Global Tree Taper Modelling: A Review of Applications, Methods, Functions, and Their Parameters

• Main Authors: Serajis Salekin, Cristian Higuera Catalán, Daniel Boczniewicz, Darius Phiri, Justin Morgenroth, Dean F. Meason, Euan G. Mason
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Forests
• Subjects: stem shape; taper; growth and yield; forest mensuration; tree structure; forest inventory
• Online Access: https://www.mdpi.com/1999-4907/12/7/913

Taper functions are important tools for forest description, modelling, assessment, and management. A large number of studies have been conducted to develop and improve taper functions; however, few review studies have been dedicated to addressing their development and parameters. This review summarises the development of taper functions by considering their parameterisation, geographic and species-specific limitations, and applications. This study showed that there has been an increase in the number of studies of taper function and contemporary methods have been developed for the establishment of these functions. The reviewed studies also show that taper functions have been developed from simple equations in the early 1900s to complex functions in modern times. Early taper functions included polynomial, sigmoid, principal component analysis (PCA), and linear mixed functions, while contemporary machine learning (ML) approaches include artificial neural network (ANN) and random forest (RF). Further analysis of the published literature also shows that most of the studies of taper functions have been carried out in Europe and the Americas, meaning most taper equations are not specifically applicable to tropical tree species. Developing well-conditioned taper functions requires reducing the variation due to species, measurement techniques, and climatic conditions, among other factors. The information presented in this study is important for understanding and developing taper functions. Future studies can focus on developing better taper functions by incorporating emerging remote sensing and geospatial datasets, and using contemporary statistical approaches such as ANN and RF.