Spatial and Temporal Assessment of Cumulative Disturbance Impacts Due to Military Training,Burning, Haying,and Their Interactions on Land Condition of Fort Riley

The effects of military training activities on the land condition of Army installations vary spatially and temporally. Training activities observably degrade land condition while also increasing biodiversity and stabilizing ecosystems. Moreover, other anthropogenic activities regularly occur on military lands such as prescribed burns and agricultural haying—adding to the dynamics of land condition. Thus, spatially and temporally assessing the impacts of military training, prescribed burning, agricultural haying, and their interactions is critical to the management of military lands. In this study, the spatial distributions and patterns of military training-induced disturbance frequency were derived using plot observation and point observation-based method, at Fort Riley, Kansas from 1989 to 2001. Moreover, spatial and variance analysis of cumulative impacts due to military training, burning, haying, and their interactions on the land condition of Fort Riley were conducted. The results showed that: (1) low disturbance intensity dominated the majority of the study area with exception of concentrated training within centralized areas; (2) high and low values of disturbance frequency were spatially clustered and had spatial patterns that differed significantly from a random distribution; and (3) interactions between prescribed burning and agricultural haying were not significant in terms of either soil erosion or disturbance intensity although their means and variances differed significantly between the burned and non-burned areas and between the hayed and non-hayed areas.     

Influence of Catchment-Scale Military Land Use on Stream Physical and Organic Matter Variables in Small Southeastern Plains Catchments (USA)

We conducted a 3-year study designed to examine the relationship between disturbance from military land use and stream physical and organic matter variables within 12 small (<5.5 km2) Southeastern Plains catchments at the Fort Benning Military Installation, Georgia, USA. Primary land-use categories were based on percentages of bare ground and road cover and nonforested land (grasslands, sparse vegetation, shrublands, fields) in catchments and natural catchments features, including soils (% sandy soils) and catchment size (area). We quantified stream flashiness (determined by slope of recession limbs of storm hydrographs), streambed instability (measured by relative changes in bed height over time), organic matter storage [coarse wood debris (CWD) relative abundance, benthic particulate organic matter (BPOM)] and stream-water dissolved organic carbon concentration (DOC). Stream flashiness was positively correlated with average storm magnitude and percent of the catchment with sandy soil, whereas streambed instability was related to percent of the catchment containing nonforested (disturbed) land. The proportions of in-stream CWD and sediment BPOM, and stream-water DOC were negatively related to the percent of bare ground and road cover in catchments. Collectively, our results suggest that the amount of catchment disturbance causing denuded vegetation and exposed, mobile soil is (1) a key terrestrial influence on stream geomorphology and hydrology and (2) a greater determinant of in-stream organic matter conditions than is natural geomorphic or topographic variation (catchment size, soil type) in these systems.     

Temporal and Spatial Relationships Between Watershed Land Use and Salt Marsh Disturbance in a Pacific Estuary

Historical and recent remote sensing data can be used to address temporal and spatial relationships between upland land cover and downstream vegetation response at the watershed scale. This is demonstrated for sub-watersheds draining into Elkhorn Slough, California, where salt marsh habitat has diminished because of the formation of sediment fans that support woody riparian vegetation. Multiple regression models were used to examine which land cover variables and physical properties of the watershed most influenced sediment fan size within 23 sub-watersheds (1.4 ha to 200 ha). Model explanatory power increased (adjusted R(2) = 0.94 vs. 0.75) among large sub-watersheds (>10 ha) and historical watershed variables, such as average farmland slope, flowpath slope, and flowpath distance between farmland and marsh, were significant. It was also possible to explain the increase in riparian vegetation by historical watershed variables for the larger sub-watersheds. Sub-watershed area is the overriding physical characteristic influencing the extent of sedimentation in a salt marsh, while percent cover of agricultural land use is the most influential land cover variable. The results also reveal that salt marsh recovery depends on relative cover of different land use classes in the watershed, with greater chances of recovery associated with less intensive agriculture. This research reveals a potential delay between watershed impacts and wetland response that can be best revealed when conducting multi-temporal analyses on larger watersheds.     

Quantifying Terrestrial Ecosystem Carbon Dynamics in the Jinsha Watershed, Upper Yangtze, China from 1975 to 2000

Quantifying the spatial and temporal dynamics of carbon stocks in terrestrial ecosystems and carbon fluxes between the terrestrial biosphere and the atmosphere is critical to our understanding of regional patterns of carbon budgets. Here we use the General Ensemble biogeochemical Modeling System to simulate the terrestrial ecosystem carbon dynamics in the Jinsha watershed of China’s upper Yangtze basin from 1975 to 2000, based on unique combinations of spatial and temporal dynamics of major driving forces, such as climate, soil properties, nitrogen deposition, and land use and land cover changes. Our analysis demonstrates that the Jinsha watershed ecosystems acted as a carbon sink during the period of 1975–2000, with an average rate of 0.36 Mg/ha/yr, primarily resulting from regional climate variation and local land use and land cover change. Vegetation biomass accumulation accounted for 90.6% of the sink, while soil organic carbon loss before 1992 led to a lower net gain of carbon in the watershed, and after that soils became a small sink. Ecosystem carbon sink/source patterns showed a high degree of spatial heterogeneity. Carbon sinks were associated with forest areas without disturbances, whereas carbon sources were primarily caused by stand-replacing disturbances. It is critical to adequately represent the detailed fast-changing dynamics of land use activities in regional biogeochemical models to determine the spatial and temporal evolution of regional carbon sink/source patterns.     

Assessing Surface Water Quality and Its Relation with Urban Land Cover Changes in the Lake Calumet Area, Greater Chicago

Urban land use and land cover change significantly affect spatial and temporal patterns of runoff, which in turn impacts surface water quality. With the exponential growth in urban areas over the past three decades, changes in land use and land cover to cater for the growth of cities has been a conspicuous spectacle in urban spaces. The main goal of this study was to assess the impacts of land cover change on runoff and surface water quality using a partial area hydrology framework. The study employed ArcHydro GIS extension and a modified version of Long-Term Hydrologic and Nonpoint Source Pollution model (L-THIA-NPS) in estimating runoff and nonpoint source pollutant concentration around Lake Calumet between 1992 and 2001. Data employed include National Land Cover Data set, rainfall data, digital elevation model (DEM), Soil Survey Geographic (SSURGO) data, and The United States Environmental Protection Agency’s STORET (storage and retrieval) water quality data. The model was able to predict surface water quality reasonably well over the study period. Sensitivity analysis facilitated a manual calibration of the model. Model validation was executed by comparing simulated results following calibration and observed water quality data for the study area. The study demonstrates that the level of concentration of nonpoint source pollutants in surface water within an urban watershed heavily depends on the spatiotemporal variations in areas that contribute towards runoff compared to the spatial extent of change in major land use/land cover.     

Optimal spatial resolution for collection of ground data and multi-sensor image mapping of a soil erosion cover factor

Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.     

An erosion-based land classification system for military installations

The universal soil loss equation (USLE) has been integrated with a geographic information system known as the geographical resources analysis support system (GRASS) to create a land classification system for use by military trainers and land managers to minimize the environmental impacts of military training activities. The USLE provides an estimate of current average annual sheet and rill erosion based upon factors representing climate, soil erodibility, topography, cover, and conservation support practices. The erosion estimate is compared to erosion tolerance values to produce an expression of the current erosion status. An index of inherent site erodibility is also achieved through manipulation of the USLE. Based on published soil surveys, satellite imagery, and ground-truth vegetation transects, data layers are created within GRASS for each of the component factors of the USLE. Appropriate mathematical operations are performed with the data layers, and color-coded maps are produced that represent the erosion status and erodibility index for each 50-m × 50-m area of soil surface. These maps aid military trainers and land managers in scheduling appropriate kinds and intensities of military training activities.     

Development of a Bird Integrity Index: Measuring Avian Response to Disturbance in the Blue Mountains of Oregon, USA

The Bird Integrity Index (BII) presented here uses bird assemblage information to assess human impacts to 28 stream reaches in the Blue Mountains of eastern Oregon. Eighty-one candidate metrics were extracted from bird survey data for testing. The metrics represented aspects of bird taxonomic richness, tolerance or intolerance to human disturbance, dietary preferences, foraging techniques, and nesting strategies that were expected to be positively or negatively affected by human activities in the region. To evaluate the responsiveness of each metric, it was plotted against an index of reach and watershed disturbance that included attributes of land use/land cover, road density, riparian cover, mining impacts, and percent area in clearcut and partial-cut logging. Nine of the 81 candidate bird metrics remained after eliminating unresponsive and highly correlated metrics. Individual metric scores ranged from 0 to 10, and BII scores varied between 0 and 100. BII scores varied from 78.6 for a minimally disturbed, reference stream reach to 30.4 for the most highly disturbed stream reach. The BII responded clearly to varying riparian conditions and to the cumulative effects of disturbances, such as logging, grazing, and mining, which are common in the mountains of eastern Oregon. This BII for eastern Oregon was compared to an earlier BII developed for the agricultural and urban disturbance regime of the Willamette Valley in western Oregon. The BII presented here was sensitive enough to distinguish differences in condition among stream riparian zones with disturbances that were not as obvious or irreversible as those in the agricultural/urban conditions of western Oregon.     

Space and Time Scales in Human-Landscape Systems

Exploring spatial and temporal scales provides a way to understand human alteration of landscape processes and human responses to these processes. We address three topics relevant to human-landscape systems: (1) scales of human impacts on geomorphic processes, (2) spatial and temporal scales in river restoration, and (3) time scales of natural disasters and behavioral and institutional responses. Studies showing dramatic recent change in sediment yields from uplands to the ocean via rivers illustrate the increasingly vast spatial extent and quick rate of human landscape change in the last two millennia, but especially in the second half of the twentieth century. Recent river restoration efforts are typically small in spatial and temporal scale compared to the historical human changes to ecosystem processes, but the cumulative effectiveness of multiple small restoration projects in achieving large ecosystem goals has yet to be demonstrated. The mismatch between infrequent natural disasters and individual risk perception, media coverage, and institutional response to natural disasters results in un-preparedness and unsustainable land use and building practices.     

Urbanization and the Loss of Resource Lands in the Chesapeake Bay Watershed

We made use of land cover maps, and land use change associated with urbanization, to provide estimates of the loss of natural resource lands (forest, agriculture, and wetland areas) across the 168,000 km² Chesapeake Bay watershed. We conducted extensive accuracy assessments of the satellite-derived maps, most of which were produced by us using widely available multitemporal Landsat imagery. The change in urbanization was derived from impervious surface area maps (the built environment) for 1990 and 2000, from which we estimated the loss of resource lands that occurred during this decade. Within the watershed, we observed a 61% increase in developed land (from 5,177 to 8,363 km²). Most of this new development (64%) occurred on agricultural and grasslands, whereas 33% occurred on forested land. Some smaller municipalities lost as much as 17% of their forest lands and 36% of their agricultural lands to development, although in the outlying counties losses ranged from 0% to 1.4% for forests and 0% to 2.6% for agriculture. Fast-growing urban areas surrounded by forested land experienced the most loss of forest to impervious surfaces. These estimates could be used for the monitoring of the impacts of development across the Chesapeake Bay watershed, and the approach has utility for other regions nationwide. In turn, the results and the approach can help jurisdictions set goals for resource land protection and acquisition that are consistent with regional restoration goals.     

LANDSCAPE‐SCALE ANALYSIS AND MANAGEMENT OF CUMULATIVE IMPACTS TO RIPARIAN ECOSYSTEMS: PAST,PRESENT, AND FUTURE1

ABSTRACT: Analyses of cumulative impacts to riparian systems is an important yet elusive goal. Previous analyses have focused on comparing the number of hectares impacted to the number of hectares restored, without addressing the loss of riparian function or the effect of the spatial distribution of impacts. This paper presents an analysis of the spatial distribution of development‐related impacts to riparian ecosystems, that were authorized under Section 404 of the Clean Water Act. Impacts on habitat structure, contiguity, and landscape context were evaluated using functional indices scaled to regional reference sites. Impact sites were mapped using GIS and analyzed for spatial associations. Positive spatial autocorrelation (i.e. clustering of impact sites) resulted from the piecemeal approach to impact assessment, which failed to prevent cumulative impacts. Numerous small projects in close proximity have resulted in adverse impacts to entire stream reaches or have fragmented the aquatic resources to a point where overall functional capacity is impaired. Additionally, the ecological functions of unaffected areas have been diminished due to their proximity to degraded areas. A proactive approach to managing cumulative impacts is currently being used in Orange County, California as part of a Corps of Engineers sponsored Special Area Management Plan (SAMP). The SAMP process is evaluating the ecological conditions and physical processes of the study watersheds and attempting to plan future development in a manner that will guard against cumulative impacts.     

Effect of land cover data on nitrous oxide inventory in fen meadows

Landscape representations based on land cover databases differ significantly from the real landscape. Using a land cover database with high uncertainty as input for emission inventory analyses can cause propagation of systematic and random errors. The objective of this study was to analyze how different land cover representations introduce systematic errors into the results of regional N2O emission inventories. Surface areas of grassland, ditches, and ditch banks were estimated for two polders in the Dutch fen meadow landscape using five land cover representations: four commonly used databases and a detailed field map, which most closely resembles the real landscape. These estimated surface areas were scaled up to the Dutch western fen meadow landscape. Based on the estimated surface areas agricultural N2O emissions were estimated using different inventory techniques. All four common databases overestimated the grassland area when compared to the field map. This caused a considerable overestimation of agricultural N2O emissions, ranging from 9% for more detailed databases to 11% for the coarsest database. The effect of poor land cover representation was larger for an inventory method based on a process model than for inventory methods based on simple emission factors. Although the effect of errors in land cover representations may be small compared to the effect of uncertainties in emission factors, these effects are systematic (i.e., cause bias) and do not cancel out by spatial upscaling. Moreover, bias in land cover representations can be quantified or reduced by careful selection of the land cover database.     

Estimating the Variability and Confidence of Land Use and Imperviousness Relationships at a Regional Scale1

Abstract: Impervious cover is a commonly used metric to help explain or predict anthropogenic impacts on aquatic resources; often it is used as a surrogate for intensity of human impacts when evaluating effects on aquatic resources. The most common way to estimate imperviousness is based on relationships with land use. Few studies have evaluated how the relationship between impervious surface and land use varies among geographies with different levels of development and between types of imagery used to assign land use type. In this study, we assess variability in estimates of imperviousness based on two locally available land use datasets: one based on aerial imagery (2‐m resolution) and another based on satellite imagery (30‐m resolution). The ranges and variability in imperviousness within land use categories were assessed at several spatial scales, including within counties, between counties, and between watersheds. Results indicate that there was considerable variability for all developed land use types. Estimated impervious cover often varied over a range of 20‐40% points within a land use category. Furthermore, there were clear spatial patterns both between and within counties, with impervious cover for a given land use type being higher near the urban centers and lower at the margins of development. Estimates of imperviousness for 12 study watersheds indicated that variability increased with increasing watershed development, making it difficult to confidently set management or regulatory targets based on impervious cover. This study suggests that locally derived, high resolution satellite or aerial imagery should be used to estimate imperviousness when a high level of accuracy and precision is required for regulatory or management decisions. Furthermore, the error associated with impervious land use relationships should be accounted for when using impervious cover in runoff or water quality models, or when making management decisions regarding stream health.     

Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area

The Houston-Galveston Area (HGA) is one of the most severe ozone non-attainment regions in the US. To study the effectiveness of controlling anthropogenic emissions to mitigate regional ozone nonattainment problems, it is necessary to utilize adequate datasets describing the environmental conditions that influence the photochemical reactivity of the ambient atmosphere. Compared to the anthropogenic emissions from point and mobile sources, there are large uncertainties in the locations and amounts of biogenic emissions. For regional air quality modeling applications, biogenic emissions are not directly measured but are usually estimated with meteorological data such as photo-synthetically active solar radiation, surface temperature, land type, and vegetation database. In this paper, we characterize these meteorological input parameters and two different land use land cover datasets available for HGA: the conventional biogenic vegetation/land use data and satellite-derived high-resolution land cover data. We describe the procedures used for the estimation of biogenic emissions with the satellite derived land cover data and leaf mass density information. Air quality model simulations were performed using both the original and the new biogenic emissions estimates. The results showed that there were considerable uncertainties in biogenic emissions inputs. Subsequently, ozone predictions were affected up to 10 ppb, but the magnitudes and locations of peak ozone varied each day depending on the upwind or downwind positions of the biogenic emission sources relative to the anthropogenic NOx and VOC sources. Although the assessment had limitations such as heterogeneity in the spatial resolutions, the study highlighted the significance of biogenic emissions uncertainty on air quality predictions. However, the study did not allow extrapolation of the directional changes in air quality corresponding to the changes in LULC because the two datasets were based on vastly different LULC category definitions and uncertainties in the vegetation distributions.     

Modeling Hydrological and Environmental Consequences of Climate Change and Urbanization in the Boise River Watershed,Idaho

The potential impacts driven by climate variability and urbanization in the Boise River Watershed (BRW), located in southwestern Idaho, are evaluated. The outcomes from Global Circulation Models (GCMs) and land use and land cover (LULC) analysis have been incorporated into a hydrological and environmental modeling framework to characterize how climate variability and urbanization can affect the local hydrology and environment at the BRW. The combined impacts of future climate and LULC change are also evaluated relative to the historical baseline conditions. For modeling exercises, Hydrological Simulation Program‐Fortran (HSPF) is used in parallel computing and statistical techniques, including spatial downscaling and bias correlation, are employed to evaluate climate consequences derived from GCMs as well. The implications of climate variability and land use change driven by urbanization are then observed to evaluate how these overall global challenges can affect water quantity and quality conditions at the BRW. The results show the combined impacts of both climate change and urbanization can lead to more seasonal variability of streamflow (from ?27.5% to 12.5%) and water quality, including sediment (from ?36.5% to 49.3%), nitrogen (from ?24% to 124.2%), and phosphorus (from ?13.3% to 21.2%) during summer and early fall over the next several decades.     

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.     

Deforestation and Forest Management in Southern Ethiopia: Investigations in the Chencha and Arbaminch Areas

Long-term human impacts are considered to be the prime cause of unsustainable forest exploitation in Ethiopia. Yet there exist well-established systems and a wealth of local experience in maintaining and managing forests. This study explores the trends and driving forces of deforestation plus traditional practices regarding sustainable forest use and management in the Chencha and Arbaminch areas, Southern Ethiopia. Satellite image analysis (images from 1972, 1984 and 2006) combined with field surveys were used to detect and map changes in forest cover. Household interviews and group discussions with experienced and knowledgeable persons were also employed. The results show a 23 % decline in forest cover between 1972 and 2006 with the most significant change from 1986 to 2006. Change was greatest in the lowlands and remarkable episodic forest changes also occurred, suggesting nonlinear spatial and temporal forest cover dynamics. According to farmers, the main driver of deforestation is agricultural land expansion in response to local population increases and a decline in agricultural production. Growing local and regional fuel wood demand is another chief cause. Despite these issues, remarkable relicts of natural forests remain and trees on farmland, around homesteads and on fields in every village are basic elements of farm activities and social systems. This demonstrates the effect of cumulative traditional knowledge and long-term local experience with forest management and preservation. Therefore, these practices should be promoted and advanced through the integration of local knowledge and forest management practices in the design and implementation of sustainable environmental planning and management.     

Impact of Changing Climate and Land Cover on Flood Magnitudes in the Delaware River Basin,USA

Changing climate and land cover are expected to impact flood hydrology in the Delaware River Basin over the 21st Century. HEC‐HMS models (U.S. Army Corps of Engineers Hydrologic Engineering Center‐Hydrologic Modeling System) were developed for five case study watersheds selected to represent a range of scale, soil types, climate, and land cover. Model results indicate that climate change alone could affect peak flood discharges by ?6% to +58% a wide range that reflects regional variation in projected rainfall and snowmelt and local watershed conditions. Land cover changes could increase peak flood discharges up to 10% in four of the five watersheds. In those watersheds, the combination of climate and land cover change increase modeled peak flood discharges by up to 66% and runoff volumes by up to 44%. Precipitation projections are a key source of uncertainty, but there is a high likelihood of greater precipitation falling on a more urbanized landscape that produces larger floods. The influence of climate and land cover changes on flood hydrology for the modeled watersheds varies according to future time period, climate scenario, watershed land cover and soil conditions, and flood frequency. The impacts of climate change alone are typically greater than land cover change but there is substantial geographic variation, with urbanization the greater influence on some small, developing watersheds.     

Remote sensing for land management and planning

The primary role of remote sensing in land management and planning has been to provide information concerning the physical characteristics of the land which influence the management of individual land parcels or the allocation of lands to various uses These physical characteristics have typically been assessed through aerial photography, which is used to develop resource maps and to monitor changing environmental conditions These uses are well developed and currently well integrated into the planning infrastructure at local, state, and federal levels in the United States.Many newly emerging uses of remote sensing involve digital images which are collected, stored, and processed automatically by electromechanical scanning devices and electronic computers Some scanning devices operate from aircraft or spacecraft to scan ground scenes directly; others scan conventional aerial transparencies to yield digital images. Digital imagery offers the potential for computer-based automated map production, a process that can significantly increase the amount and timeliness of information available to land managers and planners.Future uses of remote sensing in land planning and management will involve geographic information systems, which store resource information in a geocoded format. Geographic information systems allow the automated integration of disparate types of resource data through various types of spatial models so that with accompanying sample ground data, information in the form of thematic maps and/ or aerially aggregated statistics can be produced Key issues confronting the development and integration of geographic information systems into planning pathways are restoration and rectification of digital images, automated techniques for combining both quantitative and qualitative types of data in information-extracting procedures, and the compatibility of alternative data storage modes     

Forecasts of habitat loss and fragmentation due to urban growth are sensitive to source of input data

The conversion of natural habitat to urban settlements is a primary driver of biodiversity loss, and species' persistence is threatened by the extent, location, and spatial pattern of development. Urban growth models are widely used to anticipate future development and to inform conservation management, but the source of spatial input to these models may contribute to uncertainty in their predictions. We compared two sources of historic urban maps, used as input for model calibration, to determine how differences in definition and scale of urban extent affect the resulting spatial predictions from a widely used urban growth model for San Diego County, CA under three conservation scenarios. The results showed that rate, extent, and spatial pattern of predicted urban development, and associated habitat loss, may vary substantially depending on the source of input data, regardless of how much land is excluded from development. Although the datasets we compared both represented urban land, different types of land use/land cover included in the definition of urban land and different minimum mapping units contributed to the discrepancies. Varying temporal resolution of the input datasets also contributed to differences in projected rates of development. Differential predicted impacts to vegetation types illustrate how the choice of spatial input data may lead to different conclusions relative to conservation. Although the study cannot reveal whether one dataset is better than another, modelers should carefully consider that geographical reality can be represented differently, and should carefully choose the definition and scale of their data to fit their research objectives.