A framework for evaluating forest landscape model predictions using empirical data and knowledge

  • Authors: Wang, Wen J.; He, Hong S.; Spetich, Martin A.; Shifley, Stephen R.; Thompson, Frank R.; Dijak, William D.; Wang, Qia.
  • Publication Year: 2014
  • Publication Series: Scientific Journal (JRNL)
  • Source: Environmental Modelling & Software. 62: 230-239.
  • DOI: 10.1016/j.envsoft.2014.09.003

Abstract

Evaluation of forest landscape model (FLM) predictions is indispensable to establish the credibility of predictions. We present a framework that evaluates short- and long-term FLM predictions at site and landscape scales. Site-scale evaluation is conducted through comparing raster cell-level predictions with inventory plot data whereas landscape-scale evaluation is conducted through comparing predictions stratified by extraneous drivers with aggregated values in inventory plots. Long-term predictions are evaluated using empirical data and knowledge.We demonstrate the applicability of the framework using LANDIS PRO FLM. We showed how inventory data were used to initialize the landscape and calibrate model parameters. Evaluation of the short-term LANDIS PRO predictions based on multiple metrics showed good overall performance at site and landscape scales. The predicted long-term stand development patterns were consistent with the established theories of stand dynamics. The predicted longterm forest composition and successional trajectories conformed well to empirical old-growth studies in the region.

  • Citation: Wang, Wen J.; He, Hong S.; Spetich, Martin A.; Shifley, Stephen R.; Thompson, Frank R.; Dijak, William D.; Wang, Qia. 2014. A framework for evaluating forest landscape model predictions using empirical data and knowledge. Environmental Modelling & Software. 62: 230-239.
  • Keywords: LANDIS PRO, Validation, U.S. Forest Service Inventory and Analysis, (FIA) data, Stand density management diagrams, (SDMDs), Oak forests, Prediction
  • Posted Date: October 3, 2014
  • Modified Date: October 16, 2014
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