Projections, Resampling, Georeferencing & Georectification Assignment

Introduction 

Projection, or the process of converting a round globe into a flat map, causes distortion. Different  projections cause different types of distortion. 

Consequently, one of the first and most important decisions a cartographer must make when making a  map is which projection to use. The number of projections available can be overwhelming. If you  know how projections are made and classified, you can make an informed decision about which  projection best suits your needs. 

Learning Objectives

•  To understand how projections distort data properties 
•  Determine the most appropriate projections for your analysis 
•  To learn about zones and their effects 
•  To understand metadata and the importance of knowing the projection information for spatial  datasets  
•  To Georectify an image automatically and manually 
•  Understand rectification errors 

As has been previously discussed in lectures and labs, making a map involves recording the earth's  features on a three-dimensional globe and then projecting them onto a flat map (Figure 1). Because  each projection is based on a geographic coordinate system, this is where the process begins. 

Figure 1: From 3D to 2D 

Converting a sphere to a flat surface results in distortion. The question a cartographer must ask is,  "What spatial property do I need to preserve in my map to accomplish my goal?" 

Projections are categorized according to the four spatial properties they preserve: shape, area, distance  and direction (azimuth).

Some projections preserve more than one spatial property: 

• Conformal projections preserve shape, but they also preserve azimuth fairly well.
• Equivalent or equal-area projections preserve area and can also preserve distance reasonably  well. 

These spatial properties are also used to categorize projections, and often are found within a  projection's name. For example, the name of the Albers Equal Area Conic projection tells you that it  will preserve the spatial property of area. 

Geometric shapes—specifically planes, cones, and cylinders (Figure 2)—help visualize projections  and give us an idea of the distortions we might expect from certain types of projections. 

Figure 2: visualizing projections 

However, we still must often decide which projection is the best to use. What you have learned so far will help you choose the best projection for a given scenario. When deciding which projection to use  for your map, ask yourself the following questions: 

•  What is the purpose of your map? 
•  What spatial properties need to be preserved? 
•  What is the spatial extent of the map? 

Answering these questions will help you identify the most important spatial properties to preserve,  and ultimately, determine the projection that is best suited for your needs. 

Here are a few iconic and commonly used projections. 

Figure 3: Winkel Triple projection- averages the coordinates from the equirectangular (equidistant cylindrical) and  Aitoff projections 

Figure 4: Mollweide Projection: An equal-area projection designed for small-scale maps

Figure 5: Albers Equal Area: Best suited for land masses extending in an east-to-west orientation rather than those  lying north to south

Figure 6: Lambert conformal: Similar to the Albers conic equal area projection except that Lambert conformal  conic portrays shape more accurately than area 

Figure 7: Mercator: Cylindrical projection; originally created to display accurate compass bearings for sea travel; all  local shapes are accurate and clearly defined 

Figure 8: Web Mercator

...and The Universal Transverse Mercator (UTM). 

Figure 9: UTM: Cylindrical projection; a specialized application of the transverse Mercator projection 

All projections are based on a geographic coordinate system, either local or global. The World  Geodetic System 1984 (WGS84) (Figure 10), for example, does not focus on any one location on the  planet as its datum is found within the centre of the earth. 

Figure 10: WGS 1984 

Sometimes, your data will not be in the same format as the projection you want to use. To prevent  misalignment issues, you may need to reproject the data. This is especially true if your datasets use  different geographic coordinate systems.

Like most things in geography, distortion is scale dependant. For example, the UTM is conformal;  however, most UTM-based maps use a scale that preserves distance, direction and area fairly  accurately (Example: Figure 11). 

Figure 11: Topographic map = “A map that represents the vertical and horizontal positions of features, showing  relief in some measurable form, such as contour lines, hypsometric tints, and relief shading” (Esri, 2015) 

UTM-based maps also tend to have a large scale ratio and small spatial extent.

Figure 12: Use of Different Projection for different purposes 

Distortion is most prominent at the global scale. As the map moves towards a local scale, it becomes  more and more possible to "hide" distortion in unseen or unused parts of the map. 

As the scale ratio approaches 1:1, the specific projection that you use becomes less important. At such  a scale, the map feature is the same size as the actual feature and the earth's curvature is barely  noticeable.

Part 1: Working with Projections 

In this part of the lab, you will determine the most appropriate projection for two scenarios. When  choosing a projection, remember to consider the following questions: 

•  What is the spatial extent of your subject matter? 
•  Where on the globe are you mapping? 
•  What does your map need to do? 
•  Which spatial parameters do you need to preserve? 

Estimated completion time: 20 minutes 

Step 1: Assess your needs: Dot density 

Scenario 1:  

You need to choose an appropriate projection for a dot map density (discussed in a podcast)  that shows populations for each country. 

•  Start ArcMap. 
•  Browse to your Lab2 _Data folder > then to the ProjectionBasics10_0 folder  ? Open choosing_projections.mxd. 

Answer the following questions to determine the best projection for the dot density map. 

• At what scale will you be working in? 
• Which spatial property is critical to a dot density map? 
• Now that you know the kind of projection that you need and the properties your map must  preserve, which of the following projections are appropriate? 
• Albers Equal Area Conic 
• Azimuthal Equidistant (world) 
• Cylindrical Equal Area (world) 
• Mollweide (world) 
• Sinusoidal (world) 
• Transverse Mercator 
• Winkel Triple (world) 

Step 2: Choose a projection: Dot density 

Now you need to choose your final projection. 

• Open the Data Frame Properties dialog box > click the Coordinate System tab, if necessary.
• Apply a few projections that you think might meet your needs. 
• Make notes on the differences.

Step 3: Assess your needs: Trail map 

Scenario 2:  

You have been asked to create a topographic map of the area around Crater Lake, Oregon for  outdoor enthusiasts who will hike on and off the trails. 

•  In the table of contents: right-click the Crater Lake Data Frame name > Activate. At what scale is the trail map? 
  • Landform / local 
  • County 
  • Country 
  • Continent 

Hikers will need to be able to interpret landforms on the map and correlate them to what they see  around them. 

What spatial property is most important for you to preserve? 

•  Shape 
•  Area 
•  Distance 
•  Direction 

Which popular projection was described earlier as a local-scale conformal projection that is often  used in topographic navigation? 

•  Winkel Triple 
•  Web Mercator 
•  Mercator 
•  Universal Transverse Mercator (UTM) 

Step 4: Examine misaligned data: Trail map 

You have identified the projection for your map. Someone has collected coordinates for “points of  interest” that will help users plan their hikes. 

•  In the table of contents: turn off Contour Lines and turn on Points of interest.
•  Zoom to the full extent. (Tip: Use the Full Extent icon) 

Notice that the points do not line up with your map. In reality, they encircle Crater Lake, but are  located due east on your map. The points were collected and manually entered into a spreadsheet,  which was then imported into a shapefile. Based on these facts, it is likely that the cause of this  problem is related to the projection used by the shapefile. 

•  In the table of contents: right-click Points of interest > Properties. 
•  In the Layer properties dialog box, click the Source tab.

Which referencing system has been used for this data? 

Which referencing system has been used for the Topo layer? 

Step 5: Reproject a feature class: Trail map 

Now that you know what is causing the misalignment of your data, you will use a geoprocessing tool  to remedy the situation. 

•  On the Standard toolbar: click the Search window button . Dock this window to one side  of the ArcMap GUI. 
•  In the Search window, do the following: 
  •  Click the Tools link 
  •  In the text box, type Define Projection 
  •  Click the Search button . 
  •  Click the Define Projection (Data Management) result to open the tool. 
•  In the Define Projection tool dialog box: for Input Dataset or Feature Class, choose Points of  interest. 

Note: A warning icon indicates that the feature class already has a defined projection. This is a  reminder that this tool will alter the source data so that it is based on another projection.  There is no "undo" functionality, but you could re-define the projection if necessary. 

•  To specify the coordinate system, click the Spatial Reference button . 
•  In the Spatial Reference Properties dialog box, find the referencing system of the Topo layer  > OK 

Hint: You could import the same projection as the Topo map:

•  Click OK to run the tool. 

Notice that the points now encircle the lake. 

•  Zoom to the Lightning Spring bookmark. 

Now that the points are projected in the UTM zone appropriate for the spatial extent, they align  perfectly. 

•  Close ArcMap without saving your changes.

Part 2: From Analog to Digital: Georeferencing 

Now that you have worked with a scanned and projected topographic map, it is important to  understand the process of how the map went from an analog paper map to a digital map (Figure 2.1) that can be used with other geographic data in the GIS (i.e. it wasn’t magic). To do this we use a  process called georeferencing. 

Figure 2.1: Basics of georeferencing 

Georeferencing is the process of aligning geographic data to a known coordinate system so that it can  be viewed, queried, and analyzed with other geographic data (Figure 2.2). This process creates  additional information within the file itself or in supplementary accompanying files that specifies how  GIS software should properly place and draw the data. 

Figure 2.2:

ArcGIS provides a variety of tools to fix a raster's spatial properties and coordinates, depending on the  nature of the problem.  

Georeferencing is required only when the dataset has not been integrated to any coordinate system  and does not have a defined extent. You can check those settings in ArcCatalog, under dataset  properties. 

 

Figure 2.3:

Most of the raster datasets that you obtain from the government or commercial sources are already  georeferenced, and they should be ready for you to use. However, your data will occasionally have a  different format than the projection you want to use. To prevent misalignment issues, you may need to  reproject the data or adjust it. It is also possible that your data is georeferenced, but the coordinate  system has not been defined, even though you know what it is. In that case, you would use the Define  Projection tool. (Such cases will not be addressed in this course.) 

Converting raw raster data into GIS data 

Georeferencing involves transformations, which convert an image from pixel space to a defined real world coordinate system. Transformations are based on the ground control points on the raster and on  the reference data. The reference data can be either in vector or raster format, as long as it covers the  same area and has spatial information. 

Converting raster data from pixel space into a coordinate system 

In simple form, raster data represents the real world as an array of cells arranged in rows and columns.  Every cell in a raster has unique row and column position identifiers. To integrate a raster image with  a spatial coordinate system, you need to define its origin in a planar Cartesian coordinate system (Figure 2.4). Scanned maps, satellite images, aerial photographs, and pictures are examples of raster  data.  

The cell sizes of the raster in Cartesian coordinates are arbitrary. ArcGIS can draw the raster dataset,  but it will not line up with the other spatial data. Instead, the raster will display at the origin of the  current coordinate system.

Figure 2.4:

The georeferencing process involves selecting a location on the raster image and specifying which  coordinate it represents in your desired real-world coordinate system. 

Input data 

To align your raster properly with its real-world location, you must be mindful of the input data  quality and its relevance for your study area. All the transformations in the georeferencing process are  based on the assumption that the reference data is perfectly accurate. The data transformations will  resample your raster data and change its properties.  

To convert your raster data into GIS data, you need reference data that covers the same area as your  target area or any other spatial information about your data (like known coordinates). Reference data  is a layer with a known spatial reference that displays in the correct coordinates' space. 

Key points to keep in mind when choosing reference data: 

• The reference data should be in the desired coordinate system that you will use to align with  and georeference your raster data. For example, if the scanned map were created in a State  Plane coordinate system, your reference data should possess the same system. Projection  defines not only the coordinate system but also the units and many other measures. 
• The output raster will have the same (or larger) cell size as its inputs. In a GIS, your results  will only be as accurate as your least accurate dataset.  
• You can always resample a raster dataset to have a larger cell size. However, you will not  obtain any greater detail by resampling your raster to have a smaller cell size; you will simply  increase the size of the raster on the disk. If you are limited to using reference data that has  lower resolution (larger cell size) than the image you are processing, it may be worthwhile to  store a copy of your data at its smallest and most accurate cell size, while simultaneously  resampling it to match that of your largest and least accurate. This could increase your  analysis processing speed. 

Raster georeferencing methods 

The process of georeferencing involves identifying a series of control points — known x,y  coordinates — that link locations on the raster dataset with locations in the spatially referenced data  (target data). Control points are locations that can be accurately identified on the raster dataset and in  the geographic coordinate system. You then use those control points to build a transformation that  will shift the raster dataset from its existing location to the spatially correct location. The connection  between one control point on the raster dataset (the “from” point), and the corresponding control point  on the aligned target data (the “to” point), is a “link”. 

There are two methods for creating links in georeferencing: auto registration and manual  georeferencing (Table 2.1). Auto registration is quick and convenient, but it works only when the  reference data is similar to the raster image. Manual referencing is more time-consuming, but it gives  you control over the results of the georeferencing and works with all kinds of raster data. 

Table 2.1 

For more help on Georeferencing, and key Georeferencing vocabulary, see: 

http://resources.arcgis.com/en/help/main/10.2/index.html#//009t000000mn000000

ENVT4411 GIS Applications 

Part 2: Exercise 1: Georeference a raster using auto registration 

Comparisons of multi-temporal aerial photographs are a valuable tool for monitoring change. To use  aerial photographs either in combination with other spatial data, or to compare them against aerial  photographs from other timeframes, they must be georectified. In the previous section, you learned  about data projections. In this lab you will see how primary data is first georeferenced in geographic  space then transformed through georectification, and how measurements between aerial photographs  from different time frames can then be compared. 

For this lab you have two historical aerial photographs of Fremantle. One was taken in 1958, has been  scanned at a resolution of 900 dots per inch (dpi), and requires georectification. The other was taken  in 2007 and is already scanned, mosaicked (joined with other aerial photographs to create a  continuous image), and georectified. You will use the locations in the georectified image to  georeference the other image. Using this georeferencing, you will then georectify the image. 

Estimated completion time: 

Step 1: Add reference data 

• Start ArcMap with a new blank map. 
• Ensure the Catalog Window is open ( ). 
• In the Catalog window: connect to the GeoreferenceRaster folder in your Lab2_Data folder.
• Expand the GeoreferenceRaster folder. 
• Drag Freo_2007.ecw into the map display. 

Step 2: Add the raster dataset 

• From your GeoreferenceRaster folder, click Freo_Unregistered.tif > drag it into the map  display. 

At this point you may receive two on-screen messages. The first one asks if you want to build  pyramids, and the second warns that your data is missing spatial information. 

• If prompted to build pyramids, click Yes.  

Building pyramids improves the display and performance of your raster data. 

• When notified that your data is missing spatial information, click OK. 

Note: You have added one layer with a spatial referencing system and one without to the map. Note  that you added the one with the referencing system first. This is vitally important to the  georeferencing process. The first layer added will define the referencing system of that  Data Frame. 

• Add the Georeferencing toolbar (Figure 2.5) 

Tip: Top of GUI > right-click the gray area > Georeferencing

Figure 2.5: Georeferencing toolbar. 

• If desired, dock the Georeferencing toolbar at the top of the GUI. 
• From the drop-down list next to the Georeferencing toolbar, select Freo_Unregistered.tif 

Note: All georeferencing operations will be applied to the layer that is set as the Georeferencing  layer. 

Note: The Georeferencing toolbar layer can display raster layers, image service layers, and CAD  layers as valid types. The layer must either be in the same coordinate system as the data frame  or have no spatial reference defined. 

Step 3: Prepare for georeferencing 

• Zoom to the Freo_2007.ecw layer. 
• On the Georeferencing toolbar, from the Georeferencing menu; choose Fit To Display. 

Note: Fit To Display scales and shifts the raster graphics to fit the map display area; it does not shift  it to its actual location in relation to the reference data. 

• From the Georeferencing toolbar, use the Rotate , Scale , and Shift tools to line up  the raster with its target area on the reference data.  
• Add the Effects toolbar.  
• From the Effects toolbar, choose Freo_Unregistered.tif from the drop-down list. 
• Click the Swipe Layer button , and then click and hold as you move the mouse pointer  over the map. 

This raster almost fits exactly into the Freo_2007.ecw image. 

Lining up data is the most important step in auto registration, so it is worth taking the time to do it  properly. This helps the algorithm find the similarities in the datasets, making your georeferencing  more successful. 

Step 4: Method 1: Auto registration 

• On the Georeferencing toolbar notice that there is and Auto Registration button .  

Auto registration allows you to automatically georeference your raster dataset to a referenced raster  dataset. The automated links are based on spectral signatures, so it is meant for aerial and satellite  imagery, which are similar in nature. The auto registration does not work well with scanned maps or  historical data. 

The algorithm identifies small image areas with similar textures. First, it looks for unique textures in  one image and then searches to find the same area within the second image. Therefore, to achieve a  higher success rate in auto registration, the two images need to be as similar as possible. It is also important to use the Rotate, Shift, and Scale tools to move the data to its near-exact location so that  the algorithm can quickly find the similarities. 

• Look in the Table of Contents 

You can see that the Freo_2007.ecw is comprised of 3 layers – Red Green Blue – stacked to create a  colour composite. The Freo_Unregistered.ecw layer is comprised of one image, a panchromatic  historical photo. Therefore the software will struggle to auto register the image. 

• Click the Auto Registration button .  

The Auto Registration will ultimately fail and there are a number of reasons this may have happened (see question for thought below). Understanding why something ‘does not’ work is equally as  important as knowing how a process ‘does’ work which ultimately saves time and allows you as the  spatial analyst to make the best decisions possible. If auto registration had worked, the Shift, Rotate and Scale buttons would now be greyed out and disabled. This is because “links” (ties between the  two images links areas thought to be the same) are now used to adjust the raster. Each time you add a  new link, the graphic is rescaled, shifted, and rotated to fit it. As auto registration does not work for  this image combination we will learn how to manually register an image in the next section. 

Even though auto registration cannot work for this image, it is useful to know in case you need to auto  register an image in the future:  

• You can choose to look at the links created during the Auto Registration process: View Link  Table button to evaluate your work. Currently, it will be empty as there are no links made  between the images. 
• To evaluate how well the registration process worked we use a measure called the Root Mean  Squared Error (RMSE). 
• The RMSE for the auto registration process will appear in the top right of the Link Table ? You would also be able to see and edit the number of links used  
• Remove both data sets from ArcMap 

Part 2: Exercise 2: Georeferencing using Manual Registration 

During auto registration, algorithms match points automatically based on similar textures in the  datasets. In manual georeferencing, you must do this yourself. You identify distinctive locations that  are visible in both datasets as control points, which then establish links from the original dataset to the  corresponding points in the reference dataset. 

ArcGIS uses these links to calculate the information necessary to georeference a raster to the  reference data, just as it does during auto registration. It calculates the x and y shift factors, x and y  scale factors, and the rotation angles that are needed to overlay the raster to its geographical location  on earth. 

Adding links (or Ground Control Points (GCPs) as we will call them from now on) is the most  critical part of the georeferencing process.  

When adding GCPs, keep the following points in mind: 

1. Make sure to place GCPs all over your study area, initially establishing one point in every  corner and one in the middle. However, this may not always be possible if there are not  enough identifiable features (for example, water, forests, deserts, and so on). 
2. When georeferencing an aerial image, choose points on the ground. You should not use the  tops of anything above the ground (such as houses, towers, and trees) as control points  because they appear to lean away from the camera due to the camera angle of the plane. Note  that you can use the base of any of these structures if it is on the ground and visible in both  images. 
3. Tie your links to stable, permanent points, e.g. street intersections and corners or natural  features, which are likely to be in the exact same location. Keep in mind that some features,  like rivers and streets, may have changed over time. Therefore, it is important to be familiar  with your study area (See Figure 2.6 for more).  

Figure 2.6:

In the next part of the lab, you will georeference the same image however this time you will use the  manual approach. This exercise emphasizes the need to validate and analyze the links before  transforming your data and saving the results to ensure accurate georeferencing. You will identify the  misplaced links and delete or adjust them. You will also decide how to save the georeferencing results  based on the dataset's intended use in the future. 

Estimated completion time: 

Step 1: Add reference data 

•  Start ArcMap with a new blank map. 
•  Open the Catalog window. 
•  Make sure you are still connected to your GeoreferenceRaster folder. 
•  Expand the GeoreferenceRaster folder. 
•  Drag Freo_2007.ecw then the Freo_Unregistered.tif into the map display. Think: Why are you doing it in this order? 
•  Drag the unrectified image to the top of the Table of Contents if not already there.  
•  Make sure that the Georeferencing toolbar is open and that the unrectified image is selected in  the drop down menu. 
•  Zoom to the unrectified image 

Tip: Right-click on the layer’s title and select Zoom to Layer.  

Stop: You must to identify GCPs on both images before you start selecting them. You will need  about 9 or 10 GCPs, and you should look for features that are temporally persistent. Write  them down to keep track. 

• When you are ready, use the zoom tool to view the first potential GCP more closely.  

Think: You are about to tie an area in an unrectified image to the corresponding area in a rectified  image – therefore, which image should you click on first i.e. which image are you dragging  on which? 

• On the Georeferencing toolbar, click on the red-green cross hairs button and click on your  unrectified image to select your first GCP.  
• In the Table of Contents (TOC) right click the georectified image and zoom to this layer.  ? Zoom into the corresponding ground control point (using the scroll button on your mouse) ? Click on the corresponding GCP on the rectified image 

You have placed you first GCP! Once you have selected 2 or 3 GCPs your unrectified image will  move into place relative to the rectified image (Figure 2.7).  

• Repeat this process 9 or 10 times trying to select ground control points well distributed. Tips: 
  • You will need to remember this as a process: right click on unrectified image > zoom to layer  > select GCP > right click on rectified image > zoom to layer > add corresponding GCP >  repeat 
  • You may also need to switch the top image on and off in the TOC to be able to see the  rectified image 
  • Use the Effects toolbar to help you judge the accuracy of your rectification once you’ve  added 5 or 6 points.

Figure 2.7: The Unrectified image begins to apepar on top of the referenced image. 

Step 2. Examining your georeferencing error  

Once you’ve added 9 or 10 GCPs, you need to know how well you’ve chosen in order to know how  accurately the rectification will be. 

• Inspect the ground control points you have chosen by looking at the link table (Figure 2.8),  using the transformation formula “first order polynomial” (click the button at the right of the  Georeferencing toolbar ). 

Figure 2.8: Link Table shows the residuals for each GCP along with Total RMS Error. 

• Examine the residuals and the total Root Mean Squared (RMS) error to see how well your transformation went.  

A low total RMS error does not necessarily mean that the image is geolocated properly, it only means  that the GCPs are in agreement spatially. Thus, it is really describing how consistent the information  is from all GCPs.

A GCP with high residuals shows that, in relation to the other GCPs, there is poor spatial location for  that ground control point. If you have points with very high residuals they can be omitted and a new  RMS error is calculated. While points with high RMS tend to be poorly located this can also be  caused by a few closely located points in the image causing a distortion and making an accurately  located point appear off. Before removing ground control points remember that you want to maintain  good spread of points throughout the image and remove points your are less confident of first (where  there is difficulty selecting a clear boundary to line up points between both images). Alternatively,  you can add more or correct existing ground control points.  

Step 3. Georectifying the aerial photograph  

• When you are happy with your RMS error, select Rectify from the Georeferencing menu  (Figure 2.9). 
• Set the cell size to 1.0 (meters).  
• Select Bilinear Interpolation as the Resampling Type.  

Figure 2.9: Rectify in the Georeferencing menu. 

• Ensure that the “Output Location” is the folder you are working with (Figure 2.10).  ? If not, click the folder icon, click the “connect to folder” icon , navigate to the correct  folder, then Add.  
• The file name is given separately underneath in “Name” section.  

Note: You can save the image as an ESRI Grid, a TIF or an Erdas Imagine file.  ? Save the file as an Erdas Imagine file.  

Think: What is the name of your output file? You must always keep track of what you are calling a  file and where you are saving it to!!!

Figure 2.10: The “Save As” dialog box for creating your final, georectified image. Note the diffent parts of  the dialog box i.e. for output location, name, type: all must be completed in turn. 

• Check that the projection for the data frame is GDA 1994 MGAS Zone 50. If not, set it.  

Hint: View > Data Frame Properties > Coordinate System > Predefined > National > Australia >  GDA 1994 MGA Zone 50 > OK) 

Step 4. Make a Map 

• Make a map showing the historic georectified image overlaid on the rectified image.  Make sure your maps includes all of the necessary pieces of information (i.e. legend, title,  scale bar etc.). Save this map in a file format of your choice. I suggest .jpg or .png.  

Question(s) for Thought: (1-2 paragraphs of well developed prose) 

Why did the auto registration process not work in this lab? Hint – use ArcGIS Desktop Help  for clues. 

Reflecting on the RMSE for your manually registered image, if you wanted to achieve a  higher accuracy over a particular part of your scanned image, how could you do this using  GCPs? In addition, what does RMSE actually tell you and more importantly what does it not  tell you? 

Outputs: 

1. Typed answer to the Question(s) for Thought. (Lab Portfolio) 
2. The final map (step 4) you created in Exercise 2 of this Lab. (Lab Portfolio)

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