Ground state atomic nitrogen N([Formula: see text]) was analyzed using two-photon absorption laser-induced fluorescence (TALIF) in sub-atmospheric pressure nitrogen pulsed barrier discharge under needle-to-hemisphere electrode configuration. By reducing the pressure from 90 to 30 kPa, the discharge form transitioned from multiple filaments to a single column, improving the reacting region uniformity. The TALIF measurement revealed that the amount of atomic nitrogen near the needle anode increased over tens of microseconds after the discharge, and this N-production during afterglow was enhanced by reducing the pressure. Reducing the pressure from 90 to 30 kPa extended the half-life period of atomic nitrogen near the anode by 350 [Formula: see text], while maintaining the peak amount of atomic nitrogen. The lifetime extension with the same amount of atomic nitrogen helped improving the chemical activity near the anode. The origin of the N-production during afterglow was not identified as a single factor, but its time constant indicated the contribution of N[Formula: see text] quenched by the ground state atomic nitrogen, along with the quenching of N[Formula: see text], which was previously considered as a major source of afterglow production of the ground state atomic nitrogen. Under 30 kPa, higher discharge energy resulted in faster and larger amount of atomic nitrogen production during afterglow, which indicates the involvement of highly excited particles including metastable atomic nitrogen. In contrast, the decay rate of atomic nitrogen did not depend on the discharge energy. This suggests that the increasing discharge energy broadens the N-productive region while maintaining the local N density.
A low-cost but accurate remote-sensing-based forest-monitoring tool is necessary for regularly inventorying tree-level parameters and stand-level attributes to achieve sustainable management of timber production forests. Lidar technology is precise for multi-temporal data collection but expensive. A low-cost UAV-based optical sensing method is an economical and flexible alternative for collecting high-resolution images for generating point cloud data and orthophotos for mapping but lacks height accuracy. This study proposes a protocol of integrating a UAV equipped without an RTK instrument and airborne lidar sensors (ALS) for characterizing tree parameters and stand attributes for use in plantation forest management. The proposed method primarily relies on the ALS-based digital elevation model data (ALS-DEM), UAV-based structure-from-motion technique generated digital surface model data (UAV-SfM-DSM), and their derivative canopy height model data (UAV-SfM-CHM). Following traditional forest inventory approaches, a few middle-aged and mature stands of Hinoki cypress (Chamaecyparis obtusa) plantation forests were used to investigate the performance of characterizing forest parameters via the canopy height model. Results show that the proposed method can improve UAV-SfM point cloud referencing transformation accuracy. With the derived CHM data, this method can estimate tree height with an RMSE ranging from 0.43 m to 1.65 m, equivalent to a PRMSE of 2.40–7.84%. The tree height estimates between UAV-based and ALS-based approaches are highly correlated (R2 = 0.98, p < 0.0001), similarly, the height annual growth rate (HAGR) is also significantly correlated (R2 = 0.78, p < 0.0001). The percentage HAGR of Hinoki trees behaves as an exponential decay function of the tree height over an 8-year management period. The stand-level parameters stand density, stand volume stocks, stand basal area, and relative spacing are with an error rate of less than 20% for both UAV-based and ALS-based approaches. Intensive management with regular thinning helps the plantation forests retain a clear crown shape feature, therefore, benefitting tree segmentation for deriving tree parameters and stand attributes.
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