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With the emergence and advancement of airborne laser scanning technology over the past decade, individual tree height can be easily measured over a large area of forests with a comparable degree of accuracy to conventional ground-based methods. In laser scanning based large-scale forest
inventories, the need to predict diameter from remotely sensed tree height calls for a systematic evaluation of equation forms as the first step towards a well-developed approach to developing diameter–height equations. This study evaluated more than 30 height–diameter equations
in the forest biometrics literature to select candidates for deriving equation forms for diameter–height equations. The evaluation was based on four criteria: (i) the height–diameter function is inversable; (ii) the inverse function is continuous and monotonically
increasing over a specified working range of total tree height; (iii) diameter at breast height is equal to zero when tree height equals breast height in the inverse function; and preferably, (iv) the inverse function has an inflection point that is consistent with biological
expectations. A total of 12 candidate equation forms were derived, which included five two-parameter and seven three-parameter equations. The estimation properties and predictive performance of these 12 equation forms were further evaluated and compared through repeated sampling and fitting
using data from 3581 trees destructively sampled for taper measurements from Pinus radiata D. Don plantations across New South Wales, Australia. Three equation forms, including the constrained Richards,
Weibull, and the combined power and exponential function, displayed superior prediction accuracy and estimation properties and so were recommended as the primary equation forms for developing diameter–height equations. The remaining equation forms were marred by either lower prediction
accuracy or poorer estimation properties or both. The three recommended equation forms should only serve as basic deterministic specifications upon which other tree and stand variables should be incorporated as predictors to further improve their predictive performance.
Forest Science Centre, New South Wales Department of Primary Industries, P.O. Box 100, Beecroft, NSW 2119, Australia. 2:
Department of Forest and Ecosystem Science, University of Melbourne, Melbourne, VIC 3052, Australia. 3:
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China. 4:
Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China.
Publication date: April 14, 2012
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Published since 1971, this monthly journal features articles, reviews, notes and commentaries on all aspects of forest science, including biometrics and mensuration, conservation, disturbance, ecology, economics, entomology, fire, genetics, management, operations, pathology, physiology, policy, remote sensing, social science, soil, silviculture, wildlife and wood science, contributed by internationally respected scientists. It also publishes special issues dedicated to a topic of current interest.