Predicting Southern Appalachian overstory vegetation with digital terrain data

Paul V. Bolstad, Wayne Swank, James Vose

Research output: Contribution to journalArticlepeer-review

98 Scopus citations

Abstract

Vegetation in mountainous regions responds to small-scale variation in terrain, largely due to effects on both temperature and soil moisture. However there are few studies of quantitative, terrain-based methods for predicting vegetation composition. This study investigated relationships between forest composition, elevation, and a derived index of terrain shape, and evaluates methods for predicting forest composition. Trees were measured on 406 permanent plots within the boundaries of the Coweeta Hydrologic Lab, located in the Southern Appalachian Mountains of western North Carolina, USA. All plots were in control watersheds, without human or major natural disturbance since 1923. Plots were 0.08 ha and arrayed on transects, with approximately 380 meters between parallel transects. Breast-height diameters were measured on all trees. Elevation and terrain shape (cove, ridge, sideslope) were estimated for each plot. Density (trees/ha) and basal area were summarized by species and by forest type (cove, xeric oak-pine, northern hardwoods, and mixed deciduous). Plot data were combined with a digital elevation data (DEM), and a derived index of terrain shape at two sampling resolutions: 30 m (US Geological Survey), and 80 m (Defense Mapping Agency) sources. Vegetation maps were produced using each of four different methods: 1) linear regression with and without log transformations against elevation and terrain variables combined with cartographic overlay, 2) kriging, 3) co-kriging, and 4) a mosaic diagram. Predicted vegetation was compared to known vegetation at each of 77 independent, withheld data points, and an error matrix was determined for each mapping method. We observed strong relationships between some species and elevation and/or terrain shape. Cove and xeric oak/pine species basal areas were positively and negatively related to concave landscape locations, respectively, while species typically found in the mixed deciduous and northern hardwood types were not. Most northern hardwood species occurred more frequently and at higher basal areas as elevation increased, while most other species did not respond to elevation. The regression and mosaic diagram mapping approaches had significantly higher mapping accuracies than kriging and co-kriging. There were significant effects of DEM resolution on map accuracy, with maps based on 30 m DEM data significantly more accurate than those based on 80 m data. Taken together, these results indicate that both the mapping method and terrain data resolution significantly affect the resultant vegetation maps, even when using relatively high resolution data. Landscape or regional models based on 100 m or lower resolution terrain data may significantly under-represent terrain-related variation in vegetation composition.

Original languageEnglish (US)
Pages (from-to)271-283
Number of pages13
JournalLandscape Ecology
Volume13
Issue number5
DOIs
StatePublished - 1998

Bibliographical note

Funding Information:
This work was supported in part by NSF grants BSR-9011661and DEB-9407701. We wish to thank the numerous members of the field crews responsible for data collection. We also wish to thank Bruce McCoy for digital data development, and Katherine Elliot and Dean Urban for reviews of earlier versions of this manuscript.

Keywords

  • Community
  • Composition
  • DEM
  • Deciduous forests
  • Landscape
  • Prediction
  • Terrain shape

Fingerprint

Dive into the research topics of 'Predicting Southern Appalachian overstory vegetation with digital terrain data'. Together they form a unique fingerprint.

Cite this