Total tree height (h) is often difficult to measure in natural forests. Regression models based on easily accessed variables like DBH (d) can be an alternative, since their assumptions are validated. The aims of this study are to: (i) calibrate specific and generic h-d models for three forest types (Seasonal Deciduous Forest, DEC; Mixed Ombrophilous Forest, MIX; and Dense Rainforest, DEN) in Santa Catarina state testing the regression assumptions and evaluating model quality; (ii) verify different h-d relationship between forest types. The dataset (1,766 measured tree h and 3,150 estimated h) was collected by Santa Catarina Forest and Floristic Inventory (IFFSC) in 418 systematically located sample plots. Models were calibrated for two datasets, one containing hypsometer measurements, the other h estimations made by field crews. Specific models were calibrated for species with at least 30 sampled trees. Residual normality, randomness and heteroskedasticity were evaluated by analytical methods. Confidence bands were generated by the Working-Hotelling method; z test for means was applied to compare models based on the two databases. The statistical parameters such as corrected Akaike Information Criterion provided evidences that logarithmic models were better adjusted to the data. Both datasets were statistically different for DEN and MIX. Differences in h-d relationships were found between forest types. The use of calibrated h-d models is an alternative for studying the relationships between these variables and to assess vertical structure patterns of forest communities, when h measurements are not feasible, although, for situations that more accurate h values are needed, they will not always provide reliable predictions.
Tree height prediction; h-d relationship; forest inventory; regression assumptions; model calibration
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