GIS AUTOMATION TECHNIQUES

Context of the problem:

In this exercise you will use various tools you have learned in lectures to create agro-climatic zones

of Tanzania. The major variables that determine crop growth are temperature and soil moisture.

Plants can germinate even if there is no soil if there is a right temperature and water. Other variables

such as soils are important but not a necessary condition for plant growth.

Due to that context you have been given the Tanzanian Minimum, Maximum annual temperature

and Potential Evapotranspiration in Tanzania (vector files layers, Min_Isotherm.shp ,

Max_Isotherm.shp and Evapotr.shp) and the Tanzanian annual rainfall Map (a vector layer

Isohyets.shp that shows the distribution of annual temperature). You have also been given the

Tanzanian map boundary map in a vector format to show the area of study.

Solution of the problem

A. Creation of a map that shows distribution of moisture in Tanzania

Moisture distribution can be calculated as a ratio between Annual Rainfall and Potential

Evapotranspiration. Potential evapotranspiration is amount of water removed in the soil through

evaporation after rainfall and is measured in mm as rainfall. Before we calculate this ratio that

shows the distribution of moisture we need to convert the vector files in to raster images.

Exercise 1: Conversation of isolines into to raster images that shows the continuous information

of Temperature, rainfall, and Evapotranspiration, we will employ the batch processing with Topo

to Raster interpolation tool.

STEPS:

I. Four vector layers supplied (Min_Isotherm.shp, Max_Isotherm.shp Evapotranp.shp and

Isohyets.shp), was added.

Figure 1 : Four vector files layors (Min_Isotherm.shp, Max_Isotherm.shp,

Evapotrant.shp, and Isoheyts.shp)

II. By opening the attribute table of each layer identification of the field that contains data can

be done (the temperature fields are given in the Fahrenheit units and the rainfall and

evapotranspiration in mm). The data value fields will be used to create the raster layers)

III. To convert vector to raster, Spatial Analyst tool>> Interpolation>> then Right Click Top

to Raster and Select Batch. Batch widow was opened and add Min_Isotherm.shp,

Max_Isotherm.shp, Evapotranp.shp and Isohyets.shp

Figure 2: Batch processing window

IV. Validating the data button before Clicking OK to run the batch processing. Check Values

also generates output dataset names. The Check Values button validates the entire batch

grid's content. Check Values also generates output dataset names.

V. After the batch is completed running four files will be produced and displayed as below:

ann_rain 1

min_temp

max_temp

evs_temp

Figure 3: Raster display of four output layers ann_rain for isohytes Min_Temp for

Min_Isotherm.shp output, Max_temp for Max_Isotherm output and Evp_Trans for

Evapotransp.shp output

Figure4: batch processed display

Exercise 2: Creating the moisture distribution map as ration of Rainfall and

Evapotranspiration.

This operation was done by simple Math and Divide Tool when the Rainfall is divided by

evapotranspiration, the process that is known as image rationing. But also can be run using the

image calculator. We will use the image calculator although the equation is not complicated.

STEPS:

I. Open Arc Map >> Click a toolbox >> spatial analyst >> Map Algebra >> Raster calculator,

Raster Calculator was opened:

II. Following formula was entered “Ann_Rain” / “Evp_Trans” implying annual rainfall

divide by potential evapotrasipitation, the output file Moist_Av i.e., moisture availability

was assigned.

III. Click OK. Raster Calculator run and the Moisture availability raster file (Moist_Av)

produced. As per below display

Figure 5: Moisture Availability

Exercise 3: Production of Agro -climate zone of Tanzania

Agro-climatic zone is an important concept for agricultural planning. The concept is used to

identify regions suitable for particular crop production. Agro climatic zones for any particular

place can be determined by the combination of moisture availability and temperature zones.

Challenge 1: Produce the average annual temperature using a step by step procedure, show the

procedure and produce the result.

STEPS:

I. Image calculator in the Arc Map was opened and entering the following

formula((((Min_Temp + Max_Temp)/2)-32)*5)/9. This is a complicated formula that

calculates average annual temperature in 0C from the Mean annual Minimum Temperature

and Maximum Temperature in Fahrenheit.

II. Formula was entered by clicking the Files and the operators in the image calculator.

III. Output Five was called Av_An_T

Figure 6: Average Annual temperature in 0C

There in the previous steps we have produced the average annual temperature Av_An_T and the

moisture availability Moist_Av which need to be zoned and combined to produce agro climate

zones:

Exercise 3.1 Creating a moisture availability zones and Average annual temperature zone

First the temperature zone model was created. The model classifies the average Annual

temperature Av_An_T into zones using Table 2 provided and then clip the temperature zone to

Tanzania Country boundary

Steps:

I. In ArMap, Click on Modeler builder window was opened

II. Drag and drop Av_An_T on Model builder Window >> Select the file >> Click Add in the

Add data Window, the oval shaped circle in the Model Builder Window labeled Av_An_T

appeared

III. Then reclassify tool was found by click on the tool box (Spatial Analyst Tools >> Reclass

>> Reclassify) and drag and drop the tool on the model builder window

IV. The path was changed by Double Click on the Output Raster and change name from the

default folder to working folder and call the output raster file Temp_Zones.

V. The oval cycle Av_An_T was connected to Reclassify box, by clicking on the connect

tool and click on the Av_An_T oval cycle and move the magic wand that appears to

Reclassify box, and then select input raster

VI. The reclassify tool was opened by Double Click on the Reclassify box >> Change the

values in the Reclassification window to use the values in Table 2>> Reverse the New

Values. Such action will reverse the values such that low temperature zone are assigned

low value code and high temperature high value codes.

VII. Then raster was clipped by Open Tool box again >> Data Management >> Raster >> Raster

Possessing >> Clip, Drag and drop the Clip Tool on the Model Builder Window

VIII. The Output Temp_Zones was connected from Reclassify to Clip tools using a connect

tool. The tool changed the color to show that it has input

IX. Double click the Clip Tool to open the input window, Add Tz_Boundary to Output extent

(optional), Check the box, Use the Input Feature For Clipping Geometry (Optional), Write

Tz_Temp_Zones for Output raster

X. Click Ok to return to the model builder Window.

XI. Click on Auto Layout, The four blue and green boxes icon on the model builder window

toolbar. After inputting of all information your mode will look as follows:

Figure 6: Mode Builder Window toolbar for tz_temp_zone

Then click run on model to results the map display as seen in attached below:

Figure 7: Tanzania temperature Zones Map

By repeating the previous steps on exercise 3.1 above the below model was created and run to

produce Tanzania moisture availability map

Figure 8: Mode Builder Window toolbar for tz_moist_zone

Figure 9: Running the model completed

Figure 10: Tanzania moist zones Map

Exercise 3.1.1: Saving the Model.

Model you can be saved and reused. The Model is always saved in the toolbox. To serve the model

there are series of steps adding toolbox and save the model in the toolbox created. The model was

saved as moist zone and can be rerun to other analysis. It displayed as per below snapshot:

Figure 11: Saved Model

Figure 12: Moisture Zone availability Model

Exercise 3.2 Creation of Agro climatic Zone from Moisture availability zone and Temperature

Zone.

Steps:

1. By using batch processing of model builder both Tz_Temp_Zones and Tz_Moist_Zones

were converted to vector (Use the Tool Raster to Polygon)

Figure 13: Temperature polygon map produced

Figure 14: Moisture polygon map produced

2. Union Tool from Geoprocessing was used to combine the 2 vectors produced in Step 1

above and named Tz_Ag_Z. (The Single vector layer with a tables containing the

Temperature and Vector Values was produced and displayed as per below snapshot)

Figure 15: TZ_AG_Z map (Union of two polygon)

3. By studying the GRIDCODE and GRIDCODE_1 Fields, these are the values of the

Temperature zones and Field Zones, if the Value of GRIDCODE is 0 and the Values of

GRIDCODE_1 is any other value and vice versa it means those rows have no combination

of temperature zones and moisture availability zones. Therefore, they do not produce

Agro-climate Zone therefore we need to delate them and remain with row that have

GRIDCODE and GRIDECODE_1values other than 0.

Figure 16: Temperature zones and Field Zones

Challenge 3: Write a SQL statement to select All values with GRIDCODE value 0 and

GRIDECODE_1values 0

1. Start editor was started and select the GRIDCODE and GRIDECODE_1 with values 0 and

delete them ( by use of the SQL Tool, Query by attribute)

2. After deleting rows with GRIDCODE value 0 and GRIDECODE_1values 0 stop editing

and serve edits. The below tables were produced:

Figure 17: SQL Tool, Query by attribute

Figure 18: After deleting rows with GRIDCODE value 0 and GRIDECODE_1values 0 (Agro

climatic Zones of Tanzania (FID)

3. FID in the map was visualized to produce The Tanzanian Agro Climate Zone Map. (It is

not possible to symbolize FID in ArcGIS. Therefore New Field was crated with the FID

Values)

4. This was done by Add Field and Call it TzAgClZ

5. Then Populate TzAgClZ Field with FID result by right clinking of the Field and Select

Field Calculator>>formula [FID] *1 was entered in the field calculator window, then OK

(The FID Valued duplicated in the TzAgClZ Field.)

6. Then Tz_Ag_Z was visualized using the Unique Value, and TzAgClZ Field was chosen

as the input values. (A map of Tanzania Agro climate Map displayed as seen in attached

below snapshot)

7. The Map was labeled with the Values of both GRIDCODE and GRIDECODE_1 to see the

moisture and Temperature combination of each zone.

Figure 19: The map showing moisture and Temperature combination of each zone

Challenge 4: Create a Model that produce Agro climate Zones using all steps in this practical.

Figure 20: Overall Agro climate Zones