Andrew Evans. 1. , Rick Odom. 2,. W. Mark Ford. 3. , Lynn Resler. 2. , Steve Prisley2. 1Texas A&M Department of Geography, 2Virginia Tech Department of ...
Andrew
1 Evans , 1Texas
Introduction
Rick
2, Odom W.
Mark
Deciduous Forest
Lynn
2 Resler ,
Species Composition includes at least two of the following species: B. alleghaniensis, A. rubrum, F. grandifolia
Contains one or more of the following species: Abies fraseri, Picea rubens
Study Area
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TN
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3
5 6 8
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NC
Northern Red Oak Forest Type
Northern Hardwood Forest Type
Spruce-fir Forest Type
Northern Hardwood Forest Type Northern Red Oak Forest Type Spruce-fir Forest Type Montane Pine Community
Results:
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Steve Prisley2
A&M Department of Geography, 2Virginia Tech Department of Geography, 3USGS Fish and Wildlife Cooperative Unit
Decision Tree and Forest Type Photographs
In forested mountain landscapes where topography controls or influences many biophysical characteristics, such as microclimate, modeling terrain attributes within a geographic information system (GIS) provides an effective approach for tackling this problem. Topographic characteristics that are often directly correlated with environmental gradients of interest can be accurately and efficiently modeled for large geographic areas using widely available digital elevation data and spatial analysis software. A predictive model provides a baseline condition for predicting the impact of climate change in the southern Appalachians to high elevation forest types such as northern hardwoods that are presumed to be a risk for decline. The objectives of our study were to (1) define the northern hardwood forest type in western North Carolina, adjacent portions of eastern Tennessee, and southwestern Virginia and (2) to determine if the geographic distribution of northern hardwood forest types can be accurately determined from digital terrain modeling.
3 Ford ,
The 332 sites we sampled encompassed a wide range of terrain conditions, 179 were characterized as the northern hardwood forest type and the remaining 153 sites were composed of the high elevation northern red oak forest type (n = 80), and the red spruceFraser fir forest type (n = 73). Our model containing Elevation + TEI, the latitude variable, and all interactions between the variables was shown to be the best approximating model for the overall study area based on AICc score. Elevation + TEI indicated that the predicted occurrence of the northern hardwood forest type was positively correlated with quadratic effects of elevation, and negatively correlated with the interaction of elevation and LFI along with the quadratic effects of the latitudinal variable . Empirically, there was some minor support for the Elevation + Aspect model because it was within approximately 11 AICc units of the Elevation + TEI model. The accuracy of the predictive model can be seen in the table at right.
Predicted Northern Hardwood Forest Type Maps
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Discussion:
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Methods: We sampled vegetation characteristics at 332 points across 11 study areas in western North Carolina, adjacent portions of eastern Tennessee, and southwestern Virginia (see map.). All sample points were in high elevation areas (>1214 meters) and fell within the Blue Ridge Parkway National Park (BRPNP), Pisgah, Nantahala, Cherokee or Jefferson national forests, or on lands owned by the Eastern Band of Cherokee Indians. Digital Data • Derived Terrain Variables (10 meter resolution DEMs National Elevation Dataset (http://ned.usgs.gov/) Elevation Slope gradient Aspect Topographic exposure index (TEI) * Slope curvature
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Accuracy Assessment
References Predicted Northern Red Oak
Observed
*Topographic exposure index (TEI) was derived by subtracting the average elevation of an area (defined as a circular area with radius equal to 1000m) surrounding each cell in the DEM using the Raster Calculator tool.
Statistical Analysis: We created a series of six overarching a priori model categories based on previous research which were then inputted as multinomial regression models in the R statistical software 1) Elevation (Nowacki and Wendt, 2009) 2) Elevation and TEI (Odom and McNab, 2000) 3) Elevation and Aspect (Daniels et al., 1999) 4) Elevation and Curvature (McNab, 1989; Bolstad et al., 1998), 5) Slope and TEI (Yoke and Rennie, 1996) 6) Elevation, Aspect and Slope (North Carolina Wildlife Resources Commission, 2005) 7) Global using all parameters. Our best performing model, based on AICc, was then used to create a predictive map of the northern hardwood forest type.
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Our use of GIS to delineate northern hardwood forests in the southern Appalachians gives managers in the region a tool to better understand the distribution and composition of high-elevation forests important for the management of endangered and endemic species restricted to these high elevation forests. Previous studies have indicated that most physiographic regions can be modeled with some degree of accuracy in the southern Appalachians via digital terrain analysis within a GIS (McCombs, 1997; Bolstad et al., 1998; Odom and McNab, 2000; Narayanaraj et al., 2010). Our results show, based on the varying success of the models, that a perceptible relationship exists between the northern hardwood forest type and the variables we examined.
Forest Types Northern Hardwood Northern Red Oak Spruce-fir Total
Northern Hardwood
Overall Model Accuracy: 67% Correctly Predicted Northern Hardwood Presence: 80%
143 39 34 216
Spruce-fir 22 41 1 64
14 0 38 52
Total 179 80 73 332
Bolstad, P.V., Swank, W., Vose, J., 1998. Predicting southern Appalachian overstory vegetation with digital terrain data. Landscape Ecology 13, 271-283. Daniels, R.B., Kleiss, H.J., Ditzler, C.A., 1999. Soil systems in North Carolina. North Carolina State Univeristy, Raleigh, North Carolina, 60-101. McCombs, J.W., 1997. Geographic information systems topographic factor maps for wildlife management. M.S. Thesis, Virginia Polytechnice Institute and State University, Blacksburg. McNab, W.H., 1989. Terrain shape index - quantifying effect of minor landforms on tree height. Forest Science 35, 91-104. Narayanaraj, G., Bolstad, P.V., Elliott, K.J., Vose, J.M., 2010. Terrain and landform influence on Tsuga canadensis (L.) Carrière (eastern hemlock) distribution in the southern Appalachian Mountains. Castanea 75, 118. North Carolina Wildlife Resources Commission, 2005. North Carolina wildlife action plan. Raleigh, NC. Nowacki, G.J., Wendt, D., 2009. The current distribution, predictive modeling, and restoration potential of red spruce in West Virginia. Rench, J.S., Schuler, T.M. (Eds.), Proceedings from The conference on the ecology and management of high-elevation forests in the central and southern Appalachian Mountains.Slaty Fork WV: U.S. Department of Agriculture, U.S. Forest Service, Northern Research Station, Newtown Square, Pennsylvania 163-178. Odom, R.H., McNab, W.H., 2000. Using digital terrain modeling to predict ecological types in the Balsam Mountains of Western North Carolina. Asheville, N.C.: U.S. Department of Agriculture, Forest Service, Southern Research Station, 12 pp. Yoke, K.A., Rennie, J.C., 1996. Landscape ecosystem classification in the Cherokee National Forest, east Tennessee, U.S.A. Environmental Monitoring and Assessment 39, 323-338.
