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GG 369 Wilfrid Laurier University Geographic Information Systems Lab Report

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Course Code: GG 369: Geographic Information Systems Assignment: Introduction to ArcMap (ArcGIS) Value: This work is worth 10% of your final grade. Due Date: The lab is due before (that is right, BEFORE) your lab slot begins the week of February 14th. There is a MyLS Dropbox made for each lab slot. Format: There is no need for a formal format (APA, title pages) just make sure everything is labeled properly. Your name and date should be on each map (it is also in the instructions). Whenever possible you should avoid working with data within ArcGIS remotely (via the cloud). Sometimes (all the time?) Arc is quite input/output intensive and if they are on a network, cloud, or other remote disk (like a USB key)everything will get as slow as molasses on a January day. Things tend to speed things up is to save your files to “local” storage Please note that you may find a step missing. Well as we move along in the labs, I am going to assume you remember how to complete steps from lab #1. So, if you see the instruction to ‘export map as PDF’ or ‘make layout’ we covered this in lab #1. 1 Part 1: Fun with Rasters! (while we generally try to use local examples the data for this example is quite ‘fun’ as the elevation changes are drastic) A Digital elevation model (DEM) is just a raster that has elevation values attached to it (see the lecture for some information on raster). In a perfect world all our cell sizes would be perfect, but it is rarely the case. Different cell sizes, errors in the data, unclear resolutions are all things that can plague a dataset. Within ArcGIS we may use raster operations, or functions, to improve our DEM data. This first map will have us combine data of different resolutions. We often have data from various sources, for example, DEMs at approximately 30m and 10 m. In Ontario most DEM data was 30m until recently where some 5m data has become available. Having inconsistencies is very common. Our first task here will be combining two DEMs, valley3, at 3-meter cell size, and valley9, with a 9-meter resolution. We want to use the higher resolution data where we have it but use the lower resolution data elsewhere. Start ArcGIS Pro and add both the valley3 and valley9 DEMs to an empty Map Frame. Calculate the hillshade for both data sets (using standard default settings and Model shadows) (ArcToolbox→Spatial Analyst Tools –> Surface –> Hillshade). You should be able to see a little toolbox at the top. This is the Arc Toolbox and it is a little overwhelming. Don’t worry though! 2 Please read this link: You will see a screen like this one. The input raster will be one of the two you have inputted. The Output raster will be new so give is a reasonable name. Inspect these hillshades carefully, and note the enhanced detail with the 3 meter DEM, as shown below. The figure on the right is from the hillshade of valley3 (or whatever you have named it), on the left the hillshade of valley9. Note the greater definition of the small streams and streambanks. Remove the two hillshades from the data view to reduce clutter. 3 We can use the raster calculator to join these two data sets. Note that we want to use the detailed valley3 data where we have it, and the coarser valley9 data everywhere else. Resampling To avoid unforeseen results, we should first convert our data sets to a common resolution, in this case the 9 meter data to a 3-meter cell size. This does not make the 9-meter data better. Basically, we are making a copy of the data at a finer resolution. Use ArcToolBox –> Data Management Tools –> Raster –> Raster Processing -> Resample In the resultant window, specify the valley9 as input, an output filename, a 3meter cell size, and a bilinear resampling (the textbook describes the differences among resampling methods). Name the output raster valley9to_3. After the resampling is done, examine the valley9to_3 and verify that it has a 3meter resolution, either via the table of contents – properties, then source/raster information, or by zooming on the map and using the measure tool. 4 We can now combine the two data sets. Although there are many ways to do this, perhaps one of the simplest is with clever use of two raster functions – IsNull and Con. In short, the IsNull returns True whenever a cell comparison or value is Null. The con function takes three values, the first is a true/false test, and second is the value to assign to an output grid if the test is true, and the third is the value to assign if the test is false. Using the Raster calculator, we can nest these functions: >ArcToolBox → Spatial Analyst Tools → Map Algebra → Raster Calculator Con(IsNull(“valley3″),”valley 9to_3″,”valley3”) Name the output something like combinedDEM 5 The Raster calculator is super picky and, can be, very frustrating. Make sure you take your time. This function first tests if the cell of Valley3 is Null. This is true everywhere in our study area outside the data region of Valley3. When the value is Null, the con function assigns the value found in Valley9_to3 to the cell in the output data set. When the value is not Null, the con function assigns the value found in Valley3. Examine the combineDEM raster. Compute and inspect a hillshade and verify that it has the higher detail contributed by the Valley3 data set. Once you are happy create a layout using the combined data (NOT the hillshade). Please make sure all Map elements are present. This is the part where you need to figure something out. If you remember we can change the symbology of anything in the Table of Contents. What I need you do is make the river channel visible in the map you hand in. I will give you a hit with the word ‘histogram’ 6 Deliverable 2 Open a new blank map Raster Filters We’d now like to introduce filtering as a tool to fix “noisy” data. This is to say small errors in the data created from processing or interpolation (remember that word!). This is often used with interpolated surfaces, particularly LiDAR data, and similar tools are used near edges for mosaiced DEMs and other continuous surfaces. Load the DEM named Shasta, and calculate a hillshade surface for the Shasta DEM. Leave the Azimuth as is, set the Altitude to 25, and check the model shadows box. Make sure you have read this ( as you need to know what is happening in the background. Inspect the output hillshade layer and notice the funny artifacts. These are both data “spikes,” white points with a long thin shadow trailing to the southeast, or “pits,” dark areas on the northwest with a white “edge” on the southeast. How do we remove these? As described in the text, we may use a lowpass filter to identify and get rid of this speckle. We may apply a low-pass filter to the SHASTA dem (not the hillshade you just made)with the ToolBox –> Spatial Analyst Tools –>Neighborhood –> Filter command. Specify the Shasta DEM as the input, and a filter type of Low pass, and specify an output name, like Shasta_lo. Run the filter, calculate a hillshade for this new layer, using the default parameters, and do not Model shadows. Inspect the output, looking at the areas that have previously had spikes and pits (zoom way in). The before and after figure below left and right, show the same corresponding area above, it shows the reduction in the size of the spikes and pits, although they are still visible. We could stop here, and just accept the filtered data layer. But if we look carefully at the filtered and unfiltered hillshade, we’ll see we pay a cost for filtering. We lose some of the fine detail, apparent in the image of the unfiltered hillshade to the left, below, compared to the filtered hillshade to the right. 7 We’d like to keep both, and we can. Open the Raster calculator (remember where that is?) First, we should subtract the filtered DEM layer from the original Shasta layer. Calculate the hillshade for the difference layer you just created. 8 The figure at right shows a zoomed in version of the hillshade of the difference output, with the largest differences at and around the spikes and pits, seen as squares of contrasting shades. We can now then replace the cells were the difference is large. Sure, large is a relative term and, in this case, I am giving you a number. You could easily use trial and error. Here, a threshold of about 15 works alright We want to replace the cells in the shasta image when they are more than 15 meters different from the filtered surface. Otherwise, we leave the Shasta surface alone, and hence, we don’t get any of the degradation in detail in otherwise good data. Open the Raster Calculator apply the following function, also shown within the raster calculator in the figure following: Con(Abs(“difference”) > 15, “shasta_lo”,”shasta”) Cut and paste may not work here. If the absolute value of the difference is greater than 15, we write the filtered value to the output. Otherwise, we write the original data value. 
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