Get started with imagery for Africa

The Digital Earth Africa program (or DE Africa) provides freely available, ready-to-use satellite imagery and other earth observation data for the African continent. Its goal is to enable policy makers, scientists, the private sector, and civil society to make use of that data to address social, environmental, and economic changes on the continent.

Esri and its Africa GeoPortal initiative entered a collaborative partnership with DE Africa to extend the impact of the project. One of the outcomes is the Digital Earth Africa Explorer, a web app that enables users to seamlessly explore some of the DE Africa data. In this tutorial, you will become familiar with the app and understand what imagery is and how to use it, based on real-life examples. This is the first tutorial in the Explore satellite Imagery for Africa series, and it is meant for beginners.

This tutorial was last tested on August 16, 2023.

Requirements

Get started with imagery for Africa

Get familiar with the Digital Earth Africa Explorer app

First, you'll open the web app, become familiar with some imagery concepts, and learn how you can highlight vegetation throughout the African continent.

1. Open the Digital Earth Africa Explorer app.

   The app opens to the display of the entire African continent represented with Sentinel-2 satellite imagery.

   

   Note:

   Sentinel-2 is an earth observation satellite program from the European Space Agency, which was launched in 2015 and produces high-quality imagery for the entire earth. Learn more about the Sentinel-2 program.

   Sentinel-2 produces multispectral imagery, which means that it captures information for several wavelength ranges. Some of these wavelength ranges are visible to the human eye, such as blue, green, and red, and some are not, such as near infrared, shortwave infrared, and coastal aerosol. Each of these ranges is called a spectral band, and Sentinel-2 imagery contains 13 such bands. The following diagram shows where the 13 bands fall on the electromagnetic spectrum.

   

   The 13 Sentinel-2 spectral bands are shown on the electromagnetic spectrum.

   The full list of bands includes:

   • Band 1—Coastal aerosol
   • Band 2—Blue
   • Band 3—Green
   • Band 4—Red
   • Band 5—Vegetation red edge
   • Band 6—Vegetation red edge
   • Band 7—Vegetation red edge
   • Band 8—Near infrared (NIR)
   • Band 8A—Near infrared (NIR) narrow
   • Band 9—Water vapor
   • Band 10—Shortwave infrared (SWIR) - Cirrus
   • Band 11—Shortwave infrared (SWIR)
   • Band 12—Shortwave infrared (SWIR)

   Multispectral imagery can provide many interesting insights on the landscape observed.

   One way to use the bands is to choose three of them and to combine them to produce a composite image. There are many possible band combinations and each one shows the imagery differently. Some combinations can be particularly good at highlighting specific types of landscape features or properties.

   

   Three spectral bands (left) combined into a composite image (right).

   You'll now learn about the current band combination displayed in the app.

2. If necessary, click the Zoom Out button once to see the entire continent.

   

3. On the sidebar, click the Explore Imagery button.

   

4. In the Explore Imagery window, for Layer, verify that Sentinel-2 Annual GeoMAD is selected. For Rendering, verify that Natural Color with DRA is selected.

   

   Natural Color with DRA is the current band combination. It combines the blue, green, and red bands, which shows colors close to what the human eye would usually see. DRA stands for dynamic range adjustment, and it is a technique to improve the contrast of the image.

   Tip:

   In this tutorial, you can ignore the messages in red that appear in the Explore Imagery window, such as Zoom in to select images. Also, you can drag the Explore Imagery window to move it anywhere in the app to ensure it doesn't obstruct interesting features.

   The map displays mostly earthy tones such as beige, brown, and dark green on the African continent.

   

   This image is how you would see the earth from space. The Natural Color band combination can be useful for some applications, but analysts often choose other combinations based on the specific features they want to highlight.

   You'll change the band combination to color infrared, which combines the near infrared, red, and green bands. This combination is particularly useful to highlight vegetation, which it displays in bright red.

5. In the Explore Imagery window, for Rendering, choose Color Infrared with DRA.

   

   Note:

   The band combination or other processes you apply to the imagery to change its display are called Rendering.

   After a few moments, the imagery representing Africa updates, displaying the areas high in vegetation in bright red. Labels and country boundaries displayed on top of the imagery can help you get oriented.

   

   Note:

   The other continents did not change their display. This is because the app contains only multispectral imagery for Africa. For the rest of the world, you are seeing a simple imagery basemap, which does not support this type of multispectral rendering.

   Central Africa is strikingly dominated by red tones, signaling a strong presence of vegetation. The brightest red color corresponds to the equatorial region mostly covered in rain forests and filled with lush vegetation. In slightly less bright red are tropical climate regions also rich in vegetation.

   

   In northern Africa, the Sahara desert appears mostly in white and beige tones, which signal an absence of vegetation. All along the Mediterranean coast, there are several zones rich in vegetation, supported by the Mediterranean climate.

   

   In southern Africa, the eastern wetter regions are quite rich in vegetation, whereas the west is much more arid and low in vegetation, with the presence of the Kalahari and Namib deserts.

   

   Satellite imagery displayed in color infrared offers a powerful summary of the vegetation distribution in the African continent.

6. Click the Zoom In button twice.

   

7. If necessary, drag the map with the mouse to pan until the map is centered on the bright-red central Africa.

   

   Diagonal stripes cross the map. They correspond to the paths that the satellites take to capture the imagery one picture (or scene) after the other. What you are seeing is not one single large image of the whole continent. It is thousands of individual images stitched (or mosaicked) together. At times, the boundaries between the images show slightly, creating this diagonal stripe effect.

   Additionally, there are no clouds in any of these images. This is surprising since satellites fly about the clouds and often capture images partially obstructed by them.

   

   An example of imagery containing clouds.

   The reason you are not seeing clouds on the map is because the current imagery layer, Sentinel-2 Annual GeoMAD, is not made of simple satellite images. Instead, it provides a year-by-year summary of the imagery, where clouds and other small issues have been removed.

   Note:

   Currently, the Sentinel-2 Annual GeoMAD layer in the app contains the year-by-year summary images for the years 2016 to 2019. Learn more about how the GeoMAD layer is created in the DE Africa documentation.

Assess the vegetation distribution in Abidjan

While it is interesting to detect patterns over an entire continent, imagery is also commonly used to observe local areas. You'll assess the vegetation distribution in the region of Abidjan, Ivory Coast. With more than 4 million inhabitants, Abidjan is Ivory Coast's largest city.

A view of the city of Abidjan.

You'll first locate Abidjan on the map.

1. If necessary, close the Explore Imagery window. In the search box, type Abidjan and press Enter.

   

   The map updates to the new location. After a few moments, the imagery appears in a higher-resolution version, showing more details.

2. Close the Search result window, as you don't need it any longer.

   

   With the color infrared with DRA rendering, vegetation continues to be displayed in bright red tones, Abidjan's built-up urban areas appear in bluish gray, and water bodies appear in bluish black.

   

3. Examine the vegetation distribution in the region.

   At a glance, is the region rich in vegetation? What type of climate is it likely to have? And what about the city of Abidjan; does it seem densely built-up? Does it contain vegetation-covered areas?

4. Zoom in and pan through different areas of the city and the region to further your observation.

   Note:

   To zoom in or out, besides the zoom buttons, you can also use the mouse's wheel button. Each time you zoom in or pan, the imagery may take a few moments to refresh.

   Besides labels and regional boundaries, the main roads are drawn over the imagery. This can be useful for some purposes, but for now, you'll benefit from seeing the imagery less obstructed. Since this extra information is part of the basemap, you'll switch to a different basemap with less information.

5. On the sidebar, click the Basemap Gallery button.

   

6. In the Basemap Gallery window, choose Light Gray Canvas.

   

   Much of the extra information displayed over the imagery disappears. The underlying basemap also changes to a light gray theme, although it is currently covered with the imagery, so you can't see it.

7. Close the Basemap Gallery window.

   To continue your assessment of the region, you'll try another rendering: Agriculture with DRA.

8. Reopen the Explore Imagery window. For Rendering, choose Agriculture with DRA.

   

   On the map, the imagery updates.

   

   The agriculture band combination uses shortwave infrared, near infrared, and blue bands. It is great to highlight vegetation (in bright green), and it is often used for agriculture applications, as its name indicates. However, it also shows other features clearly: the built-up urban environment appears in pink or purple, water bodies in dark blue, and sand beaches and raw earth in orange or light brown. As a result, this rendering is quite multipurpose.

9. Continue your observation of the city with the new rendering.

   Can you gain additional or different insights thanks to the new rendering? For instance, it is easier to distinguish between built-up and sandy areas than it was with color infrared. It is also easier to identify small water bodies within the city.

10. Zoom in once and pan to observe the southern part of the city.

    

    The Ébrié lagoon lies south of Abidjan, separated for almost all of its length from the Atlantic Ocean by a narrow coastal strip. In orange yellow, sandy beaches line most of the Atlantic coast. Other sand beaches can also be seen around Port-Bouët. As for the vegetation distribution, some of these southern areas are densely urban and others largely vegetated.

11. Pan to examine the northern part of the city.

    Most strikingly, in the western side of the city lies the Parc National du Banco, a large old-growth forest preserve. The neighborhood around the city seems densely urban, while the eastern side of the city has more of a mix of built-up and vegetated patches.

    

12. Zoom out to examine the vegetation surrounding the city. Zoom in and pan to explore various details in the region.

    

    Abidjan is surrounded with vegetation. The city is generally located in the midst of the Eastern Guinean forest ecoregion, which is constituted of tropical moist broadleaf forests. The zone around Abidjan displays remaining patches of this old-growth forest alternating with cultivated land for the production of coffee, cocoa, bananas, and oil palms. On the map, the forested areas appear in darker green, and the fields appear as small rectangles or specs of lighter green, such as, near the town of Ahoutoue, northeast of the city.

    

    You've visually assessed the distribution of vegetation in the city of Abidjan and its region. This knowledge is useful for urban and regional planners, environmentalists, or a farming cooperative looking to expand its agricultural activities. You now know how you can visualize detailed imagery for any location in Africa with the Digital Earth Africa Explorer app.

Look for an oasis in Egypt

You'll continue your exploration of imagery in a different country, Egypt. This time, your goal is to identify and analyze an oasis in the midst of the desert. You'll first observe moisture levels in the entire country, distinguishing between dry and moisture-rich areas. Then, you'll identify the oasis and analyze what it is made of. In the process, you'll learn to use spectral indices, which are meant to identify specific landscape properties. First, you'll reset the imagery rendering and the basemap and locate Egypt on the map.

1. In the Explore Imagery window, for Layer, verify that Sentinel-2 Annual GeoMAD is selected. For Rendering, choose Natural Color with DRA.

   

   The map updates to the colors that the human eye would usually see.

2. On the sidebar, click the Basemap Gallery button. In the Basemap Gallery window, choose Imagery with Labels.

   

   It will be useful to have country boundaries and labels for your upcoming exploration.

3. Close the Basemap Gallery window.
4. In the search box, type Egypt and press Enter.

   The map updates to the new location, and after a few moments, the Sentinel-2 Annual GeoMAD imagery appears for that location.

5. Close the Search result window.

   

   With the Natural Color with DRA rendering, most of the country appears in beige tones with some dark green areas.

   You want to quickly identify the moisture level throughout the country. Any body of water (such as rivers, lakes, or seas) but also any areas covered with vegetation are high in moisture. This is in contrast with desert or built-up areas, which are very low in moisture.

   You'll use a spectral index named moisture index. A spectral index is a different way of using multispectral imagery, in contrast to the color combinations you have seen until now. It applies a mathematical calculation to compute a ratio between different bands for every pixel in the imagery, with the goal of highlighting a specific phenomenon. In the case of the moisture index, the bands involved are near infrared (NIR) and shortwave infrared (SWIR), and the formula is as follows:

   (NIR - SWIR) / (NIR + SWIR)

   Note:

   In the app, you don't need to do the math yourself; spectral indices are available as ready-to-use renderings.

6. In the Explore Imagery window, for Rendering, choose Moisture Index (NDMI).

   

   After a few moments, the map updates to show moisture-rich pixels in blue tones and the low-moisture pixels in orange brown.

   

   Much of Egypt is arid and low in moisture. This makes sense, as the Sahara desert extends through most of the country. Two striking exceptions are the Nile river's valley and delta, which are very high in moisture and can be seen in the eastern side of the country. The Mediterranean Sea, Gulf of Suez, and Red Sea waters surrounding Egypt also appear as high moisture.

7. Zoom to the Nile valley and delta, as shown in the following example image:

   

   Tip:

   To zoom to a specific extent, press the Shift key while you draw a box around the area you want to zoom to.

   After a few moments, the map updates to show the imagery in more detail. The Nile river ensures the irrigation of its valley and entire delta, keeping them filled with lush vegetation. There is one heart-shaped area that seems high in moisture, even though it is detached from the Nile valley, as shown in the following example image:

   

   This is El Fayoum (or Faiyum, or Fayum), a large oasis that has existed since ancient times. You'll examine it in more detail.

   

   A view of the El Fayoum oasis.

   First, you'll change the basemap to remove some of the extra information.

8. On the sidebar, click the Basemap Gallery button and update to the Light Gray Canvas basemap.
9. Zoom in to the El Fayoum oasis.

   

   After a few moments, the imagery updates. The heart-shaped area has a lot of moisture overall. Within the oasis, the town of El Fayoum and other villages throughout the oasis appear as low-moisture orange-brown dots.

   The moisture index is an efficient tool to quickly identify high-moisture areas in a mostly desert-covered region.

   Note:

   The moisture index can also be used to monitor drought levels and measure desertification trends.

   You now want to understand what the high-moisture area in the El Fayoum oasis is made of. First, you'll apply a different index specialized in identifying water bodies: the normalized difference water index (NDWI). Its formula relies on the near infrared and green bands:

   (NIR – Green) / (NIR + Green)

10. In the Explore Imagery window, for Rendering, choose Water Index (NDWI).

    

    The map updates, displaying the water pixels in blue. The nonwater pixels appear in light to dark gray.

    

    You can identify several water bodies. First, to the north is the main lake of the El Fayoum oasis, lake Qarun. There are also smaller lakes, southwest of the oasis. On the east side, you can distinctly see the Nile river flowing through the Nile valley.

11. Zoom in on the stem that connects the oasis to the Nile valley, as indicated in the following example image:

    

12. Continue zooming in and pan to view the area delineated in the following example image:

    

    Water streams that run through the stem are highlighted in light blue.

    

    These are two canals. The more winding one is Bahr Yusef, the main canal that has brought water to the oasis from the Nile river since ancient times. The straighter one is a secondary canal. Some distance away from the canals, there are a few small water reservoirs and ponds.

    NDWI enabled you to quickly identify water bodies of various sizes in the area.

    Note:

    More advanced techniques would enable you to extract the pixels identified as water and save them to a separate layer to reuse later in other analyses.

    You'll now use an index specialized in identifying vegetation: the normalized difference vegetation index (NDVI). Its formula relies on the near infrared and red bands:

    (NIR – Red) / (NIR + Red)

13. Zoom out to see the entire oasis.
14. In the Explore Imagery window, for Rendering, choose Vegetation Index (NDVI).

    

    The map updates to show the vegetation pixels in light to dark green tones, and the nonvegetation pixels in brown and beige tones. The dark green pixels represent the thickest, healthiest vegetation.

    

    Most of the oasis is covered in vegetation.

15. Zoom in to the heart of the oasis and pan around to examine the vegetation.

    

    Much of the vegetation displays as a mosaic of small rectangles in medium and light green: these are cultivated fields. Some darker green, more uniform areas correspond to tree groves. The vegetation in the El Fayoum oasis includes crops such as cotton, clover, and cereals. Olive groves and fruit orchards are common, and palm trees are regularly interspersed with other crops. (Fayum)

    In medium brown, you can identify towns and villages between the vegetated areas, and in yellow, the roads that connect them.

    

    Thanks to the water and vegetation spectral indices, you now know that the high-moisture El Fayum oasis contains a large lake, canals, cultivated fields, groves, towns and villages, and more.

    You identified an oasis in the midst of the Egyptian desert and analyzed what it was made of. You learned about spectral indices, which compute ratios between spectral bands, and specialize in identifying specific land-cover types and landscape properties. Spectral indices can allow you to quickly and precisely identify high-moisture areas, vegetation, water, and many other interesting elements in your area of study.

Visualize the construction of Africa's largest oil refinery

Until now, you've explored imagery that represents a single moment in time using the Sentinel-2 Annual GeoMAD imagery layer. But what if you wanted to track trends over time? What if you wanted to compare Abidjan's urban development today and 10 years ago, or see how the lake in the El Fayoum oasis may have expanded or shrunk over the last few decades?

Next, you'll learn how to visualize change over time. As an example, you'll visualize the development of the Dangote oil refinery, which has been under construction for several years in the Lekki region in Nigeria. When completed, it will be the largest oil refinery in Africa. It will be able to meet the country's entire domestic fuel demand, as well as export refined products, and it will create 135,000 permanent jobs in the region.

Your goals are to understand when the project started on the ground and track the different construction steps. To visualize the project site over time, you'll use the Landsat imagery layer.

Note:

Landsat is an earth observation satellite program from the United States. It has been providing imagery for the entire earth continuously since 1972. Like Sentinel-2, the imagery it produces is multispectral. You can learn more about Landsat on the program's website.

The Landsat layer included in the app provides a large selection of Landsat imagery from 1972 to present.

The Dangote oil refinery is located near the town of Idaso, Nigeria. You'll look for it. But first, you'll switch back to a more neutral rendering.

1. In the Explore Imagery window, for Rendering, choose Natural Color with DRA. Close the Explore Imagery window.
2. In the search box, type Idaso, Nigeria.

   The search engine suggests Idaso, Epe, Lagos, NGA (Epe being the district, and Lagos, the state where Idaso is located).

   

3. Click Idaso, Epe, Lagos, NGA.

   The map updates to the new location.

4. Close the Search result window.
5. Zoom out until you can see the entire Dangote oil refinery site, as in the following example image:

   

   Positioned near the Gulf of Guinea's coast, the refinery site appears in mostly white and light gray tones and is the size of a small city. The current view is the Sentinel-2 Annual GeoMAD imagery layer for 2019. You'll switch to the Landsat layer.

6. In the Explore Imagery window, set the following parameters:
   • For Layer, choose Landsat.
   • For Rendering, verify that Natural Color with DRA is selected.

   

7. If necessary, for Date, adjust the slider until the date shown is December 28, 2019.

   Note:

   Based on your computer's time zone, some of the dates may appear off by one day. Also, since Landsat imagery is constantly updated, the date of the most recent image you see in the time slider may be more recent than in the example image.

   

   On the map, the imagery updates.

   

   The imagery ends abruptly just west of the refinery site, showing the light gray basemap beyond that. When zoomed to this level, the Landsat layer shows only one single image (or scene) at a time, captured on a specific date.

   In the Explore Imagery window, in the Date section, the time slider shows all available individual images, from the early days of the Landsat program until the most recent dates. The oldest image available for this area is from November 1972 and the most recent is no more than a few weeks old. The slider is currently on the December 28, 2019 date, so that's the one displayed on the map.

   You'll now start scrolling through time.

8. Drag the slider to November 6, 1972 (or November 7, 1972, depending on your time zone).

   

   The imagery updates. These first few scenes, captured with older Landsat satellites, are not as high quality as later images. However, some of these older scenes can still be useful to understand how the world has changed. You'll ignore the first couple of scenes, which are mostly showing a cloudy landscape.

9. On the right side of the timeline, click the plus button to move to the next scene of the area chronologically.

   

10. Click the plus button until you arrive at December 17, 1984 (or December 18, 1984).

    

    While the scene is grainy, it's clear that the site of the future refinery is completely undeveloped and either covered with vegetation (in dark green) or showing a few bare earth patches (in brown tones). South of the site, along the coast, you can see the town of Idaso and other built-up areas (in light brown). North of the side is the Lekki lagoon (in bluish black).

11. Continue clicking the plus button to observe how the area changes.

    Note:

    Unlike the Sentinel-2 Annual GeoMAD layer, which summarizes the imagery data and provides cloudless images, the Landsat layer is made of individual scenes, which can contain clouds. As a result, you need to review the scenes and identify the ones that best suit your needs.

    Also, as you scroll from one scene to the next, you may notice that not all scenes have the same extent. Some seem to extend toward the east side, others toward the west side. This depends on the specific position of the satellite when it captured each scene.

    Observe in particular the imagery on the following dates:

    • December 12 or 13, 1999—The refinery site continues to be untouched.
    • January 2 or 3, 2011—Although somewhat cloudy, this scene shows the constructions of roads and some buildings, west of the future refinery. This is part of the Lekki Free Trade Zone, a project meant to ease the installation and development of businesses and industries in the region.
    • December 18, 2013—The first signs of work on the refinery site appear at its southern site.
    • January 15, 2015—Some of the ground has been excavated, as the site is being prepared.
    • January 4, 2017—Most of the site is now bare of vegetation and has been actively prepared, displaying in bright white.
    • January 1, 2019—A grid of roads and many buildings has appeared.
    • February 2, 2022—The development of many large industrial buildings continues. On the coast, a harbor structure has also appeared, south of the refinery site.

    Tip:

    Alternatively, you can click the Show images in drop down list button on the left of the time slider to switch to a drop-down date list.

    In the most recent scenes, while the refinery is not fully functional yet, the construction seems to be making great progress. As new images are added to the Landsat layer, it will be possible to continue monitoring further changes on the site.

    

    The Dangote refinery site on February 2, 2022.

    You have observed the Dangote refinery site over time. You learned that Landsat imagery dates back to the 1970s, allowing you to visualize change over several decades anywhere in Africa. This provides countless opportunities to observe and better understand environmental changes, urban development, as well as industrial and civil engineering developments.

Monitor a lake's water level in Ghana

You now want to track change through time for an essential resource: water. You'll use the Water Observation from Space (WOfS) Annual Summary layer, which is derived from Landsat imagery and focuses strictly on showing the presence of water across the African continent.

Note:

The WOfS Annual Summary layer provides a yearly summary of where and how frequently water was seen throughout the year across the entire African continent. This allows users to understand where water bodies are located and how they change over time, where inundations may have happened, and other water movements. Currently, this layer contains data from 1983 to 2020. Learn more about the WOfS dataset.

As an example, you'll use the layer to monitor the water level of Lake Volta in Ghana. Lake Volta is a large body of water that was created by the construction of the Akosombo Dam in 1961-1965. The dam produces a great part of Ghana's electricity, as well as electricity exported to nearby countries.

A view of Lake Volta.

The lake is also a major source of livelihood in the region, with activities such as fisheries and tourism. Fluctuations in the water level of the lake can have great consequences on the quantity of electricity generated and the economic activity in the region. In the context of climate change, it is ever more important to monitor and understand these fluctuations.

You'll go to Lake Volta's location and display the WOfS Annual Summary layer.

1. In the search box, type Lake Volta, Ghana and press Enter.

   The map updates to the new location.

2. Close the Search result window.
3. In the Explore Imagery window, for Layer, choose WOfS Annual Summary. For Rendering, verify that WOfS Annual Frequency is selected.

   

   The imagery updates to the WOfS layer for 2021 (dated December 31, 2020 or January 1, 2021).

   For this workflow, you'll focus on the water level variation for a portion of Lake Volta at the mouth of the Obosum river, one of the lake's tributaries.

   

   Your goal is to visualize the water presence each year from 2012 to 2021.

4. On the map, pan slightly southward until you see the Obosum river's mouth clearly.

   

5. In the Explore Imagery window, under Date, drag the time slider to December 31, 2011 or January 1, 2012.

   

   In 2011, the water level for this portion of the lake was quite high.

   

   The symbology used for the layer is the following:

   • Darker blue—These pixels were found to constantly contain water during the entire year.
   • Green and yellow—These pixels contained water sometimes during the course of the year.
   • Red—These pixels rarely contained water.
   • Pixels without color—Didn't contain water at any point in the year.

   The lake is mostly dark blue. Thin lines of green, yellow, or red close to the shorelines represent slight seasonal variation: during the dryer season, the water level lowers slightly and the lake's surface area retracts slightly.

6. In the Explore Imagery window, click the plus button to display the next four years and examine the map for each year.

   

   The Obosum river in 2016.

   The lake's surface area seems to have shrunk each year from 2012 to 2016. For reference, you can compare the water level to the gray basemap, which shows the lake's most expanded shorelines in medium gray.

7. Click the plus button to display the next five years.

   

   The Obosum river's mouth in 2021.

   Now the lake's surface area is expanding again, as the water level goes back up.

   Another interesting way of visualizing these changes is to compare imagery at two dates with the Compare Imagery tool.

8. On the sidebar, click the Compare Imagery button.

   

   The Compare Imagery window appears. With this tool, you can specify two images to display on the left and right sides of the map, and swipe between the two.

9. In the Compare Imagery window, set the following options for the left image:
   • Verify that Left Image is selected.
   • For Layer, choose WOfS Annual Summary.
   • For Rendering, verify that WOfS Annual Frequency is selected.
   • Check the Select a date check box.
   • Drag the time slider to December 31, 2011 or January 1, 2012.

   

10. Set the following options for the right image:
    • Click Right Image to select it.
    • For Layer, choose WOfS Annual Summary.
    • For Rendering, verify that WOfS Annual Frequency is selected.
    • Check the Select a date check box.
    • Drag the time slider to December 31, 2015 or January 1, 2016.

    

11. Grab the swipe handle and swipe repeatedly from left to right to compare the two images.

    

    The Compare Imagery tool allows you to examine in detail the differences between two images.

12. Optionally, in the Compare Imagery window, choose other dates between 2012 and 2021 for the left and right images, and compare them with the swipe tool.

    The change in water level is particularly visible at the mouth of the Obosum river because of the specificity of the terrain. However, you can also observe similar trends in the rest of the lake.

    Research indicates that such fluctuations of Lake Volta's water level are caused in large part to extended periods of lower precipitation in the region, leading to drought conditions. Interestingly, because of the complex dynamics introduced by the Akosombo dam, the impact of drought periods on the lake's water level can be delayed by up to a couple of years (Ndehedehe & al.). This is the case in the data you visualized, as lower levels of precipitation were reported in the region from 2010 to 2013, and you observed the lake's water level lowering from 2012 to 2016.

    You've monitored the water level of Lake Volta at the mouth of the Obosum river. You learned that the WOfS Annual Summary layer allows you to closely monitor water bodies all over Africa, and provides crucial information to manage water resources more proactively and sustainably. You also learned how to compare two images with the Compare Imagery tool.

Save your visualization results

When you obtain an interesting visualization in the map, you may want to save it for later use. In this section, you will learn how to do that, either as a local image file on your computer, or as an online imagery layer. As an example, you will save the NDVI rendition of the El Fayoum oasis that you visualized earlier in the tutorial.

First, you'll switch back to that view.

1. In the search box, type El Fayoum and press Enter. Close the Search result window.
2. Zoom out until you see the entire oasis.
3. Open the Explore Imagery window and choose the following options:
   • For Layer, choose Sentinel-2 Annual GeoMAD.
   • For Rendering, choose Vegetation Index (NDVI).
   • For Date, choose January 1, 2020.

   

   The map updates to the imagery view you would like to save.

   

4. On the sidebar, click the Image Export button.

   

5. In the Image Export window, choose Default Image.

   

   The first option is to save the current imagery view to your computer.

6. For Save location, choose Save to disk. For TIFF download options, verify that As displayed is selected.

   

7. Click Save.

   After a few moments, the TIFF image downloads to your computer, usually to your Downloads folder. Depending on your web browser settings, it may also automatically be displayed in preview mode. You can share this image as you wish, for instance, including it in a report document that you are writing.

   Another option is to save the current imagery view as an online imagery layer. This requires an ArcGIS Online account.

   Note:

   If you don't have an ArcGIS Online account, you can get one for free through the Africa GeoPortal (click Sign In, click Create an Africa GeoPortal account, and follow the instructions). Africa GeoPortal is an open mapping community supported by Esri, working together to provide data and insights across Africa.

   Alternatively, you can create a free ArcGIS Public account (click Create an ArcGIS public account and follow the instructions).

   ArcGIS Online allows you to create dynamic web maps, which you can share with your community.

8. In the Image Export window, set the following options:
   • For Save location, choose Save to portal.
   • For Title (required), type Vegetation in the El Fayoum oasis.
   • For Description, type Sentinel-2 Annual Geomad layer with NDVI rendering, showing the presence of healthy vegetation.
   • For Tags (required), type Imagery, Egypt, vegetation.

   

9. Click Preview. In the Sign in window, sign in using your ArcGIS username and password.

   In the Image Export window, a thumbnail preview appears.

10. Click Save.

    

    You'll look at the imagery you just saved on ArcGIS Online.

11. Go to ArcGIS Online and sign in using the same account you used to save the image.
12. In the ribbon, click Content.

    

13. In the list of content, click Vegetation in the El Fayoum oasis.

    

14. On the Vegetation in the El Fayoum item page, click Open in Map Viewer.

    

    The layer appears in the ArcGIS Online Map Viewer.

    

    You can now save this map and share it with your colleagues or community. You can also add more data layers or other types of useful information.

    Note:

    To learn more about how to create your own web maps with ArcGIS Online, see Get started with ArcGIS Online.

Explore more locations

To continue the exploration, the Digital Earth Africa Explorer app includes several bookmarked locations. You can examine one or several of them. To gain more insights, make sure you change the imagery layer and apply different band combinations and spectral indices, as you learned to do in this tutorial. The bookmarks are the following:

• Lagos, Nigeria
• Dakar, Senegal
• Apamprama forest reserve, Ghana
• Maasai Mau Forest, Kenya
• Effiduase, Ghana
• Gulu Municipality, Uganda
• Ife Municipality, Nigeria
• Flood Plains Asaba Onitsha, Nigeria
• Weija Reservoir, Ghana
• Bahi Swamp, Tanzania
• Lake Ngami, Botswana

1. In the Explore Imagery window, for Layer, select Sentinel-2 Annual GeoMAD. For Rendering, verify that Natural Color with DRA is selected.
2. For Date, ensure that the most recent date is selected.
3. On the sidebar, click the Bookmarks button.

   

4. In the Bookmarks window, scroll down the list of bookmarks, and click one of them.

   

   The map updates to that location.

5. Explore the area and try to learn about it.

   Are there any water bodies, and how have they evolved over time? How is the vegetation distributed? Are there any human dwellings or urban areas?

6. Explore several other bookmarks.
7. Alternatively, use the search box to look for specific areas that interest you, and continue the exploration further.

In this tutorial, you traveled across the African continent using the Digital Earth Africa Explorer app. You learned about multispectral imagery and some of its capabilities. You used different spectral band combinations and indices to monitor land cover and landscape properties. You also visualized change through time. You can now start using imagery to monitor your own areas of interest and gain crucial insights to help tackle important issues.

You can find more tutorials like this in the Explore satellite Imagery for Africa series.

Acknowledgements

• More about the DE Africa program and Esri / Africa GeoPortal partnership: Esri and its Africa GeoPortal initiative entered a collaborative partnership with DE Africa to extend the impact of the DE Africa earth observations data. This partnership has led to the development of the web app used in this tutorial, Digital Earth Africa Explorer. Esri is also making imagery products from DE Africa available as imagery services through the Africa GeoPortal. Users can use these imagery services along with the visualization and analysis tools provided on the portal. The goal is to enable users in Africa to develop their imagery skills and perform imagery analyses to help them address common challenges in Africa, such as food security, water resource management, and nature conservation.
• All imagery layers used in this tutorial are provided by Digital Earth Africa and accessed through the Digital Earth Africa Explorer app.
• Cited paper: Christopher E. Ndehedehe C. E., et al. Analysis of hydrological variability over the Volta river basin using in-situ data and satellite observations, Journal of Hydrology: Regional Studies, Volume 12, 2017, pp. 88-110, https://doi.org/10.1016/j.ejrh.2017.04.005
• Photo credits: Gallery image by Lina Loos, Unsplash.com; Abidjan plateau by RyansWorld, Wikipedia Commons; Faiyum Oasis by Zorbey Tunçer, Wikipedia Commons; A Portion of the course of the Volta Lake by Bakudie, Wikipedia Commons.
• Light Gray Canvas sources: Esri, HERE, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• World Imagery sources: Esri, Maxar, Earthstar Geographics, and the GIS User Community