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wiki:grasskenyaextract [2017/12/05 22:53] (current)
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 +====== Extracting Information from Images ======
 +In this exercise you will complete a small remote sensing project. ​ We begin by importing a new Landsat image into GRASS and performing several data preparation operations. ​ Once the data are ready to use you will create a vegetation index and a map layer for degrees Celsius. ​ You will then do a basic change detection. ​ This will be followed with land cover classifications of the region.
 +The Landsat image we will be using covers the same southern part of Kenya, but is larger than the area we worked with previously. ​ The image was acquired on 19 August 2010.  The data are in the compressed and archived format **tar.gz** as the data are provided by the USGS.
 +The file is called **LT51680612010231MLK00.tar.gz**\\
 +It is located in the folder: **/​home/​user/​ost4sem/​exercise/​KenyaGIS/​Landsat**.
 +Open the File Manager (bottom of the Virtual Machine - third icon from the left). ​ Navigate to the Landsat folder and right-click on the filename. ​ Select **Extract Here** to unpack all of the data layers.
 +If GRASS is currently open, **Remove** any maps in the Layer Manager. ​ If it is not open, reopen GRASS. ​ We will use the same Location **KenyaUTM** and Mapset **user** as we did earlier.
 +==== Preparing data for use in GRASS ====
 +=== Import each band of the Landsat scene ===
 +We will use the graphic tool to import the first layer, then use the Command console to quickly imprt the remaining data layers
 +  * From the menu select **File | Import raster data | Common formats import []**
 +  * Browse to the KenyaGIS/​Landsat folder and select file **LT51680612010231MLK00_B1.TIF**
 +  * This file is the first band of the Landsat image, contianing data for the Blue band
 +  * Click Open then Import
 +  * The new map layer is added to the Layer Manager and the Map Display window
 +  * Click in the Command console panel of the Layer Manager and press the keyboard Up Arrow to display the command
 +  * Edit the command by replacing the **B1** with **B2** for both the input and output file names
 +  * Hit the Enter key to run the command and the second data layer is imported
 +  * Repeat this until all seven bands are imported into GRASS
 +  * If you make an error entering the band numbers you can remove the bad dataset\\ **File | Manage maps | Delete [g.remove]**
 +  * Close the Import window when you are done
 +  * The Layer Manager should now have all seven bands loaded
 +=== Region ===
 +The Map Display should show the same area that was displayed in the earlier exercise. ​ This is because the default Region was set to this area.  You can learn more about this region from the Command console.
 +  * Type **g.list region** to list the names of any Regions defined in the Location
 +  * Type g.region -p to display all of the region information
 +  * In the Map Display window click on the **Zoom to selected map layer** to display the full map layer
 +  * On the bottom of the Display window change Coordinates to Show comp. extent to display the region boundary
 +  * Change back to Coordinates and the region display is retained
 +Region is a powerful part of GRASS. ​ You can use this to easily subset data, change cell size, and control raster processing. ​ You are encouraged to read about the tool g.region in the GRASS documentation.
 +=== Explore the data ===
 +Take few minutes to explore these data layers. ​ Toggle off and on various layers. ​ Use the Query tool to examine cell values. ​ Make layers 4 and 6 active and query the scene. ​ Note the large difference in values. ​ Band 4 represents optical data of the Near Infrared wavelength and Band 6 represents emissive (thermal) data.
 +==== Data Preparation ====
 +The data units, as provided in the original files, are Digital Numbers (DN).  These are rescaled radiance values. ​ We can improve the quality of the data and make it more comparable to other images by converting these data into Top of Atmosphere Reflectance. ​ We will use the GRASS TOAR tool to accomplish this.
 +  * Load the tool **Imagery | Satellite images tools | Landsat DN to radiance/​reflectance [i.landsat.toar]**
 +  * In the //​Basename//​ pane use the drop-down arrow to select the first layer.
 +  * Only keep the common part of the name up to **B** and remove the last part of the name **1@user**
 +  * Enter a Prefix of **2010TOARb**
 +  * Click on the //​Metadata//​ tab
 +  * In the //Name of Landsat metatdata// pane click Browse and navigate to the folder where the original data are located
 +  * Select the file with the name that ends with **MTL.txt** and click Open
 +  * This may take a couple of minutes to run
 +  * Close the TOAR tool when done
 +=== Explore these new data ===
 +  * Load the new band 4 and make both the original and new band 4 layers active
 +  * Use the Query tool to compare values. ​ The original layer has DNs and the new file has reflectance values ranging from 0 to 1
 +  * Compare the original and new band 6 layers. ​ The original layer had emissive values and the new file has degrees Kelvin
 +=== Subset the data ===
 +These data have been improved to represent the solar energy reflected from the surface of the earth and captured at the satellite. ​ Now we want to subset the data to the Nairobi Region. ​ We will use Mapcalc to multiply the data by 1.  Since the Region has been defined for the Location, this will subset the data to this area.
 +  * Open the raster calculator **Raster | Raster map calculator**
 +  * Select **2010TOARb1**in the Insert existing map box
 +  * After the Mapset name user enter multiply Operator followed by 1
 +  * The formula should look like this:   ​2010TOARb1@user * 1
 +  * Enter and output name **2010b1** and click Run
 +  * We need to do this for bands 2, through 7 next.
 +  * Change the Expression and Output from **b1** to **b2** and click Run
 +  * Repeat for the remaining bands
 +<​note>​The new image has been subset to the Region size.  It also is using the Rainbow Color Table. ​ We will change this to grey later</​note>​
 +=== Final Preparation ===
 +Now we need to change the Color Table and create an Image Group and Subgroup and Color Balance the layers
 +== Color Table ==
 +  * In the Layer Manager right-click on **2010b1** and select **Set color table**
 +  * Click on the Define tab, pick the **grey** color table and click Run
 +  * Click Copy and paste the command into the Command console
 +  * Change the band number and hit enter
 +  * Click in the console, hit the Up arrow, change the band number and hit Enter again
 +  * Repeat until all six bands have been changed
 +  * Close the Color table window
 +== Create Group ==
 +  * Select **Imagery | Develop images and groups | Create/edit group []**
 +  * Enter the new group name **2010TOAR**
 +  * Click the **+Add** button and enter the Pattern **2010b** and click OK
 +  * Now check the box **Edit/​create subgroup,** enter the name **2010Sub,​** select all bands and click OK
 +== Color Balance Images ==
 +Satellite images are typically quite dark before they are enhanced. ​ To see this use the Add Image tool {{:​wiki:​addimage.jpg?​35|}} and select the new bands 3, 2, 1 to construct the RGB image. ​ It looks very dark.  We can quickly improve this.
 +  * Select **Imagery | Manage image colors | Color balance [i.colors.enhance]**
 +  * For the Red layer select the map **2010b3**
 +  * For the Green layer select the map **2010b2**
 +  * For the Blue layer select the map **2010b1**
 +  * Toggle off then on the RGB image display
 +  * Repeat the Color Balance for the RGB 432 and RGB 543 combinations
 +==== Create New Data Layers ====
 +== Convert degrees Kelvin to Celsius ==
 +This is the same function that you did in the earlier exercise. ​ Use the Raster map Calculator to subtract 273.15 from the Kelvin layer **2010b6**. ​ Name the new file **2010Celsius**. ​ Change the Color Table to Celsius and use the Query tool to verify the data values.
 +== Create the NDVI layer ==
 +Recall from the lecture that this formula is the Near Infred layer minus the Red layer divided byt the sum of the two layers. ​ For Landsat the bands would be **(4 - 3) / (4 + 3)**
 +  * Use the Map Calculator to construct this formula
 +  * Make sure you use the Insert existing map pane to replace the numbers 4 and 3 with the layers 2010b4 and 2010b3
 +  * Enter a new filename 2010NDVI
 +  * Use the Query tool to verify that the data values are between -1.0 and +1.0
 +  * Change the Color Table to NDVI
 +==== Change detection ====
 +The ability to measure change over time is a another powerful feature of working with satellite images. ​ We will look at how the temperature and NDVI have changed from 1995 to 2010.
 +== Temperature Change ==
 +  * Remove all map layers from the Layer Manager then open the **1995Celsius** and **2010Celsius** layers
 +  * Use the Map Calculator to subtract the **1995Celsius** layer from the **2010Celsius** layer
 +  * Enter a new filename 2010Cminus1995C
 +  * Make all three layer active and use the Query tool to verify your results
 +  * If this looks correct, change the Color Table to Differences
 +  * Red and pink areas reflect increases in temperature and shades of blue are decreases
 +== NDVI Change ==
 +  * Add the 2010 and 1995 NDVI layers and perform the same difference calculation
 +  * Use the Query tool to verify your result and if it looks good, change the Color Table
 +  * Use the Map Swipe tool **File | Map Swipe** to see if changes in temperature vegetation are related
 +This is just a brief exposure to change detection. ​ After you develop land cover classifications you can explore how forest have changed over time, examine urban sprawl, and many other changes to the landscape.
 +==== Classification ====
 +Land cover classification provides a means to categorize and quantify the landscape. ​ We will perform a quick unsupervised classification of the new 2010 image. ​ This will be followed by a supervised classification of the 1995 image used earlier. ​ In GRASS classifications are a two-step process. ​ You create the training data, then apply thenm in the Maximum Likelihood Classifier.
 +=== Unsupervised Classification ===
 +  * Select **Imagery | Clustering input for unsupervised classification**
 +  * In the imagery group pane select the **2010TOAR** group created earlier
 +  * In the subgroup pane select the **2010sub** subgroup
 +  * Enter a filename **Unsup10class**
 +  * Click on the **Setting** tab and enter 10 in the //Initial number of classes// pane
 +  * Click Run to create the classification signature file
 +  * If there are no error messages close the window
 +  * Select **Imagery | Classify image | Maximum likelihood classification**
 +  * Enter the same Group and Subgroup as above
 +  * Enter **Unsup10class** for the //Name of Input file containing signatures//​
 +  * Enter the filename **10ClassUnsup** for the output and click Run
 +=== Supervised Classification ===
 +This method of classifying an image is a bit more complex. ​ It requires the user to create training regions before running the Maximum Likelihood Classifier. ​ Here is a view of the Classification tool.
 +{{ :​wiki:​supclass.jpg?​500 |}}
 +This tool requires many inputs. ​ After you load the tool you should move the mouse over each icon to learn which tools to use.
 +== Setup ==
 +  * Select **Imagery | Classify image | Interactive input for supervised classification**
 +  * Move your mouse over the icons to discover the many funtiosn you will be using
 +  * First we must define the image group
 +  * - Click on the Select Imagery Group tool in the upper left
 +  * - Select the **1995Visible** group and **1995sub** created in the earlier exercise
 +  * In the **Training Areas Display** select the Add RGB tool
 +  * For the Red display layer scroll down to the Mapset:​Permanet data and select the **1995Jan30b5** layer
 +  * Select the **b4** and **b3** layers for the Green and Blue layers and click OK
 +  * This will produce a flase color display with vegetation bright green, water black, and soils pink and purple
 +  * In the Preview Display enter a band combination of 3, 2, and 1 to produce a natural color display
 +== Define Classes ==
 +  * Open the Class Manager tool and add several classes
 +  * - Click the +Add button and enter the Class name **Water** and select the color blue
 +  * - Add classes for Veg as green and Soil as orange
 +  * Close the dialog when done
 +== Create training regions ==
 +  * Zoom very close in to the northeast corner to create the Water class training region
 +  * - Make sure Water is selected near the Class Manager icon
 +  * - Use the **Digitize new area** tool to create a couple of polygons containing only water
 +  * - Zoom out the the full image then in to the very center
 +  * - Create another Water polygon in the dark rectangular area.  Zoom in more if you do not see it
 +  * Create a Veg traing region
 +  * - Change the Active class to Veg
 +  * - Zoom in to the bright green area just north of the settling ponds
 +  * - Draw a polygon here - make sure it is displayed as green
 +  * - Zoom out to the entire image then back in to the dark green area to the northwest
 +  * - Create another Veg polygon here
 +  * Create the Soil traing region
 +  * - Change the active class to Soil
 +  * - Zoom in to two of the pink areas and create polygons
 +== Calculate the class signatures ==
 +  * Click the **Run analysis** tool.  This will take a bit of time
 +  * When it is finished it will add a color overlay in the Preview Display window and will display class histograms along the left side of the window
 +  * Click on the **Save signature file** tool, enter the name **FirstSupClassSigs** and click OK
 +Now that you have created the training regions you will run the maximum Likelihood Classifier as you did above.
 +  * Select **Imagery | Classify image | Maximum likelihood classification**
 +  * Enter the 1995 Group and Subgroup used above
 +  * Enter **FirstSupClassSigs** for the //Name of Input file containing signatures//​
 +  * Enter the filename **FirstSupClass** for the output and click Run
 +  * If you get no error messages then yo uwill see your new classification
 +This was just a brief introduction to land cover classification. ​ You are encouraged to rad the GRASS manual and experiment with the many options to create and apply training regions.
wiki/grasskenyaextract.txt ยท Last modified: 2017/12/05 22:53 (external edit)