Modeling forest site productivity using mapped geospatial attributes within a South Carolina landscape, USA

Abstract

Spatially explicit mapping of forest productivity is important to assess many forest management alternatives. We assessed the relationship between mapped variables and site index of forests ranging from southern pine plantations to natural hardwoods on a 74,000-ha landscape in South Carolina, USA. Mapped features used in the analysis were soil association, land use condition in 1951, depth to groundwater, slope and aspect. Basal area, species composition, age and height were the tree variables measured. Linear modelling identified that plot basal area, depth to groundwater, soils association and the interactions between depth to groundwater and forest group, and between land use in 1951 and forest group were related to site index (SI) (R2 =0.37), but this model had regression attenuation. We then used structural equation modeling to incorporate error-in-measurement corrections for basal area and groundwater to remove bias in the model. We validated this model using 89 independent observations and found the 95% confidence intervals for the slope and intercept of an observed vs. predicted site index error-corrected regression included zero and one, respectively, indicating a good fit. With error in measurement incorporated, only basal area, soil association, and the interaction between forest groups and land use were important predictors (R2 =0.57). Thus, we were able to develop an unbiased model of SI that could be applied to create a spatially explicit map based primarily on soils as modified by past (land use and forest type) and recent forest management (basal area).

  • Citation: Parresol, B.R.; Scott, D.A.; Zarnoch, S.J.; Edwards, L.A.; Blake, J.I. 2017. Modeling forest site productivity using mapped geospatial attributes within a South Carolina landscape, USA. Forest Ecology and Management. 406: 196-207.
  • Keywords: Regression attenuation, Site index, Spatial analysis, Structural equation modeling
  • Posted Date: October 18, 2017
  • Modified Date: November 1, 2018
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