Display the lake

To calculate the lake's change in area over time, you'll compare imagery of the lake taken by Landsat satellites between 1984 and 2014. The Landsat satellite program has been in operation over 40 years, making its imagery vital for monitoring major planetary changes. You'll classify the pixel values of the imagery into categories based on land cover. Then, you'll display only land cover of Lake Poyang, isolating the lake from the rest of the image.

Open the project

First, you'll download the project and open it in ArcGIS Pro.

  1. Go to the Classify Land Cover to Measure Shrinking Lakes group.

    The group has one item: a project package called Lake Poyang Project by Learn_ArcGIS.

  2. For Lake Poyang Project, click the options button and choose Download.

    Lake Poyang Project Download option

  3. Start ArcGIS Pro. If prompted, sign in using your licensed ArcGIS account.

    If you don't have ArcGIS Pro or an ArcGIS account, you can sign up for an ArcGIS free trial.

    When you open ArcGIS Pro, you're given the option to create a new project or open an existing one. If you've created a project before, you'll see a list of recent projects.

  4. Click Open another project (if you've used ArcGIS Pro before) or Open an existing project (if you haven't.)

    The Open Project window appears. Next, you'll search for the project you downloaded.

  5. In the Open Project window, browse to the location where you downloaded the Lake_Poyang_Project.ppkx file. Double-click the package to open it.

    Default project

    The project opens to central-eastern China. Three layers (and the basemap) are turned on: the Three Gorges Dam, the Yangtze River, and Lake Poyang. Lake Poyang is several hundred miles downstream of the Three Gorges Dam.

Compare Lake Poyang over time

The project also contains three imagery layers, which are currently turned off. These layers show Lake Poyang at the peak of its rainy season during three different years: 1984, 2001, and 2014.

  1. In the Contents pane, right-click the Lake Poyang layer and choose Zoom To Layer.

    Lake Poyang

    Lake Poyang is mostly long and narrow, extending south from the Yangtze River. Its narrow shape means that even small shrinkages in lake surface area can lead to fragmentation of aquatic habitat. Additionally, several cities around the lake depend on the fishing and transport trades provided by the lake. On a national level, the lake is China's largest source of freshwater. The shrinking of the lake can have devastating ecological and economic ramifications.

  2. In the Contents pane, uncheck the box next to the Lake Poyang layer to turn it off.
  3. Check the box next to the June 1984.tif layer to turn it on. Pan and zoom the map so that the entire image is visible.

    Lake Poyang in 1984

    This image shows the lake in June 1984. The image was acquired by the Landsat 5 satellite. The Landsat satellite program is a joint initiative between two American government agencies: the United States Geological Survey and the National Aeronautics and Space Administration.

    The imagery shows a bright and clear distinction between the blue lake and the green vegetation nearby. Although these colors may seem natural, they are actually a combination of colors on the electromagnetic spectrum that are normally invisible to the human eye.

  4. In the Contents pane, click the arrow next to the June 1984.tif layer.

    Lake Poyang band combinations

    The colors on the electromagnetic spectrum, known as bands, are listed under the layer. Imagery is usually depicted with a combination of three bands, from which an RGB (red, green, blue) composite is created. This image uses Near Infrared 2 for the red color, Near Infrared 1 for the green color, and Red for the blue color. The band designations for various Landsat satellites are listed in the following table:

    Band Landsat 5 Landsat 7 Landsat 8




    Coastal aerosol










    Near Infrared 1

    Near Infrared 1



    Near Infrared 2

    Far Infrared

    Near Infrared




    Far Infrared 1


    Mid Infrared

    Far Infrared 2

    Far Infrared 2







    Thermal Infrared 1


    Thermal Infrared 2

    A list of what each band shows best can be found in the lesson Assess Burn Scars with Satellite Imagery. All three images in your project use band combinations that emphasize vegetation, making the boundaries between the lake and the surrounding landscape more clear and distinct. Next, you'll compare the 1984 imagery to the later imagery to see how the lake has changed.

  5. Check the box next to the June 2001 layer to turn it on.

    Lake Poyang in 2001

    This image was taken by Landsat 7 instead of Landsat 5, so its colors are different. Without a side-by-side comparison, it's difficult to see exactly what has changed. You'll use the Swipe tool to compare the images side-by-side.

  6. In the Contents pane, click the June 2001.tif layer to select it.
  7. On the ribbon at the top of the application, click the Appearance tab. In the Effects group, click Swipe.

    Swipe tool

    When you move the pointer over the map, it changes to an arrow.

  8. Drag the map in the direction the arrow is pointing.

    Swipe on map

    The selected layer is hidden where you drag the cursor. You can now compare the two images. As you drag the Swipe tool back and forth (or up and down), you'll see that most of the change happens on the southern and eastern ends of the lake. Areas where the lake has receded are generally a dull orange, because there is no vegetation there. Overall, in 2001, the lake had visibly less surface area than in 1984. Both of these images were taken before the Three Gorges Dam's opening in 2008, so the cause of the lake's decline during this period may be due to drought or other meteorological trends.

  9. In the Contents pane, turn on the May 2014.tif layer.

    Lake Poyang in 2014

    This image was taken by Landsat 8. The orange areas that indicated bare earth from the receding lake in the 2001 image now show up as bright green due to vegetation growth, indicating long-term water level change.

  10. In the Contents pane, click the May 2014.tif layer to select it.
  11. Use the Swipe tool to compare the 2014 image to the 2001 image.

    The lake appears to have undergone additional surface area loss, mostly in the southern and western parts of the lake. Visually, it's clear that the lake has diminished between 1984 and 2014 (at least during the rainy season, when all three of these images were taken), although the exact amount is unknown.

  12. On the ribbon, click the Map tab. In the Navigate group, click the Explore button.

    The Swipe tool is disabled and you can navigate the map normally again.

Classify land cover in 1984

To quantify the change in lake surface area over time, you'll classify the land cover in each image. Every individual pixel, or cell, in an image has a value for each band. In Landsat imagery, these values correspond to colors. As you can see from the vibrant imagery of Lake Poyang, however, there are many possible color values for all varieties of shades and hues. By classifying the image, you'll group similar values together into a single value that represents a locational feature, or class, such as water, vegetation, or urban areas. You can use these classes to find the area of the feature you want (in this case, Lake Poyang).

You'll use a particular type of classification technique, called an unsupervised classification, in which the software uses statistical analysis to decide which values to group together into classes. You'll only need to specify how many classes there will be. To make this classification, you'll use the Iso Cluster Unsupervised Classification tool.

  1. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Tools.

    Tools button

    The Geoprocessing pane opens.

  2. In the Geoprocessing pane, type Iso Cluster Unsupervised Classification in the search box. Click the result with the same name.

    Search for Iso Cluster tool

    The Iso Cluster Unsupervised Classification tool opens. This tool runs an unsupervised classification on the imagery, or raster, layer of your choosing. It uses the Iso Cluster algorithm to determine the characteristics of natural groupings of cells and creates an output layer based on the number of classes you want. You'll run the tool three times, once for each of the image layers in your map.

  3. In the Iso Cluster Unsupervised Classification tool parameters, for Input raster bands, choose June 1984.tif.
  4. For Number of classes, type 4.

    You really just want to see the lake, so there is no need for a large number of classes.

  5. Change the name of the Output classified raster to Iso_1984. Leave the other parameters unchanged.

    Paramaters of Iso Cluster tool

  6. Click Run.

    After the tool finishes, the output layer is added to the map. Your layer may have different colors than the example image.

    Classified 1984 image

    The new layer resembles the original June 1984 imagery, but the colors have changed and it looks much more pixelated. All image layers comprise grids of pixels, also known as cells, but in the original imagery the pixels had thousands of different colors. The Iso Cluster Unsupervised Classification tool took all the pixels in the original image and sorted them into four value classes, each with its own color (the output colors are randomly generated). All water values were classified into a single value, while the other values represent vegetation or cloud cover.

  7. In the Contents pane, for the Iso_1984 layer, right-click Value 1 and change the color to Yogo Blue.

    Yogo Blue color

  8. Change the other values (2, 3, and 4) to No color.

    Only the water value remains visible. You'll compare it to the original June 1984 image to make sure the classification is correct.

  9. In the Contents pane, turn off the May 2014.tif and June 2001.tif layers.

    Compare classification and imagery

  10. In the Contents pane, click the Iso_1984 layer to select it. Use the Swipe tool to compare the two 1984 layers.

    Although the lake boundaries mostly match up, the classified value also includes smaller bodies of water around the lake. You'll remove some of these smaller water bodies in the next lesson. There is also a portion of the lake that was not classified into the same value as the rest of the lake due to cloud cover.

    Clouds in image

    Clouds often obscure ground features in satellite imagery. The cloud cover in this image is relatively minor, so it won't have a major impact on the analysis, but the analysis could be improved by using imagery with even less cloud cover.

  11. Turn off the June 1984.tif layer. Reactivate the Explore tool.

Classify land cover in 2001 and 2014

Next, you'll classify the other two images to see how the lake has changed over time. The Geoprocessing pane is already open to the Iso Cluster Unsupervised Classification tool, so you'll only change the parameters before running the tool again.

  1. In the Geoprocessing pane, for Input raster bands, change June 1984.tif to June 2001.tif.
  2. For Output classified raster, change the output name to Iso_2001. Leave the other parameters unchanged.

    Parameters for Iso Cluster tool

  3. Click Run.

    Classified 2001 image

    As before, the water values were classified in Value 1 of the new layer.

  4. In the Contents pane, for the Iso_2001 layer, change the color of Value 1 to Light Apple.

    Light Apple color

  5. Change the other values (2, 3, and 4) to No color.

    Compare 2001 and 1984

    The visible blue areas indicate areas that were water in 1984 but not in 2001, more clearly showing the lake's reduction between the two years. Lastly, you'll classify the 2014 image.

  6. In the Geoprocessing pane, change the Input raster bands parameter to May 2014.tif. Change the name of the Output classified raster to Iso_2014.
  7. Click Run.

    Classified 2014 image

  8. In the Contents pane, for the Iso_2014 layer, change the color of Value 1 to Mango.

    Mango color

  9. Change the other values (2, 3, and 4) to No color.

    Compare 2014 to 2001 and 1984

  10. On the ribbon, click the Project tab and click Save As. Save the project with the name Poyang Land Cover.

You've visually compared imagery of the same lake from three different times spanning a period of 30 years, noticing a trend of water loss. You also classified each image to show land cover, creating a single value for water from the many water values in the original image. Next, you'll smooth out the lake boundaries and remove many of the smaller water features that were classified alongside Lake Poyang. You'll then calculate the area of the lake for each year and determine the rate at which the lake is decreasing.

Calculate area over time

Previously, you classified land cover to show Lake Poyang in three different years: 1984, 2001, and 2014. Next, you'll clean up your classified images with generalization analysis tools to remove small errors or minor water bodies around the lake. You'll also smooth the lake's boundaries. After preparing your images, you'll calculate the area of the lake over the past 30 years and determine how much it has changed.

Filter individual pixels

First, you'll clean up the small, individual pixels that are not part of Lake Poyang. Some of these pixels belong to tiny ponds or water bodies, while others were classified incorrectly. Either way, they should not be counted when calculating Lake Poyang's area, so you'll run a geoprocessing tool to eliminate as many of them as possible.

  1. If necessary, open your Poyang Land Cover project in ArcGIS Pro.
  2. Click the Analysis tab and click Tools to open the Geoprocessing pane.

    If the Geoprocessing pane is already open to a specific tool, click the Back button in the upper left of the pane to return to the search box.

  3. In the search box, type Majority Filter. Click the Majority Filter tool.

    Search for Majority Filter tool

    The Majority Filter tool is a data generalization tool. It replaces cells in an image or raster layer based on the value of the majority of the neighboring cells. If a cell has a value of 1 but three of its four neighboring cells have a value of 2, the tool will change the 1 value to fit the surrounding values. You'll run the tool three times, once for each classified image.

  4. For Input raster, choose Iso_1984.
  5. Change the Output raster name to Filter_1984.

    The other parameters let you choose how many neighboring cells the tool will use and whether a majority of contiguous cells must be the same value or if only half must be. In order to generalize the maximum amount of individual pixels and create a greater smoothing effect, you'll use half.

  6. For Replacement threshold, choose Half.

    Majority Filter tool

  7. Click Run.

    Filtered 1984 image

    The generalization removed many of the individual pixels, but many still remain. Additional generalization might be warranted, but generalization also runs the risk of removing data that you do want (in this case, it risks generalizing the values that represent Lake Poyang). You'll fix some of the remaining issues when you smooth out the boundaries later, but for now, you'll run the tool on the other image layers.

  8. In the Geoprocessing pane, change Input raster to Iso_2001 and the Output raster name to Filter_2001.
  9. Click Run.

    The generalized 2001 image is added to the map.

  10. Run the Majority Filter tool on the Iso_2014 raster. Name the output raster Filter_2014.

    The generalized 2014 image is added to the map. Now that you have generalized versions of the three classified images, you no longer need the original classified images on the map.

  11. In the Contents pane, right-click the Iso_2014 layer and choose Remove.

    Remove layer

  12. Remove the Iso_2001 and Iso_1984 layers.

    If you need to access these layers again, you can find them (as well as all the other layers you create in this project) in the poyang database in the Catalog pane.

Clean image boundaries

You've removed some of the individual pixels in each image. Next, you'll clean the boundaries between values in each image to remove the pixelated, granular edges.

  1. In the Geoprocessing pane, click the Back button to return to the search box.

    Back button

  2. Search for and open the Boundary Clean tool.

    Search for Boundary Clean tool

    The Boundary Clean tool smooths boundaries between classes (also known as zones) by expanding the boundaries and then shrinking them back to their original size. Doing so generally removes individual pixels and replaces them with the value of the pixels around them. Its results achieve a similar effect as the Majority Filter tool, but it uses a different process to achieve that effect.

  3. For Input raster, choose Filter_1984.
  4. Change the Output raster name to Clean_1984.

    Boundary Clean tool

    The Sorting technique parameter determines whether values with larger or smaller areas are prioritized during expansion, and the check box determines the number of times the process is run. You'll run the process twice to maximize generalization, and you won't give priority to either area size.

  5. Click Run.

    Cleaned 1984 image

    The differences are small, but the boundaries between values are smoothed out. Additionally, more of the small, individual pixels scattered throughout the image are removed. While a few remain, the generalization tools have cleaned up the image substantially. If you want to see the differences for yourself, try using the Swipe tool and zooming in close to the image to compare. Next, you'll run the Boundary Clean tool on the other images.

  6. Run the Boundary Clean tool on the Filter_2001 raster. Change the Output raster name to Clean_2001.

    The new 2001 image is added to the map.

  7. Run the Boundary Clean tool on the Filter_2014 raster. Change the Output raster name to Clean_2014.

    The new 2014 image is added to the map. You no longer need the images created by the Majority Filter tool, so you'll remove them.

  8. In the Contents pane, remove the Filter_2014, Filter_2001, and Filter_1984 layers.

    If you want to visually compare the extent of Lake Poyang again, change the symbol for Values 2, 3, and 4 in all three layers to No color.

Determine change in area

You've cleaned up your original classified image to remove a lot of the small errors or stray pixel groupings. Next, you'll calculate the area of Lake Poyang. When you originally classified the images into four distinct values, the number of pixels with each value was automatically added to the attribute tables of the layers. Using these pixel counts, you'll determine the hectares of the lake in each image, starting with 1984.

  1. In the Contents pane, right-click the Clean_1984 layer and choose Attribute Table.

    Attribute table

    The table opens. Each of the layer's four values has a pixel count. Value 1, which corresponds with water, has approximately 3 million pixels. That's a lot of pixels, but how big is a pixel in real-world terms? You'll find out by checking the image's resolution, which measures how many real-world units correspond to a single pixel.

  2. Keep the attribute table open. In the Contents pane, right-click the Clean_1984 layer and choose Properties.

    The Layer Properties window opens.

  3. On the left side of the Layer Properties window, click Source.

    Source tab

    The Source tab contains information about the layer's data type and location on your computer, the extent of the data, and how the data is being projected on the map.

  4. Click Raster Information.

    Raster Information

    The Cell Size X and Cell Size Y parameters refer to the length (X) and height (Y) of each cell or pixel. In this case, each pixel on the map corresponds to a real-world area of 30 units by 30 units. However, you still don't know the unit of measurement. Is it 30 inches? 30 kilometers? You want to calculate hectares, so knowing the unit of measurement is important.

  5. Click Raster Information again to close it. Click Spatial Reference.

    Spatial Reference

    The Linear Unit parameter refers to the unit of measurement that all spatial calculations involving the layer use by default. In this instance, the unit is meter, which means that each pixel represents a 30-meter by 30-meter (or 900-square-meter) area in the real world.

  6. Close the Layer Properties window.

    To find the area of each value in the image, you'll multiply the pixel count by 900 to convert it to square meters. Then, you'll divide the result by 10,000, which is the number of square meters in a hectare. The overall formula is as follows:

    Hectares = (Count Ă— 900) / 10,000

  7. In the attribute table, click the Add Field button.

    Add Field button

    The Fields view opens, allowing you to manage the fields in the attribute table. A new field is added to the end of the list.

  8. For the new field, change the Field Name value to Hectares. Change Data Type to Float.

    Hectares field

    Float is a data type that allows numbers with decimals.

  9. On the ribbon, on the Fields tab, in the Changes group, click Save.

    Save changes

  10. Close the Fields view to return to the attribute table.

    The table now contains the Hectares field, but it has no values. Next, you'll calculate the hectares for each value using the conversion equation discussed earlier.

  11. Right-click the heading of the Hectares field and choose Calculate Field.

    Calculate Field option

    The Geoprocessing pane opens to the Calculate Field tool.

  12. Confirm that the Input Table is Clean_1984 and the Field Name is Hectares.
  13. Under Hectares =, create the expression (!Count! * 900) / 10000.

    You can add the Count field to the expression by double-clicking Count in the Fields box.


  14. Click Run.

    Hectares field values

    The Hectares field in the attribute table is populated with the area in hectares of each value in the image. Value 1, which shows water, is approximately 270,000 hectares—the area of the lake in 1984.

  15. Close the attribute table.
  16. Calculate the hectares of the lake in 2001. (Repeat steps 7 through 14, using the Clean_2001 layer.)
  17. Calculate the hectares of the lake in 2014.

    The area of the lake in 2001 is approximately 250,000 hectares, while the area in 2014 is approximately 200,000 hectares. Between 1984 and 2014, nearly 70,000 hectares of Lake Poyang were lost: close to 2,300 hectares a year. Worse yet, the rate of decrease seems to have gotten higher since 2001. While only approximately 20,000 hectares were lost in the 17 years between 1984 and 2001 (close to 1,200 hectares per year), approximately 50,000 hectares were lost in the 13 years between 2001 and 2014 (close to 3,900 hectares per year).

  18. Save the project.

In this lesson, you classified Landsat imagery of Lake Poyang for three different time periods to calculate how much the lake's area has changed. Your findings indicate a severe problem: the lake has lost thousands of hectares in just 30 years, and the rate of loss is increasing. It's possible that the increased rate is due to the construction of the Three Gorges Dam in 2008. Also possible is that a prolonged drought has contributed to the increased rate. Your calculations don't reveal the causes of Lake Poyang's reduction, but they do provide factual evidence of a serious problem and provide a starting point for environmental scientists and others to conduct further research.

You can find more lessons in the Learn ArcGIS Lesson Gallery.