Located in the heart of the Upper Midwest, Fort McCoy is the only U.S. Army installation in Wisconsin. The installation has provided support and facilities for the field and classroom training of more than 100,000 military personnel from all services each year since 1984. The Fort McCoy complex is situated on 60,000 acres, 46,000 of which are contiguous live-fire and maneuver areas. Fort McCoy provides reserve- and active-component forces with the networked, integrated, interoperable training resources required to support the Army’s training strategies using a full spectrum of facilities, ranges, and training areas. From 1990 to the present day, new construction projects have served to modernize the post’s infrastructure, facilities, and training areas (The Real McCoy, 2019).
In order to minimize maintenance costs and ensure the long-term utility of military training lands, it is necessary to inventory and classify the lands relative to their environmental condition and their ability to sustain various kinds and intensities of military training in the future (Warren, Diersing, Thompson, and Goran, 1989).
Spatial information has always been important to military commanders; an understanding of terrain, for example, is an essential military skill. GIS has a key
role to play in creating, editing, analyzing, querying, and displaying geographical data in order to help the commander understand the influence of terrain on the conduct of the battle (Swann, 1999). Geographic Information Systems (GIS) play a pivotal role in military operations. As the operational picture and battlefield develop, everything is essentially spatial in nature.
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The Department of Defense (DoD) is responsible for administering more than 25 million acres of federally owned land in the United States making it the fifth largest federal land managing agency. Often the factors affecting land condition are of different scales and their values are of different magnitudes (Mendoza, Anderson, and Gertner, 2002).
Different missions and focuses play an integral part in the asset and resource planning related to any such land usage and mission sets, from training considerations, environmental considerations, and organizational considerations. As stated by Swann (1999), asset and resource management have always been a problem.
Due to the nature and intensity of the activities occurring on many military training areas, management of those lands can be a complex problem. The scale of the problem is enormous. The number of buildings, the length of roads, complexity of infrastructure, and area of land involved are similar to that of a large local government user. With assets and resources for action and usage being often dispersed nationally and internationally, effective management is problematic (Swann, 1999).
In this study, the Fort McCoy military installation was analyzed using imagery spanning 18 years to identify land use change in the following classes: (a) complexes, (b) blacktop, (c) agricultural/green space, and (d) forested lands. The maximum likelihood classification tool and imagery interpretation was used to explore imagery to evaluate land use changes amongst the 2004 and 2018 years to explore changes in land use. Time periods selected were based on availability of finer resolution imagery, time periods of global military needs, and resource constraints.
Methods
Study Area
Fort McCoy is a United States Army installation on 60,000 acres between Sparta and Tomah, Wisconsin, in Monroe County (Figure 1).
Fort McCoy is located in Monroe County between the cities of Sparta and Tomah and roughly 30 miles east of La Crosse in west-central Wisconsin. The installation is divided by State Highway 21. Since its creation in 1909, the post has been used primarily as a military training center (Figure 2).
From 1990 to the present day, new construction projects have served to modernize the post’s infrastructure, facilities, and training areas. The installation has provided support and facilities for the training of more than 100,000 personnel annually since 1984. Today, the post provides full-scale support to its customers at each juncture of its training triad — transient, institutional, and exercise (The Real McCoy, 2019).
Most of Fort McCoy’s 1,000 buildings with 5 million square feet of area are within the triangular shaped Cantonment Area that covers approximately 2,600 acres. The Cantonment Area is surrounded by approximately 114,000 acres of maneuver area, 7,600 acres of impact area, 1,400 acres of ranges, and 640 acres in airfield of land owned by the government.
Work Flow
The analysis entailed a variety of processes from initiation to completion. According to Bangerte (2017), logical data flows focus on what happens in a particular information flow and what general processes occurred.
General work flow processes involved in analysis included data collection, data preparation, classification system establishment, classification tool identification, training sample creation, raster output analysis, raster to GRID conversions, class acreage change analysis, results, and discussion as to explanations for land cover changes. ArcMap was used for classifications, conversions, and overall analysis of land cover changes.
Data Collection
Imagery Acquisition
Imagery for this study came from a variety of sources and was evaluated for image resolution and suitability for image classification. The Fort McCoy Public Affairs and Geographic Information Systems/Integrated Training Area Management office were contacted in attempts to obtain installation GIS and imagery/vector data. All installation procured data is treated as FOUO (For Official Use Only) and thus unavailable for general public distribution without the proper declassification. Consequently, ample public imagery sources were available and subsequently used for input data for the study.
Publicly available imagery included aerial imagery from the National Agriculture Imagery Program (NAIP) within the United States Department of Agriculture (USDA), U.S Geological Survey (USGS) EarthExplorer, and the Monroe County Land Information Office (GIS Office).
Imagery downloaded came in digital ortho quarter tiles (DOQQs) or compressed county mosaics (CCM). Imagery came in either .tiff or .sid files depending on the downloading source.
Data Preparation
Satellite imagery gathered of the Fort McCoy (Figure 3) area included years 2004 and 2018.
Preparing, exploring, and later grouping the images into one image (mosaic) was an essential step to start the study in order to successfully run classification tools.
Imagery had to be mosaiced for the study area before classification tools could be run successfully (Figures 4 and 5). A mosaic is a combination or merge of two or more images. Mosaicking the imagery made the process streamlined only using one image as the raster input and made a variety of clustered images into a standardized image set for each year, creating an easier method of visualizing the study area as one image instead of multiple images.
Input imagery data sources were mosaiced together using ESRI’s ArcGIS raster dataset tools. The Mosaic tool merges multiple existing raster datasets into a single existing raster dataset.
Classification System
Military land often serve a variety of additional uses such as: timber production, agriculture, livestock grazing, off-road vehicle recreation, and hunting. In light of the potential cumulative effects of larger-scale and more intense military training, coupled with other uses, the military community has become increasingly aware of the need to maintain or improve the condition of its lands (Warren and Bagley, 1992).
The Land Condition Trend Analysis (LCTA) program is the Army’s standard for land inventory and monitoring, employing standardized methods of natural resources, data collection, analyses, and reporting designed to meet multiple goals and objectives within owned lands (Anderson, Guertin, and Price, 1996).
Standard LCTA methodology was used in combination with the Anderson classification system (Anderson, Hardy, Roach, and Witmer, 1976) to create the following classes (a) Complexes, (b) Blacktop, (c) Agricultural/Green Space, and (d) Forested Lands (Table 1).
Maximum Likelihood Classification
Maximum likelihood classifier (MLC) is the most widely adopted parametric classification algorithm (Manandhar, 2009). An MLC was chosen due to errors in object classification within unsupervised classification.
Rozenstein and Karnieli (2011) state training maximum likelihood classifiers are more properly applied to an area for which one is more familiar with, and as such, creating classifiers with knowledge of the area provided rationale in utilizing MLC for this study.
| Class | Description |
| Forested Lands | Areas characterized by tree cover. Includes areas with deciduous, evergreen, and/or mixed forest types. |
| Complexes | A group of similar buildings or facilities i.e barracks, training facilities, administrative buildings, etc. |
| Agricultural/ Green Space | Areas characterized by vegetation managed for production of food, feed, or fiber. Includes pasture, hay, row crops, small grains, fallow, open fields and recreational land. |
| Blacktop | Areas characterized by a constructed material including low/high density land surfaces such as roads and parking lots that repel rainwater and do not permit it to soak into the ground (i.e roads, parking lots, highways, gravel lots). |
Training Samples
Maximum image classifications require training classifiers to assign pixels or objects to a given class using training samples. Representative training samples
for all land cover types (classes) identified in the image had to be attained before conducting land change assessments.
Histogram analysis was used for training sample grouping (Figure 6 and Figure 7). Due to the histograms following a normal distribution, having similar peaks, and overlaps between samples created, they were merged into their respective identified classes and used to create separate signature files for each year of imagery. Training samples that had close peaks and overlap between identified samples were merged and saved as a signature file and used as input for the maximum likelihood tool for classification output.
Analysis
Maximum likelihood classifications were conducted for each respective mosaic image of the study area. Classification area was defined by creating a polygon around the area of interest defined by what is called the Cantonment Area (Figure 8).
A polygon was drawn encompassing the study area of the Cantonment Area for land cover analysis characterized by zones of development within the general triangular base structure. The polygon was used as the processing extent dimensions for the classification tool in raster outputs, but does not encompass the maneuver, impact, ranges, and airfield areas.
A maximum likelihood classification was conducted on the raster bands and produced a classified raster output clipped to the study area polygon encompassing approximately 1,987 acres. (Figure 9).
While the classification process utilized the study area polygon, the output extend for a raster is defined by the rectangular extent of the polygon as shown in Figure 9. The final analyzed area contains land fully operated by the Fort. The size of the maximum likelihood classification results is approximately 4% of all the total land contained within the Fort McCoy property.
Landcover area changes were evaluated by determining changes in area between both images of 2004 and 2018 after classifications were calculated.
Calculating Areas
Maximum likelihood outputs were converted to integer grid formats to be able to determine area changes in square meters for each land class (Figure 10).
Changes in land area were evaluated for 2004 and 2018. Using the count field in the value attribute table for each raster dataset, area totals in square meters were calculated for each land use category and subsequently converted into acres as land parcels typically use acres for defining parcel size and ownership.
Calculating class conversions between the years was conducted through converting raster data to features. Each raster was converted using the Raster to Polygon tool. Amount of change between each land cover classification was calculated by using the Union tool and Dissolve tool. The Union tool was run to create overlapping features from both polygon feature datasets and the Dissolve tool was run to merge like attributes using the class attribute for both years.
Results
Land Cover Change Analysis
The acreage for each land cover class was calculated for all four categories along with percentage of change from 2004 to 2018 in an area of approximately 1,987 acres (Table 2).
| Class | 2004 Acreage | 2018 Acreage | Percent Change |
| Forested Lands | 572.51 | 421.31 | -26.41% |
| Complexes | 353.91 | 282.75 | -20.10% |
| Agricultural/Green Space | 656.23 | 756.83 | 15.33% |
| Blacktop | 403.38 | 524.77 | 30.09% |
All classes saw change over the 14-year span. From 2004 to 2018, blacktop realized the greatest increase of 30%. Agricultural/green space lands increased by 15%, complexes saw a decrease of land by 20%, and forested lands saw a decrease of land by 26%. Differences in land cover classes are reflected in Appendix B and Appendix C.
In 2018, the majority of land cover from 2004 were forested lands losing about 39% to agricultural/green space; complexes losing 38% to blacktop, agricultural/green space losing 16% to blacktop, and blacktop losing about 17% respectively. Table 3 represents class changes between 2004 and 2018.
Accuracy Assessment
The classification process was straightforward; however, the result can be incorrect if training samples were not well chosen. New signature files were created for each year set but errors could have still taken place. Image resolution in earlier time periods and interpretation of the area, can be subjective and also could have added to the potential inaccuracy of the study.
Image resolution in between time periods differed with images used. The tiling format of the 2018 NAIP imagery was based on a 3.75′ x 3.75′ quarter quadrangle with a 300-pixel buffer on all four sides using 4 band colors and 32-bit pixels and a .6 x .6 cell size as compared to the 2004 NAIP imagery based on a 3.75′ x 3.75′ quarter quadrangle with a 360 meter buffer on all four sides using only 3 band colors with 24 bit pixels and 1 x 1 cell size.
The limited number of identified classes in this study did seem to cause some issues but not impact classification categories in repeated attempts to refine classes. Reject fraction for both classifications were set at 0.0 which means that every cell was classified into the assigned four categories.
Conclusions
Land cover changes are continually occurring due to a variety of reasons including land purchases, eminent domain, new zoning, and joint planning efforts the study area and the land surrounding it can be can be expected to follow the general flux of this trend as more land is acquisitioned, expanded upon, and developed as the trends seems to suggest.
Historically, large military installations represent high economic multiplier effects in the local economy. According to the Mississippi River Regional Planning Commission (2013), the estimated economic impact of Fort McCoy on the local economy in FY 2011 was $1.31 billion; in FY 2002, it was $357.8 million, which is 27.0% of the 2011 number (Appendix E). Appendix E from the Mississippi River Regional Planning Commission (2013) shows the economic impact from 2001-2010 that Fort McCoy has on the local communities.
According to Department of Defense Instruction 3030.03, it is DoD policy to work toward achieving compatibility between military installations and neighboring civilian communities by a joint compatible land use planning and control process conducted by the local community in cooperation with the local military installation.
The expansion and significant land cover increases can be attributed to the continued growth of the local community, the symbiotic relationship between Fort McCoy and local communities, and the ever-changing economic and global environment.
References
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About the Author
Christian Rodriguez is currently a graduate student at the Missouri University of Science and Technology. This research article/technical paper is a continuation of a land cover change project he worked on for his graduate program in Geological Engineering. It involved remote sensing, land cover change analysis, imagery classifications, and the implementation of raster analysis tools.
