new window should open now.
Below the field Output layer is a button with 3 points. Click on it and write “NDWI” in the file name input field. Click Save.
Copy the following formular and paste it into the field named Raster Calculator Expression: ( \"B03@1\" - \"B08@1\" ) / ( \"B03@1\" + + \"B08@1\" )
Click Ok.
Wait for it… Voila.

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That’s all. Almost.

Step 7: Bring colour into the game

To make the data easier to understand, we want that no water is yellow/green and water is black… joke. Water should be, of course, blue. For that we will colorize the grey image.

Double-click the NDWI layer in the layers panel. A new window will appear.
Choose Symbology on the left.
Change the Render type from Singleband gray to Singleband pseudocolor.
In the Min input write -1 and in the Max input 1.
On the right side of the color ramp dropdown is an arrow downwards. Click on it.
Then choose All color ramps and select YlGnBu (the third option from bottom).
Below the table with the colored squares, click on classify.
Click Apply and Ok.

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Well done, now it's looking much nicer, right?

Step 8: Analyze your data

There are now two ways to take a closer look at the data. Either via QGis itself or you export. Both have their advantages and disadvantages. I will explain them to you in the following:

Use QGis to move around the data
QGis offers a variety of ways to dive deep into your data. You can zoom in and out of the layers, move freely on the map and make individual layers more transparent to get an even better overview.

To make your NDWI layer more transparent so that you can see the basemap better, you first have to hide the two bands. We don't need them any more. Just click on the tick in front of the layer. My recommendation: Only tick the layers you really need. That way you keep the overview. In our case it is the NDWI layer and the basemap:

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Then click on the small brush in the layers panel. A new area has opened on the right side. Click on Transparency to set the transparency for each layer under Global Transparency. Make sure that you have selected the right layer. I set the transparency a little lower so that I can see the basemap underneath better. This makes it easier for me to orientate myself.

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Export the image
To be able to share your data with others or to print them, it is useful to export the data - for example as PDF or JPG. You can then share the files with other people without them having to have QGis installed. Practical, isn't it?

To do this, simply take the following steps:

Go to Project in the menu.
Select Import/Export and choose Export Map to Image or Export Map to PDF.
If you have selected PDF: Check the box Create Geospatial PDF (GeoPDF).
Now click Save.
Select the location where you want to save the file, enter a file name and click Save.

"]}},{"value":"snow-cover","content":{"interpret":["

How to read the colors:
White colored areas correspond to area that is covered by snow. Black areas indicate snow-free surface.

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Dive deeper into this topic:
You should never blindly rely on data. They know no context. That means the correct reading and interpretation of the data is up to you. Therefore, you should familiarize yourself with the NDSI, its strengths and weaknesses, before publishing your findings or making decisions based on them.

Here we have some further resources for you:

Article: General introduction to NDSI (EOS)
Article: Limitation of NDSI (Bikesh Bade)

\n"],"introduction":["

In the next 20-30 minutes we will detect snow on a satellite image showing the region you have chosen. For this we will use a scientific indicator - the Normalized Difference Snow Index. Or NDSI for short. Don't worry, it sounds more complicated than it is. We will guide you step-by-step through this process.

At the end you will get a picture where snow is white, surprise, and areas without snow are black. This gives you a quick overview of snow covered areas and helps you to monitor changes in snow covers.

Don't worry, if you get stuck, just use our forum. We and other users are happy to help you.

Sounds good? Then go ahead...

\n"],"process":["

What do you want to do?

Where do you want to search?

What time range do you want to investigate?