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Abstract

The volume of bark is a function of bark thickness and tree diameter, thus bark and tree volume calculations depend on accurate determination of bark thickness. Bark thickness is affected by a number of inherent and external factors. The purpose of this study was to examine the factors affecting bark thickness and to derive models predicting bark thickness for Picea orientalis (L.) Link. This study indicated that bark thickness at breast height was strongly correlated with geographical aspect, tree age and diameter classes, and bark thickness increased as tree age or diameter class increased in sunny or shady aspect. Diameter over bark at breast height explained 50% of the variations in double bark thickness at breast height on shady aspects and 68% of the variations in double bark thickness of P. orientalis at breast height on sunny aspects. By adding tree age to the functions, the coefficient of determination increased by 1–2%. Thus, not only tree ages and diameters, but also the aspect in which trees grow should be taken into account to calculate the amounts of wood and bark in P. orientalis.     

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New forest inventory methods must be developed in order to create good conditions for decision‐making regarding ecological and economical issues in forestry. There are good field measurement methods to use, but they are often very expensive. Coherent all radio band sensing (CARABAS) is a newly developed synthetic aperture radar (SAR) sensor. It differs significantly from the earlier SARs by using longer wavelengths. The CARABAS sensor is more adapted to the scatterers of interest in the forest, due to its longer wavelengths. In this study, CARABAS imagery is compared with forest tree volume. Regression analysis was used to relate radar backscattering to forest tree volume. Due to the large range of incidence angle (45°‐68°), the CARABAS image had to be radiometrically relative‐calibrated. Radar backscattering from five forest stands with similar volume contents were plotted against the distance from the sensor. The plot revealed a linear relationship between these variables. By linear regression on that material the other pixels were relative‐calibrated in the image. Finally, radar backscattering was related to forest stand volumes by using linear regression. The results showed that the backscattering component of the CARABAS imagery is highly correlated to forest tree volume (R² = 74.9%). In this material, there seemed to be no saturation level of the backscattering component up to 300 m³ ha^?1.