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

• Published in: Environmental modelling & software : with environment data news 2014-12, Vol.62, p.230-239
• Main Authors: Wang, Wen J., He, Hong S., Spetich, Martin A., Shifley, Stephen R., Thompson, Frank R., Dijak, William D., Wang, Qia
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Biological and medical sciences; Empirical analysis; Forestry; Forests; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; General forest ecology; Generalities. Production, biomass. Quality of wood and forest products. General forest ecology; Inventories; LANDIS PRO; Landscapes; Mathematical models; Methods and techniques (sampling, tagging, trapping, modelling...); Oak forests; Prediction; Stand density management diagrams (SDMDs); Stands; Stockpiling; Supports; U.S. Forest Service Inventory and Analysis (FIA) data; Validation
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2014.09.003

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 long-term forest composition and successional trajectories conformed well to empirical old-growth studies in the region. •We present a framework for evaluating the short- and long-term forest landscape model predictions at site and landscape scales.•Site-scale evaluation is conducted through comparing cell-level predictions with inventory plot data.•Landscape-scale evaluation is conducted through comparing predictions stratified by extraneous drivers with aggregated values in inventory plots.•We successfully evaluated the LANDIS PRO forest landscape model predictions using empirical data and knowledge and showed reasonable performances at both scales.