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The biomass value was then assigned to all REs intercepted or traversed by the inventory plot.
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The cost of forest inventory and LiDAR acquisition are strongly related to the inventory plot size and the LiDAR pulse density, respectively.
The real locations of the inventory plots can differ by up to 1 km in Germany (Henning 2016) and by 0.8 11.6 km in the USA (O′Connell et al. 2014).
Possibly, the inventory plots from the two inventories are at different development stages with consequently different structure-productivity relationships.
Outerwood stress-wave velocity (V) was measured in 32 of the inventory plots.
Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany.
This is overlaid by the location of the field inventory plots.
Stand productivity for the US plots was derived from the periodic annual increment growth between the two inventories for the US inventory plots where more than one inventory was conducted (Liang et al. 2016).
In our case, as most of the variation in productivity among the sample plots was already explained by location and climate, for the US inventory plots, effects of species and structure were very small but still present.
Here, we focus on a 50 ha forest inventory plot managed by the Center for Tropical Forest Science CTFSS) that contains some of the largest trees on the Island (up to 54 m).Although most of the study site is located in the main plateau of BCI (<10° in slope), its south-eastern edge shows slopes that exceed 30 40° [21].
Equation 1 includes the number of trees counted per inventory plot N, the diameter at breast height of the ith tree per plot, d i, and the number of stems per ha represented by the ith tree, n i.
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