Errors In Terrain-Based Model Predictions Caused By Altered Forest Inventory Plot Locations In The Southern Appalachian Mountains, USA
Keywords:
uncertainty, errors in measurements, GIS, FSQIAbstract
Forest modeling applications that cover large geographic areas can benefit from the use of widely-held knowledge about relationships between forest attributes and topographic variables. A noteworthy example involves the coupling of field survey data from the Forest Inventory Analysis (FIA) program of USDA Forest Service with digital elevation model (DEM) data in attempts to explain how topographic characteristics influence forest productivity, vegetation composition, fire behavior, and other phenomena. Because U.S. federal law prohibits the release of actual FIA plot coordinates, only altered coordinates are released to the public. Here, terrain-based variables derived from a 10 m DEM using actual FIA plot locations were compared to those from altered plot locations in a region of the Southern Appalachian Mountains of western North Carolina, USA. Variables examined included simple terrain attributes such as percent slope and azimuth of aspect, and composite attributes such as terrain shape index, flow accumulation, slope position and forest site quality index. Results showed little correspondence between variables from altered plot locations compared to those derived using actual locations. Further, FIA field measurements of percent slope and azimuth of aspect showed little correspondence with corresponding DEM-derived estimates from the actual plot coordinates. In order to effectively link FIA plot data with DEM-derived topographic variables in mountainous regions like the Southern Appalachians, access to actual plot coordinates or terrain variables derived from them may be required.
References
Bailey, R.G. 2009. Ecosystem Geography: From Ecoregions to Sites. 2nd ed. Springer, New York. 251 p.
Best, D.J., and Roberts, D.E. 1975. Algorithm AS 89: The upper tail probabilities of Spearman's rho. Journal of the Royal Statistical Society. Series C (Applied Statistics) 24(3): 377-379.
Bolstad, P., Jenks, A., Berkin, J., Horne, K., and Reading, W.H. 2005. A comparison of autonomous, WAAS, real-time, and post-processed global positioning systems (GPS) accuracies in northern forests. Northern Journal of Applied Forestry 22(1): 5-11.
Bolstad, P.V., Swank, W., and Vose, J. 1998. Predicting Southern Appalachian overstory vegetation with digital terrain data. Landscape Ecology 13(5): 271-283.
Bolstad, P.V., Vose, J.M., and McNulty, S.G. 2001. Forest productivity, leaf area, and terrain in southern Appalachian deciduous forests. Forest Science 47(3): 419-427.
Carmean, W.H. 1970. Site quality for eastern hardwoods. P. 36-56. in Silviculture of oaks and associated species. R. D. Lane (ed). USDA, Forest Service, Northeastern Forest Experiment Station, Upper Darby, PA.
Coulston, J.W., Reams, G.A., McRoberts, R.E., and Smith, W.D. 2006a. Practical considerations when using perturbed forest inventory plot locations to develop spatial models: a case study. P. 81-86 in Proceedings of the sixth annual forest inventory and analysis symposium. Gen. Tech. Rep. WO-70. R.E. McRoberts, G.A. Reams, P.C. VanDeusen, and W.H. McWilliams (eds). USDA, Forest Service, Denver, CO.
Coulston, J.W., Riitters, K.H., McRoberts, R.E., Reams, G.A., and Smith, W.D. 2006b. True versus perturbed forest inventory plot locations for modeling: a simulation study. Canadian Journal of Forest Research 36(3): 801-807.
Davis, F.W., and Goetz, S. 1990. Modeling vegetation pattern using digital terrain data. Landscape Ecology 4: 69-80.
Davis, F.W., Seo, C., and Zielinski, W.J. 2007. Regional variation in home-range-scale habitat models for fisher (Martes pennanti) in California. Ecological Applications 17(8): 2195-2213.
Deng, Y.X., Chen, X.F., and Kheir, R.B. 2007. An exploratory procedure defining a local topographic index for mountainous vegetation conditions. Giscience & Remote Sensing 44(4): 383-401.
Fenneman, N.M., and Johnson, D.W. 1946. Physiographic divisions of the conterminous United States. U.S. Geological Survey special map series, scale 1:7,000,000. Washington D.C.
Fisher, R.A. 1915. Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 10(4): 507-521.
Forest Inventory and Analysis. 2007. Forest inventory and analysis national core field guide - Volume 1: field data collection procedures for phase II plots version 4.0. USDA Forest Service (ed). Forest Inventory and Analysis National Office.
Forest Inventory and Analysis. 2009. FIA DataMart - FIADB version 4.0. USDA Forest Service, Forest Inventory and Analysis National Program, Arlington VA.
Franklin, J. 1995. Predictive vegetation mapping: Geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 19(4): 474-499.
Guldin, R.W., King, S.L., and Scott, C.T. 2006. Vision for the future of FIA: paean to progress, possibilities, and partners. P. 1-8 in Proceedings of the sixth annual forest inventory and analysis symposium. Gen. Tech. Rep. WO-70. R.E. McRoberts, G.A. Reams, P.C. VanDeusen, and W.H. McWilliams (eds). USDA, Forest Service, Denver, CO.
Higgins, J.J. 2004. Introduction to Modern Nonparametric Statistics. Brooks/Cole - Thomson Learning, Pacific Grove, California. 366 p.
Horn, B.K.P. 1981. Hillshading and the reflectance map. Proceedings of the IEEE 69(1): 14-47.
Horsch, B. 2003. Modelling the spatial distribution of montane and subalpine forests in the central Alps using digital elevation models. Ecological Modelling 168(3): 267-282.
Iverson, L.R., Dale, M.E., Scott, C.T., and Prasad, A. 1997. A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (USA). Landscape Ecology 12(5): 331-348.
Landis, J.R., and Koch, G.G. 1977. Measurement of observer agreement for categorical data. Biometrics 33(1): 159-174.
LaPoint, E. 2005. Access and use of FIA data through FIA spatial data services. P. 199-202 in Proceedings of the fifth annual forest inventory and analysis symposium. Gen. Tech. Rep. WO-69. R.E. McRoberts, G.A. Reams, P.C. VanDeusen, and W.H. McWilliams (eds). USDA, Forest Service, New Orleans, LA.
Liknes, G.C., Holden, G.R., Nelson, M.D., and McRoberts, R.E. 2005. Spatially locating FIA plots from pixel values. P. 99-103 in Proceedings of the fourth annual forest inventory and analysis symposium. Gen. Tech. Rep. NC-252. Ronald E. McRoberts, Gregory A. Reams, Paul C. Van Deusen, William H. McWilliams, and Chris J. Cieszewski (eds). USDA, Forest Service, North Central Research Station, St. Paul, MN.
Lister, A., Scott, C., King, S., Hoppus, M., Butler, B., and Griffith, D. 2005. Strategies for preserving owner privacy in the national information management system of the USDA Forest Service's Forest Inventory and Analysis unit. P. 163-166 in Proceedings of the fourth annual forest inventory and analysis symposium. Gen. Tech. Rep. NC-252. Ronald E. McRoberts, Gregory A. Reams, Paul C. Van Deusen, William H. McWilliams, and Chris J. Cieszewski (eds). USDA Forest Service, North Central Research Station, St. Paul, MN.
McNab, W.H. 1989. Terrain shape index - quantifying effect of minor landforms on tree height. Forest Science 35(1): 91-104.
McNab, W.H. 1993. A topographic index to quantify the effect of mesoscale landform on site productivity. Canadian Journal of Forest Research 23(6): 1100-1107.
McRoberts, R.E. 2010. The effects of rectification and Global Positioning System errors on satellite image-based estimates of forest area. Remote Sensing of Environment 114(8): 1710-1717.
McRoberts, R.E., Holden, G.R., Nelson, M.D., Liknes, G.C., Moser, W.K., Lister, A.J., King, S.L., LaPoint, E.B., Coulston, J.W., Smith, W.B., and Reams, G.A. 2005. Estimating and circumventing the effects of perturbing and swapping inventory plot locations. Journal of Forestry 103(6): 275-279.
Meiners, T.M., Smith, D.W., Sharik, T.L., and Beck, D.E. 1984. Soil and plant water-stress in an Appalachian oak forest in relation to topography and stand age. Plant and Soil 80(2): 171-179.
Roise, J.P., and Betters, D.R. 1981. An aspect transformation with regard to elevation for site productivity models. Forest Science 27(3): 483-486.
Ross, M.S., Sharik, T.L., and Smith, D.W. 1986. Oak regeneration after clear felling in southwest Virginia. Forest Science 32(1): 157-169.
Smith, W.B. 2002. Forest inventory and analysis: a national inventory and monitoring program. Environmental Pollution 116: S233-S242.
Stage, A.R. 1976. Expression for effect of aspect, slope, and habitat type on tree growth. Forest Science 22(4): 457-460.
Stage, A.R., and Salas, C. 2007. Interactions of elevation, aspect, and slope in models of forest species composition and productivity. Forest Science 53(4): 486-492.
Stehman, S.V. 1997. Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment 62(1): 77-89.
Tenenbaum, D.E., Band, L.E., Kenworthy, S.T., and Tague, C.L. 2006. Analysis of soil moisture patterns in forested and suburban catchments in Baltimore, Maryland, using high-resolution photogrammetric and LIDAR digital elevation datasets. Hydrological Processes 20(2): 219-240.
Tirpak, J.M., Jones-Farrand, D.T., Thompson, F.R., Twedt, D.J., Baxter, C.K., Fitzgerald, J.A., and Uihlein, W.B. 2009. Assessing ecoregional-scale habitat suitability index models for priority landbirds. Journal of Wildlife Management 73(8): 1307-1315.
Whittaker, R.H. 1956. Vegetation of the Great Smoky Mountains. Ecological Monographs 26(1): 2-80.
Whittaker, R.H. 1966. Forest dimensions and production in the Great Smoky Mountains. Ecology 47(1): 103-121.
Zarnetske, P.L., Edwards, T.C., and Moisen, G.G. 2007. Habitat classification modeling with incomplete data: Pushing the habitat envelope. Ecological Applications 17(6): 1714-1726.
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