Model conservation suitability

In this tutorial, you will take the role of a Kenyan conservation professional using spatially informed decision making tools to prioritize areas for conservation to reach 30x30 in Kenya.

You will use layers from ArcGIS Living Atlas of the World to determine how much area is currently conserved and how much more needs to be conserved to meet that goal. Once you have done so, you will use Suitability Modeler in ArcGIS Pro to analyze multiple data layers to determine high priority areas for conservation in order to meet targets of the UN Kunming-Montreal Global Biodiversity Framework (GBF).

In conservation planning, the challenge is often to determine how to do the most good, given a set of goals and criteria. The goals in this case are the conservation targets specified by the GBF. Of these targets, several are explicitly spatial. For example, Target 1 and Target 3 are to identify areas of importance, to manage them to prevent their loss, and to conserve 30 percent of landscapes.

The criteria that support your decision-making are represented by spatial layers. Depending on the specific goals and specific locations, you will use different layers, including data from national mapping agencies and your own organization, but many of these layers are freely available online.

The approach outlined in this tutorial is intended to demonstrate some of the data available for conservation, and the tools that you can apply. It is not intended to prescribe a workflow or specific layers.

This tutorial was last tested on April 1, 2025, using ArcGIS Pro 3.4. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

View final result

Requirements

• ArcGIS Pro (see options for software access)
• ArcGIS Spatial Analyst extension

Determine the currently protected area

The first task will be to determine the currently protected area in Kenya. You will search for data, explore its documentation, and use it in ArcGIS Pro to determine the percentage of Kenya that is currently protected. You will use this information later to help determine which areas to prioritize for conservation.

Search for data

First, you'll examine data layers available in ArcGIS Living Atlas that may be useful for your analysis.

ArcGIS Living Atlas is a collection of curated authoritative data layers, maps, and apps hosted in ArcGIS Online by Esri and the GIS community. If you have an ArcGIS organization account, you have access to this vast collection with a browser and in ArcGIS Pro.

1. In a browser, go to ArcGIS Living Atlas.
2. Click Sign In. Sign in using your ArcGIS organizational account.

   Note:

   If you don't have an organizational account, see options for software access.

3. In the ArcGIS Living Atlas search box, type world countries generalized, and in the search results, click World Countries Generalized.

   

4. In the search results, click World Countries Generalized.

   

   This layer is a feature layer, hosted by Esri and shared as authoritative content with ArcGIS Living Atlas.

   

   From this page, you can get more information about the layer, open it in the map viewer or a scene, and add it to ArcGIS Pro.

   The country polygon features in this layer can be used in analyses. They can be extracted and used to define a study area, and to clip other layers. Later in this tutorial, you'll use a polygon from this layer to determine the conserved area in Kenya and calculate how much more needs to be conserved to meet the 30x30 goals.

   To find this layer again, it's a good idea to make a note of the layer name, World Countries Generalized, and the item ID from the URL (2b93b06dc0dc4e809d3c8db5cb96ba69).

   You'll explore a bit more before beginning your analysis.

5. In the ArcGIS Living Atlas search box, type world database of protected areas. Click WDPA – World Database of Protected Areas.

   

6. In the search results, click the WDPA – World Database of Protected Areas layer.

   

   This feature layer is a joint project between UN Environment World Conservation Monitoring Centre (UNEP-WCMC) and the International Union for Conservation of Nature (IUCN). It is an authoritative source, showing marine and terrestrial areas of conservation with a variety of different environmental attributes.

   The layer is provided by IUCN and WCMC to support scientific analysis and conservation planning, policy, and management. This data may not be used for commercial purposes without written permission. The data used in this tutorial is used with permission of UNEP-WCMC.

   Note:

   When you are investigating a data layer that might be useful to you, it is important to study its details page, which provides important context information about the data, including who compiled it, how it was collected, what it is intended to be used for, and whether or not there are use constraints on the data or who can use it.

   This layer's name is WDPA - World Database of Protected Areas and its item ID is ae78aeb913a343d69e950b53e29076f7.

   Later in this tutorial you'll use the polygons of protected areas in Kenya to determine the fraction of Kenya that is under conservation management, and you'll determine how much more needs to be conserved to meet the 30x30 goals.

Add data to a project

You've seen data in the ArcGIS Living Atlas website. Next, you'll open ArcGIS Pro, add ArcGIS Living Atlas data to a project, and examine the data.

1. Download the Model conservation suitability project package.

   Project packages are a way of sharing ArcGIS Pro projects and data. They are compressed files that, when opened, extract a copy of the project to your C:\Users\[user name]\Documents\ArcGIS\Packages folder.

2. Locate the downloaded file on your computer. Double-click Model conservation suitability.ppkx.

   Note:

   Most web browsers download files to your computer's Downloads folder by default.

   

   The project package is extracted and the project opens to a map showing a basemap. A number of layers are in the Contents pane, but they are currently turned off. Next, you'll add a layer from ArcGIS Living Atlas.

3. On the ribbon, on the View tab, in the Windows group, click Catalog Pane.

   

4. In the Catalog pane, click Portal.

   

5. Click the Living Atlas button.

   

6. Copy the item ID value for the World Countries Generalized layer, 2b93b06dc0dc4e809d3c8db5cb96ba69. Paste it in the search box and press Enter.

   

7. In the search results, right-click World Countries Generalized and choose Add To Current Map.

   

   The layer appears on the map.

   

8. In the Catalog pane, in the search box, type World Database of Protected Areas and press Enter.

   

9. In the search results, right-click WDPA - World Database of Protected Areas and choose Add To Current Map.

   

   The WDPA – World Database of Protected Areas layer appears on the map.

   

Extract a polygon

You've seen how to search for data from ArcGIS Living Atlas and add it to ArcGIS Pro. Next, you'll use this data to analyze the protected area of Kenya and determine how much more needs to be conserved to reach the 30x30 goal.

1. On the ribbon, on the Map tab, in the Inquiry section, click Locate.

   

   The Locate tool is useful for finding places.

2. In the Locate pane, click Layer Search.

   

   The Layer search option searches within the layers currently on the map.

3. In the search box, type Kenya and press Enter.

   

4. In the results, right-click Kenya and choose Add To Selection.

   

   The Kenya polygon is selected.

5. Right-click Kenya and choose Zoom To.

   

   The map zooms to Kenya.

   

6. Close the Locate pane.
7. In the Contents pane, right-click the World_Countries_Generalized layer, point to Data, and choose Export Features.

   

   The Export Features tool appears. The Input Features parameter is set to the World_Countries_Generalized layer. The tool detects that there is a single feature selected. It will export only the selected features by default, although you can use the switch to ignore the selection and export all of the features.

8. For the Output Feature Class name, type KenyaFeature.

   The default location for the new feature class is the project geodatabase.

9. Click OK.

   

   The KenyaFeature layer is added to the map. You no longer need the World_Countries_Generalized layer.

10. In the Contents pane, right-click the World_Countries_Generalized layer and choose Remove.

    

11. Click the symbol for KenyaFeature.

    

12. In the Symbology pane, click Black Outline.

    

    The country polygon is drawn with the black outline symbol and no fill, so you can see the other layers.

    

13. Close the Symbology pane.

Extract protected areas

The next step to determining how much of Kenya is currently protected is to extract protected areas in Kenya from the WDPA layer. The WDPA layer has data for the entire world. You do not need the full layer, so you'll select the features that are relevant for Kenya and export them to a new layer. To facilitate the selection, you'll make the protected areas the only selectable layer.

1. In the Contents pane, click the List By Selection button.

   

   All layers are listed and the feature layers have a check box. You can turn off a layer's selectability so features in that layer are not selected when using the Select tool. You only want to select polygons in the WDPA layer.

2. In the Selection section, expand WDPA - World Database of Protected Areas and uncheck WDPA_point_Latest.

   

   Now, only the polygons are selectable. You'll also make the boundary of Kenya unselectable.

3. Uncheck the box next to KenyaFeature.

   

   Now that you have modified the selectable layers, you'll make the selection.

4. On the ribbon, click the Map tab. In the Selection group, click Select.

   

   You'll draw a rectangle around Kenya to select features from the WDPA layer. Your selection will yield features that fall outside of Kenya, but you'll use other tools to clip the selection to the Kenya boundary.

5. On the map, draw a rectangle around the entire boundary of Kenya.

   

   Polygons in and around Kenya are selected, while no features in other layers are selected.

   

   For purposes of this simplified analysis, it does not matter what type of protection each area is under. The polygons in this layer overlap and extend beyond the Kenya boundary. You don't want to double-count the areas that fall under multiple conservation jurisdictions, so you'll narrow down selected features using the Kenya boundary. Once you have the WDPA polygons within Kenya, you'll dissolve the boundaries between them to eliminate the overlaps. First, you'll export the selected features to a new layer.

   Note:

   For official calculation and reporting of protected areas, some other steps are usually taken to prepare the data. For example, the Man and the Biosphere (MAB) areas are typically excluded, and protected areas for a specific country are selected by attribute selection on areas with an ISO3 value of the country's code (in this case, it would be KEN), rather than by clipping to the generalized country boundary. For more details on WDPA reporting procedures, please see Calculating protected area and OECM coverage and theUser Manual for the World Database on Protected Areas and world database on other effective area-based conservation measures.

6. In the Contents pane, click the List By Drawing Order button.

   

7. Expand WDPA - World Database of Protected Areas. Right-click WDPA_poly_latest, point to Data, and choose Export Features.
8. For Output Feature Class, change the name to WDPA_poly_Latest. Click Run.

   You no longer need the source WDPA – World Database of Protected Areas layer, so you'll remove it from the map.

9. In the Contents pane, right-click WDPA – World Database of Protected Areas and choose Remove.

   

   You have exported the selection. Next, you'll clip the WDPA polygons to the Kenya boundary so you are only working with protected areas within Kenya.

   Note:

   Some hosted layers may have restrictions or custom extents that don't allow certain operations like clipping. These restrictions are why you exported the selection to a new feature class. Once the features are locally stored, you won't encounter these restrictions.

10. Click the Analysis tab. In the Geoprocessing section, click Tools.

    

11. In the Geoprocessing pane, search for and open the Pairwise Clip tool.

    

12. Enter the following parameters:
    • For Input Features, choose WDPA_poly_Latest.
    • For Clip Features, choose KenyaFeature.
    • For Output Feature Class, type WDPA_Kenya_Clip.

    

13. Click Run.

    The tool runs and the protected areas are clipped to the Kenya boundary.

14. Remove the WDPA_poly_Latest layer from the map.

    

    Now that you have clipped the polygons to the boundary, you'll dissolve the boundaries between the polygons to remove overlaps and clean up the data.

15. In the Geoprocessing pane, click the Back button.

    

16. Search for and open the Pairwise Dissolve tool. Set the following parameters:
    • For Input Features, choose WDPA_Kenya_Clip.
    • For Output Feature Class, type WDPA_Kenya_04_25.

    

    The World Database of Protected Areas layer is updated monthly. The terms of use advise only using the latest version. It is recommended that you add the month and year (in this case, 04_25) to the extracted output of the tool to keep track of when this version of the data was extracted. You'll need this information for citing the data, if you publish any work based on this layer.

    This tool will create multipart features by default. In this case, that is what you want; all the conserved areas should be merged into a single feature, with many geographically separate parts. If you turn off the Create multipart features parameter, only features that touch or overlap will be merged.

17. Click Run.
18. Remove the WDPA_Kenya_Clip layer.

    The dissolved layer looks the same as the input layer, but now the layer contains one feature instead of over 400.

Calculate the area of Kenya

Now that you have a polygon representing the country of Kenya and a polygon representing the protected areas in Kenya, you can calculate the percentage of Kenya that is currently under some protection.

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

   

2. In the table, scroll to the Shape_Area column.

   The area of the feature is 586683013946.813599 square meters, as meters are the linear units of the coordinate system of this layer. The coordinate system for this data is Web Mercator, which is not ideal for area calculations, so you will add a field to contain a geodetically calculated area.

3. At the top of the table, click the Add Field button.

   

4. In the Fields list, for Field Name, type Area_KM.

   

5. For Data Type, choose Double.

   

6. On the ribbon, on the Fields tab, in the Manage Edits section, click Save.

   

7. Close the Fields tab.
8. In the table, right-click the Area_KM field name and choose Calculate Geometry.

   

   The Calculate Geometry tool allows you to add calculated data based on feature geometry to an attribute.

9. In the Calculate Geometry tool, in the Geometry Attributes section, for Field, choose Area_KM.
10. For Property, choose Area (geodesic).
11. For Area Unit, choose Square Kilometers.

    

    Using geodesic area provides a more accurate area value. Setting the Area Unit parameter to Square Kilometers simplifies the calculation you'll make later.

12. Click OK.

    The area is calculated at 581,864 square kilometers. This number differs from the official area of Kenya by a small amount because the country boundaries in this layer are generalized. For the purposes of this analysis, the value is accurate enough.

Calculate the protected area

Next, you'll calculate the area of the protected areas layer. You'll repeat the process you used to calculate the area of Kenya on the WDPA_Kenya_04_25 layer.

1. In the Contents pane, open the attribute table for WDPA_Kenya_04_25.
2. In the attribute table, click the Add Field button.

   The Fields view appears.

3. For Field Name, type Area_KM. For Data type, choose Double.
4. On the ribbon, on the Fields tab, in the Manage Edits section, click Save.
5. Close the Fields pane.
6. In the WDPA_Kenya_04_25 table, right-click the Area_KM field name and choose Calculate Geometry.
7. In the Calculate Geometry tool, set Field to Area_KM, Property to Area (geodesic), and Area Unit to Square Kilometers.

   

8. Click OK.

   The area is calculated. It is about 102,338 square kilometers.

   Note:

   Your Area_KM value may be different as the WDPA data is updated regularly.

   

   The total area of Kenya is 581,864 square kilometers. The protected area of 102,338 square kilometers is about 17.5 percent of the country's total area.

9. Close both tables.
10. On the Quick Access toolbar, click the Save Project button.

    

Kenya is currently about halfway to its UN 30x30 target. To reach 30 percent, another 87,000 square kilometers or so will need to be protected. Now the question is, which 87,000 km² should be conserved? Where are the greatest biodiversity conservation opportunities in Kenya?

Next, you'll use Suitability Modeler in the ArcGIS Spatial Analyst extension to help answer that question.

Determine the high priority areas

Now that you've identified how much more area needs to be conserved to reach the UN 30x30 target, you'll work on the problem of determining which areas are highest priority to add to the protected area. You'll use data from ArcGIS Living Atlas and the ArcGIS Spatial Analyst extension Suitability Modeler to determine additional areas of high importance for conservation.

Examine the data layers

Earlier, you reviewed some layers in ArcGIS Living Atlas and learned how to add those layers to ArcGIS Pro. Next, you'll examine the data to learn about the layers you'll be considering in your analysis.

1. In the Contents pane, uncheck the WDPA_Kenya_04_25 layer.

   

2. In the Contents pane, check the GlobalTerrRaR_025deg layer.

   

3. In the Contents pane, expand the GlobalTerrRaR_025deg layer to see its symbology.

   

   This layer has bivariate symbology. Bivariate symbology allows you to see a quantitative relationship between two variables. The GlobalTerrRaR_025deg layer shows species richness and species rarity calculated for terrestrial vertebrate species (birds, reptiles, mammals, and amphibians). Species richness quantifies how much species diversity a given location has, which is an important measure to consider, as protecting an area with high richness protects many species.

   Species rarity quantifies how uncommon the species found in a given area are. This measure is also important, because if a given area is one of a few areas suitable for an uncommon species, protecting that area may be your only opportunity to protect the rare species.

   

   Inspecting this layer shows that the southwestern portion of Kenya is high in both species richness and species rarity (dark blue), while the region in the northeast generally has moderate species rarity and low species richness (light blue). There are certain areas, particularly along the coastline and around north of Metro Nairobi, that stand out as areas of high species rarity.

   Note:

   This layer is published by the Map of Life and is a product of Half-Earth Project, the EO Wilson Foundation, and Yale University to support conservation of biodiversity. The layer is also available in ArcGIS Living Atlas. The layer's terms of use prohibit use for commercial purposes or personal gain. The layer is used in this tutorial with the permission of Map of Life and Yale University.

4. Uncheck the GlobalTerrRaR_025deg layer.
5. Check the World Distance to Water layer.

   This layer provides a landscape-scale estimate of the distance from large water bodies such as lakes and rivers, including the ocean. The distribution of water affects the distribution of many species. In addition, a component of Target 3 of the GBF is to integrate terrestrial and marine protected areas.

   

   The map shows distance to coastlines and inland water sources like Lake Victoria and rivers. The northeastern portion of the country stands out as the area that is more arid in comparison to the rest.

   Note:

   This layer is published by Esri, USGS, and ESA. It is available in ArcGIS Living Atlas.

6. Uncheck the World Distance to Water layer.
7. Check and expand the European Space Agency WorldCover 2021 Land Cover layer.

   

   This layer shows land cover classes derived from satellite imagery at a 10-meter resolution. This layer contains highly accurate information about the current landscape worldwide.

   

   To prioritize ecosystem functions and services, in accordance with 30x30 targets, you want to prioritize areas with tree cover and shrub land for conservation over crop lands or urban areas.

   Note:

   This layer is from the WorldCover Consortium and European Space Agency for the years 2020/2021. The data was derived from worldwide Sentinel satellite imagery. It is available in ArcGIS Living Atlas.

8. Uncheck the European Space Agency WorldCover 2021 Land Cover layer.
9. Check and expand the Land Cover Vulnerability Change 2050 - Country layer.

   

   This layer was produced by Clark Labs at Clark University, looking at trends in land cover change and extrapolating those trends into the future to the year 2050.

   

   Some areas are more vulnerable to change given pressures like urbanization or deforestation. Dark purple areas are less vulnerable, while the ones drawn in bright yellow on the map show areas that are most imperiled in need of conservation before they are converted into something less sustainable. Using this layer supports GBF Target 1, which relates to preventing the loss of areas of high biodiversity importance.

   Note:

   This layer is available in ArcGIS Living Atlas. It is shared under a Creative Commons By Attribution license.

   Now that you have investigated the data, you'll use Suitability Modeler to weight them against each other to determine which additional 15 percent of the country to recommend for conservation to support the 30x30 goals.

Rasterize data for the analysis

You'll work on a clipped, locally saved set of layers in Suitability Modeler to speed processing.

1. In the map pane, click the Kenya map tab.

   

   This map contains a copy of the layers from the previous map, with some small changes. First, for processing speed, each of the layers was clipped using the Kenya boundary feature and stored as local data in the project. The suffix _Kenya was added to the layer names to indicate the change.

   

   To simplify the suitability analysis, the species richness and rarity bivariate layer was duplicated and each layer was independently symbolized by the richness or rarity attributes, with symbology adjusted to emphasize each attribute separately.

   

   Also, the distance to water layer was symbolized with a stretch and new color scheme.

   

   There are two more data preparation steps before you can start the analysis. Suitability Modeler works with raster data. The species richness and species rarity layers are feature layers, so you'll need to convert them to rasters.

2. In the Geoprocessing pane, in the search box, type feature to raster.
3. Click Feature to Raster .

   

4. In the Feature to Raster tool, set the following parameters:
   • For Input features, choose GlobalTerrRaR_025_Richness_Kenya.
   • For Field, choose Rich_all.
   • For Output raster, type GlobalTerrRaR_025_Richness_Raster_Kenya.
   • For Output cell size, type 1000.

   

   The cell size of 1,000 meters, or 1 kilometer, is a compromise between the relatively large polygons and the need to capture their boundaries.

5. Click Run.

   The raster layer, GlobalTerrRaR_025_Richness_Raster_Kenya, is added to the map. Before closing the tool, you'll run it again on the species rarity layer.

6. For Input features, choose GlobalTerrRaR_025_Rarity_Kenya.
7. For Field, choose or confirm Rar_all is selected.
8. For Output raster, type GlobalTerrRaR_025_Rarity_Raster_Kenya. Confirm Output cell size is set to 1000.

   

9. Click Run.

   The GlobalTerrRaR_025_Rarity_Raster_Kenya raster layer is added to the map. You no longer need the original richness and rarity feature layers.

10. In the Contents pane, right-click GlobalTerrRaR_025_Rarity_Kenya and choose Remove.
11. In the Contents pane, right-click GlobalTerrRaR_025_Richness_Kenya and choose Remove.

    Now that these layers have been converted to raster format, you are ready to use Suitability Modeler.

Start a suitability analysis

Next, you'll start a new suitability analysis model and add rasters to it.

1. On the ribbon, on the Analysis tab, in the Workflows group, click Suitability Modeler.

   

   The ribbon shows the Suitability Modeler tab. The Suitability Modeler pane appears. First, you'll create a new model.

2. For Model name, type Kenya conservation priorities.

   

3. For Output suitability raster, type Conservation_suitability_Kenya.

   

   The Kenya conservation priorities model layer is added to the Contents pane. Next, you'll add the layers of information to the model.

4. Click the Suitability tab.

   

5. In the Criteria section, for Input Rasters, click the arrow. In the menu, click Select all and click Add.

   

   The input rasters are added to the model.

   

   You've added rasters to the suitability model.

Transform layers

The next step in the suitability modeling workflow is to transform the rasters. The first raster you'll transform is the distance to water raster. This raster is a continuous raster. You'll transform it to use 10 categories of distance to water.

1. In the Criteria table, select Distance_to_Water_Kenya.

   

   The Transformation Pane view appears and shows the Distance_to_Water_Kenya layer. In this view, the distribution of the data in the raster is shown as a histogram.

   

   The view also shows the field being shown and the range of values for each class.

   

   The Range of Classes transformation was selected for this data based on its properties. If you examine the default mapping of values, the low values in the input raster are assigned low suitability values. You should examine the transformation for each layer and determine if it works well for the problem that you are modeling. For this particular raster, low values mean short distances to water, which should be ranked as high suitability.

   If you were going to use the Range of Classes transformation for this data, you would click the Reverse button to reverse the ordering of the classes. Doing so would rank locations close to water high and locations far from water low. Instead, you'll choose a different type of transformation.

2. Click Continuous Functions.

   

   The MSSmall function was determined to be the best fit.

   

   The Transformation Pane view shows the curve that fits the data in a histogram of the transformed values.

   

   The function that fits this data is MSSmall. This function rescales input data based on the mean and standard deviation where smaller values in the input raster have higher preference. Under this function, the short and intermediate distance areas are classified as highly suitable, while the long distance areas are classified as low suitability. This function is a more accurate model of suitability for this problem.

3. In the Contents pane, ensure that the Kenya conservation priorities layer is checked.
4. In the Contents pane, uncheck the Conservation_suitability_Kenya output raster layer.

   

   You'll examine the Conservation_suitability_Kenya output raster at the end of the suitability analysis.

   When the Transformed Distance_to_Water_Kenya layer was added in the Transformation pane, it was also added to the Contents pane and to the Kenya map.

   

   In this layer, red represents greater distances from water, and green represents lesser distances.

   Note:

   If your map has much more red than green, it's possible you used the default Range of Classes transformation. If the southern portion of the map appears to be mostly red, with some areas of green in the northeast, the suitability values are reversed, and places with long distances to water are being ranked high, while places closer to water are ranked low.

   This transformation converts the linear distance values in the original raster to a 1-10 scale to allow it to be added to the other layers in the overlay. Next, you'll transform another layer.

5. In the Suitability Modeler tool, in Criteria table, select GlobalTerrRaR_025_Rarity_Raster_Kenya.

   

   The Transformation Pane view updates to show the range of classes for the rarity layer.

   

   For this layer, a higher value for rarity should receive a higher suitability value. The histogram of values is also updated.

   

   The layer is added to the Contents pane in the Kenya conservation priorities group layer and to the Kenya map.

   

   Note:

   As you explore the suitability layers in the next few steps, if you don't see a layer on the map, drag the layer up in the Contents pane or turn off the layers above it.

6. In the Criteria table, select GlobalTerrRaR_025_Richness_Raster_Kenya.

   

   The Transformation Pane view updates to show the range of classes for the richness layer.

   

   For this layer, higher values for richness are mapped to higher values for suitability. The view also shows the histogram of values.

   

   The layer is also added to the map.

   

7. In the Criteria table, select LandCoverVulnerability_Kenya.

   

   For this layer, higher values for vulnerability should have higher values for suitability.

   

   The layer is also added to the map.

   

   You've transformed most of the input criteria. The land cover layer is a categorical variable, so you'll transform it a bit differently.

Reclassify the land cover variable

The next data preparation step for this analysis is to specify a categorical transformation for the land cover layer. You'll prioritize certain land cover classes over others based on their value for biodiversity.

The raster contains unique categories. To make it easier to reclassify them, you'll change the field that will be used for the transformation.

1. In the Criteria table, select EuropeanSpaceAgencyWorldcover_Kenya.

   

   The Transformation Pane view shows the current default mapping, but it is based on the object ID values of the raster attribute table. You'll change it to use the land cover class name.

2. In the Transformation Pane view, click Value and choose ClassName.

   

   Now the Transformation Pane view shows the mapping of class names to values.

   

   To prioritize ecosystem functions and services, you'll rank tree covered land highest.

3. In the Suitability column, for TreeCover, click the value, type 10, and press Enter.

   

   This is the highest value. Shrubland is also important from a biodiversity perspective, so you'll give it a slightly lower suitability ranking of 9.

4. In the Suitability column, for Shrubland, click the value, type 9, and press Enter.

   Grassland is also important, so you'll give it a slightly lower suitability ranking of 8.

5. In the Suitability column, for Grassland, click the value, type 8, and press Enter.

   

   The least important areas to conserve are urban built-up land.

6. In the Suitability column, for Built-up, click the value, type 1, and press Enter.

   Crop land is also less important in this analysis, so you'll give it a low value of 2.

7. In the Suitability column, for Cropland, click the value, type 2, and press Enter.

   Bare or sparsely vegetated areas are also less important, so you'll give that class a value of 3.

8. In the Suitability column, for Bare or sparse vegetation, click the value, type 3, and press Enter.

   This mapping accounts for most of the land cover in Kenya.

   

9. Scroll down to see the remaining values.

   

   Mangroves and herbaceous wetlands have high value for biodiversity, so you'll leave the default suitability values of 10 and 9, respectively. Snow and ice covered land is rare in Kenya, and permanent water bodies are important for aquatic biodiversity. You'll accept the default values for these.

   The distribution of transformed values is shown in the histogram.

   

   Next, you'll preview the results of the suitability model.

10. In the Contents pane, check the Conservation_suitability_Kenya layer.

    

    The map shows the model results.

    

    These preliminary results look good, so you'll run the model to produce a raster copy of the output in your project geodatabase.

Specify an output resolution and run the model

Before you run the model, you should set the cell size for the output. By default, the cell size is the maximum of the cell sizes in each input layer. In this analysis, you have several high resolution layers and two very low resolution (species richness and rarity) layers. By default, the output will have a resolution of 1 kilometer, matching the two lower resolution layers.

A low resolution output will be processed more quickly and take up less space on disk but will also be less useful for land management decision making. A high resolution output takes longer to process and more disk space to store, but will be more useful for decision making. You'll set the output resolution to an intermediate value as a compromise.

1. In the Suitability Modeler pane, click the Environments tab.

   

2. Scroll to and expand the Raster Analysis section.
3. For Cell Size, choose Same as layer Distance_to_Water_Kenya.

   

   This layer has a cell size of about 200 meters, which is between the highest resolution data (10 meters) and the lowest resolution (1 kilometer).

4. Click the Parameters tab.

   

5. In the Suitability Modeler pane, click Run.

   

   The model runs and produces the Conservation_suitability_Kenya raster layer. The model may take a few minutes, depending on your computer.

   

   In the output, you can see the impact of the lower resolution data in the large blocky 30 kilometer pixel footprints, but the result also retains some of the fine-grained data quality of the higher resolution layers. This output resolution is acceptable for this analysis. The values range from 50, representing 10 points, or maximum value, for each of the input layers, to 16.8 points, representing low scores from each of the input layers. High values in the output represent areas that have a high priority for conservation, based on the values you assigned in the model.

   The result is based on the data but also on your professional knowledge and judgment about which layers to include, the importance of the different layers, and the transformation and the weighting of the values. Modeling is a nonlinear, iterative process, and is subject to some inherent constraints.

Locate regions

The Suitability Modeler allows you to locate regions, based on the model result layer. Next, you'll identify four regions that make up a total area equal to the remaining 15 percent needed to meet the 30x30 goal.

1. In the Suitability Modeler pane, click the Locate tab.

   

2. For Input suitability map, choose Conservation_suitability_Kenya.
3. For the Total area, type 87000.

   This value is the area you calculated would need to be added as conserved land to meet the 30x30 goals.

4. For Area units, choose Square kilometers.
5. For Output raster, type Kenya_conservation_areas.

   

6. For Number of regions, type 4.

   

   The warning beside the Number of regions parameter indicates that the tool will use the combinatorial method of selecting regions, because the value you choose, 4, is less than 8. The region growth and search parameters section of the Locate pane will allow you to change the method, but it is not necessary in this case. The combinatorial method is better for small numbers of regions. If you chose to create more than 8 regions, the tool would default to the sequential selection method, which is more efficient for larger numbers of regions.

7. For Maximum distance between regions, type 50.
8. For Distance units, choose Kilometers.

   

   You can set a minimum and maximum area for each region and a minimum distance between regions if you want to ensure that they are dispersed, but it is not necessary for this analysis.

9. For Input suitability map or feature of existing regions, click the Browse button.
10. Click Databases.

    

11. Double-click the model_conservation_suitability.gdb geodatabase.

    

12. Click WDPA_04_25_Kenya. Click OK.

    This layer, which you created earlier in the other map, has the existing conserved areas. These areas will allow the tool to avoid adding existing conserved areas to the proposed conservation areas.

    

13. Click Run.

    The tool runs and the new Kenya_conservation_areas layer is added to the map.

    

    Note:

    The colors on the map may differ from the example image.

    The result shows four regions, totaling 87,000 km² within the country of Kenya. It does not include areas that are currently conserved, but does prioritize those that are continuous. It also prioritizes areas that have high species richness, species rarity, access to water, mostly tree cover, grassland and shrub land, and high vulnerability to change.

    The areas you see in gray, red, blue, and yellow are areas the model has identified as prime conservation areas. These are areas with high biodiversity and important ecosystem functions that could be degraded if not conserved. One of them is close to the coastline, which fits into the UN target of integrating into larger seascapes. Some of them are close to the urban centers and, and the richly biodiverse and tree-covered parts of Kenya.

In this tutorial, you identified the area of Kenya currently protected and determined the additional area that needs to be protected to meet the UN 30x30 goals. You conducted an analysis aligned with the Global Biodiversity Framework targets to conserve areas with significant biodiversity and importance to ecosystem functions. You used data from ArcGIS Living Atlas in Suitability Modeler to create a weighted overlay of the transformed layers. You used the Locate function to identify compact areas for conservation, building upon existing conserved areas and trading off various factors.

To go further, you could convert the Kenya_conservation_areas raster to a feature layer and share it in a web map. This could be used to communicate your analysis results to your colleagues and to the broader community. You could tell the story of this analysis, 30x30 goals, and conservation in your area by including the map as part of a story using ArcGIS StoryMaps.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• WDPA – World Database of Protected Areas - UNEP-WCMC and IUCN (2024), Protected Planet: The World Database on Protected Areas (WDPA), online, 04/2024 version downloaded, Cambridge, UK: UNEP-WCMC and IUCN. Available at www.protectedplanet.net.
• UNEP-WCMC (2019). User Manual for the World Database on Protected Areas and world database on other effective area-based conservation measures: 1.6. UNEP-WCMC: Cambridge, UK. Available at: http://wcmc.io/WDPA_Manual
• GlobalTerrRaR_025deg - Map of Life, Yale University - Jetz, W., McPherson, J. M., and Guralnick, R. P. (2012). Integrating biodiversity distribution knowledge: toward a global map of life. Trends in Ecology and Evolution 27:151-159. DOI:10.1016/j.tree.2011.09.007.
• World Distance to Water - Source: Esri, USGS, ESA
• European Space Agency WorldCover 2020 Land Cover - © ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium
• World Topographic Map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• World Hillshade layer sources: Esri, Maxar, Airbus DS, USGS, NGA, NASA, CGIAR, N Robinson, NCEAS, NLS, OS, NMA, Geodatastyrelsen, Rijkswaterstaat, GSA, Geoland, FEMA, Intermap, and the GIS user community
• Land Cover Vulnerability Change 2050 – Country - Clark Labs
• This tutorial is based on a demonstration presented by Abigail Fitzgibbon.