Raster Modeling

Goal and Objective: The goal of this exercise was to become more familiar with the geoprocessing tools that are more associated with raster modeling. Such tools include reclassify, raster calculator, and viewshed. With these tools I was able to create a suitability model using influences that include landcover, slope, water-table depth, distance to railroads, and the geology of the county of Trempealeau County, WI. I was also able to create a risk model using the factors of streams, prime farmland, residential/populated areas, schools, and parks.

Methods: Creating these maps I used model building to help myself see the process of where I was getting my information and where the new data was going. The ranking for my models were 3 to1. 3 being the highest suitability to 1 being the lowest. The scale for my risk model is 1 being the highest risk and 3 being the lowest risk.

- Geologic Criteria



Figure 1: Map showing suitability based on Geology



 

This may be the most important factor for a sand mining company because they need to find ground where the grain size will make their mining work. The high suitability areas in this map contain Jordan and Wonecon Formations. I decided to set all the other formations as low suitability.

- Landcover Criteria

Figure 2: Map showing suitability of Landcover

The amount of vegetation in a certain area can effect whether or not a company will want to take the time to clear it for mining. The areas with a rank of 1 are developed and shrub areas. Areas with forest vegetation are set at 2. The most suitable areas, ranked at 3, are barren lands, pastures, and herbaceous lands.

- Railroads Criteria

Figure 3: Map showing distance to rail terminal

 This map shows the range in distances it is to the nearest rail terminal. Sand mining companies will want to pick an area where it will be closest to the terminal so that they will use less money for transportation. Using the Euclidean distance tool I was able to find ranges where 3 is the most suitable and 1 is the least suitable.


- Slope Criteria

Figure 4: Map showing slope suitability

It is much easier to mine on a slope that is more flat than uphill. Using the slope tool and a DEM a.o.i I created this model. Slope with less than 10% is ranked 3. 10% to 25% is ranked 2. Anything beyond 25% is 1.

- Water-Table Depth Criteria

Figure 5: Map showing water-table depth suitability

This data had to be downloaded from the Wisconsin Geological Survey website. With this data I had to convert it into a raster so that it could be used further in creating my suitability model. It is important for sand mine companies to know what the water-table depth is because the more water in the area the more pumping they have to do. The highest suitability here is 3 while the lowest is 1.

- Suitability Model

Figure 6: Map of Suitability in Trempealeau County, WI

After creating all the suitability models I added them all together using the raster calculator so that I could come up with this model. The areas with the highest suitability are ranked 3 and the areas that would have the lowest for sand mine companies are 1.

Figure 7: Model Builder used to make suitability maps

- Stream Risk Model

Figure 8: Distances created to show how close the risk is to streams

It is important for mining companies to know how close they are to streams because they can effect the environmental health of the stream. The Euclidean distance tool helped me rank the distances where the risk will be high or low. Areas with a risk of 1 are high, 2 are medium, and 3 is low.

- Prime Farmland Risk Model

Figure 9: Map showing prime farmland at risk

Since farming is one of the state's economical advantages a company would not want to damage any useful farmland in the area. The highest ranking is 1 while the lowest is 3.

- Population Risk Model

Figure 10: Areas where population would be most effected.

 

It is important for sand mines not to set up their mines close to neighborhoods and populated towns. Drilling can cause contaminated water and there is much noise with transportation. The ranking of 1 is where there is a good amount of population. The lower rankings are areas that are scarcely populated.

- Schools Risk Model

Figure 11: Areas that show the risk for schools

You could say the importance for keeping away mines away from populated areas is the same for schools/school districts. To get the schools I created a layer feature from the sites feature class and added it to my geodatabase. I then used the Euclidean distance tool to buffer and create this model. The areas ranked 1 are where the schools are. The majority of the area around the schools have a medium risk.

- Parks Risk Model

Figure 12: Risk for parks in the county

 For a factor of my own I chose parks that are within Trempealeau County. I took the Euclidean distance tool to make a buffer and then reclassified the rankings to create this model. The ranking of 1 has the parks in it. The lowest rank is 3.

- Risk Model

Figure 13: Risk Model for Trempealeau County, WI 

After combining all of the risk models together I came up with this final risk model. The areas with red to yellow have a lower risk and areas with green to yellow have a higher risk. the low risk areas would be better for the sand mines.

Figure 14: Model Builder used to create the risk models

Results and Discussion:

- Overlay Model



Figure 15: The final map showing a final suitability model 

After using the raster calculator to add the final suitability and risk models I came up with this final model. The areas with high suitability are more green and areas with low suitability are more red. There is a good amount of space where a company could choose their next mine. It is interesting to see how many factors can go into creating an area that is going to do its best to make the sand mine companies happy and the residents living in the county happy.


Conclusion: Creating models using ArcMap geoprocessing tools gives someone an opportunity to answer questions that effect the surrounding communities. These models could help the people who live in Trempealeau County understand more about what factors go in choosing a location for a sand mine. This model helps get information across that shows potential areas in which sand mines could pop up.



Sources:

http://wgnhs.uwex.edu/maps-data/gis-data/