5/16/17
Raster Modeling
Goals and Objectives:
As the final part of the ongoing sand mine suitability project the overall objective was to determine suitable areas for sand mining with the use of raster analysis. Due to the controversy and potential hazards surrounding sand mining (Sand Mining ), a risk assessment was also performed. Therefore, if a mine location was suitable the potential risk the mine posed on the surroundings was assessed.
The criteria and objectives for sand mine suitability was determined and is listed as below.
- Generate a spatial data layer to meet geologic criteria
- Generate a spatial data layer to meet land use land cover criteria
- Generate a spatial data layer to meet distance to railroads criteria
- Generate a spatial data layer to meet the slope criteria
- Generate a spatial data layer to meet the water-table depth criteria
- Combine the five criteria into a suitability index model
- Exclude the non-suitable land cover types
The correct geology is important because not all sand is of the quality required for mining. The Jordan and Wonewoc formations were selected as quality areas. Another important factor for determining suitability was the current land use. Urban and developed areas would represent areas that were highly unsuitable for sand mining. Rail proximity was also important. The closer the mine could be located to a rail sight the more cost effective the mine could be. Mines would also have to be located in areas that possessed a moderate slope. Areas with a high slope could potentially hinder mining operations. Finally, sand mines need to use great quantities of water for cleaning the sand. Therefore, mine locations were chosen in areas that have a very high water table.
The criteria and objectives for sand mine risk was determined and is listed below.
As previously stated, sand mines pose a potential risk to the surrounding area. Streams are an important natural resource and can potentially be polluted and impacted by sand mining. Proximity to streams was assessed by taking large to moderate streams and creating a risk index surrounding those streams. The further away from a stream the less risky. Farmland is another key natural resource that is important to Trempealeau. Areas of prime farmland were identified by determining areas that were highly erodible or not. Areas that were not highly erodible represented areas of prime farmland. These areas would receive a high risk factor. Due to noise and potential pollution, mines that could potentially be located next to urban areas would possess a high amount of risk. Therefore, urban areas were assigned as areas of high risk. Trempealeau County possesses several schools located in rural areas. This is why schools were included in the risk assessment. Mines that could potentially be near schools would present a high risk to that particular school. The final risk factor that was included in the assessment was the proximity to wilderness areas. Wilderness areas are an important and dwindling natural resource that are frequently shared by the public. Therefore, due to the potential impact that sand mining could have an wilderness areas, these areas were selected as areas of high risk. Finally, the suitability and risk models were to be combined to determine the best possible areas for sand mining.
Methods:
Model builder was used to generate the results to the correct specifications. Figure 1 below shows the model that was used to determine sand mine suitable areas.
Figure 1.
First, the ideal locations that possessed the correct ground water depth were determined. This was done by converting the geology features to a raster format. The reclassify tool was then used to classify areas with ground water close to the surface, moderate groundwater depth, and deep ground water depth.
Next, the Trempealeau County DEM was used to calculate areas with adequate slope. The slope and block statistics tools were used to determine the correct slope values. The reclassify tool was used to separate the slope values into highly, moderate, and low suitability areas.
After the slope was calculated, proximity to rail lines was determined. The rail terminals class was imputed into the euclidean distance tool. The distances were then reclassified into areas that were close, moderately close, and far away from rail terminals.
Suitable land was then calculated. This was done in two steps. First, areas that were completely unsuitable for sand mining such as urban areas were determined and would later be excluded. The other areas were then separated into three classes where the most highly suitable areas were baron and undeveloped land.
Finally, adequate geology was determined. Areas of the Jordan and Wonewoc formations were selected as highly suitable and the other areas were marked as unsuitable. The raster calculator tool was then used to add the five previous rasters together. This would make a map that showed areas that were suitable for sand mining.
Model builder was further used to calculate risk and merge the suitability and risk models (Figure 2).
Figure 2.
Proximity to streams was calculated by using the euclidean distance tool, then areas around the streams were reclassified into areas either close to, moderately, or far away from streams.
Prime farmland locations were determined by first changing the feature class into a raster by using the polygons to raster tool. In this instance areas that were high erodible were selected as areas poorest for farming. The reclassify tool was then used to separate areas into prime, moderate, and poor farming locations.
Next, urban risk was assessed by using the euclidean distance tool to calculate distance. Areas within 640 meters of urban areas were selected as high risk areas. This was included in the reclassify tool. As for assessing the risks on schools, the schools had to be converted into a raster format by using the polygons to raster tool. The same procedure was then used to calculate the risk on schools as the was the risk on urban areas where areas within 640 meters of schools were selected as high risk areas.
After risk to urban areas and schools were assessed, the potential sand mine risk to wildlife areas was calculated. The feature to raster tool was used to convert the data into the proper format. Then euclidean distance was implemented to determine land proximity to wilderness areas. When the areas were reclassified, locations within 1000 meters of a wilderness areas where selected as high risk.
Finally, raster calculator was used to add the five risk raster together to yield which areas were at the highest risk to sand mining. Both the risk and suitability models were reclassified and added together using the raster calculator. This final result showed which areas were best for sand mining.
Results and Discussion:
Below in Figure 3 are the five different suitability factors. As displayed below Trempealeau County contains a large amount of the Jordan and Wonewoc formations. A large portion of the county also possessed a very high water table where the proper sandstone are located. Overall, the county contains large areas of either suitable or highly suitable land. However, areas in the northern and eastern sections of the county are located far away from any rail terminals. This could potentially cause issues with removing the sand from the mine location.
Figure 3. Suitability models for sand mining in Trempealeau County.
Below in Figure 4 are the five different risk factors. Trempealeau County has many streams, this is evident by looking at the proximity to streams results. Large portions of the county are located near streams. On the other hand, many areas of the county are highly erodible. It should also be noted that there are many, although not large, portions of the county that are located near schools and wilderness areas. Overall, the largest risk sand mining imposes is on stream health.
Figure 4. Risk factors for sand mining in Trempealeau County.
Below are the suitability and risk models (Figure 5).
Figure 5. Suitability and risk models.
The final result was the combination of the suitability and risk models (Figure 6). Displayed in red are the best areas for sand mining. As seen below Trempealeau County contains many areas that possess the correct conditions for sand mining. Although the county contains many of the correct areas for mining, these areas of often located near some of the worst areas for mining. Overall, the best locations to put sand mines in Trempealeau County are in the west central portion of the county. The worst areas to put sand mines in Trempealeau County are located in the southern portion of the county.
Figure 6. Final sand mine model showing the best mine locations.
Conclusion:
Although there is much controversy surrounding sand mining in Wisconsin, it has been demonstrated that there are some low risk areas to mine. The final decision to sand mine will have to be made by the local populations, and proper assessment and planning will be critical. Therefore, if a mine were to open operations in an area it will pose the lowest amount of risk and controversy.
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