, 2006), this is probably due to Licor underestimations of LAI (

, 2006), this is probably due to Licor underestimations of LAI ( Sampson and Allen, 1995); hence, predicted LAI values from the developed equation were low compared to litter trap estimates check details ( Gresham, 1982 and Dalla-Tea and Jokela, 1991) but in agreement with Licor measurements ( Sampson et al., 2003).

In addition, an unrealistic estimated LAI value (0.12) collected in one of the heavily thinned plots of the RW18 study was deleted from the dataset. Small footprint lidar data were acquired for all the study areas in late August 2008. The system was an Optech ATLM 3100 with an integrated Applanix DSS 4K × 4K DSS camera. The data have multiple returns with a sampling density of 5 pulses per square meter, with at least 4 returns per pulse. The scan angle was less than 15°. Instrument vertical accuracy over bare ground is 15 cm, and horizontal selleck inhibitor accuracy is 0.5 m. Ground returns were already extracted by the lidar provider, and the data were reviewed to determine whether the ground return classification had any flaws. Based on the size of the lidar dataset, these study sites represent a relatively small area, which is an advantage in terms of the computation time necessary to run interpolation models. Therefore, the kriging method was applied to the provided ground returns to generate a digital elevation model (DEM) for the area (Popescu et al., 2002). Next, lidar data points

per plot were separated in three classes: “ground returns” (height above the ground, hag = 0 m), “all returns” (hag > 0.2 m), and “vegetation returns” (hag > 1 m). Vegetation returns were classified using a 1 m threshold because the instrument used to estimate LAI in situ was held at approximately 1 m above Nutlin-3 mouse the ground. The metrics derived from the ground returns class (Gr) were: frequency (count) of returns and frequency (count) of pulses (Table 1). The metrics derived

from the all returns class (All) were: frequency (count), mean height, standard deviation, coefficient of variation, minimum, maximum, percentiles (10, 20, 25, 40, 50, 75, and 90), and frequency (count) of pulses (Magnussen and Boudewyn, 1998, Popescu et al., 2002 and Holmgren, 2004). The metrics derived from the vegetation returns class (Veg) were the same described for the all returns class with the addition of the mode. The distribution of intensity values (I) were described using the mean, minimum, maximum, standard deviation, and coefficient of variation. First, second, third and fourth returns were classified as such and divided by the total number of “vegetation returns” (R). The laser penetration index (LPI) ( Barilotti et al., 2005), developed taking into account the transmission of the laser beams through the canopy, uses the number of ground returns. It is based on the same principles than the instruments to indirectly measure LAI on the ground (measuring the solar light transmission or reflectance through vegetation).

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