Exercise Ten - Selecting locations for tea cultivation

Exercise

10

Selecting the most suitable locations for tea cultivation

in Kenya

In this exercise, you will have the opportunity to select the most suitable locations for expanding tea cultivation in Kenya using GIS and spatial analysis. You will use public domain spatial data from the World Resources Institute as your source for your analysis.

One of the maps you will produce in this exercise through the analysis of the most suitable locations for tea cultivation in Kenya using ArcGIS for Desktop.

Exercises for The GIS Guide to Public Domain Data

Context

Tea is an important world cash crop. Kenya is Africa’s leading tea producer and fourth largest in the world after India, China, and Sri Lanka. Farmers have been producing tea in Kenya since 1903; the main type grown is Assam—C Sinensis variety Assamica (Masters) Kitamura—due to its high yield potential. Even with such high production levels, more than 60 percent of tea in Kenya is grown on small or family-run farms, organized and managed by the Kenya Tea Development Agency Ltd. (KTDA) (http://www.ktdateas.com). The remaining 40 percent is grown by large, privately-owned plantations. KTDA is a cooperative of growers that represents 28 percent of Kenya’s total export earnings and is the country's largest tea cooperative. With over 400,000 growers in its network, cultivating over 86,000 hectares of land, it produces over 700 million kilograms of tea annually.

Tea, Camellia sinensis, is an evergreen plant whose leaves and leaf buds are used to produce tea. This plant grows best in tropical and subtropical climates, although some varieties can tolerate marine and more temperate climates. Tea also grows best on well-drained fertile acid soil on fairly high lands. Kenya, with its volcanic soils, tropical climate, and hilly terrain, is an ideal environment for growing tea. As tea cultivation and production is dependent upon both physical and human geography, a GIS application is an excellent tool to analyze the current and potential growth of tea in Kenya.

http://www.ktdateas.com/

Problem

Hearing about your GIS skills, the Kenya Tea Development Company has hired you to select additional lands that might be suitable for expanding tea cultivation. According to the KTDA, “tea is grown on tropical red loam mixed with volcanic soils found in high-altitude areas of Kenya. The suitable altitude range is between 1,500 and 2,250 meters above sea level. Tea does well in an environment with well-distributed rainfall ranging from 1,200 to 2,500 millimeters annually with long sunny intervals.” However, less than 8 percent of the land in Kenya is used for crop and feed production and less than 20 percent of the land in the country is suitable for cultivation. Of that 20 percent, only 12 percent is classified as high potential (with adequate rainfall) agricultural land and about 8 percent is medium potential land.

The rest of the land is arid or semiarid. In addition, there is a strong tradition of land conservation in Kenya, with a significant portion of the country’s land classified as protected in natural areas or national parks. The lands you will select for potential tea cultivation sites need to have the following characteristics:

Tea must be grown on moderately high ground,

between 1,500 meters and 2,250 meters above sea level.

Tea must be on land that has an annual rainfall between 1,200 millimeters and 2,500 millimeters.

The tea cultivation sites cannot be within 500 meters of an urban area.

Problem (contd.)

Tea cultivation sites cannot be in wetland areas because of unsuitable growing conditions and wetland conservation directives

Tea cultivation sites cannot be within 100 meters of a river due to the potential for flooding and to protect the riparian zone

Tea cultivation sites cannot be within 250 meters of a major town

Tea cultivation sites cannot be in a protected area

Tea cultivation sites cannot be in a water body because of the obvious unsuitable growing conditions

Tea cultivation sites must be within 5 kilometers of a major road to reduce transport costs

Tea cultivation sites must be on agricultural land partly or wholly designated as herbaceous crop

Resources Skills Required

Performing tabular data joining, sorting and querying, and analysis

Performing spatial data sorting and querying, and analysis

Overlaying, erasing, separating, and other geoprocessing operations

Symbolizing and classifying data

Making decisions in a GIS environment

TIME This exercise contains fifty-eight questions and will require three to five hours to complete. SOFTWARE ArcGIS 10.0 or later, from Esri.

Work package 1: Managing the data

Create a folder on your computer or on the network where you will store the data. Make sure this folder has a logical name so that you will remember what its contents are. The data for this exercise were created by the World Resources Institute (WRI). Access ArcCatalog and navigate to the location where you will store and work with your data. Right-click the folder, select New, and create a new File Geodatabase and name it Kenya_wri_data.gdb.

Steps 1 - 5

1) Data management

Visit the World Resources Institute at: http://www.wri.org 2) World Resources

Institute data 2.1) What is the WRI? 2.2) Why does the WRI produce spatial data and why does it use GIS in its work?

Visit the WRI Download Kenya GIS Data resource at http://www.wri.org/publication/content/9291 and download the following eleven data layers: Under Agriculture: ke_agriculture Areas of cropland in Kenya Under Land cover & land form: ke_urban Urban areas in Kenya ke_wetlands Wetland areas in Kenya

http://www.wri.org/

http://www.wri.org/publication/content/9291


Under Base data: ke_district_boundaries.zip District administrative boundaries in Kenya ke_major-rivers.zip {permanent and non-permanent rivers in Kenya ke_major-road.zips Major roads in Kenya ke_major-towns.zip Major towns in Kenya ke_protected-areas.zip Protected areas in Kenya ke_waterbodies.zip Waterbodies in Kenya Under Elevation: dem_250m.zip Kenya’s digital elevation model at 250-meter resolution Under Rainfall: ke_totann.zip Average annual rainfall in Kenya.

Steps 1 - 5

2) World Resources

Institute data (contd.)

2.3) How many layers did you download?

3) Extract data Unzip all of your files.

3.1) How many data layers are vector and how many are raster?

Because the DEM and the Total Annual rainfall layers are raster datasets, and as ArcGrids they are associated with folders and files, you should create folders for these datasets as you unzip them. Note that the wetlands file had an embedded path name and therefore, when you unzip it, it is stored in the ke_wetlands folder.


Access ArcMap. Set your workspaces in this project so you will be storing the data in the correct and logical location. To do this, from the Geoprocessing menu select Environments to open the Environment Settings dialog. Set the current workspace and the scratch workspace to the geodatabase that you are using for this exercise, as shown next.

Steps 1 - 5

4) Set up workspaces

Setting up the current and scratch workspaces for the exercise.

Use Windows > Catalog in ArcMap to open the Catalog window. Right-click the ke_agriculture shapefile, select Properties, and then select the XY Coordinate System tab.

Work package 1: Managing the data Steps 1 - 5

4) Set up workspaces

(contd.)

Because the Albers projection does preserve area, you decide to use it for this project, because you need to compute areas accurately.

4.1) What is the coordinate system of this dataset? 4.2) What is the coordinate system of the other vector layers that you downloaded for this exercise?

Notice that the DEM is the layer that has a different coordinate system than all of the other layers.

4.3) What is the coordinate system of the DEM?

In the Help or via an online resource, read about the projection that this coordinate system is based upon.

4.4) Is the Albers projection an equal area projection?

5) Project data Use Toolboxes > System Toolboxes > Data Management Tools > Projections and Transformations > Feature > Project. Project your agriculture.shp file to the same projection as the DEM (the Albers projection). Use Import in your projection and select the DEM dataset. Name your output agriculture_albers and save it in your working folder, the file geodatabase that you established earlier.

Work package 1: Managing the data Steps 1 - 5

5) Project data (contd.)

Repeat the process for all of your other vector layers. Save them in the Albers projection and as feature classes in your file geodatabase. Make sure that you do not use any dashes or spaces in your feature class names; only underscores are acceptable. Note that water bodies does not contain any projection information. Therefore, you will need to first Define Projection on the waterbodies layer and give it the same projection as that of the other layers (WGS 84 Geographic). Then, use Project on the data and project it into WGS 84 Albers. Repeat the process for your other raster layer—the total annual precipitation—but use the Raster > Project Raster tool. Save the result as a raster in your file geodatabase. Repeat the process for the other layers, but use Import > Feature Class (Single) because the other layers are vector layers. Note that the situation you encountered earlier (different data layers having different projections, or no projection at all) is common with spatial data portals. Often, the GIS analyst or administrator who uploads data to the portal lacks the time to fully document or project each layer. The result is a variety of useful data themes, but more work for you, the end-user. It also means that you need to pay close attention to each data layer and any metadata available, so you can best work with the data. This is a key skill that will serve you well in the future.

End of work package

Work package 2: Describing the data

Another best practice is to become familiar with each of the data layers. Add each of your data layers to your map document if you have not already added them. For each layer in your map document, fill out the following data table, adding rows as necessary to describe each data layer.

Step 6

6) Describing the data

Save your map document now, and often as you work through this exercise

Layer (Name)

Data Type (Point, Line, Polygon, Raster)

Possible attribute (on which to map)

End of work package

Work package 3: Analyzing elevation and rainfall Steps 7 - 17

7) Examine elevation data

7.1) Describe the pattern of elevation across Kenya. 7.2) Which two districts would you describe as having some of the highest land in Kenya? Which two districts would you describe as having some of the lowest land in Kenya?

Identifying suitable elevations for tea cultivation.

You are ready to begin analyzing your data to solve the problem of selecting tea cultivation sites. Let’s turn our attention first to criterion 1: Land between 1,500 and 2,250 meters above sea level.

8) Using the raster calculator

Expand the Catalog tab on the right (you may need to use Windows > Catalog to open up the Catalog window), go to Toolboxes > System Toolboxes > Spatial Analysis Tools > Map Algebra > Raster Calculator (or simply search for Raster Calculator and open the tool). Name your output elev_ok and store it in your geodatabase as follows (note the use parentheses).


9) Analyze elevation data

9.1) What is the size of each cell in the x and y dimensions? This is the spatial resolution of your data. 9.2) How much land area does each cell occupy? Show your work.

Analyze your elevation data. Right-click elev_ok and examine the properties. Under source, examine the cell size.

10) Analyze elevation

attributes

Open the table for the elev_ok layer.

10.1) Fill in the following information: Value = 0 where: ______________________________________________. Value = 1 where: ______________________________________________.

11) Identify acceptable

elevation

Select the row in the table where the elevations are acceptable for growing tea.

11.1) Describe the pattern of elevation acceptable for growing tea across Kenya.

12) Analyze rainfall data

Analyze the annual rainfall data layer.

12.1) Describe the pattern of annual rainfall across Kenya.

Recall criterion 2: Lands that have an annual rainfall between 1,200 millimeters and 2,500 millimeters.


13) Using raster calculator

13.1) What expression did you use? 13.2) Describe the pattern of acceptable annual rainfall across Kenya.

Use the Raster Calculator again to determine areas with acceptable rainfall. Name your output raster layer rainfall_ok.

14) Combine criteria

Next, combine criteria 1 and 2 with Raster Calculator as follows, naming your resulting raster layer elev_ok_rainfall_ok and storing it in your geodatabase.

Using the Raster Calculator to evaluate acceptable rainfall areas.


14) Combine criteria (contd.) 14.1) Describe areas of acceptable elevation and acceptable rainfall.

14.2) Describe the pattern of acceptable annual rainfall across Kenya.

15) Identify acceptable

elevation and rainfall

Since you are using a combination of vector and raster datasets, at some point in your analysis, you will need to convert the vector layers under consideration to raster or convert the rasters to vectors to achieve the final goal—areas that meet all criteria for tea cultivation. For the purpose of this exercise, we will convert the raster data to vector and complete the remaining analysis using only vector data.

Open the table for elev_ok_rainfall_ok and select the row representing conditions where the elevation and rainfall is acceptable.


16) Convert data

17) Compare raster and vector

layers

Use Search, find the Raster to Polygon tool, and convert your raster data to vector.

Open the table for elev_ok_rainfall_ok and select the row representing conditions where the elevation and rainfall is acceptable.

Converting the raster elevation and rainfall data to vector format.

17.1) Do you notice any differences between the two data sources?


17) Compare raster and vector

layers (contd.)

The reason you selected the acceptable row before you converted it to vector is because your result would only contain polygons for the acceptable areas. If you had not selected the acceptable row beforehand, your resulting vector layer would have contained all areas of Kenya, with codes indicating whether the land was acceptable or not. From that layer, you would have had to select the acceptable area. So, either way would have worked, but by selecting the row beforehand, you saved a step.

End of work package

Work package 4: Analyzing urban areas, rivers, and agriculture Steps 18 - 24

18) Identify land away from urban

areas

You are now ready to turn your attention to criterion 3: Your tea cultivation site cannot be within 500 meters of an urban area. In order to determine which land is distant from urban areas, you must first determine which land is near urban areas, and then switch the selection. Expand the Catalog tab on the right (you may need to use Windows > Catalog ), go to Toolboxes > System Toolboxes > Analysis Tools > Proximity > Buffer, or simply search for Buffer to open the buffer tool. Complete the required fields as follows, with urban areas as your input. Name your output urban_500m_buffer, as shown next.

Generating a 500-meter buffer around urban areas.


19) Examine new layer

Examine your new layer.

19.1) Is your new layer a point, line, or polygon feature class? Why? 19.2) What is the shape of your buffers? Why?

20) Create 100-meter buffer

Repeat the process to create 100-meter buffers around rivers (criterion 5) and 250 meters around major towns (criterion 6).

20.1) What shape are your river buffers? Why? 20.2) What shape are your towns buffers? Why? 20.3) Are your towns buffers 250 meters across or 500 meters across? Why?

21) Create 5-km buffer

Now, create 5-kilometer buffers around roads.

21.1) How did you accomplish this?


22) Examine agricultural land

Next, examine your agricultural land. Symbolize the layer as Unique Value. Note that there are four different types of herbaceous crops—irrigated, isolated, rainfed, and scattered, as listed next: Irrigated herbaceous crop Isolated (in natural vegetation or other) rainfed herbaceous crop (field

density 10–20 percent polygon area) Rainfed herbaceous crop Scattered (in natural vegetation or other) rainfed herbaceous crop (field

density 20–40 percent of polygon area) Make all of the agricultural area that includes any herbaceous crop appear in the same color.

22.1) Describe the pattern of agricultural land in Kenya. 22.2) Describe the pattern of herbaceous crops in Kenya.


23) Select agricultural land

Select the appropriate agricultural land. Use Selection > Select By Attribute, and select all of the herbaceous crop areas. Use three OR statements to connect the four types of herbaceous crops, as shown next.

Identifying the herbaceous crop areas.

When you are finished, use Verify to make sure your statement contains no syntax errors and then click OK.


23) Select agricultural land

(contd.)

When done, examine your map. You may wish to change the selection color from the default of cyan to yellow or something easy to notice. Use Selection > Selection Options.

23.1) How many polygons are now selected?

24) Create new layer

Create a layer from the suitable agricultural lands. Right-click the agriculture layer, and choose > Selection > Create layer from selected features. This will create a layer file just for the areas meeting the agricultural requirement. Note that it is just a layer file, rather than a new dataset. This layer file still points to the original data file named agriculture_albers.

24.1) How many records are in your agriculture_albers selection layer? Why?

End of work package

Work package 5: Combining agriculture, elevation, and rainfall Steps 25 - 27

Now you are ready to intersect the suitable agricultural areas with the areas suitable in terms of elevation and rainfall.

25) Create new layer

Search on Intersect and complete the required fields for the Intersect tool. Name your output elev_ok_rainfall_ok_agric_ok, as shown next.

Intersecting the agriculture, elevation, and rainfall data.

Work package 5: Combining agriculture, elevation, and rainfall Steps 25 - 27

When you are finished, open the attribute table, right-click the field SHAPE_AREA, and calculate statistics for it.

26) Calculate statistics

26.1) In what units is the map data stored? 26.2) How much area in square kilometers are you now considering for tea cultivation? Show your work.

27) Examine combined data

Examine your elevation-rainfall-agriculture layer. Zoom to the extent of this layer.

27.1) Describe the pattern of this layer. 27.2) In which districts of Kenya are you considering tea cultivation (field DISTNAME)?

Work package 6: Excluding land from analysis Steps 28 - 36

You are getting closer to your solution. You now have a layer representing criteria 1, 2, and 10 (elevation, rainfall, and agricultural). You are now ready to remove the areas near urban areas, wetlands, areas near rivers, areas near major towns, protected areas, and waterbodies. You will do this with the Erase tool.

28) Examine combined data

Remove areas near urban areas from your consideration. In the Systems Toolboxes, find the Analysis > Overlay > Erase tool, and first erase the urban area buffers. Name your output feature class elev_rainfall_agric_urban_ok, as shown next.

Removing urban areas from further consideration of suitable areas for tea cultivation.

Note: Erase may require the ArcInfo license for ArcGIS. You can use a combination of selections if you need to avoid using Erase.


29) Compare layers

Zoom to the scale of 1:140,000 and compare the elev_ok_rainfall_ok_agric_ok layer with the elev_rainfall_agric_urban_ok layer.

Remove wetlands from your consideration—run Erase to remove wetlands and name your result elev_rainfall_agric_urban_wetlands_ok.

29.1) Describe at least two differences.

30) Eliminate wetlands

31) Eliminate areas near rivers

Remove areas near rivers from your consideration—run Erase on your new layer to remove areas near rivers (your river buffers layer) and then name your result elev_rain_ag_urb_wet_riv_ok.

31.1) Examine your new layer and describe the result.

32) Eliminate areas near town

Remove areas near major towns from your consideration—run Erase on your new layer to remove areas near major towns (your towns buffer layer) and then name your result elev_rain_ag_urb_wet_riv_town_ok.

33) Eliminate protected areas

Remove protected areas from your consideration—repeat the process, running Erase on your new layer to remove protected areas, naming your result elev_rain_ag_urb_wet_riv_town_prot_ok


34) Eliminate waterbodies

Remove waterbodies from your consideration— run Erase on your new layer to remove waterbodies and name your result elev_rain_ag_urb_wet_riv_town_prot_water_ok.

As you did earlier, open the attribute table for your latest layer, right-click the field SHAPE_AREA, and calculate statistics for it.

34.1) How many polygons are now suitable for tea cultivation?

35) Calculate statistics

35.1) How much area in square kilometers are you now considering for tea cultivation? 35.2) How does this figure compare to that which you obtained earlier? Why?

36) Examine data Examine the various layers in your map document.

36.1) Do you have many holes in the area under consideration? Do they represent areas near cities, rivers, protected areas, waterbodies, and other features?

End of work package

Work package 7: Analyzing roads Steps 37- 38

You have now considered nearly all of the criteria except for roads. Next, you will consider the impact of roads on your tea cultivation sites.

Recall that earlier you created a buffer layer representing all land within 5 kilometers of roads. Symbolize your roads buffer as Unique Value on the field KENROAD_ID. This field’s values of 1 and 2 came from the original road values, with 1 representing major roads and 2 representing minor roads. Since you want to consider major roads only, use Select By Attribute to select the buffers KENROAD_ID = 1.

37) Symbolize roads buffer

Notice that the roads buffers are actually composed of many different segments, which are remnants from the buffering of individual road segments. These segments will interfere with your final measurement of the tea cultivation sites, so you will need to dissolve the interior boundaries in the buffers.

37.1) How many buffers are now selected?

Work package 7: Analyzing roads Steps 37- 38

To remove these segments, run Dissolve on the roads_5km_buffer layer. Accept the default name of roads_5km_buffer_Dissolve, and be sure that you uncheck Create multipart features as shown next.

38) Using dissolve

Removing internal polygons from the buffered roads data.

Notice that Dissolve, like most other geoprocessing commands, operated only on the selected set, which were your major roads.

End of work package

Work package 8: Creating the final study sites layer Steps 39- 44

You are now ready to intersect major roads with the layer that represents the other nine criteria. Unlike for some of the other features, you want to consider areas near roads, rather than areas far from roads.

39) Using intersect

Using intersect to identify areas near roads.

Use the Data Management > Analysis Tools > Overlay > Intersect tool to intersect your dissolved roads buffers with the layer that meets the other nine criteria (elev_rain_ag_urb_wet_riv_town_prot_water_ok). Use these two inputs with an appropriately named output, such as final_sites, as shown next.

39.1) Describe the final_sites layer.


40) Calculating the minimum

total area

Separating individual polygons from multipart features.

You were just about to create a report and final map when you receive an e-mail from the tea development agency, indicating that they want one more factor to be considered. Tea plants can only be planted every 1.5 meters to avoid overcrowding and for maximum sunlight and production of tea leaves.

40.1) If 100,000 plants are considered a minimum below which it is not economically feasible to grow tea, and if tea can only be planted at a density of one plant every 1.5 square meters, what is the minimum total area that should be considered? Show your work.

41) Identifying polygons

Now you need to determine (based on your calculation in the previous step) which polygons are large enough to be considered.

42) Using separate command

Because of the way the polygons are stored, you need to separate the multipart polygons into single polygons. Search for the Separate command and run it to create a new layer called final_sites_single, as shown next.

41.1) Examine your final_sites layer. Can you determine which individual polygons meet the size criterion? If not, what is the preventing you from doing this?


43) Selecting land parcels

Use Select By Attribute on the final_sites_single layer to select the land parcels that meet the size criterion.

43.1) How many land parcels are large enough to meet your final consideration for tea cultivation?

44) Examine largest polygon

Zoom to the largest polygon under consideration.

44.1) What town is on the south end of this polygon? 44.2) Select what you believe would be the three best land parcels for your tea cultivation project. For each parcel, provide the following information: location within Kenya elevation closest major town and distance to that town distance to Nairobi

End of work package

Work package 9: Presenting and assessing your analysis Steps 45- 57

45) Create layout Create a final map showing the data for your report to KTDA. All of the information you wish to include in the final plot will be in a layout view. Access the layout view by selecting the button on the lower left of the data frame. Alternatively, you could use the View menu and select Layout View. You may also wish to use Customize > Toolbars > Layout to display the layout tools. Using the Insert menu, add:

a scale bar in kilometers a title your name the date a text box explaining how you determined the tea cultivation sites a photograph of tea cultivation any other information you think is pertinent

46) Copy layout Create a digital copy of your layout.

47) Save Save your map document.


48) Assess your results

Considering the data you have analysed in this exercise, answer the following:

48.1) Assess the ease of use of working with the WRI data in terms of accessing, downloading, and using the data within a GIS environment. 48.2) Assess the quality of the WRI data in terms of scale, spatial accuracy, attribute completeness, and metadata. 48.3) Would you use WRI data again in the future? Why or why not? 48.4) What other data layers on the WRI website might you have wanted to use to deepen your analysis of selecting suitable tea cultivation sites?

Read about growing and processing tea on the site you were examining earlier: http://www.ktdateas.com. Select “The Tea” and examine the article “From Bush to Cup (Tea Life Cycle).”

48.5) What other data layers that were not on the WRI website might you have wanted to use to deepen your analysis of selecting suitable tea cultivation sites? 48.6) What organizations might produce the data that you need? 48.7) What factors have you considered that would also be important in other regions of the world? Are there any factors that would not be important considerations elsewhere in the world but are important in Kenya? 48.8) Summarize in a few sentences what you have learned about the spatial pattern of suitable tea cultivation sites in this exercise.

http://www.ktdateas.com/


48) Assess your results (contd.) 48.9) How did GIS help you in your analysis of tea cultivation and Kenya’s

cultural and physical geography? 48.10) Create a ten-minute presentation for the KTDA that summarizes your results and your recommendations on the potential tea cultivation areas in the country.

End of work package

1) Name two methods and two tools for accessing data that have appeared in the past one to two years. 2) What implication does the trend of incorporating GIS data into services have for you, the GIS data user? 3) Which new trends impact the fee vs. free debate in the provision of spatial data by organizations? 4) Why is choice on data themes, formats, access points, and scales both a challenge and a benefit of using GIS in the twenty-first century? 5) Which are the three most important things that you believe you learned from reading this book and working through the exercises? 6) Which of the challenge areas posed by the International Group on Earth Observations do you consider to be the most important? How do you think GIS and spatial analysis will be able to help tackle these challenges? 7) From which organization did you use data from for your study of tea cultivation sites in Kenya? What is your assessment of the quality and variety of data from that organization?

Chapter 10 quiz

8) What steps did you have to perform on the data for the Kenya tea analysis before you were able to use it? Compare the number of steps involved with the steps you went through for other exercises and with other organizations’ data. Was the number of steps involved in processing in this exercise acceptable to you as a GIS data user? Why or why not? 9) Did the scale at which the data were collected affect your tea analysis? Why or why not? 10) Name one thing about the Kenya tea study that frustrated you, one thing that surprised you, one thing that you were confused about, and one thing that you were pleased with in the course of your analysis. For each item, provide a reason why you selected that item and describe what you might do about that issue in the future.

Chapter 10 quiz (contd.)

Exercise Ten - Selecting locations for tea cultivation

Education

Transcript of Exercise Ten - Selecting locations for tea cultivation