Determine optimal habitat conditions
To tackle the difficult problem of dwindling mountain lion populations and the increased fragmentation of mountain lion habitat, you'll first explore the study area and answer the question, What makes a mountain lion habitat ideal? In this lesson, you'll download an ArcGIS Pro project package that contains a map of the Los Angeles area and a few key data layers about known areas of mountain lion habitat. Based on this information, as well as some supplementary research, you'll compile a list of criteria that define optimal mountain lion habitat. This list of criteria will be fundamental in your subsequent analysis of suitable wildlife corridors to connect existing habitat areas.
Download and open the project package
First, you'll download and open a project package in ArcGIS Pro. You'll use this project package and the data it contains as a starting point for your analysis of potential mountain lion wildlife corridors.
- Download the Mountain_Lion_Corridors compressed folder.
- Locate the downloaded file on your computer.
Depending on your web browser, you may have been prompted to choose the file's location before you began the download. Most browsers download to your computer's Downloads folder by default.
- Right-click the file and extract it to a location you can easily find, such as your Documents folder.
- Open the extracted Mountain_Lion_Corridors folder.
Depending on your operating system and file browser, the appearance of the files in your folder may differ from the example image.
The folder contains the beginning data for the project. Most of the data is stored inside geodatabases (.gdb) and toolboxes (.tbx) and can't be accessed in a usable format outside of ArcGIS software. The folder also contains an ArcGIS Pro project file (.aprx). You'll open the file in ArcGIS Pro to access the rest of the data.
- 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 one that already exists. Depending on whether you've created a project in ArcGIS Pro before, the options vary slightly.
In ArcGIS Pro, you can personalize the appearance of the user interface with either a light or a dark theme. In these lessons, the example images will use the dark theme, but you can use the theme that you prefer. If you want to change the theme, from the start screen, click Settings, and then click Options. Alternatively, from an open project, click the Project tab, then click Options. In the Options window, click the General tab. Under Application Theme, change the Theme using the drop-down menu. You'll need to restart ArcGIS Pro for changes to take effect.
- If this is your first time using ArcGIS Pro, click Open an existing project. If you have created a project in ArcGIS Pro before, under the list of your recent projects, click Open another project.
The Open Project window opens. Here you can browse to projects on your computer or projects on your portal (for most users, the default portal is ArcGIS Online).
- In the Open Project window, browse to the location of your extracted folder. Double-click Mountain_Lion_Corridors.aprx to open it.
If you have used ArcGIS Pro before and customized the default layout, your interface may look different from the example images and explanatory text.
The project contains a map of the greater Los Angeles metropolitan area, which encompasses five counties: Ventura, Los Angeles, Orange, San Bernardino, and Riverside. Mountain lions are found in all of these counties (as well as much of the western United States), although the focus area of your analysis will be within Los Angeles and Ventura counties, where frequent mountain lion encounters have been reported.
Above the map is the ribbon, which contains several tabs with various tools and options for navigating or working with the map. To the left of the map is the Contents pane, which contains a list of all elements on the map. Currently, the map has three layers: one for the counties in the Los Angeles area; one for the oceans, landforms, terrain, and water bodies that provide geographic context for the counties; and one called World Boundaries and Places that is currently turned off. If a layer is turned off (denoted by the check box next to the layer name), it is not visible on the map.
- In the Contents pane, click the checkbox for the World Boundaries and Places layer to turn it on.
National and state boundaries appear on the map, as well as significant cities.
The added geographic context indicates that the greater Los Angeles metropolitan area is located in Southern California, close to the United States-Mexico border. The distribution of cities roughly demonstrates the most populated location: the low-lying coastal region in the southwest of the metropolitan area.
The data on this map isn't all of the data that you downloaded. To the right of the map view is the Catalog pane, which lists all of the maps, data, and folders associated with the project.
- In the Catalog pane, expand Folders and expand Mountain_Lion_Corridors.
The Mountain_Lion_Corridors folder shown in this pane is the same one that you downloaded and extracted. It contains the same geodatabases and toolboxes, but now you can use the data inside. The Results geodatabase contains example copies of all of the data that you'll create during these lessons. If you make a mistake, get stuck, or simply want to skip a step or two, you can use the data in this geodatabase to help you. Likewise, the Results toolbox contains example copies of the geoprocessing models that you'll create during these lessons.
The Mountain_Lion_Corridors toolbox is currently empty, but it's where you'll store the models that you create. The Mountain_Lion_Corridors geodatabase contains all of the starting data for the project.
- Expand the Mountain_Lion_Corridors geodatabase.
The geodatabase contains eight items. Each of these items can be added to a map as a layer. From the titles of the layers, you can get a pretty good idea of what each layer shows. The Los Angeles Counties layer is here, but the World Boundaries and Places and World Physical Map layers aren't. The latter two layers are actually hosted on ArcGIS Online, which means you can display them on your map without having to save them on your computer.
Explore the study area
Now that you've downloaded and opened the project, you'll explore the study area for your analysis. Although the current map shows the entire greater Los Angeles metropolitan area, the main focus of your study will be the mountainous areas of Los Angeles and Ventura counties. You'll open another map that you downloaded with your project that focuses particularly on the study area.
- In the Catalog pane, expand Maps.
This project comes with six maps. Other than the Greater Los Angeles map, none of these maps are currently open, so you can't see what they look like.
- Double-click the Mountain Lion Study Area map to open it.
The study area map is similar to the Greater Los Angeles map, but it contains a layer called Core Mountain Lion Habitats. The habitats themselves are difficult to see at this zoom extent, but they have large labels that stand out. You'll explore the habitat areas soon, but first you'll close the Greater Los Angeles map you looked at previously. Above the map viewer, tabs indicate which maps are open and which map is currently active in the viewer. You can switch between open maps by clicking the appropriate tab.
If you can't see all of the labels for the mountain lion habitats, try zooming in.
- Above the map viewer, point to the Greater Los Angeles map tab and click the Close button.
Next, you'll explore the mountain lion habitat areas.
- On the ribbon, in the Map tab, in the Navigate group, confirm that Explore is selected.
The Explore button allows you to pan the map by dragging it. You can also zoom in or out using the mouse scroll wheel. If you don't have a mouse with a scroll wheel, you can also zoom by pressing the Shift key while drawing a box on the map where you want to zoom to.
- Use the navigation controls to explore the map.
If you zoom or pan too far, it might be difficult to find your way back to the data. You can use a bookmark that was created with the map to return to the original extent. On the ribbon, on the Map tab, in the Navigate group, click Bookmarks and choose Greater Los Angeles Area.
Now that you've explored the map on your own, you'll focus on the specific study area specified for your analysis. This map includes a layer called Study Area, but the layer is turned off.
- In the Contents pane, turn on the Study Area layer. Then, right-click the layer name and choose Zoom To Layer.
The map automatically zooms to the layer extent, which includes the study area you'll focus on for your analysis.
The study area represents a smaller extent within the greater Los Angeles metropolitan area. It contains the four core mountain lion habitats. By limiting analysis to this smaller extent, you'll process less data and improve the time it takes for geoprocessing tools to run. The core mountain lion habitats identified in Los Padres, Santa Monica, and San Gabriel represent larger natural areas containing mountain lion populations surrounded by urban development and roads. The area located in Santa Susana, which was established by the National Park Service and is located just outside of Pico Canyon Park, does not represent a significant core area but serves as an important geographic gateway for linking the larger but geographically disconnected core areas to each other. Your goal is to find the best corridors to connect these habitats.
Mountain lions are solitary animals that tend to avoid human contact and are extremely competitive with other mountain lions. Individual mountain lions have large range areas, varying between 50 and 100 square miles. When available habitat is scarce, mountain lion ranges may overlap, which increases competition. Dense vegetation, such as shrubland and forest, help mountain lions hunt and stalk their prey (and are also land-cover types preferred by mule deer and other prey species). In addition, as indicated by the location of the habitat areas on the map, mountain lions prefer rugged, mountainous terrain. This Cougar Facts page, created by the Cougar Fund, contains additional information about mountain lions.
- On the ribbon, on the Map tab, in the Navigate group, click Bookmarks and explore the four bookmarks for each of the core mountain lion habitat areas: Los Padres, Santa Susana, San Gabriel, and Santa Monica.
Many of the habitat areas are close to natural areas or protected forests, meaning they'll be safe from future development. The habitat areas are also untouched by roads or freeways, which are dangerous for mountain lions.
- When you finish exploring the area, return to the extent of the Study Area layer.
Identify suitability criteria and data
One of the ongoing challenges of wildlife biology is determining which habitat variables are most important to a given species. There's almost no limit to the different models you could potentially create and test. In this case, you'll focus on the two main challenges facing urban mountain lions: finding prey and avoiding contact with humans. You'll use the following criteria:
- Ruggedness of terrain - Rugged terrain serves a dual purpose of helping mountain lions stalk their prey and discouraging human development.
- Dense land cover - This also provides cover for stalking prey.
- Protected areas - Though mountain lions themselves don't know whether an area is protected, this ensures that urban development won't destroy the habitat in the future.
- Distance from roads - Not only are roads dangerous for mountain lions, but they also serve as a proxy for human development.
You'll use these four criteria to help determine which areas can become suitable mountain lion corridors. To represent these criteria, you'll need the appropriate data. When you downloaded the project, you also downloaded several data layers that can be used to model these criteria. You'll explore these data layers and determine how you might best use them in your analysis.
- In the Catalog pane, open the Analysis Criteria map.
This map contains almost all of the data you downloaded with the project. Currently, most of the data layers are turned off.
- In the Contents pane, turn on the Core Mountain Lion Habitats layer.
The other layer that is turned on in this map is the Elevation layer, which you downloaded with the project. Unlike most of the layers you've looked at so far, which depict defined shapes, the Elevation layer depicts a grid of pixels, with each pixel having a unique value (which is why the layer is symbolized with a variety of shades between white and black, as opposed to a single color). This type of layer is called a raster layer. For the Elevation layer in particular, each pixel represents elevation above sea level. Areas with lighter colors have higher elevations. This information will be vital for calculating the ruggedness of terrain, but it doesn't help you visualize the terrain's appearance.
- Turn on the Terrain: Hillshade layer.
A hillshade layer is a kind of layer that is derived from an elevation layer. It depicts changes in elevation in a way that looks similar to actual mountainous landforms. The layer indicates that much of this area is dramatic, rugged terrain, although there are several low-lying valley areas between the core habitats. Like the World Physical Map layer that you saw in the Greater Los Angeles map, the hillshade layer was not downloaded onto your machine. Instead, it's hosted on ArcGIS Online as part of ArcGIS Living Atlas of the World, a curated collection of geographic data and information.
If you're ever unsure where a layer on your map is hosted, you can check its properties. In the Contents pane, double-click the layer name to open the Layer Properties window. Click the Source tab and expand the Data Source group. The Location parameter gives the URL or file path where the layer is stored.
Next, you'll compare the elevation and hillshade layers directly by swiping between them.
- In the Contents pane, click Terrain: Hillshade to select it.
Selecting a layer causes contextual tabs to become available on the ribbon, with options tailored to the type of data you selected.
- On the ribbon, click the Appearance tab. In the Effects group, click Swipe.
When you point to the map, the cursor changes.
- Drag the map up and down or left to right to peel back the Terrain: Hillshade layer and compare it to the Elevation layer below.
The comparison indicates that lighter-colored areas in the elevation layer correspond to more rugged-looking areas in the hillshade layer. This comparison also indicates that both of these layers are based on similar data, only presented in a different way. These layers will help you determine the ruggedness of terrain, but you'll need to perform some analysis on them before they can be used for that purpose. Next, you'll explore data that might be useful for determining which areas have the dense land cover mountain lions use to hunt.
- When finished, click the Map tab and click the Explore button to return to standard navigation controls. Turn off both the Terrain: Hillshade and Elevation layers.
- Turn on the Land Cover layer.
This layer is another raster layer, although it doesn't have a simple black-to-white color gradient like the elevation and hillshade layers did. To understand what this layer shows, you'll need to investigate its symbology.
- In the Contents pane, click the arrow next to the Land Cover layer to expand its symbology.
Each pixel color corresponds to a specific type of land cover. The red colors are developed urban areas, while the green colors tend to correspond to forests or grasslands. The brown areas are shrubland or herbaceous areas. Forests and shrubland offer ideal protection for mountain lions and their prey. In addition, hungry mountain lions will roam into agricultural and open areas in search of food. In contrast, they will avoid areas with a high degree of human activity.
- Zoom to the Santa Susana bookmark.
When zoomed in, the land-cover layer becomes more pixelated. Pixels in a raster layer have a set size, corresponding to the real-world area each pixel covers. An image's pixel size is also called its resolution. The resolution of this layer is 30 meters, meaning each pixel represents a real-world area of 30 square meters.
To check the resolution of a raster layer, open its Layer Properties window by double-clicking its name in the Contents pane. Click Source and expand Raster Information. The Cell Size parameters give the resolution of the layer. Most of the raster layers in your initial dataset have a 30-meter resolution, although the layers hosted on Living Atlas have more detailed resolutions.
- Return to the extent of the study area. Turn off the Land Cover layer and turn on the Protected Status layer.
Each pixel in this layer is symbolized in one of five shades of red, corresponding to the protection status of the area. (As with the land-cover data, you can check the symbology of this layer in the Contents pane). A protected status of 0, 1, or 2 (the lightest red colors) corresponds to the strongest protection status, limiting human disturbance and activity. A protected status of 3 indicates some level of protection from development. A protected status of 4 (the darkest red) indicates no known mandate for protection.
Like the Land Cover layer, the Protected Status layer is fairly straightforward once you're familiar with the symbology. More protection is more suitable for cougars. Lastly, you'll investigate some data related to the final criterion you identified: distance from roads.
- Leave the Protected Status layer turned on and turn on the Roads layer.
Unlike the other data layers you've been using, the roads layer isn't a raster layer. It's a vector layer, which means that it contains distinct features. For this layer, the features are in the form of lines, but other vector layers may have points or polygons. (The Core Mountain Lion Habitats layer, for instance, is a vector layer with polygons).
Each line represents a road. Although there are different kinds of roads, ranging from small mountain paths to major highways, the roads layer will mostly be used to describe and understand the distribution of human activity. Based on what you've already learned, roads are dangerous to mountain lions and mountain lions generally tend to avoid humans. Areas that are farther away from people will thus be more suitable.
- Turn off the Protected Status layer and turn on the Cougar Distribution layer.
Unlike many of the other layers you've looked at so far, you won't use this layer in your analysis of suitable mountain lion corridors. Instead, it'll provide contextual information that will help you better understand where mountain lions are located. Green areas indicate current mountain lion distribution. Typically, mountain lions are not found in areas with dense road cover, as those are often areas of high human habitation. Later, you'll transform the road features into a raster layer that can be used in conjunction with the other raster layers in your analysis.
- On the Quick Access Toolbar, click Save. If you receive a message that tells you the project was created in an earlier version of the software, click Yes to save the map with your current version.
Your exploration of the source data and study area has made it clear that the mountain lion in the greater Los Angeles area is threatened by dense development. At the same time, many areas remain that can serve as suitable mountain lion habitat. To ensure the survival of the species, action must be taken soon. In this lesson, you established four key criteria for assessing the suitability of an area for mountain lion habitation: ruggedness of terrain, dense land cover, protected status, and distance from roads. In the next lesson, you'll prepare your data for analysis and convert some of the initial layers into more usable datasets.
Prepare data for analysis
In the previous lesson, you familiarized yourself with the study area, examined the initial data, and decided on four criteria to define optimal mountain lion habitat: ruggedness, land cover, protected status, and distance from roads. While you have data that directly corresponds to some of these criteria, a few datasets need some work. In particular, your elevation data doesn't directly indicate ruggedness, and your roads data doesn't measure how far an area is from the roads. To change your initial data into layers more suitable for your analysis, you'll run geoprocessing tools that perform functions on data to create a new output layer.
While you can run geoprocessing tools on their own, it's likely that you'll need to run many tools to create your ultimate suitability analysis output. Furthermore, the parameters you set for each tool are in some ways subjective. For instance, how far from roads is far enough? How rugged should terrain be? In order to have the ability to repeat your analysis steps, make incremental changes, and record your workflow for others to use, you'll create a geoprocessing model. A model is a series of geoprocessing tools that run in an order that you specify. It is useful for both documentation and iteration, allowing you to modify tool parameters without having to run each tool again individually. That way, you can more quickly experiment with how you determine suitable mountain lion habitat.
Set the environment parameters
Before you begin any complex analysis, it's a good idea to set geoprocessing environment parameters. These parameters will carry over among all of the geoprocessing tools you run and are helpful for limiting the extent of your data or determining how data will be saved. You'll also open a new map specifically for performing your analysis.
- If necessary, open your Mountain_Lion_Corridors project in ArcGIS Pro.
- In the Catalog pane, open the Analysis Steps map.
This map contains almost all of the same layers as the Analysis Criteria map (the exception being the Cougar Distribution layer, which won't be used in your analysis). First, you'll set an option so that output layers created by geoprocessing tools will overwrite any existing layers with the same name. This option is useful when you intend to repeat an analysis workflow multiple times but don't want to clutter your project with extra layers. This option might be enabled by default, but it's good to confirm that it's active before you begin your analysis.
- On the ribbon, click the Project tab.
- On the left column, click Options.
The Options window opens.
- Click the Geoprocessing tab. Confirm that Allow geoprocessing tools to overwrite existing datasets is checked.
- Click OK. On the upper left of the page, click the back arrow to return to your project.
The rest of the settings you'll choose are specific to this project, so you must choose them within the project itself. (Settings changed through the Options window, such as the one you just changed, will be saved across all of your projects in ArcGIS Pro.)
- On the ribbon, click the Analysis tab. In the Geoprocessing group, click Environments.
The Environments window opens. First, you'll ensure that the default workspace (the location where new layers created by geoprocessing tools are saved) is the geodatabase connected to your project.
- For Workspace, confirm that both Current Workspace and Scratch Workspace are set to Mountain_Lion_Corridors.gdb.
Both workspaces indicate the location where data is saved, but the scratch workspace is intended for data you don't want to maintain. For your purposes, it'll suffice to save all of your data in the same place.
- For Output Coordinate System, choose Elevation.
The coordinate system automatically changes to NAD 1983 UTM Zone 11N, the coordinate system of the Elevation layer. By choosing the default coordinate system, you'll ensure that your data is projected consistently.
- For Extent, choose Study Area. For Snap Raster, choose Elevation.
The extent changes to a set of coordinates consistent with the extent of the Study Area layer. This parameter guarantees that all output layers are automatically clipped to your study area. By reducing the size of your output, you lower processing time and ensure that your results cover only the area you're interested in. Meanwhile, the Snap Raster parameter causes all output raster layers to match the cell alignment of the chosen layer, meaning pixels will overlap exactly.
- For Cell Size, choose Same as layer Elevation.
The Cell Size box populates with the path to the location where your Elevation layer is stored. As mentioned when you first explored your initial data, most of your initial raster layers have a cell size, or resolution, of 30 meters. This parameter ensures that all output raster layers will have the same cell size. A 30-meter resolution is small enough to capture changes in the landscape but not so small as to ramp up processing time, so it's an acceptable cell size to use.
- Click OK.
Transform elevation into ruggedness
Next, you'll run analysis tools to prepare the initial data layers that don't quite match up with the criteria you chose for mountain lion habitat suitability. You have data for elevation, but you want data that indicates the ruggedness of the terrain. For this workflow, ruggedness will be defined by large and dramatic shifts in elevation. One of the ways you can calculate these changes is in elevation is with the Focal Statistics geoprocessing tool, which compares each cell value in a raster layer with the cells that surround it. You'll begin your geoprocessing model with this tool.
- On the Analysis tab, click ModelBuilder.
A new model opens in the map viewer. Currently, the model is blank. You can add data and geoprocessing tools to the model to create an automated workflow. First, you'll add the Elevation layer and the Focal Statistics tool. The Contents pane shows the layers in the previously open map, including all of your initial data layers, so you can drag the layer from there.
By default, the model is stored in your project toolbox. If you accidentally close the model's view tab at any time, you can open it again though the Contents pane. To do so, expand Toolboxes, then Mountain_Lion_Corridors.tbx. Right-click the model and click Edit.
- In the Contents pane, drag the Elevation layer into the blank space of the model.
The layer is displayed in the model as a blue oval with the layer's name. Blue ovals are input data variables: the initial data on the map that you'll transform with a geoprocessing tool. You can reposition and resize input data variables, as well as all other model elements, in any way you like. Next, you'll add the Focal Statistics tool. You can locate it by searching the list of available geoprocessing tools.
- On the ribbon, on the ModelBuilder tab, in the Insert group, click Tools.
The Geoprocessing pane opens. You can use this pane to access all geoprocessing tools in ArcGIS Pro.
- In the search box, type Focal Statistics.
- In the search results, drag the Focal Statistics (Spatial Analyst Tools) tool into the model, to the right of the Elevation input data variable.
The tool is currently depicted as a gray rectangle, connected to an output that is also gray. Both the tool and the output are gray because they haven't been connected to the input layer yet.
- Make sure that the Elevation input is not selected. Drag an arrow between it and the tool to connect them.
When you release the mouse button and connect the two model elements, a list of connection options opens.
- Click Input raster.
The tool becomes yellow and the output becomes green, indicating that they are now active and that the tool is ready to run. Before you run it, you'll set additional parameters to fine-tune your results.
- In the model, double-click the Focal Statistics tool to open its parameters.
Your goal with this geoprocessing tool is to transform a raster layer with data about elevation into a raster layer with data about the change in elevation. The tool makes that calculation by comparing a cell's value to its neighborhood (the cells nearby). You can adjust the parameters to change the size of the neighborhood. The default neighborhood is a three-cell-by-three-cell rectangle that surrounds each cell, which is sufficient for your analysis. However, you want to change the statistic that is calculated from the average of the cells in the neighborhood to the range between the largest and smallest cells in the neighborhood.
- For Output raster, change the raster name to Ruggedness. For Statistics type, choose Range.
If you want to learn more about any tool parameter, point to the parameter and then point to the information icon that appears next to it for a detailed explanation. You can also click the question mark icon in the upper right corner of the tool to read more.
- Click OK.
The name of the model element for the output raster changes to Ruggedness, based on the name you chose in the tool parameters. The tool hasn't run yet, but the parameters have been saved. Lastly, you'll edit the output model element so that the output layer is added to the map when the tool runs.
- Right-click the Ruggedness model element and click Add To Display.
You'll save the model before running it.
- On the ribbon, on the ModelBuilder tab, in the Model group, click Save.
The model is saved. You can find it in the Catalog pane, in the Mountain_Lion_Corridors toolbox.
- In the Run group, click Run.
The model runs. The Model window opens and informs you when the model is finished. A gray drop shadow is also added to the Focal Statistics box, indicating that the tool has finished running. The Ruggedness layer is added to the Contents pane, although you're currently not viewing the map it's on.
- Click the Analysis Steps tab to make the map active.
Layers created by models are added to the last map that was open before you ran the model. If you can't find the layer in the Analysis Steps map, try the other open maps.
The Ruggedness layer depicts the change in elevation on a color range from black to white. Darker pixels have less change in elevation, while lighter pixels have more. It seems as though the low-lying valleys also tend to have less ruggedness, which makes sense. You'll change the layer's symbology to make differences in ruggedness more apparent.
- In the Contents pane, click the gradient symbol for the Ruggedness layer.
The Symbology - Ruggedness pane opens. You'll symbolize the layer with three distinct colors based on statistical classifications in the data.
- Under Primary symbology, choose Classify. Change Classes to 3. Expand the Color scheme drop-down menu and check the Show Names checkbox. Scroll down and choose Prediction.
The changes occur automatically on the map.
It's now more clear which areas are especially rugged. You'll compare your ruggedness layer to the hillshade layer for an even clearer idea of the terrain.
- Turn on the Terrain: Hillshade layer.
To view both layers at once, you'll make the Ruggedness layer transparent.
- In the Contents pane, click the Ruggedness layer to select it. On the ribbon, click the Appearance tab. In the Effects group, change the Layer Transparency slider to 50.0%.
The transparency is automatically applied.
- Zoom in and explore the map.
When compared to a more realistic depiction of the terrain, the slopes closer to the mountain peaks seem to be the most rugged.
- Try comparing the Ruggedness layer to some of the other layers, such as the Roads layer. (Adjust symbology if necessary.)
Roads are often sparse on rugged terrain, as they're difficult to build, but some roads do appear on the less severe yellow areas. As such, there isn't an exact overlap between ruggedness and distance from roads, meaning you can't define suitable mountain lion habitat by only the ruggedness layer.
Transform roads into distance from roads
The layer of roads is a good indicator of development and human activity, as well as prominent dangers to mountain lions. However, not only is it a vector layer, making it difficult to analyze alongside your raster layers, but it also doesn't directly indicate what is suitable mountain lion habitat. You'll fix both problems by running a geoprocessing tool to create a raster layer that indicates each cell's distance from the nearest road. Cells with a higher distance value will be more suitable habitat.
- Return to your model in the map viewer. From the Contents pane, drag the Roads layer into the model, below the Elevation layer.
You may notice small shadows under the Focal Statistics tool and the Ruggedness output layer. These shadows indicate that the tool has been run and the output has been generated as part of the model.
To calculate distance from roads, you'll use the Euclidean Distance tool. This tool calculates the straight-line distance from each cell in a raster layer to a source layer that you specify. In this case, your source layer will be the Roads layer. The distance is measured from cell center to cell center.
- Open the Geoprocessing pane and search for Euclidean Distance.
If you closed the Geoprocessing pane or can't find it, you can open it from the ModelBuilder tab by clicking the Tools button. You can also open the pane from the Analysis tab.
- From the search results, drag the Euclidean Distance tool into the model, next to the Roads input.
This tool has three outputs, a distance raster, a direction raster, and an out back direction raster. The direction raster indicates the direction from each cell to the closest source layer feature, and the out back direction raster identifies the next cell along the shortest path back to the closest source while avoiding barriers. You're not interested in the direction between mountain lion habitat and roads, so you'll ignore the output direction rasters.
- Draw a line between the Roads input and the Euclidean Distance tool to connect them. When you release the mouse button, choose Input raster or feature source data.
The tool and the output distance raster become active, but the output direction raster does not. Because the output direction raster is a secondary output for the tool, it isn't created unless you modify the tool parameters to include it. You'll modify the tool parameters, but only to change the name of the output distance raster.
- Double-click the Euclidean Distance tool to open its parameters.
- For Output distance raster, change the output name to Distance_to_Roads.
You'll leave the other parameters unchanged, including the blank Output direction raster parameter.
- Click OK. In the model, right-click the Distance_to_Roads output variable and choose Add To Display.
Next, you'll run the tool. However, you don't want to run the Focal Statistics tool again. You can run only part of a model by selecting the parts you want to run.
- In the model, draw a rectangle around all five components of the Euclidean Distance tool, including its input and all outputs.
When you release the mouse button after drawing the rectangle, all four elements are selected, as indicated by the handles that appear around them.
- Right-click the selected tool and choose Run.
The tool runs. The Model window opens to confirm that the analysis was successful and the new layer is added to the Contents pane.
- Close the Model window. On the ModelBuilder tab, in the Model group, click Save.
Check Close on Completion if you'd like the Model window to close automatically the next time you run an analysis.
- Make the Analysis Steps map active in the map viewer. Zoom to the full extent of the study area and turn off all layers except the Distance_to_Roads layer.
The symbology of the layer, displayed in the Contents pane, indicates that the cell values range from 0 to over 10,000 meters (approximately 7 miles). At best, mountain lion habitat in the area will only be about 7 miles from the nearest road.
- Change the symbology of the Distance_to_Roads layer using the following parameters:
- Primary symbology: Classify
- Method: Natural Breaks (Jenks)
- Classes: 3
- Color scheme: Prediction
- On the Appearance tab, make the Distance_to_Roads layer 50.0 percent transparent. Turn on the Terrain: Hillshade layer.
The majority of the area, in blue, is relatively close to roads, even a lot of the mountainous area. There are very few areas that are exceptionally far from roads, indicating how widespread development in the area has become. The yellow areas, which are moderately far from roads, are more common, but still relatively scarce. Mountain lions generally try to avoid crossing roads, but when roads can be found almost anywhere, they often have little choice if they want to maintain their large hunting ranges.
- Save the project.
In this lesson, you set geoprocessing environments to prepare for your analysis. Then, you started your model by transforming your elevation and roads data into layers that more directly indicate suitable mountain lion habitat. Your other initial data layers, which show land cover and protected status, don't need any changes, so your data is now ready for analysis. In the next lesson, you'll build a geoprocessing model that will combine all four of your layers (representing each of your four criteria) and determine the most suitable places for mountain lions to live.
Build a suitability model
In the previous lesson, you began your model to determine suitable locations for mountain lion habitat. All of the data layers you intend to use in your model are now raster layers that indicate favorable and unfavorable areas for mountain lions. However, your four layers still use four different classification and categorization methods. Distance to roads, for instance, is measured in meters, while ruggedness is measured in change in elevation, and protected areas and land cover use their own unique classification systems. Because of these differences, you can't currently combine these layers—that would be like trying to add meters to kilograms. To overcome this problem, you'll reclassify all four data layers to use the same classification scheme, based on favorability to mountain lions. In this scheme, each layer will have only three values: low favorability (3), medium favorability (2), and high favorability (1). Once you've reclassified all four layers, you'll create a new layer that combines them to show the most suitable places for mountain lions.
Add data and tools to the model
Your model already has two of the layers you want to use in your analysis: Ruggedness and Distance to Roads. You'll add the other two layers, Protected Status and Land Cover. Then, you'll add the geoprocessing tools needed to reclassify the layers.
- If necessary, open your Mountain_Lion_Corridors project in ArcGIS Pro.
- Drag the Land Cover and Protected Status layers into your model view, below the output layers for the previous tools.
- In the Geoprocessing pane, search for Reclassify.
Depending on what toolbox extensions you have enabled, the search results may return multiple tools called Reclassify. Next to the tool name, the type of tool is explained in parentheses. You want the version of the tool that is tailored toward spatial analysis.
- Drag the Reclassify (Spatial Analyst Tools) tool into the model, next to the Ruggedness output.
- Drag the same tool into the model three more times. Position one next to the Distance_to_Roads output, one next to the Land Cover input, and one next to the Protected Status input.
You now have a Reclassify tool for each of your four data layers.
Next, you'll reclassify the layer that shows ruggedness. When you symbolized the Ruggedness layer, you used a statistical classification method to automatically sort the wide range of ruggedness values into three classes. You'll use a similar method to reclassify your data, using a classification scheme where 3 is least favorable for mountain lions and 1 is most favorable. More ruggedness is better for mountain lions, because they can more easily stalk and hunt their prey.
- Draw a line to connect the Ruggedness output to the first Reclassify tool. Choose Input raster.
The tool and its output become active. The Reclassify tool has additional mandatory parameters besides the input layer, but in this case, they were filled in with defaults. These defaults don't meet your needs, though, so you must still adjust the parameters.
- Double-click the connected Reclassify tool to open its parameters.
When you open the tool parameters, you might notice that the name of the input raster is Ruggedness:2. The 2 was added to make the layer name unique, because names are not duplicated in models. Despite the different name, the input layer is unchanged.
The remaining mandatory parameter (marked by a red asterisk) is the Reclassification parameter. This parameter is displayed as a table that contains both the old values of the input dataset and the new values that you want them to be reclassified to. First, you'll add the old values. However, the ruggedness values range from 0 to over 200, so displaying them all is impractical. Instead, you'll automatically sort the old values into three classes.
- Below the Reclassification table, click Classify.
The Equal Interval Classification window opens. Equal interval is a mathematical way to classify data, similar to the natural breaks method you used when you symbolized the ruggedness values. While natural breaks searched the data for statistically significant groups in the data, equal interval sorts data into groups with equal ranges of values.
- For Number of Classes, type 3. Click OK.
The table is populated.
The Start and End columns indicate the beginning and ending value of each range of values. The New column indicates the value that the range will be reclassified into. For instance, after the tool runs, all values in the original layer from 0 to 74.463531 will be reclassified to the value 1. However, the default table has some problems. First, the values for each range have a large number of decimal places that aren't very meaningful. You'll tweak the default values slightly to more rounded numerals. Secondly, in your new classification scheme, a value of 1 is going to represent the most favorable land for mountain lions, so the higher ruggedness values should be reclassified to 1 instead.
- Double-click the first cell in the End column to edit it. Change the value to 70.
- Change the remaining values to match the values in the following table:
Start End New
The End value for the final data range is unchanged, because it's the maximum value in the dataset. The NODATA fields represent any areas that aren't classified or aren't being displayed.
With this reclassification table, the most rugged areas will be reclassified to 1, while the least rugged areas will be reclassified to 3.
- For Output raster, change the name of the output layer to Ruggedness_Reclassified.
- Click OK.
The parameters are saved. You'll hold off on running the tool until after the model is complete.
- Save your model.
Reclassify distance to roads
Next, you'll reclassify distance to roads. You'll use a similar reclassification method to the one you used for ruggedness. Areas that are farther from roads are more suitable for mountain lions, so you'll reclassify them to 1. Areas close to roads are dangerous for mountain lions and signify human habitation, so you'll reclassify them to 3.
- Connect the Distance_to_Roads output to the second Reclassify tool as the Input raster.
- Double-click the second Reclassify tool to open its parameters.
- Below the Reclassification table, click Classify.
- In the Equal Interval Classification window, for Number of classes, type 3. Click OK.
Three classes with an equal range of values are created. Unlike ruggedness, however, an even distribution of value ranges isn't a good way to classify distance from roads. 2,000 meters, for instance, is already over a mile away from roads. According to the Cougar Facts page you looked at earlier, mountain lion ranges can span 50 to 100 miles, which means that nowhere in the Los Angeles area will mountain lions be completely free from the threat of roads and human civilization. However, if a mountain lion corridor is a few thousand meters from a road, it should keep the animals relatively safe from threats.
The decisions you make when reclassifying your data layers are ultimately value judgments and best guesses. That's why you're building the model; if you find that your results are unsatisfactory, you can tweak some of the parameters you set and test a different way of reclassifying the values.
- Update the Reclassification table with the following values:
Start End New
- For Output raster, change the output name to Distance_to_Roads_Reclassified.
- Click OK.
- Save your model.
Reclassify land cover
The two remaining datasets you need to reclassify are different from the previous two because they contain qualitative, rather than quantitative, data. Instead of a broad range of hundreds of values, these datasets contain only a handful of values, each with a specific meaning. For instance, when you looked at land cover in a previous lesson, it contained 15 unique values that corresponded to a specific type of land cover, from open water to barren land to cultivated crops. To reclassify these values, you'll decide whether each land-cover type is not suitable for mountain lions, moderately suitable for mountain lions, or most suitable for mountain lions.
- Connect the Land Cover input to the third Reclassify tool as the Input raster.
The tool and its output do not become active, unlike the previous tools you used in the model. This means the mandatory parameters could not all be filled with automatic defaults. Despite this apparent difference, you'll adjust the tool parameters in the same way.
- Double-click the third Reclassify tool to modify its parameters.
- For Reclass Field, confirm that Value is chosen.
- Below the Reclassification table, click Unique.
The table is populated with numeric values. Each numeric value represents one of the land-cover types you saw previously in the map legend while exploring the data. You'll only need to change the values in the New column to either 1, 2, or 3, depending on how suitable the land-cover type is for mountain lions. Forest and shrublands are the best land cover for mountain lions, although they can also make use of agricultural or open areas. Developed land, meanwhile, is not suitable for mountain lions.
- In the Reclassification table, update the New column with the following values:
11 (Open Water)
21 (Developed, Open Space)
22 (Developed, Low Intensity)
23 (Developed, Medium Intensity)
24 (Developed, High Intensity)
31 (Barren Land)
41 (Deciduous Forest)
42 (Evergreen Forest)
43 (Mixed Forest)
82 (Cultivated Crops)
90 (Woody Wetlands)
95 (Emergent Herbaceous Wetlands)
As before, these reclassification decisions are subjective and can be tweaked after the model is run.
- For Output raster, change the output name to Land_Cover_Reclassified.
- Click OK.
- Save the model.
Reclassify protected status
The last dataset to reclassify indicates the protected status of the land. It also has qualitative data that is already classified into five unique categories. Of the current categories, 0, 1, and 2 represent land that has many strong protections. A value of 3 represents area with some protections, while 4 has no protections.
- Connect the Protected Status input to the fourth Reclassify tool as the Input raster.
- Double-click the fourth Reclassify tool.
- For the Reclass field, confirm that Value is chosen.
This tool's Reclassification table is already populated. You'll change the new reclassification values so that areas with high protection are considered suitable for mountain lions. Unlike the previous times you reclassified data, you'll also reclassify NODATA cells. When you first looked at the protected areas in one of the previous lessons, you learned that areas with NODATA are not errors, but places that have no known protection. NODATA cells are thus less suitable for mountain lions because they aren't protected.
- In the Reclassification table, update the New column with the following values:
- For Output raster, change the output name to Protected_Status_Reclassified.
- Click OK.
All four Reclassify tools are now active with the correct parameters.
- Save the model.
Create a weighted suitability layer
You've added tools to your model that will reclassify your data layers to all have the same three classifications: 1 (most suitable), 2 (moderately suitable), and 3 (least suitable). Next, you'll combine the reclassified layers to determine which areas are the most suitable. You'll use the Weighted Sum tool to add the values in each of the four reclassified raster layers, with certain weights given to each layer. The weights allow you to make some layers more important than others. For instance, while ruggedness is good for mountain lions, a proper land-cover type is probably more important—so you would weight land cover over ruggedness when combining the layers. The tool multiplies the designated field values for each input raster by the given weight, and then adds the four values together to create a new value.
- In the Geoprocessing pane, search for Weighted Sum.
- Drag the Weighted Sum (Spatial Analyst Tools) tool into the model, to the right of the four reclassified layers.
The Weighted Sum tool can use multiple input layers. You'll connect all four reclassified layers to the tool.
- Drag a line from the Ruggedness_Reclassified output to the Weighted Sum tool. Choose Input rasters.
- In the same way, connect the other reclassified layers to the tool.
Next, you'll edit the tool parameters to provide the correct weight for each input.
- Double-click the Weighted Sum tool to open its parameters.
All four reclassified layers are added as input rasters. The default weight for each layer is 1, which would apply no weight to the values. However, if you wanted to give each layer the same weight, it would be better to give them a weight of 0.25. Each value is multiplied by its layer's weight. If the four layers all had a weight of 1, it would cause the values in the suitability layer to range between 4 (if all four input values were 1) and 12 (if all four input values were 3). To maintain a suitability scale of 1 to 3 in your output layer, it's a good idea to adjust the weights so that all four weights combined equal 1.
Furthermore, you won't weight each input equally. You'll give more weight to land cover and protected status, as those factors tend to be the most optimal conditions for mountain lions. Because you created a model of the workflow, you can always go back and adjust the weights afterward if you don't like your results.
- For Ruggedness_Reclassified and Distance_to_Roads_Reclassified, change the Weight to 0.1. For Land_Cover_Reclassified and Protected_Status_Reclassified, change the Weight to 0.4.
The four weights, when added together, equal 1.
- For Output raster, change the output name to Suitability_Surface.
- Click OK.
You also want to be able to visualize your suitability layer on the map.
- In the model, right-click the Suitability_Surface output and choose Add To Display.
- On the ribbon, on the ModelBuilder tab, in the Run group, click Run.
The model runs and the new layer is added to the Contents pane.
- If necessary, close the Model window. Make the Analysis Steps map active in the map viewer.
- Turn off all layers except for Suitability_Surface.
The layer has a randomly assigned symbology that isn't particularly meaningful, making it difficult to determine which areas are more suitable than others. All of the values in the layer range from 1 to 3, but there are decimal values in between those integers, so symbolizing the layer with only three classes will likely remove important distinctions in the data.
- Change the symbology for the Suitability_Surface layer using the following parameters:
- Symbology: Classify
- Method: Natural Breaks (Jenks)
- Classes: 9
- Color scheme: Red to Green
The layer symbology changes automatically.
With this symbology, the darker red areas are those that are most suitable for mountain lions, having favorable values in most of the four criteria you used to determine suitable habitat. Green areas are the least suitable for mountain lions, while orange and yellow areas are moderately suitable.
- Make the Suitability_Surface layer 50 percent transparent. Turn on the Terrain: Hillshade and Core Mountain Lion Habitats layers.
Almost all highly suitable mountain lion habitat is located in mountainous regions, while not all mountainous regions are highly suitable for mountain lions. Furthermore, the core mountain lion habitats are all located in areas that are highly suitable for mountain lions, with the exception of Santa Susana, which is only moderately suitable. Your suitability model corresponds to observed mountain lion habitats, so although it's your first attempt and could be refined further, your results aren't wildly off base.
One concerning element of your results is that there are no obvious corridors of highly suitable land that connect the four core habitats. Likely, any corridors created between these four areas will have to use land that is only moderately suitable for mountain lions. For now, you'll clean up and save your model.
- Make your model active in the map viewer. On the ribbon, on the ModelBuilder tab, in the View group, click Auto Layout.
The elements in your model are automatically organized in a logical way, with the elements being equally spaced and evenly aligned. (If you don't like how your model was reorganized, you can adjust the position of model elements as you like.) Later, you'll add more elements to your model in order to determine the best places to put corridors between the four core habitats. Before you do that, you'll group the suitability model elements to keep them separate from the future corridor model elements.
- Draw a box around all elements in your model to select them.
Your model layout may differ from the layout in the example images.
- On the ModelBuilder tab, in the Group group, click Group.
The selected model elements are grouped, signified by a yellow box that is added around them. Next, you'll save the grouped elements as a separate model, in case you want to return to only the suitability part of the workflow.
- In the model, right-click the group and choose Save as Model.
- Name the model Suitability Model and click Save.
The saved model is added to the Mountain_Lion_Corridors toolbox.
- Save the current model and save the project.
In this lesson, you built a model that creates a suitability surface for mountain lion habitat from four input layers. You designed the model to reclassify each layer on a classification scheme where 1 was the most suitable habitat and 3 was the least suitable. Then, you ran the model and investigated the results. In the next lesson, you'll add a tool to your model that will determine the best locations for mountain lion corridors.
Build a corridor model
In the previous lesson, you built a model that determines the suitability of areas for mountain lion habitat. Your results indicated that the four core habitat areas are located in places at least moderately suitable for mountain lions. However, you still don't know where potential corridors might go to connect the fragmented habitat areas. You'll add a new geoprocessing tool to your model to automatically determine the most suitable routes (or corridors) between the habitat areas for mountain lions to travel.
Modeling the location of corridors can be thought of as determining the path of least resistance. Factors that facilitate movement have low resistance or cost, and factors that hinder movement have high resistance or cost. For your analysis, you'll transform your suitability surface into a cost surface that determines areas of high and low cost to travel. Based on this surface, you'll generate lowest-cost lines between the core habitat areas. These lines can be used as the basis for mountain lion corridors.
Identify least-cost paths
To create corridors that have the least resistance or cost for mountain lions, you'll use the Cost Connectivity geoprocessing tool. This tool identifies an optimum network of least-cost paths based on contiguous cell groups of the same or similar value. When there are no routes through an area with suitable terrain, the routes may travel through less suitable areas—hence the name least-cost, rather than low-cost. The tool will use two inputs: the suitability surface that is the final output of your model so far, and the core mountain lion habitats.
- If necessary, open your Mountain_Lion_Corridors project in ArcGIS Pro.
- Drag the Core Mountain Lion Habitats layer into the model, near the Suitability_Surface output.
Next, you'll add the Cost Connectivity tool.
- If necessary, open the Geoprocessing pane. Search for Cost Connectivity.
- Drag the Cost Connectivity tool into the model, near the Suitability_Surface and Core Mountain Lion Habitats inputs.
- Drag a line to connect the Core Mountain Lion Habitats input to the Cost Connectivity tool. Choose to connect the input as Input raster or feature region data.
The input regions that you want to connect can be either raster data or vector data. The Core Mountain Lion Habitats layer is a vector dataset.
- Connect the Suitability_Surface element to the Cost Connectivity tool as the Input cost raster.
The tool becomes active. The cost raster determines impedance or cost to movement. You classified your suitability surface on a scale of 1 to 3, with 1 being the most suitable terrain. The tool considers lower values to have lower cost, so it will attempt to connect your input regions via routes with the most suitable terrain.
- Double-click the Cost Connectivity tool to open its parameters.
The tool has only four parameters. The first two are the inputs that you already connected. The next two are the output layers.
- For Output feature class, change the output name to Mountain_Lion_Corridors.
You can also use this tool to create an optional, more advanced output. The first output will connect the geographically closest regions. The second output, however, connects the regions with the lowest total cost to travel between. You'll have the tool create this optional output so you can compare it to the first output.
- For Output feature class of neighboring connections, type Mountain_Lion_Corridors_Advanced.
- Click OK.
- Right-click each of the two outputs of the tool and choose Add To Display.
Before you run the model, you'll reorganize and group it as its own model component.
- Reorganize your model so that the inputs and outputs of the Cost Connectivity tool are close together.
- Drag a box around the Cost Connectivity tool, its two outputs, and the Core Mountain Lion Habitats input.
- On the ribbon, on the ModelBuilder tab, in the Group group, click Group.
Your model now has two distinct groups. One contains the workflow for creating the suitability surface, and one contains the workflow for connecting the habitat areas with least-cost paths.
- Right-click the new group and choose Save as Model.
- Name the model Corridor Model and click Save.
Run the corridor model
Now that you've built the model to identify mountain lion corridors, you'll run it and explore the results.
- In the model, click the Cost Connectivity tool to select it.
- Right-click the selected tool and choose Run.
The tool runs (it may take a few seconds to process all of the data). Once it finishes, two layers are added to the Contents pane: Mountain_Lion_Corridors and Mountain_Lion_Corridors_Advanced.
- If necessary, close the Model window. Make the Analysis Steps map active in the map viewer.
The new layers are line vector layers. Depending on their symbology (which is assigned by default), they may be difficult to see.
- Turn off all layers except the Mountain_Lion_Corridors and Core Mountain Lion Habitats layers.
The corridors layer has created three lines. Each line connects one of the core habitat areas to the central Santa Susana area.
- Turn off the Mountain_Lion_Corridors layer and turn on the Mountain_Lion_Corridors_Advanced layer.
This layer has the same routes as the previous layer, but it adds three new routes that connect the outer habitat areas to one another. These routes were added in the advanced corridors layer because they indicate the lowest cost routes from one habitat to another, even if the connected habitats are geographically distant.
- Make your model active in the map viewer.
Your original model has the default name for models. Now that you've created two additional models, showing each section of the workflow, it's a good idea to rename this model to something more meaningful.
- Save the model. On the model's tab above the map viewer, click Close.
- If necessary, open the Catalog pane. Expand Toolboxes and the Mountain_Lion_Corridors toolbox.
Your completed model is called Model, while the two models you derived from it have more descriptive names.
- Right-click Model and choose Properties.
- In the Tool Properties window, change Name to SuitabilityCorridorModel. Change Label to Suitability and Corridor Model.
- Click OK.
The model is renamed.
- Save the project.
In this lesson, you finished your model by determining the least-cost paths between the core mountain lion habitats. These least-cost paths represent the most suitable locations for mountain lion wildlife corridors. In the previous lesson, you'll take a more in-depth look at your results, evaluating how well your model determined suitable corridors and assessing possible improvements you can make to your model.
In the previous lesson, you finished your model and created wildlife corridors to connect core mountain lion habitat areas. In this lesson, you'll evaluate your results by comparing them to your other data layers. You'll evaluate whether the corridors created by the model are feasible pathways for mountain lions to travel between habitat areas.
Organize the results
Before you assess your model's output, you'll move the important layers to a new map to reduce clutter. You'll also symbolize your data to better visualize it.
- If necessary, open your Mountain_Lion_Corridors project in ArcGIS Pro.
- In the Catalog pane, open the Analysis Results map.
This map is currently empty except for a World Imagery basemap. This basemap will allow you to compare the corridors your model created to the actual features on the ground. Next, you'll copy pertinent layers to this map.
- Make the Analysis Steps map active in the map viewer. Right-click the Mountain_Lion_Corridors_Advanced layer and choose Copy.
- Return to the Analysis Results map. In the Contents pane, right-click the Analysis Results map item and choose Paste.
The advanced corridors are added to the map.
- Follow the same process to copy and paste the following layers into the Analysis Results map:
- Core Mountain Lion Habitats
- Terrain: Hillshade
Press Ctrl while clicking each layer to select them all at once.
Both of your corridor layers have a thin default symbology that can be difficult to see when other layers are active. You'll change the symbology of both layers to make them more visible on the map.
- In the Contents pane, click the symbol for the Mountain_Lion_Corridors layer.
The Symbology pane appears and displays a list of symbol templates.
- Near the top of the Symbology pane, click Properties.
- Change Color to Mars Red and Line width to 2 pt.
- Click Apply. Repeat the process to give the Mountain_Lion_Corridors_Advanced layer the same symbology.
The corridors now appear on the map as thick, bright red lines that stand out more clearly.
From a quick comparison of the corridors to the imagery, it looks as though the three corridors that connect the outer habitat areas to Santa Susana generally follow mountainous and uninhabited areas—the perfect terrain for mountain lions. The advanced corridors, which connect the outer habitat areas to one another, sometimes cut across inhabited areas. For instance, the corridor that connects Santa Monica to San Gabriel traverses northern Los Angeles for a significant amount of its length, making it infeasible as a mountain lion corridor. The corridor that connects Santa Monica to Los Padres also seems to skirt along some inhabited areas. From a quick glance, it seems that the advanced corridors will probably not be practical. For the rest of your evaluation, you'll focus only on the corridors that connect to Santa Susana.
- Turn off Mountain_Lion_Corridors_Advanced and turn on Mountain_Lion_Corridors.
Compare the results to other layers
Next, you'll evaluate the corridors in comparison to your other data layers. In particular, you'll look at the corridors alongside the suitability surface and roads. For your corridors to be feasible, they should generally follow suitable mountain lion areas and cross as few roads as possible.
- Turn on the Suitability_Surface layer.
Of the three corridors, the one that connects San Gabriel to Santa Susana traverses areas that are highly or moderately suitable for most of its length. It is probably the most feasible corridor for mountain lions. The other two corridors also avoid large swaths of unsuitable areas, but both of them follow only moderately suitable terrain for a large portion of their total length. While not flawless, all three corridors might be feasible for mountain lions.
- Turn off the Suitability_Surface layer and turn on the Roads layer.
All three corridors cross at least a few roads, although they all avoid the dense road clusters. When you first explored the roads dataset in the first lesson of this workflow, every road had the same symbol. You mostly used distance from roads as a proxy representation of distance from human habitation, so you didn't put much weight on the types of roads that your corridors might cross. However, now may be a good time to see whether the roads your corridors cross are small local streets or major highways.
- Symbolize the Roads layer using the Unique Values symbology type and the Carto_Desc value field.
Carto_Desc is short for Cartographic Description. This value field contains a description of the type of road. When you choose it as the field, the bottom half of the Symbology pane updates with a list of all road types. There are seven types total, ranging from highways to paths. It's much easier for a mountain lion to cross some of these road types than others.
- For each road type listed, click the default symbol and change it to a symbol you think is appropriate.
Some of the road types are just ramps for other road types. It's a good idea to symbolize the ramps the same way you symbolize the primary road type. You can also symbolize highways with a larger line width so they stand out more. The purpose of changing the symbology is to help you read the map better, so make whatever symbology changes you think are necessary.
The map symbology updates automatically once you make the changes.
Each of the three corridors crosses at least one major highway, with some of them crossing two. Because your model only measured distance from roads in general, the highways weren't considered more threatening obstacles to mountain lions than smaller streets. However, it's extremely difficult for a mountain lion to cross a highway without putting itself in extreme danger.
Compare the results to highways
You've encountered a potential problem for your corridors: major highways that pose a serious danger to mountain lions. Los Angeles is known for its extensive highway system, serving as a junction for numerous interstate freeways. One of the most important freeways in the area is US Route 101, which is one of the highways that crosses the Santa Susana to Santa Monica corridor. You'll explore the corridor's relation to this highway, as well as some of the other highways crossed by your potential corridors, and consider a possible solution to the problem.
- Turn off the Roads layer.
- On the ribbon, on the Map tab, in the Navigate group, click Bookmarks and choose US Route 101.
The highway appears on the World Imagery basemap, allowing you to visualize the way it actually looks. There are large areas of open space on either side, so from a visual standpoint, it seems your model has done a good job predicting where mountain lions might cross. In fact, the crossing point almost perfectly matches the proposed location of the California's first wildlife bridge. According to the Department of Transportation's project description, many publications have identified this spot as critical for wildlife habitat connectivity. Supporters of the project, such as Save LA Cougars, anticipate its completion in the early 2020s.
The fact that your model aligns so well with published studies and real field work is good sign. It's worth examining other places where the corridors cross major roads - they could reveal candidate sites for future wildlife bridges.
- Zoom to the other Analysis Results bookmarks to see a few more crossing locations. At each bookmark, zoom and pan around the surrounding area. Does the terrain on either side of the road look suitable for mountain lions? How significant is the urban development in each location? How wide are the roads?
These are just some of the questions that conservation professionals must ask themselves when planning complicated and costly projects such as wildlife bridges. Spatial models like this one allow them to efficiently narrow down potential sites before performing more in-depth studies on the ground.
- On your own, you can continue to identify and analyze other locations where your corridors cross major roadways.
- Save your project.
The corridors created by your model generally follow mountainous, uninhabited terrain that is perfect for mountain lions traveling between the core habitat areas. However, your evaluation of your results found a few sections of each corridor that might pose difficulties: the major highways that cut across the Los Angeles area. While the wildlife corridors you generated are not perfect, they represent the most likely routes for mountain lions attempting to travel between the core areas. These areas would thus be the best locations to focus preservation of land and creation of wildlife linkages, such as wildlife bridges.
The reality of making these corridors permanently protected will be a huge challenge, and the survival of mountain lions in Southern California is by no means guaranteed, even with reliably protected corridors. The analysis method and the model you created, while simplified for clarity, is an excellent starting point for those seeking to carry out similar analyses in their own regions of the world. You can continue to edit the parameters of tools in your model to see how the results are affected. What if you changed the weights for the Weighted Sum tool? What if you added a fifth criteria for optimal mountain lion habitat? With a documented and editable workflow, you can test theories and make adjustments as needed to perfect your analysis. Your analysis might have been enhanced further with better data on local conservation efforts, land ownership, and agriculture.
To learn more about the mountain lion and efforts to protect it, check out the Cougar Fund.
You can find more lessons in the Learn ArcGIS Lesson Gallery.