|Home | Archives | About | Login | Submissions | Notify | Contact | Search|
Copyright © 2008 by the author(s). Published here under license by The Resilience Alliance.
Go to the pdf version of this article The following is the established format for referencing this article:
Dale, V. H., F. Akhtar, M. Aldridge, L. Baskaran, M. Berry, M. Browne, M. Chang, R. Efroymson, C. Garten, Jr., E. Lingerfelt, and C. Stewart. 2008. Modeling the effects of land use on the quality of water, air, noise, and habitat for a five-county region in Georgia. Ecology and Society 13(1): 10. [online] URL: http://www.ecologyandsociety.org/vol13/iss1/art10/
Research, part of Special Feature on Crossing Scales and Disciplines to Achieve Forest Sustainability Modeling the Effects of Land Use on the Quality of Water, Air, Noise, and Habitat for a Five-County Region in Georgia
1Georgia Institute of Technology, 2University of Tennessee, 3Oak Ridge National Laboratory, 4Aberdeen Proving Ground
A computer simulation model, the Regional Simulator (RSim), was constructed to project how land-use changes affect the quality of water, air, noise, and habitat of species of special concern. RSim was designed to simulate these environmental impacts for five counties in Georgia that surround and include Fort Benning. The model combines existing data and modeling approaches to simulate the effects of land-cover changes on: nutrient export by hydrological unit; peak 8-h average ozone concentrations; noise caused by small arms and blasts; and habitat changes for the rare Red-cockaded Woodpecker (Picoides borealis) and gopher tortoise (Gopherus polyphemus). The model also includes submodules for urban growth, new urbanization influenced by existing roads, nonurban land cover transitions, and a new military training area under development at Fort Benning. The model was run under scenarios of business as usual (BAU) and greatly increased urban growth for the region. The projections show that the effects of high urban growth will likely differ from those of BAU for noise and nitrogen and phosphorus loadings to surface water, but not for peak airborne ozone concentrations, at least in the absence of associated increases in industry and transportation use or technology changes. In both scenarios, no effects of urban growth are anticipated for existing populations of the federally endangered Red-cockaded Woodpecker. In contrast, habitat for gopher tortoise in the five-county region is projected to decline by 5 and 40% in the BAU and high urban growth scenarios, respectively. RSim is designed to assess the relative environmental impacts of planned activities both inside and outside military installations and to address concerns related to encroachment and transboundary influences.
Key words: gopher tortoise; land use; landscape change; longleaf pine; nutrient export; Red-cockaded Woodpecker; simulation
A regional approach to environmental impact assessments (Munns 2006) provides the opportunity to examine the extent and spatial interactions of key drivers and processes that are affected by land-use change. Because these drivers and the factors that influence these processes change over space because of variation in features such as topography, climate, and human activities, it is important to consider their influence in a spatial context to understand the full range and extent of the causes and implications of environmental change. Such analyses can be of assistance to regional planning and hence foster sustainability by allowing potential environmental repercussions to be a part of planning.
In addition, there is a need to examine how environmental impacts can change across several stressors, environmental media, and sectors, for example, water, air, noise, and habitats for species of special concern. Although environmental laws typically segregate these impacts in the ways in which they are both reported and managed, such an artificial division can lead to inadequate understanding and hence to management problems. For example, contrary incentives can arise if one sector gains at the expense of another. In other situations, inappropriate management actions can result from a focus on only one sector, rather than the consideration of all aspects of the environment that might be affected.
As a major driver of environmental change, it is critical to understand how land-use activities affect the landscape. For example, human use can degrade or ameliorate soil properties, enhance or reduce runoff, and aggravate or alleviate drought. In turn, land use can be constrained by environmental conditions such as topography, slope, exposure, soil conditions, and climate.
With the recent advent of geographic information systems and the field of landscape ecology (Turner et al. 2001), it has been possible to use a spatial approach to environmental change. The undertaking of a regional and cross-sectoral approach to the study of environmental change requires the determination of the appropriate spatial and temporal scales of resolution and the consideration of potential feedbacks across sectors. One of the goals in such a multisector approach is to provide a means to fully understand the key components of the system, including possible cumulative impacts.
Here, we propose a regional, cross-sectoral approach to examining land-use changes and their effects and present an example of its application to a five-county region in west central Georgia, USA. We focus on the region of Georgia around and inclusive of Fort Benning for three reasons: large quantities of data are available; the region will undergo dramatic changes in the future as the military training activities and the many people that support them at Fort Knox, Kentucky, are moved to Fort Benning; and military land, on which urban growth is restricted, serves as a control against which changes on private lands can be compared. The Regional Simulator (RSim) model has been developed for this five-county region and has the ability to project future changes in the quality of water, air, and habitat, and in noise (Dale et al. 2006). This spatially explicit simulation model is structured so that the basic framework can be applied to other resource management needs and other regions. Hence, the model is designed so that it is broadly applicable to environmental management concerns. The need to apply ecosystem management approaches to military lands and regions that contain them is critical because of unique resources on these public lands and the fact that conservation issues for the entire region may jeopardize military missions if not appropriately managed. The RSim model addresses this critical need by allowing the application of ecosystem management approaches to military lands and surrounding regions. We examined relative changes that resulted from two scenarios: “business as usual” and a dramatic increase in urban growth. The analysis illustrates how a simulation model can be used as a cost-effective means to explore potential environmental ramifications of land-use changes.
We also address the issue of forest sustainability because the study region was originally dominated by longleaf pine (Pinus palustris) forest, and it is the continuance of the pine forest that allows the attainment of many other environmental goals for the region. Without the forest, some of the other environmental amenities such as wildlife habitat cannot be maintained. The environmental impacts of planning activities both inside and outside military installations need to address concerns related to encroachment and transboundary influences (Efroymson et al. 2005).
The study area for model development and application was a five-county region in west central Georgia, USA (Fig. 1). This region surrounded and included most of the 73,503-ha Fort Benning military installation, which supports both a cantonment (area of extensive infrastructure) and undeveloped areas that are used for training and in which the forest structure supports various environmental amenities. Fort Benning military activities include the training of entry-level soldiers, Infantry, and Airborne and Ranger candidates. In addition to ranges for munitions training, the installation supports expansive pine forest that receive low-intensity military use. Because these forests have been protected from urban development and because there has been a focused program of controlled burning since the 1960s, these lands currently support mature stands of longleaf pine and several rare species of plant and animal.
Because of land-use change and fire suppression throughout the southeastern United States, only approximately 4% of the original longleaf pine forest exists today; thus, the remaining forest and the species that it supports have great ecological value (Gilliam and Platt 1999). Burning is a critical management practice for longleaf pine because the seedlings first grow in what is termed a grass stage, in which the tree’s meristem is located at the base of the stem and is protected from low-intensity fire by a lush bunch of needles. A subsequent bolt of growth in the sapling moves the meristem to a height above that of ground fires, assuming that fires occur frequently enough that they are of low intensity. In the 1994 Guidelines for the Management of Red-cockaded Woodpecker on Army Lands (as cited by Beaty et al. 2003), the U.S. Army, in cooperation with the U.S. Fish and Wildlife Service, selected Fort Benning as a site designated for the protection of the federally endangered Red-cockaded Woodpecker (Picoides borealis), which nests in living longleaf pine trees. Controlled burning not only allows for the reestablishment of longleaf pine seedlings, it also reduces the ingrowth of hardwood trees in the forest.
The study region also included private lands in the counties of Harris, Talbot, Muscogee, Chattahoochee, and Marion. The city of Columbus, which abuts Fort Benning on the north side, is the center of urban development in the region and was part of the study area. Major nonurban land uses of the five-county region are forestry, agriculture, and pasture.
The region contains a complex mix of environmental pressures that can affect the quality of water, air, noise, and habitat. The urban areas had significant industrial development and intense use of fossil-fuel-based vehicles, both of which contribute to air pollution. Burning for the maintenance of longleaf pine habitat also affects air quality and soil conditions (Garten 2006). Training areas within the military installation produce loud noises as a result of small-arms activity, the firing of large-caliber arms, and military aircraft. Water quality in the region is affected by industrial activity and agricultural practices that induce runoff and required fertilizer use. In addition, the habitat of two key rare species, i.e., Red-cockaded Woodpecker and gopher tortoise (Gopherus polyphemus), can be affected by land-use practices and underlying conditions on the land (Boglioli et al. 2000, Hermann et al. 2002).
Simulating cross-sectoral environmental changes in the region
Because resource managers need to protect multiple aspects of the environmental quality of a region, the Regional Simulator (RSim) model was developed as a tool to integrate changes in a region for conditions relating to water, air, noise, and habitat (Fig. 2; Dale et al. 2006). The basic spatial unit of RSim is a 30-m pixel because most of the underlying data in the model are derived from satellite imagery reported at that scale of resolution. After much consideration, the basic time step of RSim was set to 1 yr because changes in land cover are typically reported at annual intervals. This choice means that all of the environmental changes projected by RSim are reported annually.
Where possible, RSim was built from existing models and data (Appendix 1). Urban growth in RSim is based on the SLEUTH model (Clarke et al. 1997, Clarke and Gaydos 1998, Candau 2002) supplemented by rules for low-intensity to high-intensity urbanization. Transitions for nonurban land cover are based on changes observed in the five-county region from 1990 to 1998 (Baskaran et al. 2006b). The water quality module uses nutrient export coefficients (e.g., Johnes 1996, Mattikalli and Richards 1996) combined with information on the different land uses and land covers in the region to predict the annual flux of nitrogen and phosphorus from terrestrial watersheds. The noise module uses GIS data layers of military noise exposure developed by the U.S. Army Center for Health Promotion and Preventive Medicine as part of the Fort Benning Installation Environmental Noise Management Plan (Operational Noise Program 2007). RSim builds upon noise guideline levels that were developed by the military under the U.S. Army’s Environmental Noise Program (U.S. Army 1997). RSim contains noise contour maps resulting from artillery, as projected by the Department of Defense noise simulation model BNOISE, because artillery is the greatest source of noise at Fort Benning. This approach produces noise contours that identify areas where noise levels are compatible or incompatible with noise-sensitive land uses outside of Fort Benning. The U.S. Army’s Environmental Noise Program’s guidelines define zones of high noise and accident potential and recommend compatible uses in these zones. Local planning agencies are encouraged to adopt these noise guidelines. The air quality module estimates the effect of emissions changes on ozone air quality using sensitivity coefficients available from Chang et al. (2004). The measure of ozone air quality is based on the U.S. Environmental Protection Agency’s Clean Air 8-h Ground-level Ozone rule, which designates areas in which air quality does not meet the health-based standards established in 1997 for ground-level ozone pollution (http://www.epa.gov/ozonedesignations/). This policy-based designation lets the public know whether air quality is healthy in a given area and is not designed to convey effects on plant physiology or productivity or at different temporal resolutions. The module to predict habitat for Red-cockaded Woodpecker was developed on the basis of spatial data for longleaf pine in the region (Appendix 2). The module to predict habitat for gopher tortoise was developed on the basis of an analysis of locations of gopher tortoise burrows at Fort Benning and was tested for the larger five-county region (Baskaran et al. 2006a).
Numerous future scenarios can be modeled using RSim, including both civilian and military land-cover changes. Our implementation of RSim included four specific types of scenario along with their effects on environmental conditions over the next decades: urbanization, i.e., the conversion of nonurban land cover to low-intensity urban land cover and the conversion of low-intensity urban land cover to high-intensity urban land cover; planned road expansion plus modeled urbanization; a new training area at Fort Benning; and hurricanes of various intensity. Low-intensity urban land cover included single-family-dwelling residential areas, schools, city parks, cemeteries, playing fields, and campus-like institutions. High-intensity urban land cover included paved areas with buildings and little vegetation, power substations, and occasionally grain storage buildings.
For the case considered here, RSim was run under conditions meant to simulate business-as-usual (BAU) urbanization for 40 yr into the future from 1998 compared to great increases in urban growth (see Appendix 2 for input conditions). The BAU case included typical urbanization for the region as based on regional growth patterns from 1990 to 1998, the new training area at Fort Benning (which is already under construction), and road expansion according to the Governor’s plans for the development of four-lane highways in the region. The high-growth scenario was identical except for an increase in urban growth starting in 1998. This scenario was meant to simulate changes in urban growth in the region that may result from the transfer of training from Fort Knox, Kentucky, to Fort Benning. Although many changes in the region are anticipated (Dale et al. 2006), no one has yet published an analysis of how these changes might affect land cover and other environmental conditions.
Based on the conditions and scenarios selected, the Regional Simulation (RSim) model was used to project changes in land cover (Figs. 3–5). The business as usual (BAU) case resulted in a slight increase in the area of land under high-intensity urban cover (from 4329 to 4662 ha) and a great increase in land under low-intensity urban cover (from 7914 to 10,053 ha). Land on which timber had been cleared declined sharply from 44,735 to 20,317 ha, and row crops decreased from 11,101 to 4876 ha. Pasture lands increased from 22,886 to 27,147 ha.
The high urban growth and BAU scenarios resulted in different patterns of change in urban and agricultural lands (Fig. 4A, B). The high-growth case resulted in a great increase in the area of land under both high-intensity urban cover (from 4329 to 115,789 ha) and low-intensity urban cover (from 7914 to 135,247 ha). Clearcut land declined from 44,735 to 10,963 ha, and row crops decreased from 13,101 to 1837 ha. Contrary to the BAU case, pasture lands declined from 22,886 to 7779 ha.
Forest cover changed in the BAU scenario (Fig. 5A). Both mixed forest and forested wetlands declined from 32,145 to 12,775 ha and from 27,933 to 14,310 ha, respectively. Deciduous forest and evergreen forest both increased in area from 106,439 to 118,880 ha and from 144,905 to 191,419 ha, respectively. In comparison, forest cover had quite a different pattern of change over the next 40 yr for the high urban growth scenario (Fig. 5B). All of the common forest categories declined, with mixed forest changing from 32,145 to 10,765 ha, forested wetlands from 27,933 to 10,561 ha, deciduous forest from 106,439 to 42,488 ha, and evergreen forest from 144,905 to 70,911 ha.
The water quality module projected large differences in the amount and location of major nitrogen (N) and phosphorus (P) export for the BAU scenario compared to the high urban growth scenario. In the BAU case, the greatest changes in N and P exports occurred in the watershed containing the city of Columbus (Hydrological Unit Code [HUC] 30104). In contrast, in the high urban growth scenario, the watershed northeast of Columbus (HUC 21206) had the greatest changes in these exports. The overall change in N export for the RSim region was 1.0 × 106 and 1.6 × 106 kg for the BAU and high urban growth scenarios, respectively. The overall change in P export was 1.6 × 105 and 3.7 × 105 kg for the BAU and high-growth scenarios, respectively.
In both scenarios, the peak 8-h ozone concentration over the five-county region increased from 71 ppbv (parts per billion by volume) in 1998 to about 90 ppbv in 2038. Thus, when comparing the results of the two scenarios, the additional changes in the high urban growth scenario, which are over and above those in the BAU scenario, did not yield any additional changes to the estimated change in peak 8-h ozone concentration over the five-county region. It should be noted, however, that the peak 8-h ozone concentration is but one measure of air quality. Other metrics, for example, those that measure the dose or temporal or spatial distribution of ozone, might, in fact, show differences in air quality between the two scenarios. Regardless, over the 40 yr, the increase in the peak 8-h ozone concentration from 71 to 90 ppbv was caused by the projected growth in industrial, commercial, and transportation activity. Growth in both scenarios, though, was untempered by any future regulatory controls, technological innovations, or air quality management decisions. For context, the peak 8-h ozone concentrations actually observed in the five-county region in 1998 ranged up to 104 ppbv.
Habitats of key species in the region
For both the BAU and high urban growth scenarios, RSim projected that by model year 2038, 150% of the original clusters of Red-cockaded Woodpecker will exist in the five-county region. Most of these clusters would be located in evergreen forest within the boundaries of Fort Benning; these forest stands mature to the stage at which they can support Red-cockaded Woodpecker by the end of the 40-yr model run. This quantity of new active breeding clusters would meet the U.S. Fish and Wildlife Service’s goal of 361 active clusters for Fort Benning (Beaty et al. 2003).
RSim projected that by model year 2038, there will be 181,288 and 113,639 ha of potential area of suitable gopher tortoise habitat for the BAU and high urban growth scenarios, respectively. In comparison, there was 190,918 ha of gopher tortoise habitat in the five-county region at the beginning of the simulation. The 5 and 40% reductions in potential area that can support gopher tortoise burrows reflect changes in land cover under the BAU and high urban growth scenarios, respectively. The probability of the presence of suitable gopher tortoise habitat increases when more land cover is used as pasture, clearcuts, forest, transportation corridors, row crops, or utility swaths.
For the two scenarios, the land-cover changes combined to produce different patterns of risk from noise (Fig. 6A, B). There was a moderate risk of noise complaints from areas of 6334 and 93,448 ha outside Fort Benning for the BAU and high urban growth scenarios, respectively. The areas that are likely to experience a high risk of noise complaints were relatively small in both scenarios, with 9 and 61 ha likely by 2038 for the BAU and high urban growth scenarios, respectively. RSim projected that by 2038 for the BAU and high urban growth scenarios, 8335 and 38,773 ha, respectively, of land outside of Fort Benning will be in land uses that are incompatible with noise produced from military activity.
The projected changes in land cover under the two scenarios are quite different (Figs. 4 and 5). The business as usual (BAU) case had only small changes in the urban land cover types. A sharp decline in clearcut land and a more gradual decline in row crops occurred as pasture and urban land cover increased in area. At the same time, evergreen and deciduous forest land increased in the region. In contrast, the sharp increase in high-intensity urban land cover under the high urban growth scenario is associated with a decline in all of the aforementioned land cover types. These alterations in land cover type set the stage for changes in some of the other environmental conditions.
Changes in nitrogen (N) and phosphorus (P) export to streams over the 40-yr projection are dramatic for both scenarios. For the BAU case, the watershed containing the city of Columbus has more N and P export after 40 yr than does any other watershed in the region because it continues to be the center of high urban intensity. Columbus is currently the largest city in the five-county area, and in 1998, it had the greatest concentration of urban land cover in the region. The high proportion of urban land in Columbus is related to a high proportion of paved areas, which allow runoff and industrial inputs of N and P into the water system. Over the 40-yr projection, no land-cover changes in the rural or forested landscape were great enough to overcome the large influence of Columbus on the water quality of the region. These results suggest that current and future attention to the effects of N and P export should concentrate on the city of Columbus under the BAU case. However, under the high urban growth scenario, intense urban development shifts to the northeast of Columbus (i.e., to Hydrological Unit Code 21206). This difference between the two scenarios suggests that the region needs to be prepared to support infrastructure needs and increases in N and P export for a larger region than just the Columbus area.
Both scenarios resulted in similar air quality changes projected from land-cover changes in the five-county region. There are two principal ways in which forest cover can affect air quality, and both are represented in the Regional Simulator (RSim) model. First, forests emit reactive hydrocarbons that are involved in the chemistry that forms ground-level ozone. In the southeastern United States, biogenic hydrocarbons are ubiquitous, and stoichiometrically speaking, the region is saturated with hydrocarbons. The removal of anthropogenic sources of hydrocarbons under any conceivable scenario (or the addition of more, for that matter) has no significant effect on ozone concentration. For this reason, projected changes in the local forest cover have a negligible effect on extant hydrocarbon emissions and thus ozone concentrations. The second way that forests can affect ground-level ozone is via emissions of nitrous oxide (NOx) from either burning activity in the forest or activities associated with logging or otherwise managing or using the forest (e.g., chainsaws, trucks, and all-terrain vehicles). Estimates of all of these contributions are included in the RSim current emissions inventory. However, forest-related emissions are only a small part of the total emissions inventory, and they have scant effects on the peak ozone concentration in the region, which is what RSim calculates and is the variable that is generally related to human health and vegetation growth. Further, unless the changes in forest emissions collocate with the place where the peak ozone concentration occurs, which is unlikely because the peak pollutant concentrations tend to occur near the urban areas where the more intense emissions sources are located, an effect on ozone concentration is unlikely. Lastly, forest emissions are distributed over a large area, so the effect is diluted at any one location. Even though all of these factors are included in the air quality module of RSim, there is little effect on regional air quality as calculated in the form of peak 8-h ozone concentrations produced by land-cover changes. Conversely, it is expected that air quality does affect land cover. Although this direct feedback loop has not yet been implemented in RSim, users should be aware that for both scenarios, the model projected that concentrations of ozone will exceed the secondary ozone standard that is protective of vegetation for 34 yr of the 40-yr projection period. Consequently, adverse effects on vegetation should be assumed.
The habitats for the two species that were included in the RSim model responded quite differently to projected changes in land cover under the two scenarios. The number of clusters of Red-cockaded Woodpecker had few differences between the two scenarios because almost all of the clusters are located in military lands that were not subject to urban expansion. In contrast, the habitat of gopher tortoise was strongly affected in the high urban growth scenario because that case instigates a change in several land cover types that are suitable for gopher tortoise. In the BAU case, clearcut lands undergo a steady decline from 44,735 to 20,317 ha; in contrast, in the high urban growth scenario, clearcut lands decline to approximately 10,963 ha. At the same time, pasture lands were projected to increase from approximately 22,890 to 27,150 ha in the BAU scenario, but to decline to 7800 ha in the high urban growth scenario. The decline in both clearcut and pasture lands that resulted from high urban growth reduced the amount of area suitable for gopher tortoise habitat.
The projected risk from noise is very different under the two scenarios (Fig. 6). The BAU case was associated with a slight increase in lands with moderate risk from noise and incompatible land use. In contrast, the high urban growth scenario projected dramatic increases in the area of land with moderate risk from noise and incompatible land use. Both of these scenarios display a local peak in risk from noise that occurs just before model year 2008, when the areas of land in high- and low-intensity urban categories approach similar values (Fig. 4). Before 2008, both urban land types contribute to noise risk, but the declining area of residential land after 2008 causes the noise risk to decline as well for a short period until the influence of the increasing high-intensity urban land causes another increase in the noise risk. The location of these new urban lands near the boundary of Fort Benning (Fig. 3) and within the range of noise effects is another factor that affects the sharp increase in risk from noise.
This regional, cross-sectoral analysis of the environmental effects of land-use change in west central Georgia illustrates some of the benefits of using such a holistic approach to land-use planning. A broad understanding of potential effects of land-use changes can be achieved. This information can be used to streamline management activities by allowing potential effects to be considered before a decision is made and promotes the discussion of and planning for on-the-ground repercussions of decision making. In addition, the simulation model identifies conditions under which cross-sectoral effects should be considered. For example, in the scenarios presented here, effects on air quality are negligible. At least in the absence of large changes in dominant emissions factors such as might be associated with increases in industrial and transportation uses or in technology changes, the effects of land-use changes on air quality are small. The use of the RSim model enhances the understanding of interactions between environmental effects (i.e., feedbacks and cumulative effects) and therefore allows for a greater understanding of the conditions necessary to sustain the various environmental amenities of the region.
The use of the Regional Simulator model to explore regional changes in west central Georgia, USA, projects that high urban growth can have dramatic effects on water and noise quality and on the habitat of one species of special concern, the gopher tortoise, but not another, the Red-cockaded Woodpecker. Hence, this example illustrates how management attention might be focused to promote the environmental sustainability of the region. However, only a limited set of conditions were considered in this example. The ongoing and regular use of this type of model in a planning environment is the most effective way to make use of the approach. Both the counties and the military lands in Georgia require regular updates to their planning activities, and the use of a land-use planning model in such reporting would allow the model to include both the most recent data and scenarios relevant to recent activities. Simulation models offer a cost-effective and efficient means to explore potential outcomes of resource management and land use. This analysis shows that modeling, understanding, and managing for the effects of land-use change in several sectors (i.e., air, water, noise, and habitat) requires that attention be paid to the spatial and temporal scales at which each sector operates and how the factors affecting the sectors interact.
Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link
ACKNOWLEDGMENTSThe assistance of Rusty Bufford with spatial data and Robert Addington, Thomas A. Greene, Wade Harrison, Robert Larimore, and Pete Swiderick with other information is appreciated. Hugh Westbury provided important logistic support. Discussions with Hal Balbach, John Brent, William Goran, Robert Holst, Don Imm, and Lee Mulkey were also quite helpful in implementing this project. The project was funded by a contract from the Strategic Environmental Research and Development Program (SERDP) project CS-1259 to Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by the UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725.
Baskaran, L. M., V. H. Dale, R. A. Efroymson, and W. Birkhead. 2006a. Habitat modeling within a regional context: an example using gopher tortoise. American Midland Naturalist 155(2):335-351.
Baskaran, L., V. Dale, C. Garten, D. Vogt, C. Rizy, R. Efroymson, M. Aldridge, M. Berry, M. Browne, E. Lingerfelt, F. Akhtar, M. Chang, and C. Stewart. 2006b. Estimating land-cover change in RSim: problems and constraints. Proceedings of the American Society for Photogrammetry and Remote Sensing 2006 Conference, Reno, Nevada, May 1-5, 2006.
Beaty, T. A., A. E. Bivings, T. Reid, T. L. Myers, S. D. Parris, R. Costa, T. J. Hayden, T. E. Ayers, S. M. Farley, and W. W. Woodson. 2003. Success of the Army’s 1996 red-cockaded management guidelines. Federal Facilities Environmental Journal 14(1):43-53.
Beaulac, M. N., and K. H. Reckhow. 1982. An examination of land use–nutrient export relationships. Journal of the American Water Resources Association 18(6):1013-1024.
Berry, M., J. Comiskey, and K. Minser. 1994. Parallel analysis of clusters in landscape ecology. IEEE Computational Science and Engineering 1(2):24-38.
Boglioli, M. D., W. K. Michener, and C. Guyer. 2000. Habitat selection and modification by the gopher tortoise, Gopherus polyphemus, in Georgia longleaf pine forest. Chelonian Conservation and Biology 3(4):699-705.
Candau, J. T. 2002. Temporal calibration sensitivity of the SLEUTH urban growth model. Thesis. University of California, Santa Barbara, California, USA.
Chang, M. E., A. Russell, and K. Baumann. 2004. Fall line air quality study (FAQS) final report: an analysis of air quality and options for managing it in middle Georgia. Georgia Institute of Technology, Atlanta, Georgia, USA. Available online at: http://cure.eas.gatech.edu/faqs/finalreport/.
Clarke, K. C., and L. J. Gaydos. 1998. Loose-coupling a cellular automation model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science 12(7):699-714.
Clarke, K. C., S. Hoppen, and L. Gaydos. 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B 24(2):247-261.
Constantin, J. M., M. W. Berry, and B. T. Vander Zanden. 1997. Parallelization of the Hoshen-Kopelman algorithm using a finite state machine. International Journal of Supercomputer Applications 11(1):34-48.
Dale, V., M. Aldridge, T. Arthur, L. Baskaran, M. Berry, M. Chang, R. Efroymson, C. Garten, C. Stewart, and R. Washington-Allen. 2006. Bioregional planning in central Georgia, USA. Futures 38(4):471-489.
Efroymson, R. A., V. H. Dale, L. M. Baskaran, M. Chang, M. Aldridge, and M. W. Berry. 2005. Planning transboundary ecological risk assessments at military installations. Human and Ecological Risk Assessment 11(6):1193-1215.
Garten, Jr., C. T. 2006. Predicted effects of prescribed burning and harvesting on forest recovery and sustainability in southwest Georgia, USA. Journal of Environmental Management 81(4):323-332.
Gilliam, F. S., and W. J. Platt. 1999. Effects of long-term fire exclusion on tree species composition and stand structure in an old-growth Pinus palustris (longleaf pine) forest. Plant Ecology 140(1):15-26.
Hermann, S. M., C. Guyer, J. H. Waddle, and M. G. Nelms. 2002. Sampling on private property to evaluate population status and effects of land use practices on the gopher tortoise, Gopherus polyphemus. Biological Conservation 108(3):289-298.
Johnes, P. J. 1996. Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters: the export coefficient modelling approach. Journal of Hydrology 183(3-4):323-349.
Johnes, P., B. Moss, and G. Phillips. 1996. The determination of total nitrogen and total phosphorus concentrations in freshwaters from land use, stock headage and population data: testing of a model for use in conservation and water quality management. Freshwater Biology 36(2):451-473.
Mattikalli, N. M., and K. S. Richards. 1996. Estimation of surface water quality changes in response to land use change: application of the export coefficient model using remote sensing and geographical information system. Journal of Environmental Management 48(3):263-282.
Munns, Jr., W. R. 2006. Assessing risks to wildlife populations from multiple stressors: overview of the problem and research needs. Ecology and Society 11(1): 23. [online] URL: http://www.ecologyandsociety.org/vol11/iss1/art23/.
Operational Noise Program. 2007. How is noise modeled? U.S. Army Center for Health Promotion and Preventive Medicine, Aberdeen Proving Ground, Maryland, USA. Available online at: http://chppm-www.apgea.army.mil/dehe/morenoise/TriServiceNoise/document/model.pdf.
Turner, M. G., R. H. Garner, and R. V. O’Neill. 2001. Landscape ecology in theory and practice: pattern and process. Springer, New York, New York, USA.
U.S. Army. 1997. Army regulation 200-1. Environmental protection and enhancement. Department of the Army, Washington, D.C., USA. Available online at: http://usmilitary.about.com/gi/dynamic/offsite.htm?site=http://www.usapa.army.mil/pdffiles/r200%5F1.pdf.
U.S. Environmental Protection Agency. 2004. EGAS version 5.0 beta. U.S. Environmental Protection Agency, Washington, D.C., USA. Available online at: http://www.epa.gov/ttn/ecas/egas5.htm.
|Home | Archives | About | Login | Submissions | Notify | Contact | Search|