Abstract
A systematic evaluation of nonlinear mixed-effect taper models for volume prediction was performed. Of 21 taper equations with fewer than 5 parameters each, the best 4-parameter fixed-effect model according to fitting statistics was then modified by comparing its values for the parameters total height (H), diameter at breast height (DBH), and aboveground height (h) to modeling data. Seven alternative prediction strategies were compared using the best new equation in the absence of calibration data, which is often unavailable in forestry practice. The results of this study suggest that because calibration may sometimes be a realistic option, though it is rarely used in practical applications, one of the best strategies for improving the accuracy of volume prediction is the strategy with 7 calculated total heights of 3, 6 and 9 trees in the largest, smallest and medium-size categories, respectively. We cannot use the average trees or dominant trees for calculating the random parameter for further predictions. The method described here will allow the user to make the best choices of taper type and the best random-effect calculated strategy for each practical application and situation at tree level.
Highlights
The ability to describe the stem form of a forest tree is important for practical and theoretical reasons
The performance of 5 stem taper functions shows that the function has the form of ((H-h)/(H– 1.3))b0, except for the model of (16) and (21), which does not have that structure for Cunninghamia lanceolata
The taper model developed in this paper is the best taper model for describing stands of Cunninghamia lanceolata
Summary
The ability to describe the stem form of a forest tree is important for practical and theoretical reasons. Foresters require stem profile models for estimating the volume and the value of the whole stem or a part of it [1], to various utilization limits [2, 3]. Such estimates are essential in forest planning, for example in evaluating the economics of different management regimes [4]. A comparison of these 3 types of models shows that the simple polynomial
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
