Sand Mining Suitability and Impact, Raster Analysis

- Introduction -

The final exercise in this semester long analysis of frac sand mining in western Wisconsin is to make a suitability and impact model for a subset of Trempealeau County and create a final location index based off these models. Multiple raster layers will be used including geology, land cover, and a digital elevation model (DEM). Reclassifying Euclidean distance away from certain vector features (e.g. streams, wildlife areas, residential areas) allows for further raster interpretation in carrying out this analysis. Raster calculator is used to quickly analysis multiple layers of data, creating index values that relate meaning information on both mining suitability and the inherent environmental/community impact of sand mining activity. The suitability and impact models will create raster data that will then be used to create a final mine location index showing the areas were suitability outweighs impact and vice versa.

- Methods -

 Suitability Model

Five variables were taken into consideration for the suitability model:
1) Geology
2) Land cover
3) Proximity to rail terminals
4) Slope
5) Water table depth

Each variable needed to be in raster format and given an index value ranging between 1-3. For suitability, 1 indicates low suitability and 3 indicates high suitability. Each raster created was given a scale of 30 meters. Table 1 gives each layer, its attributes, ranks, and reasons for the ranks for both the suitability and impact models. Model 1 shows the workflow for the suitability model.

Model 1: Workflow of the suitability model

Image 1: Geology bedrock vector polygons on the left. The
raster created from the vector data in the middle. The final
 reclassified and clipped raster used in analysis on the right.

The bedrock geology data was obtained as vector polygons and converted into a raster preserving the geologic names. This geology raster was then reclassified with a value of 3 given to the most desirable geologic formations and a value of 1 given to the rest.

Using a National Land Cover Dataset, the land covers with the least overburden removal were given a ranking of 3 and those with more were given rankings of 2 and 1. Some land covers were excluded from the suitability analysis including urban, agriculture, wetland, and water because mining would most likely not happen in these areas or the land cover is just unsuitable. The excluded land covers were given a value of 0 in a separate raster to remove the areas from the final product.

A reclassification of Euclidean distance away from rail terminals gave a raster of suitability dependent on how far away a location would be from a sand unloading point for trucking. The ranks were decided based on an equal interval classification that covered the entire study area with areas closer to the rail terminal having a higher rank of suitability (less distance for trucks to drive).

Slope was calculated from a DEM of the area, making sure the x, y, and z coordinates all had the same units. The slope raster was then averaged with a 3x3 window to remove the salt-and-pepper effect throughout the image. The ranks were chosen based on the assumption that mines would prefer to locate in areas with low slope rather than steep slope.

Water table contours were used to create a raster of water table depth using the 'topo to raster' tool. Several sand mining processes involve the use of water and ranks were assigned on an equal interval basis with lower water table depth receiving a higher rank.

With the five variables converted to raster data and ranked by suitability, they were added together with 'raster calculator' and then multiplied by the land cover exclusion raster to create a final suitability raster. This suitability raster shows areas of varying suitability throughout the study area as well as areas of exclusion based on the land use/land cover distinction made earlier. The values of the final suitability raster after calculation ranged from 5 to 14 and were reclassified to 1 - 10, with 0 being excluded land.

Impact Model

Five variables were taken into consideration for the impact model:
1) Prime Farmland
2) Proximity to streams
3) Proximity to populated areas
4) Proximity to schools
5) Proximity to wildlife areas

Each variable needed to be in raster format and given an index value ranging between 1-3. For environmental and community impact, 1 indicates low impact and 3 indicates high impact. Each raster created was given a scale of 30 meters. Table 1 gives each layer, its attributes, ranks, and reasons for the ranks for both the suitability and impact models. Model 2 shows the workflow for the impact model.

Model 2: Workflow of the impact model

Prime farmland was obtained as vector polygons and converted to a raster. The ranks were determined by an attribute field loosely indicating 'prime', 'not prime', or 'prime if ...'. Using this attribute, prime farmland was given a ranking of 3, prime if drained was given a ranking of 2, and not prime or prime if drained and other additional steps were given a ranking of 1.

Image X: Euclidean distance raster on the left and the
reclassification on the right, with schools data shown.

For each proximity variable, a distance of 2,100 feet or about 640 meters was used for each rank as this distance was designated a noise/dust shed for this analysis. Sand mines will often create noise and dust while in operation and these factors are often a source of complaint with local residents. Areas that fell within 2,100ft were given a ranking of 3 for likely direct noise or dust impact, areas that fell outside of 4,200ft were given a ranking of 1 for unlikely direct noise or dust impact, and areas in-between were given a ranking of 2.

The streams data available for this analysis covered the entire study area and therefore only streams with the four largest stream orders were chosen as they are the largest and potentially the most meaningful. Residential, commercial, and institutional zoning data was used to designated populated areas.

With each variable converted into raster format, 'raster calculator' was used to add the layers together and create a final impact raster. This final impact raster shows areas were the environmental and/or community impact is high or low. The values of the final impact raster ranged from 5 to 13 and were reclassified to 1 - 9.

Table 1: The layers, ranks, and reasoning behind the suitability
and impact models.

Mine Location Index

A mine location raster was then created by subtracting the impact raster from the suitability raster with 'raster calculator.' The values of this mine location index range from -6 to 9. Higher positive values indicate areas were the overall suitability of sand mining outweighs the potential impact. Higher negative values indicated areas were the overall potential impact outweighs the suitability of the land for sand mining.

Model 3: The workflow of combining the results of each model
and creating the final mine location index

- Results -

Model 4: The overall workflow of the entire process

Map 1: The suitability, impact, and mine location index rasters

- Discussion -

Ideally, to have the least environmental and community impact, new sand mining operations would locate in areas of positive value in the mine location index. As stated earlier, positive values indicate areas were the suitability of the land is greater than the impact. When interpreting the index, green colors are the best, yellows are OK, and orange/reds are the worst. Based on the variables chosen, it seems most of the best area (green) is located in the northwestern portion of the study area. The areas in orange and red located in the south and west of the study area are usually around urban, residential, or commercial areas. Any mine that begins operation in an area of orange/yellow, orange/red, or red is running the risk of negatively impacting their immediate surroundings which could cause distress to local residents and unwanted negative attention for the mine itself.

Most of the desirable area for a sand mining operation seems to fall in less populated areas which makes sense given the variables chosen for the impact analysis. Even if suitability is high and impact is low (index is high, in the green) this does not mean that no negative impact will come from the mining activity. It really means that less people are likely to witness the impacts. Three of the five variables for impact directly dealt with humans. Although, distance to wildlife areas and streams were taken into consideration so the model does account for some environmental impact regardless of human conciseness.

- Conclusion -

 To analysis sand mining in western Wisconsin, both mining suitability and environmental/community impact was explored. Suitability involved using geology, land cover, rail terminals, slope, and water table depth data to locate the best areas for a sand mining operation. Impact involved using streams, farmland, residential/commercial zoning, school locations, and wildlife area data to estimate potential impacts a sand mining operation could have on its surroundings. Combining these analyses together created a final mine location index which shows which areas should be used as mining locations based on maximum mining efficiency and minimal environmental and community impact.

- Sources -

The Office of the Assistant Secretary for Research and Technology/Bureau of Transportation Statistics (BTS) National Transportation Atlas Databases (NTAD) 2014. Rail terminals feature class. Accessed via web: http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/subject_areas/geographic_information_services/index.html

Trempealeau County Land Records. Geodatabase. Accessed via web: http://www.tremplocounty.com/landrecords/

United States Geological Survey, National Map Viewer. National Land Cover Database (NLCD) raster and DEM. Accessed via web: http://nationalmap.gov/viewer.html

Wisconsin Geological and Natural History Survey (WGNHS). Water table contours. Accessed via web: http://wgnhs.uwex.edu/maps-data/gis-data/

Wisconsin Geological and Natural History Survey (WGNHS). B.A. Brown, 1988. Bedrock Geology of Wisconsin, West-Central Sheet, WGNHS Map 104. Digitizing by Beatriz Viseu Linhares, University of Wisconsin Eau Claire (UWEC). Accessed through UWEC Geography and Anthropology Departmental resources.

Network Analysis of Sand Transportation

- Introduction -

Mined silica sand will most likely need to travel by rail to its next destination. Some mines have direct or quick access to a railway, but others will undoubtedly need to transport the sand by truck to the nearest rail terminal. These trucks will be heavy and will continuously be driven back and forth on whichever road networks are available. If each county is responsible for maintaining their roads, what will be the cost of upkeep? In this analysis, a hypothetical value of 2.2 cents is used as the cost per mile that a truck drives and it was assumed that each mine will send one truck on 50 trips a year. Network analysis was performed on the results of python script #2 and a feature class of rail terminals to determine a route for each mine to the closest terminal. Model builder in ArcMap 10.2 was used to create the route and calculate a total length of trucking per county per year (Model 1).

- Methods -

Model 1: Workflow to determine the cost in dollars
per year per county from sand trucking

A street network dataset from ESRI street map USA was used as the active network dataset for the network analysis. A new 'closest facility' layer was created with mines as incidents and terminals as facilities. In model builder, mine and terminal feature classes were added with the 'add locations' tool. The 'solve' tool was then used to route from each mine to the closest terminal and create the 'closest facility' layer. The 'select data' and 'copy features' tools were then used to select just the routes from the 'closest facility' layer and make them a new feature class. This line feature class of routes was then projected and intersected with a polygon feature class of counties. The result of the intersect was then summarized by county giving the total length of routes, and therefore, an estimation of total distance of trucking, per county. The cost of maintaining the road networks was calculated by dividing the length field by 5280 (to convert to miles), multiplying by 100 (for the number of trips, to and from) and by 2.2 (cost per mile in cents) and finally dividing by 100 (to give a hypothetical dollar amount) in a new field. The equation reads: cost = (length*trips*cost_per_mile)/(528000). A map were then made to illustrate the impact of increased trucking per county.



- Results -

Table 1: The route length and cost per year for each of the affected counties.

 

Map 1: Map illustrating the cost per year per county to maintain road networks of
likely sand trucking activity, based on hypothetical data.
 

 - Discussion -

The total amount of money each county would  have to spend was lower than expected. The county with the most trucking and highest cost was Chippewa County (~$455) followed by Wood County (~$327). The least amount of trucking and lowest cost was Burnett County (~$1.50). Six of the twenty-four counties had costs below $10 dollars a year. The counties with low cost was often due to rail terminals being located just within their border resulting in only a small portion of different truck routes coming from other counties to actually be located in that specific county. Overall, the numbers may be underestimated because of the hypothetical cost of 2.2 cents a mile and the assumption that there would only be fifty truck trips per mine to unload sand at a rail terminal per year. Map 1 clearly illustrates that counties with more mines located a distance away from rail terminals tend to experience more trucking, which only makes sense. Chippewa and Wood Counties have more individual mines in need of trucking than all but one county, and therefore, incur the largest costs per year to maintain their road networks. Trempealeau County is a slight exception to this rule because it has the largest amount of mines and does not  have the highest cost, though it does have the 7th. This is due to the fact that the mines in Trempealeau County are all located in a cluster near a rail terminal making the routes short compared to other counties.

- Conclusions -

The two important factors in determining the impacts of increased trucking on road networks per county in western Wisconsin are the number of mines that need trucking and the distance these mines are located away from the closest rail terminal. Performing network analysis by creating a 'closest facility' layer in ArcMap allowed for the length of possible routes and costs associated with maintaining the road networks to be calculated. These calculated costs are hypothetical. Another important consideration in network analysis is having a quality network dataset. The network data set used in this analysis was produced by ESRI and allowed for accurate routing. Without a high quality network dataset, routing errors are introduced which limits the effectiveness of network analysis.


- Sources -

Environmental Systems Research Institute (ESRI). Street map USA, street network dataset.

Wisconsin Department of Natural Resources (WNDR). Mine data.

Wisconsin Department of Transportation (WisDOT). Rail terminal data.

Data Normalization, Geocoding, and Error Assessment

- Introduction -

A table of sand mines in Western Wisconsin was provided by the Wisconsin DNR and each student was given the task of geocoding 15 to 20 mines. Each mine was geocoded by multiple students to showcase the user error involved in the geocoding process. The table proved by the WI DNR has not completely normalized and for some mines contained only Public Land Survey System (PLSS) addresses. This complicated the process of geocoding by introducing error. The process and errors associated with geocoding will be discussed in this post.

- Methods -

My specific mines were located in the table provided by the WI DNR and copied into a separate excel spreadsheet to eliminate any formatting that may cause errors later in ArcMap. The address field in the table was divided into its constitute parts, or normalized, so the address could be understood by the World Geocode Service address locator provided by ArcGIS Online. The normalized table was used to geocode the mine addresses in ArcMap, with the appropriate fields used as Address Input Fields. Once the address were geocoded, the match addresses and spatial locations were inspected. The PLSS addresses could not be automatically geocoded and manual placement was needed by using a PLSS feature class and satellite imagery base map. The 'Pick Address from Map' option was used for many of the address to locate the closest address to the mine entrance from a road.

Once the mines were geocoded and inspected, each student imported a shapefile of their geocoded mines into a common geodatabase. All the shapefiles were merged and each mine that corresponded to the mines I geocoded were queried out. The distance between my mines and the same mines geocoded by my classmates was calculated with the 'point distance' tool.

- Results -

Table 1: Sample of the normalization performed on the mine addresses.
The red color in the normalized table indicate PLSS address.

Table 2: The error in distance (Miles) between the same mines
geocoded by my classmates and I.

Map 1: A map showcasing the geocoding result.

- Discussion -

Of the 22 mines I was assigned, every one automatically geocoded based on the address given in the normalized table. However, only 10 were actually correct. PLSS addresses do not follow a format that the address locator could understand and were therefore matched based on town or county. These mines needed to be found manually by querying a PLSS polygon feature class of WI and then analyzing a satellite imagery base map for the closest address location. All address that were not PLSS contained the street address information in one field of the table. Some address locators can parse this information but it is always better to be thorough and normalize the address field into its constituent parts. Parts of an address that need separate fields include house number, direction prefix, street name, street type, sub-address, city, state, and zip (seen in Table 1). If this information is in the same field an address locator could become confused and not match or erroneously match the addresses. Some addresses were incomplete or spelled wrong, examples of attribute data input error.

When geocoding the mines, I either accepted the automatic match or picked an address from the map that corresponded best with the given information and interpretation of a satellite imagery base map. Some mines geocoded by my classmates were not given a specific address and the mine was located in the middle of a city or county (like the PLSS addresses). This happens with the address was not available or understandable by the address locator and the match was based on what the address locator could find (city, county, or state). One address was even located in Missouri. Each student was given the same table to normalize and geocode yet when comparing my mines to my classmates, only 8 out of 55 addresses were the same (Table 2, 0.01inch represents the same address). This demonstrates the amount of subjectivity involved in geocoding unstandardized data.

- Conclusion -

The process of geocoding can be easy or difficult depending on the quality of the data used. When data automation and compilation is unstandardized, differences and errors in attribute entry can arise. This introduces error into the geocoding process as well as a level of subjectivity on the analyst's part, potentially introducing more error. Normalization and thorough assessments of data entry quality is vital to geocode addresses, especially for data that is unstandardized.

- Sources -

Wisconsin Department of Natural Resources (DNR). Mine table.

ESRI geodatabase (2013), USA Census Data. Accessed through UWEC Department of Geography and Anthropology.

Data Gathering and Quality Assessment

- Introduction -

To analysis quartz sand mining in Wisconsin, six datasets will be downloaded from websites, geospatial gateways and online map viewers. This data includes: transportation (railways), landcover, a digital elevation model (DEM), cropland, a soil survey, and a Trempealeau county geodatabase containing various land record information for the county. Each dataset originates from a different source leading to a difference in projections, data accuracy, and metadata documentation. In this post, an overview of the data collection process is presented along with a quality assessment of the associated data.

- Methods -

Obtaining the data:

• Land records for Trempealeau County were downloaded from the Trempealeau County Land Records Department's website as a geodatabase. This geodatabase was renamed TMP.gdb and will be used as the primary location for all data gathered for this analysis. As such, each of the following datasets will need to be projected in the same projection used in the TMP.gdb and clipped to the Trempealeau County boundary.

• An updated railroad polyline shapefile was downloaded from the United States Department of Transportation Bureau of Transportation Statistics website.

• A landcover raster and a 1/3 arc Second DEM in ArcGrid format was downloaded from the National Map Viewer.

• A cropland raster was downloaded from the USDA geospatial data gateway.

• SSURGO data was downloaded from the USDA NRCS Web Soil Survey application as a polygon shape file.

Each shapefile was imported into the TMP.gdb and given the proper projection and clipped by the Trempealeau County boundary feature class. Each raster was projected, clipped(extract by mask), and imported into the TMP.gdb via a python script. The exact python script and discussion of python use can be viewed in my Python Page by clicking here or the Python tab at the top of the page.

- Results -


Map 1: Comparison of the data downloaded for the sand mining analysis

- Discussion -

Data accuracy assessment:

To assess the accuracy of the data, eight different measures of accuracy were collected from the metadata files associated with each data set. These attributes were recorded in Table 1 and include: source, scale, effective resolution, minimum mapping unit (MMU), planimetric coordinate accuracy, lineage, temporal accuracy, and attribute accuracy.


Table 1: Accuracy comparison of the data downloaded and imported into the TMP.gdb


The data originator was designated the source. Data sourced from accredited organizations is preferred because a certain level of quality is implied (though it is still important to check). For lineage, only the sources were recorded for sack of table space, though the processes also need to be considered. For temporal accuracy, either the range of data collection or the last update was recorded. For attribute accuracy, only a broad statement found in the metadata was recorded, not the entirety of the text. For some data sets, certain measures of accuracy were missing such as effective resolution, scale, planimetric coordinate accuracy, and minimum mapping unit. In these instances Table 2, Table 3, and Table 4 were consulted in an attempt to infer some of the values. In some cases the values could not be found or inferred and the value of N/A was recorded.



Table 2: Relationship between scale, MMU, and effective resolution for raster data. (Nagi)
http://blogs.esri.com/esri/arcgis/2010/12/12/on-map-scale-and-raster-resolution/

When attempting to infer values, the definitions of scale, effective resolution, MMU, and planimetric coordinate accuracy and their relationships needed to be understood. Scale is the relationship between map distance and real world distance. The scales recorded in Table 1 are representative fractions. Effective resolution is commonly denoted as the square root of the area of one pixel of raster data (30x30m pixel size equals an area of 900m^2 making the effective resolution 30m). MMU is the size of the smallest depictable feature for raster data and the size that features will retain their geometry for vector data(e.g. polygon to line or point, or whether polygons will merge or remain separate). The MMU of raster data is commonly 4 pixels. Planimetric coordinate accuracy refers to the root-mean-square (RMS) error corresponding to how well features are positioned and represented in relation to the real world.

Table 3: Relationship between planimetric accuracy(ft) and scale. (ASPRS)
http://www.asprs.org/a/society/committees/standards/1990_jul_1068-1070.pdf



Table 4: Relationship between planimetric accuracy(m) and scale. (ASPRS)
http://www.asprs.org/a/society/committees/standards/1990_jul_1068-1070.pdf


- Sources -

American Society of Photogrammetry and Remote Sensing (ASPRS). ASPRS Accuracy Standards for Large-scale Maps (1990). Accessed via web: http://www.asprs.org/a/society/committees/standards/1990_jul_1068-1070.pdf

Nagi, Rajinder. On Map scale and raster resolution (2010).  ESRI blog. Accessed via web: http://blogs.esri.com/esri/arcgis/2010/12/12/on-map-scale-and-raster-resolution/

Trempealeau County Land Records. Geodatabase. Accessed via web: http://www.tremplocounty.com/landrecords/

United States Department of Agriculture National Resource Conservation Service. Cropland raster. Accessed via web: http://datagateway.nrcs.usda.gov/

United States Department of Agriculture National Resource Conservation Service. Soil survey shapefile. Accessed via web: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

United States Geological Survey, National Map Viewer. Landcover raster and DEM. Accessed via web: http://nationalmap.gov/viewer.html

United States Department of Transportation. Railway shapefile. Accessed via web: http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html





Hydraulic Fracturing, Quartz Sand and the Dairy State

- An overview of sand mining in the state of Wisconsin and hydraulic fracturing -

Figure 1: Illustration of hydraulic fracturing
http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Also know as hydrofracking or fracing, hydraulic fracturing is a technique used for resource extraction from with rock formations. To obtain the desired resource, a well is drilled into the earth, explosives are detonated, then water, sand, and chemicals under high pressure are pumped down the well to expand and maintain the cracks formed by the explosives which allows for the resource to be removed (Figure 1).  This technique has been used for over 60 years but recently, with technological improvements in horizontal drilling, allowing for new areas of economical extraction, and a desire for new fuel deposits, an interest in hydraulic fracturing for oil and natural gas has increased dramatically. With an increase in hydraulic fracturing comes an increase in demand for sand, but not every kind of sand is appropriate for fracing purposes.



Figure 2: Wisconsin quartz sand
http://wcwrpc.org/frac-sand-factsheet.pdf

Sand for hydraulic fracturing goes by different names: quartz sand, silica sand, or frac sand. Regardless of the name, the sand is the same; it needs to be very well rounded, almost entirely quartz, and have uniform grain size (Figure 1). The largest deposits of this type of sand are found in Cambrian sandstone formations in Western Wisconsin (Figure 2, 3).

Figure 3: A snipping of a USGS Geologic map of the USA.
Areas in red are Cambrian quartz sandstone, where quartz sand is mined
http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf
 

Figure 4: Cambrian sandstone and mining site locations in Wisconsin
http://wcwrpc.org/frac-sand-factsheet.pdf

As seen in Figure 4, mining of quartz sand is primarily located in western Wisconsin ranging from Barron County to Monroe County. Special emphasis will be given to Trempealeau County in subsequent analyses and blog posts. Typically, a quartz sand mining operation will engage in seven stages: overburden removal, excavation, blasting, crushing, processing, transportation and reclamation.  Overburden is the undesired soil on top of the sought after sandstone formations. These soils and materials are removed with scrapers or excavators and dumped around the edges of the mining site creating berms that act as a noise, light, and visual barrier to the possible irritants of the mining operation for local residents. Excavation and blasting at times go hand-in-hand. During these stages the sand is removed either by straight excavation or by blasting heavily cemented sandstone. If blasting occurs, noise, vibrations, and airborne dust can become sources of contempt for local residents. After blasting, the chucks of sandstone are hauled by trucks to be crushed, either on site or elsewhere, until they become nothing more than grains. The small grains of sand are then processed in four steps: washing, drying, sorting, and storing. The is done to ensure the sand is pure and uniform in size. Once the sand is sorted it can be transported to hydraulic fracturing sites, mainly shale gas formations, around the country (Figure 5). Now that the sand has been mined, the reclamation process can be started, if it has not already. Each county has different rules to determine how the land should be treated to be considered reclaimed, though the general policy is to return the land to a proper condition for land use such as farming or residential building.

Figure 5: Areas in red depict shale gas formations
http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Throughout the mining process, fossil fuels are burned in most, if not all, stages. Groundwater is used for washing and dust control. Noise is generated through explosive detonations and use of heavy machinery. Hauling of sand and materials via trucks cause degradation of infrastructure. The removal of tons of sand will permanently alter natural landscapes causing potential ecological issues. Some of these issues have been addressed by use of  Wisconsin Department of Natural Resources (WDNR) permits and regulations, electrical equipment and generators when applicable, and pollution modeling. Though measures have been taken to reduce negative impacts of an increase in mining operations, the results have yet to be seen.

The issues related to quartz sand mining in Wisconsin are spatial in nature and GIS will be used to analyze and interpret them. As the semester continues, additional blog posts will discuss these issues in more detail.

- Sources -

Wisconsin Department of Natural Resources. (2012). Silica Sand Mining in Wisconsin. Accessed via web: http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

Wisconsin Geological and Natural History Survey. (2012). Frac Sand in Wisconsin. Accessed via web: http://wcwrpc.org/frac-sand-factsheet.pdf

The Blog and the Blogger

This is an undergraduate student blog for Geography 337, GIS II, taken at the University of Wisconsin Eau Claire. Posts will adhere to a technical report format detailing the goals, methods, and results of specific exercises focusing on sand mining in Wisconsin for hydraulic fracturing.

My name is Lee Fox and I will be completing my bachelor's degree in geography this semester. I have made class blogs for Remote Sensing of the Environment (introductory), GIS I, and Geospatial Field Methods that will no longer be updated. Along with this blog, I will also be updating another blog this semester for Advanced Remote Sensing. Over the last couple of years, I have learned to love geography for its simplicity and complexity. Each class has built on my ability to think spatially and through the medium of blogging I hope to convey what I have learned and am currently learning.