Goal and Objectives
The goals of this lab are to gather data from various sources online for Trempealeau county, to analyze the accuracy of this data, to create a loop using python to clip, project, and then place them in the Trempealeau geodatabase, and to create a series of maps with the data. There will be an emphasis put on the importance of metadata.
Methods
Download the Data
First, the raillines shapefile was downloaded from the US Department of Transpotation's website linked here. Next, the USGS National Map Viewer linked here was used to download the national landcover raster of Wisconsin along with the two DEM tiles which encompass Trempealeau county. Next, the UASDA Geospatial Data Gateway website, linked here, was used to download the crop cover raster. Then, the Trempealeau county website, linked here, was used to download the Trempealeau county geodatabase. Lastly, the soil data was downloaded from the Web Soil's Survey website, linked here.
Import the SSURGO Data
Next, some of the files from the soils data were saved as a table format from a very old personal geodatabase and needed to imported by using Microsoft Access. A macro was used to import the data from these tables into the geodatabase, by setting the correct output location. After the tables imported, the soils shapefile was imported separately using the import feature in ArcCatalog. A relationship class was then created between the soils feature class and the output tables in the Trempealeau geodatabase. This was then used to create a join between the tables and soil feature class.
Use Python to Clip, Project, and Extract Rasters
After all the data was downloaded, a python script was created which can be found in the Python Scripts post. This script clipped all of the rasters (DEM, Landcover, and Cropcover) to Trempealeau county, projected the rasters to the same coordinate system as the Trempealeau geodatabase, and placed the rasters in the Trempealeau geodatabase.
Data Accuracy
The table shown below in figure 3.0 shows the meta data collected from the data sets above. The meta collected very tediously by looking through multiple .txt files and the data providers websites. Some of the meta data couldn't be located in these .txt or websites and are appropriately marked as N/A below. This meta data refers to the level of accuracy which the data was collected at.
Meta Data →
Data Set
↓
Scale
Effective Resolution
Minimum Mapping Unit
Planimetric Coordinate Accuracy
Lineage
Temporal Accuracy
Attribute Accuracy
Soils
1 : 12,000
6 m
6 m
N/A
Web Soils Survey
2015
Tested against a master set of valid
attributes
Landcover
1: 60,000
30 m
30m
N/A
Used two-date pairs of landsate scenes
from 2006 and 2011
2011
85%- 90%
Crop cover
1: 100,000
30 m
30 m
N/A
USDA/NRCS
2006
N/A
DEM
1 : 22,000
11 m
11 m
N/A
USGS
2013
N/A
Trempealeau
Geodatabase
N/A
.01 cm
N/A
N/A
Trempealeau County
2007
N/A
Department of Transportation
1:24,000
to
1:100,000
12 m
12 m
N/A
Rederal Railroad Administration
2014
N/A
Fig 3.0: Select
Meta Data for Downloaded Data Sets
Results
A series of maps shown below in figure 3.1 was created using the data downloaded to show some of the features in Trempealeau county. These include the elevation, crop cover, railroads, and landcover. Looking at the crop cover map, there appear to be many corn fields near the streams and rivers. The majority of the crop cover not near streams and rivers is deciduous forest. Looking at the elevation and rail roads map, the rail roads align very nicely with the low elevation. This is also near the main streams in the county. In the landcover map, there are three types of landcover which stand out the most: deciduous forest, cultivated crops, and hay/pasture. It is important to note that the deciduous trees class in both the crop cover map and the landcover map align almost perfectly.
Fig 3.1: Series of Maps Clipped to Trempealeau County
Conclusion
In conclusion, the data downloaded in this lab was put to good use through using python, and by creating a series of maps. Metadata is an important part of data, without it, the data would not be credible. The meta data can be used for reference so the data being mapped can be accurately placed on the map. Learning how to download data off of the internet is a useful skill to have. In the workplace, there will be no data given like there often is in school. More than likely, the data will have to be downloaded from online, just like in this lab, and then be manipulated to the desired output. One thing that is concerning about the metadata, was how difficult it is to find. For these data sets, there were .txt files, but often they didn't contain the meta data needed to fill in the chart. If meta data were to be organized better, then it would become easier to find therefore helping the user assess the quality of the data.
Sources
Geospatial Data Gateway, NASS
Tremealeau County, Geodatabase
Multi Resolution Land Characteristics, Landcover
Web Soils Survey, soils
USGS, DEM
Import the SSURGO Data
Next, some of the files from the soils data were saved as a table format from a very old personal geodatabase and needed to imported by using Microsoft Access. A macro was used to import the data from these tables into the geodatabase, by setting the correct output location. After the tables imported, the soils shapefile was imported separately using the import feature in ArcCatalog. A relationship class was then created between the soils feature class and the output tables in the Trempealeau geodatabase. This was then used to create a join between the tables and soil feature class.
Use Python to Clip, Project, and Extract Rasters
After all the data was downloaded, a python script was created which can be found in the Python Scripts post. This script clipped all of the rasters (DEM, Landcover, and Cropcover) to Trempealeau county, projected the rasters to the same coordinate system as the Trempealeau geodatabase, and placed the rasters in the Trempealeau geodatabase.
Data Accuracy
The table shown below in figure 3.0 shows the meta data collected from the data sets above. The meta collected very tediously by looking through multiple .txt files and the data providers websites. Some of the meta data couldn't be located in these .txt or websites and are appropriately marked as N/A below. This meta data refers to the level of accuracy which the data was collected at.
|
Meta Data →
Data Set
↓
|
Scale
|
Effective Resolution
|
Minimum Mapping Unit
|
Planimetric Coordinate Accuracy
|
Lineage
|
Temporal Accuracy
|
Attribute Accuracy
|
|
Soils
|
1 : 12,000
|
6 m
|
6 m
|
N/A
|
Web Soils Survey
|
2015
|
Tested against a master set of valid
attributes
|
|
Landcover
|
1: 60,000
|
30 m
|
30m
|
N/A
|
Used two-date pairs of landsate scenes
from 2006 and 2011
|
2011
|
85%- 90%
|
|
Crop cover
|
1: 100,000
|
30 m
|
30 m
|
N/A
|
USDA/NRCS
|
2006
|
N/A
|
|
DEM
|
1 : 22,000
|
11 m
|
11 m
|
N/A
|
USGS
|
2013
|
N/A
|
|
Trempealeau
Geodatabase
|
N/A
|
.01 cm
|
N/A
|
N/A
|
Trempealeau County
|
2007
|
N/A
|
|
Department of Transportation
|
1:24,000
to
1:100,000
|
12 m
|
12 m
|
N/A
|
Rederal Railroad Administration
|
2014
|
N/A
|
Fig 3.0: Select
Meta Data for Downloaded Data Sets
Results
A series of maps shown below in figure 3.1 was created using the data downloaded to show some of the features in Trempealeau county. These include the elevation, crop cover, railroads, and landcover. Looking at the crop cover map, there appear to be many corn fields near the streams and rivers. The majority of the crop cover not near streams and rivers is deciduous forest. Looking at the elevation and rail roads map, the rail roads align very nicely with the low elevation. This is also near the main streams in the county. In the landcover map, there are three types of landcover which stand out the most: deciduous forest, cultivated crops, and hay/pasture. It is important to note that the deciduous trees class in both the crop cover map and the landcover map align almost perfectly.
|
| Fig 3.1: Series of Maps Clipped to Trempealeau County |
Conclusion
In conclusion, the data downloaded in this lab was put to good use through using python, and by creating a series of maps. Metadata is an important part of data, without it, the data would not be credible. The meta data can be used for reference so the data being mapped can be accurately placed on the map. Learning how to download data off of the internet is a useful skill to have. In the workplace, there will be no data given like there often is in school. More than likely, the data will have to be downloaded from online, just like in this lab, and then be manipulated to the desired output. One thing that is concerning about the metadata, was how difficult it is to find. For these data sets, there were .txt files, but often they didn't contain the meta data needed to fill in the chart. If meta data were to be organized better, then it would become easier to find therefore helping the user assess the quality of the data.
Sources
Geospatial Data Gateway, NASS
Tremealeau County, Geodatabase
Multi Resolution Land Characteristics, Landcover
Web Soils Survey, soils
USGS, DEM
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