The methodology that follows is for determining land use change at the sub-county level. The strength of such an analysis is that land managers can focus on specific "hot spot" locations that appear to be changing with respect to land cover or land use based on countywide data from the Census of Agriculture or other indicator. Based on the analysis the effects on wildlife, humans, or recreation can be assessed.
Additionally, fine scale analysis can help determine where and to what extent local farmland or important habitat may be lost to rural residential development, the impacts on conservation or public access opportunities, or if change is taking place in the way the public or land managers perceive.
The process consists of four steps:
The study area may be chosen based on several criteria. These may include: the need to investigate an area of sensitive habitat (i.e. a specific river corridor), an area of a county undergoing rapid development (land on the forest edge along a national forest boundary), or an area of increased recreation or agricultural intensity. The size of the study area is limited by time and available resources but parts of counties are most appropriate given the number of photos that must be acquired and studied. At least two time periods are typically studied - for most areas a decade long interval will provide insight into the nature of land use change.
Aerial photos of the study area for the two time periods are required. These are available as Digital ortho quarter-quads (DOQQ) available for free for most areas via the Internet (for Montana: www.nris.mt.gov/), or "hard copy" photos ordered from USGS in Salt Lake City, UT. DOQQs are black and white aerial photos that are ready to use in ArcView or other digital mapping program. Hard copy photos will need to be scanned into a computer. Some years are either not available or quality is not as high as other years so some flexibility is required for the analysis.
DOQQs are combined into a mosaic of the study area and then georectified to be ready for use in ArcView. Rugged topography takes longer to georectify then flat topography. Georectification and building the mosaic of the landscape is done by professional technicians and involves registering landmarks on the photomosaic to know points on the landscape - major roads, railroad crossing, bridges, etc. Technicians attempt to georectify within 20 meters of specific locations on the DOQQs.
Polygons are created and classified on the digital photomosaic using the classification scheme developed by Harrison and Potter (2001) (see table below). Each section of photo is studied to determine the type of land use/cover for each time period and a polygon is drawn around the land use area. Each polygon is given a value based on the dominate land use. For example, in the figure below Land Use T1 consists of three polygons of two different land uses - a and b.
Houses or residential areas can be mapped as point features overlaying digitized photos. Home sites can be verified by driving around the study area. Barns and outbuildings are mapped and can be useful for determining present use of the land. They are typically separated from the housing database before further analysis. The final product at this stage is at least two photomosaics of the study area with a layer of polygons depicting land use/cover, a data layer of points depicting homes, and a final layer of other buildings. Additional data layers could consist of management jurisdictions, conservation easements, recreation access points, or specific species habitat depending on the area of interest.
Land use polygons for different years can be intersected and the resulting polygon coverage depicts the type of change, the area of change, and the location of change that has occurred. This information can be displayed visually and spatially.
In the above example where a = river, b = riparian c = agriculture, in time period 1 (T1), the river is through riparian vegetation. By T2, the river has changed course, but still has riparian vegetation and by T3 a portion of the riparian vegetation has become agricultural land. The change is represented by the hyphen letters, so a transitioned to b (a-b), b went to a (b-a), and finally b to c (b-c). So the actual amount of change is only the area of the hyphen letters. Relevant questions might be how much riparian is turning into agriculture land, how much does the river change, is the total acreage of riparian land remaining the same and simply shifting, are residential structures at risk given the migration of the river corridor.
As part of an effort to understand the impact of homes in an agricultural setting on land use and the environment in general, each home site point can be assumed a spatial signature equivalent to other land use/cover types; a sort of ecological footprint on the land. Emergent research has shown that the zone of influence is largely based on dispersal distances of animals that accompany humans (pets) and their impacts on native animal species (birds and mammals) (Hansen et al, 2002; Odell et al, 2003; Theobald et al, 1997). The considerations for how to assign an area to each house is dependent on the land use/cover type where the house is placed, what elements of the landscape or environment may be impacted, and dispersal rates of organisms that may cause an impact. For example, if a house is placed in agricultural land it is likely to be a source of weeds, which can impact crop production. Dispersal distances and rates vary tremendously with different weed species and mechanisms of dispersal (wind, water, animals, etc.) (Cousens and Mortimer, 1995).
The photos below are an example of the output. The example adopts a conservative zone of influence - 100 m diameter circle (7.76 acres) for the influence zone on home sites points. The location is in the upper Paradise Valley, Park County, MT. Habitat maps, conservation easements, or other data layers of interest to land managers could be overlaid on the photo output.
Aerial photos indicating 100 m radius zones of influence around house site points (left) and 100 m radius house site zones of influence colored to show intersection with different land use/cover types (right).
The analysis described here may be particularly useful to land managers who wish to focus on perceived hot spots of change at the subcounty level. The analysis requires GIS capability and technical expertise available to most public lands agencies and is not overly data intensive except for the labor involved in air photo interpretation and preparation of the polygon layers. The investment in time and data may have significant payback with respect to highlighting fine scale changes and resultant management opportunities in those areas undergoing localized land use change.
1101—Farm (house with out buildings)
1103—Commercial Areas or Structures
2022—Dry land Annual Crop
2023—Dry land Hay
2024—Irrigated Annual Crop
3150—Grassland/ Dry land Pasture
3170—Grassland with Sagebrush
3400—Juniper with deciduous shrubs and minor component pine
3350—Sagebrush (>20% canopy)
4000—Low Density Coniferous Forest
4200—High Density Coniferous Forest
4300—Streams with mixed Deciduous/ Coniferous Forest
4500—Cut over forest
5000—Water (Yellowstone river and gravel bars, lakes and ponds)
6120—Deciduous Trees (cottonwood/willow juniper under-story possible)
6130—Shrub Riparian (young cottonwoods, willow)
6160—Riparian meadow/ bottomland pasture
6190—Broadleaf Trees on Ditch Lines
7300—Rock, Rock Outcrop
7500—Gravel Pits, Disturbed
Cousens, R. and M. Mortimer. 1995. Dynamics of Weed Populations. Cambridge University Press.
Hansen, A., R. Rasker, B. Maxwell, J. Rotella, J. Johnson, A. Wright, U. Langner, W. Cohen, R. Lawrence, M. Kraska. 2002. Ecological causes and consequences of demographic change in the new west. Bioscience 52:151-162
Odell, E.A., D. Theobald and R. L. Knight. 2003. Incorporating ecology into land use planning. APA Journal 69:72-81.
Theobald, D.M., J.R. Miller and N. Thompson Hobbs. 1997. Estimating the cumulative effects of development on wildlife habitat. Landscape and Urban Planning 39:25-36.