Predicting bark thickness with one- and two-stage regression models for three hardwood species in the southeastern US
نویسندگان
چکیده
• Bark thickness at breast height can be predicted by a species-specific, DBH-based model. Combining stem taper and bark models is suggested to predict profile. Upper lower portions of tree reliably the two-stage method. Polynomial profile more accurately than variable-exponent models. Tree bark, as outermost protective layer stems, an important indicator evaluate fire resistance properties trees assess mortality induced fire. Despite its importance, many existing were not primarily developed for predicting directly, i.e. with response variable, most past studies focused on modeling in conifers. Thus, objective this study was compare efficacy various models/methods three common hardwood species southeastern US. A total number 47,281 measurements from 2,070 used analysis. Results showed that (1.37 m or 4.5 ft above ground) varies size species, which species-specific linear regression model DBH single predictor. To profile, combination function model, method, suggested, generally performs better (one-stage method) terms bias precision. For given form, method produced reliable prediction upper one-stage With examined, segmented functions provided accurate predictions function. The results provide guidance ecologists forest managers when selecting appropriate approaches thickness.
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ژورنال
عنوان ژورنال: Forest Ecology and Management
سال: 2022
ISSN: ['0378-1127', '1872-7042']
DOI: https://doi.org/10.1016/j.foreco.2021.119778