Trempealeau County Land Data

INTRODUCTION

Following the first blog post on sand mining in West-Central Wisconsin, land data was obtained from multiple sources for Trempealeau County, Wisconsin. This exercise shows the ability to collect, manage, and edit data to compile into useful maps for further analysis--specifically land suitable both economically and environmentally for sand mining in Trempealeau County. The goals of this exercise are outlined below:

1. Download and collect data from multiple sources
2. Import and manipulate data for viewing
3. Write python script to project, clip, and aggregate data into a single geodatabase
4. Compile cartographically pleasing maps
5. Write a report on the process, accuracy, and data sources

The next section will describe the process of collection, manipulation, and editing base data into a single geodatabase, followed by the resulting maps and data shown.

METHODOLOGY

Data Collection and Manipulation

Base data used and where it was derived from is listed below:

• Bureau of Transportation statistics from the NTAD
• NLCD from the USGS
• DEM from the USGS
• NRCS soil survey from the USDA
• Land records from the Trempealeau County website

All data was freely obtained by accessing each deptartment website and directly downloading from the websites or through email request. The rasters and geodatabase were then reorganized by unzipping directly to a personal working folder. Data from the NTAD and NRCS was then manipulated before python functions were performed. 

NTAD data for updated rail lines were clipped to Trempealeau county and moved into the Trempealeau geodatabase obtained from the county land records.

The NRCS soil survey is SSURGO data which required conversion from text files to a shapefile using Microsoft Access. The new shapefile along with an existing table with the original fields were then imported into the Trempealeau geodatabase. This shapefile was then put into a 1:1 relationship class with the table and joined based on this relationship. 

Python Script

Python script was then made to reproject the NLCD, DEM, and NASS rasters to a single projection and crop around Trempealeau County. For a full view of script refer to the Python Script post under Exercise 5.

RESULTS

After the python script created reprojected the rasters, a comprehensive view of Trempealeau County was then made using ArcMap shown by Figure 1. The NLCD and NASS unique classification maps were overlain by a somewhat transparent DEM to give an idea of elevation along with land cover for environmental impacts of sand mining. The separate DEM is also shown in the bottom-right corner to give a full understanding. Both NLCD and NASS include rail and road systems for the economic conditions of sand mine site location.

Figure 1: NLCD and NASS data with elevation and separate DEM and reference maps

Although data can be collected from various sources for free, it should be done so with caution. Metadata should be examined closely for individual satellite imagery and especially when working with multiple rasters and shapefiles. Credibility of work produced will be based on accuracy and precision of base imagery collected; this includes both accuracy (how close represented features match their respective features in real life or true values) and precision (to what degree or decimal place at which data was collected).
Accuracy can be measured by positional and categorical subgroups. Scale, effective resolution, the minimum mapping unit, and planimetric coordinate accuracy all contribute to positional accuracy whereas temporal accuracy measures when the imagery was taken. Attribute accuracy contributes to categorical accuracy and is how closely the description or labels match the represented feature in the real world.

Figure 2: Geospatial accuracy for all data gathered in Trempealeau County

As Figure 2 shows, much of the metadata used to assess data quality and credibility is missing for the web soil and transportation statistics so using this data would not be wise for reference material or large projects.
Attribute accuracy would preferably need to be obtained by fieldwork. However,  ancillary data with higher accuracy can be used as reference to edit and correct inaccuracies. However, this is time consuming and often times the ancillary data should just be used instead of the image with inaccuracies.
Finally, the temporal inaccuracies present a major problem in data especially land use data where rotating crops, land use, and even elevation change with the natural process of the Earth and human-environmental interactions.

RESOURCES

"BTS | National Transportation Atlas Database." BTS | National Transportation Atlas Database. Accessed March 14, 2017. https://apps.bts.gov/publications/national_transportation_atlas_database/.

"Land Records." Land Records. Accessed March 14, 2017. http://www.tremplocounty.com/tchome/landrecords/.

NRCS. "Web Soil Survey - Home." Web Soil Survey - Home. Accessed March 14, 2017. http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.

USGS TNM 2.0 Viewer. Accessed March 14, 2017. https://viewer.nationalmap.gov/viewer/.

"USDA:NRCS:Geospatial Data Gateway:Home." USDA:NRCS:Geospatial Data Gateway:Home. Accessed March 14, 2017. https://datagateway.nrcs.usda.gov/."USGS TNM 2.0 Viewer."