The app opens to the location of Esri headquarters in Redlands, California. You notice right away that the landscape does not appear in colors that look natural, the way they would appear to the human eye. The urban areas are light purple, while some of the vegetated areas are bright green. Why is that so?

The Landsat satellite imagery captures different ranges of wavelengths on the electromagnetic spectrum, including some that are invisible to the human eye. Each range is stored separately in a spectral band and is particularly good at highlighting a type of landscape feature or property. The bands are described in the following table:

You can then combine the bands into spectral band combinations.

By default, the app uses the Agriculture band combination (bands 6, 5, and 2—or Shortwave Infrared 1, Near Infrared, and Blue). That combination highlights healthy agriculture vegetation in bright green and distinguishes it clearly from other land cover types like bare earth or urban. It is the option that is currently on. You'll now switch to the Natural Color band combination for comparison.

With this new Natural Color band combination (bands 4, 3, and 2—or Red, Green, and Blue), you can see that the imagery is dominated by earth-colored tones. Together, the Red, Green, and Blue bands make up the spectrum of light visible to the human eye. Combining these three bands approximates how the landscape would look to a person.

The Natural Color band combination can be useful for some applications, but analysts often choose other band combinations based on the specific features they want to highlight. The toolbar on the left lists several common band combinations available in the app.

A window opens, containing a list of bookmarks to interesting locations around the world.

The map extent changes from Redlands to the Sundarbans. The entire area you now see is part of the Sundarbans mangrove region.

Just from observing the area with the default Natural Color band combination, you can see a clear distinction between the heavily forested Sundarbans region and the highly urbanized city of Kolkata to the northwest. Much of the original mangrove forest was cut down, but what remains is protected by the Indian and Bangladeshi governments.

To distinguish the mangrove vegetation more clearly, you'll use the Color Infrared band combination, which combines the bands 5, 4, and 3 (or Near Infrared, Red, and Green). The Near Infrared (NIR) band distinguishes very clearly between vegetation (high value) and nonvegetation features (low value). As you can see on the spectral profile graph below, typical Forest, Urban, and Water features have very different NIR values.

Healthy vegetation appears bright red in the Color Infrared band combination.

The imagery updates.

The mangrove forest now appears bright red, signifying dense, overall healthy vegetation. The water bodies going through the mangrove—devoid of vegetation but high in sediments—appear turquoise blue. Built-up areas, such as the city of Kolkata, appear grayish or beige. Areas with agriculture appear as a lighter shade of red, signifying some vegetation presence, but less dense than in the mangrove.

Now you can see the mangrove forest in more detail. Over the broad delta region, the forest is broken up by several rivers and complex tidal waterways. Many of its small islands are accessible only by boat, which hinders on-the-ground observation and intensifies the need for satellite imagery to monitor the forest.

Healthier vegetation appears brighter red, but some areas appear in a lighter shade of red or beige. As an analyst, you could identify these areas as containing potentially less healthy vegetation and needing further investigation.

You can see a sharp contrast where the protected mangrove area ends: the land in the nonprotected area was formerly covered with mangrove forest but has now been entirely deforested. It shows as mostly beige or light pink, signifying an absence of vegetation. As an analyst, you could use these differences in color to detect illegal tree logging activity in the protected areas.

Mangrove forests are highly susceptible to changes in sea level and water salinity, as well as pollution, illegal logging, and other factors. Loss of mangroves would not only compromise the habitat of the diverse species of flora and fauna that live there (including many endangered species, such as the Bengal tiger), but also remove an important shield against monsoons for the neighboring localities. It is important to maintain the health of the forest, and imagery can help do that.

The map extent changes.

The boundaries of the oval-shaped desert are fairly clear, even when displayed in Natural Color. Since the Takla Makan Desert is primarily made up of sand dunes, its surface appears smooth and uniform from above, with few mountains or geological features. While the desert appears dry, you'll determine whether there are areas of moisture using the moisture index.

Unlike Color Infrared, which was only a combination of three different spectral bands, the Moisture Index is a more sophisticated use of spectral bands. It is an index, which is a calculation that computes a ratio between different bands. In the case of Moisture Index, the bands involved are 5 (NIR) and 6 (SWIR 1), and the formula is as follows:

(Band5-Band6)/(Band5+Band6)

If the NIR band value is higher than the SWIR 1 band value, the result of the formula will be a higher (positive) value, denoting a high moisture level. Otherwise, it will be a lower (negative) value, denoting a low moisture level. As you can see on the graph below, Lush Grass and Water features will typically result in a positive value, and Desert features in a negative value.

On the map, the Moisture Index shows moisture-rich areas in blue tones, and low moisture areas in orange tones. As expected, most of the Takla Makan Desert appears low moisture. However, the Moisture Index reveals several places that aren't as dry as they seemed with Natural Color. You'll ignore the darkest navy blue patches at the periphery, because they correspond to clouds (these are the clouds that were previously showing in white in the Natural Color view). Instead, you'll take a closer look at some of the medium blue patches, which signal actual ground-level moisture.

You zoom to a place labeled on the map as Shache.

Shache is an oasis settlement fed by a river that runs down the nearby mountains. The Moisture Index shows that this area has a fair amount of moisture overall (although the exact amount might vary depending on the season). The Moisture Index is a great tool for monitoring drought levels and measuring desertification trends.

To reveal what the moisture in the Shache area is being used for, you'll use the Agriculture band combination.

As seen in earlier sections, the Agriculture band combination (bands 6, 5, and 2, or SWIR 1, NIR, and Blue) highlights agriculture in bright green. Most of the moisture-rich area you saw when using the Moisture Index coincides with the agriculture presence that you now see in bright green. However, you can now also see the location of the river and tributary streams that irrigate this valley, clearly distinguished from the vegetated areas by their dark blue color.

Using different band combinations and indexes allows you to uncover different types of information in the imagery.

The imagery shows many small islands. The islands are formed out of coral and grouped into ring-shaped clusters called atolls. The Maldives bookmark has automatically made the Bathymetric band combination active. The Bathymetric band combination (bands 4, 3, and 1, or Red, Green, and Coastal) replaces Blue with the Coastal band, which emphasizes shallow underwater features. At the default extent, however, it's hard to tell what's underwater and what isn't.

Malé (alternatively spelled Maale) is the capital and main city of the Maldives. After you zoom, you may be able to see built-up urban environments on some of the islands. If not, you can zoom in further.

You can see that the east side of Thilafushi, in beige and brown tones, is above water and inhabited, while the west side of the island in light bluish tones is an uninhabited and mostly submerged reef. Next, you'll explore islands that are completely submerged.

The Bathymetric band combination increases the difference between submerged features (blue or turquoise blue) and emerged features (white, beige, or brown).

The map shows a portion of the Suez Canal in the Agriculture band combination. Vegetation is shown in green, and water in black or dark blue. That segment of the canal was extended in 2014, and there are now two canals that run parallel to each other. The canal on the left is the original Suez Canal, opened in 1869. The canal on the right is the addition.

The Time Line slider appears. The Time Line slider shows all available imagery from as far back in time as Landsat satellites captured imagery of this area, and it allows you to filter the imagery based on cloud cover or seasonality. The oldest image of this area, on the left of the timeline, is from September 19, 1984. Since Landsat imagery is updated periodically, the most recent image on the right may be different from the example image.

The Cloud Filter parameter allows you to choose how much cloud cover is acceptable when searching for imagery. In this case, any imagery with more than 10 percent cloud cover will be excluded.

The Season Filter parameter allows you to choose a specific season, such as spring or summer. For instance, when monitoring the evolution of a forested area over time, it can be useful to look at it only at summer dates, when the tree leaves are on.

In the year 1984, no trace of the second canal existed. You'll scroll through the timeline to watch the second canal's development as it occurred.

The next date with canal imagery is November 10, 2000, still long before the second canal's construction. One change to note is that agricultural activity (in green) has considerably increased in several locations.

Although faint, you can see the foundation of the second canal to the right of the first one.

You can also notice further development in agricultural activities.

When does the canal appear to be completed? How long did its construction take? Can you notice some changes in the agricultural methods used? By accessing the deep compendium of Landsat imagery, you can answer these questions, as well as other change-related questions across the world. Landsat imagery can track sea ice extent, lake water-level fluctuation, and urban development. It can also monitor seasonal changes, such as crop cycles and vegetation bloom. When combined with spectral band combinations, it increases the potential for imagery analysis even further.

While you are encouraged to use any location in the world, the example images in these next few steps will use the default extent of Redlands, California. You can go there by clicking the Default Extent button below the Zoom Out button.

The Build Your Own window appears. To create a band combination, you'll input the three bands to be combined. You can also adjust the stretch and gamma, which changes the contrast and brightness in the image. As a reminder, here is the list of the spectral bands available.

What bands should your combination use? It depends on what you want to see. For instance, if you want to emphasize vegetation, you can use the Near Infrared, Red, and Green bands—which happen to be the bands that make up the Color Infrared combination you used in the Sundarbans.

The imagery highlights vegetation as red, just as in the ready-made Color Infrared band combination. The order of the bands is also important, however. Because the human eye can only see the red, green, and blue ranges, those three bands (or channels) are the only ones that can be used to represent any spectral band combination. So, as you choose any three Landsat bands for your new combination, the first one will always be displayed through the Red channel, the second one through the Green channel, and the third one through the Blue channel. Together, they form a single composite image, as illustrated in the diagram below.

You'll now try the same bands in a different order.

The same vegetation features are emphasized, but they are emphasized in blue instead of red. In general, the bands that are used determine what kinds of features are highlighted, while the order of the bands determines the color. Next, you'll try a new set of bands.

What features are emphasized in the image? Are these the same features you would expect to see emphasized based on the bands you chose? If you reorder the bands, how does the color change?

With the ability to make all sorts of band combinations, look at any location in the world, and tap into images extending back many years, the possibilities of Landsat imagery seem infinite.