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Polymer microspheres are obtained by the dropwise addition of a precipitant, containing a polymeric stabilizer, into a polymer solution, containing a polymeric stabilizer. The polymer and stabilizer concentrations, the stirring speed, and the precipitation temperature determine the size and size uniformity of the microspheres. Seven polymer microspheres of polyimide, poly(ether imide), poly(ether ketone), poly-(phenylene oxide), polysulfone, poly(vinylidene fluoride), and cellulose diacetate have been prepared with dimethylacetamide as the solvent, with water as the precipitant, and with poly(vinyl alcohol) as the stabilizer. The size and size uniformity of the obtained microspheres are d ϭ 2.3-25.7 m and ⑀ ϭ 0.15-0.50, respectively (⑀ ϭ /d, where ⑀ is the dispersion coefficient, d is the average diameter, and is the standard deviation of the diameter).

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Denitrification is a major process contributing to the removal of nitrogen (N) from ecosystems, but its rate is difficult to quantify. The natural abundance of isotopes can be used to identify the occurrence of denitrification and has recently been used to quantify denitrification rates at the ecosystem level. However, the technique requires an understanding of the isotopic enrichment factor associated with denitrification, which few studies have investigated in forest soils. Here, soils collected from two tropical and two temperate forests in China were incubated under anaerobic or aerobic laboratory conditions for two weeks to determine the N and oxygen (O) isotope enrichment factors during denitrification. We found that at room temperature (20°C), NO was reduced at a rate of 0.17 to 0.35μgNgh, accompanied by the isotope fractionation of N (ε) and O (ε) of 31‰ to 65‰ (48.3±2.0‰ on average) and 11‰ to 39‰ (18.9±1.7‰ on average), respectively. The N isotope effects were, unexpectedly, much higher than reported in the literature for heterotrophic denitrification (typically ranging from 5‰ to 30‰) and in other environmental settings (e.g., groundwater, marine sediments and agricultural soils). In addition, the ratios of ΔδO:ΔδN ranged from 0.28 to 0.60 (0.38±0.02 on average), which were lower than the canonical ratios of 0.5 to 1 for denitrification reported in other terrestrial and freshwater systems. We suggest that the isotope effects of denitrification for soils may vary greatly among regions and soil types and that gaseous N losses may have been overestimated for terrestrial ecosystems in previous studies in which lower fractionation factors were applied.

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Accurate individual tree segmentation (ITS) is fundamental to forest management and to the studies of forest ecosystem. Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) shows advantages for ITS and tree height estimation at stand and landscape scale. However, dense deciduous forests with tightly interlocked tree crowns challenge the performance for ITS. Available LiDAR points through tree crown and appropriate algorithm are expected to attack the problem. In this study, a new UAV-LiDAR dataset that fused leaf-off and leaf-on point cloud (FULD) was introduced to assess the synergetic benefits for ITS and tree height estimation by comparing different types of segmentation algorithms (i.e., watershed segmentation, point cloud segmentation and layer stacking segmentation) in the dense deciduous forests of Northeast China. Field validation was conducted in the four typical stands, including mixed broadleaved forest (MBF), Mongolian oak forest (MOF), mixed broadleaf-conifer forest (MBCF) and larch plantation forest (LPF). The results showed that the combination of FULD and the layer stacking segmentation (LSS) algorithm produced the highest accuracies across all forest types (F-score: 0.70 to 0.85). The FULD also showed a better performance on tree height estimation, with a root mean square error (RMSE) of 1.54 m at individual level. Compared with using the leaf-on dataset solely, the RMSE of tree height estimation was reduced by 0.22 to 0.27 m, and 12.3% more trees were correctly segmented by the FULD, which are mainly contributed by improved detection rate at nearly all DBH levels and by improved detection accuracy at low DBH levels. The improvements are attributed to abundant points from the bole to the treetop of FULD, as well as each layer point being included for segmentation by LSS algorithm. These findings provide useful insights to guide the application of FULD when more multi-temporal LiDAR data are available in future.