Geographic Information Systems Coursework: May 2016

Modeling Sand Mining Suitability and Sand Mining Impact in Trempealeau County, Wisconsin

Using spatial analyst tools and a specific set of criteria, southern Trempealeau County, Wisconsin was analyzed for suitable locations for future sand mines. The same area was analyzed for potential impact on the community and environment.

Data Sets and Data Sources

Sand Mining Suitability

Criteria Considered:

Geology - Jordan and Wonewoc
Land use and land cover
Distance to rail terminals
Slope
Water table

There are many factors considered when selecting an optimal location for a sand mine. The desirable geologic units, Jordan and Wonewoc must be located near the surface. Some land covers are better than others; for example a mine should probably not go in the middle of a city, a shrub land would be better. Distance driven to deliver sand to rail terminal should be minimal. Slope of land should be minimal. The water table should be close to the surface for accessibility in washing sand.

All of these criteria were considered when developing a suitability model. Figure 1: Suitability Model is a flow model of all steps taken. Further specification of reclassification and other tools is described in Table 1.

Figure 1: Suitability Model

Variable	Reclassification	Ranking	Explanation
Suitable Geology	Two Classes	Ej and Ew = 3 Everything else =0	The Jordan Formation (Ej) and Wonewoc Formation (Ew) are the only places to mine, so they received the highest value (3). The other formations received a 0 because there wouldn’t be a sand min where there isn’t sand to mine.
Suitable Land Cover	Four Classes	11, 12 (Water) = 0 23, 24 (Human Infested) = 0 90, 95 (Wetlands) = 0 21, 22 (Less Human Infested) = 1 81, 82 (Agriculture) = 2 41 – 43 ( Forrest) = 2 31 (Barren) = 3 51-52 (Shrub) = 3 71-74 (Herbaceous) = 3	It is impossible to mine in open water, so all water land covers were assigned zeros. More open tracts of urban land were assigned one. Areas relatively expensive to clear and beginning mining were assigned a 2. Land covers without much land to clear were assigned a 3 because they are the cheapest to clear.
Suitable Rail	Jenks Breaks, 3 Categories	Smallest Distance = 3 Medium Distance = 2 Longest Distance = 1
Suitable Slope	Jenks Breaks, 3 Categories	Smallest Slope = 3 Medium Slope = 2 Largest Slope = 1
Water Depth	Jenks Breaks, 3 Categories	Shallowest Depth = 3 Medium Depth = 2 Greatest Depth = 1

Figure 2: Table

The Trempealeau County Geology layer was projected appropriately and turned into a raster using the Polygon to Raster tool. It was then reclassified to select the desirable geologic units, the Jordan and Wonewoc.

NLCDD2011 is the National Land Cover Data set from 2011. This raster was reclassified to select and rank the most desirable land covers for sand mining, and exclude the least desirable, like urban cities (or human infested).

Terminals_WIMN is a point feature class of rail terminals in Wisconsin and Minnesota. Euclidean Distance was used to generate a distance raster. Distances were reclassified and ranked reflecting minimal distance as desirable.

DEM is a digital elevation model for Trempealeau county. After it was projected appropriately (a projection measured meters), the slope tool was used to generate a raster describing slope. Block Statistics was used to aggregate data for simplified processing purposes. Slopes were reclassified and ranked to reflect low slope as desirable.

tr_wtline0arc are water table contour lines from the Wisconsin Geological Survey. They were converted to raster format, and reclassified to reflect shallow depth as desirable.

Raster Calculator was used to multiply all five rasters together. Recall that all these rasters rank the most desirable characteristic highest, and therefore the highest outputs are the most suitable areas for placing a new sand mine.

This data was reclassified for simplicity, and is mapped in Figure 2: Suitability for Sand Mining. Red areas are least suitable for a sand mine, while green is the most suitable.

Figure 2: Suitability for Sand Mining

Sand Mining Impact

Criteria Considered:

Proximity to streams
Prime farmland
Proximity to residential areas (noise and dust shed)
Proximity to schools (noise and dust shed)
Wildlife Areas

You'll recall from reading my GIS II Lab 1: Sand Mining Suitability Project that there are environmental and social impacts associated with sand mining. To minimize these impacts, we map where the impacts would be the greatest, and avoid them when selecting a location to mine. Proximity to major streams is considered to avoid pollution. Agriculture is one of the largest industries in Wisconsin, and definitely the heart of it. We'll want to leave prime farmland undisturbed. Proximity to residential areas and schools is considered to create a noise and dust shed and minimize noise and dust pollution in residential areas. Trempealeau County's Wildlife Areas will be preserved.

Figure 3: Impact Model depicts the flow of steps take to create an impact raster. Further specification of reclassification and other tools is described in Table 2. All reclassification was done so that areas with the greatest impact have the highest values.

Figure 3: Impact Model

One of my favorite things about Wisconsin is its abundance of natural water features, however that abundance means that every location will be close to a river. Therefore proximity was only measured on perennial rivers. The Euclidean Distance tool was used, and the resulting raster was reclassified.

Prime_Farmland was converted to a raster and reclassified.

To measure proximity to residential areas, the residential areas themselves must first be defined.

Raster Calculator was used to multiply all five rasters together. Since areas with the highest risk was reclassified with the highest values, cells with the highest values would be most greatly impacted by a new mine. Figure 4: Impact of Sand Mining displays the resulting raster. Minimal social and environmental risk is symbolized in green and very high risk areas are symbolized with red.

Figure 4: Impact of Sand Mining

Suitability Overlay

A true suitability model will consider both desirable features, like geologic unit, and risk areas, like prime farm land. A flow model for this process is displayed in Figure 5: Overlay Flow Model.

Figure 5: Overlay Flow Model

The two models (Suitability of Sand Mining and Impact of Sand Mining) were reclassified into five classes, so that the most desirable locations for mining had high values. These rasters were multiplied using Raster Calculator, and reclassified again into five categories. The resulting raster is displayed in Figure 6: Overall Suitability for Sand Mining. The most suitable areas are symbolized in green, and the least suitable areas in red.