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However, the unit cost of energy wood harvesting is clearly less sensitive to variations in stand density and volume.
A suitable spatial resolution for studying variations in stand productivity, mean tree volume, and stocking rate across stands were established that enabled useful forest-yield maps from harvester data to be developed.
Previous studies have reported significant variations in stand height, plant morphology and salinity tolerance related to the ploidy level (Hanganu et al. 1999; Pauca-Comanescu et al. 1999; Hansen et al. 2007).
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SDI clearly explains part of the variation in stand productivity.
Across the plot series there was marked variation in stand metrics and LiDAR metrics obtained at 10 pulses m−2.
Tree size had a small but positive effect and explained about 0.2 m3∙ha−1∙yr.−1 of the variation in stand productivity (Fig. 3f).
Response functions between GRper and independent variables were well defined as variation in stand and site conditions were extremely wide.
We found wide variation in stand height (7.8 22.5 m), basal area (1.4 47.9 m2 hand) and tree density (14 2199 ha−1) but single-storied structures prevailed.
LiDAR is an established technology that is increasingly being used to characterise spatial variation in stand metrics used in forest inventory.
Apart from the location which already explains a large part of the variation in stand productivity, climate played a major role.
Research is required that investigates how plot size and pulse density influence the quality of LiDAR-volume relationships across planted forest stands that include a wide variation in stand densities.
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