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Understory vegetation plays a vital role in regulating soil carbon (C) and nitrogen (N) characteristics due to differences in plant functional traits. Different understory vegetation types have been reported following aerial seeding. While aerial seeding is common in areas with serious soil erosion, few studies have been conducted to investigate changes in soil C and N cycling as affected by understory vegetation in aerially seeded plantations. Here, we studied soil C and N characteristics under two naturally formed understory vegetation types (Dicranopteris and graminoid) in aerially seeded Pinus massoniana Lamb plantations. Across the two studied understory vegetation types, soil organic C was significantly correlated with all measured soil N variables, including total N, available N, microbial biomass N and water-soluble organic N, while microbial biomass C was correlated with all measured variables except soil organic C. Dicranopteris and graminoid differed in their effects on soil C and N process. Except water-soluble organic C, all the other C and N variables were higher in soils with graminoids. The higher levels of soil organic C, microbial biomass C, total N, available N, microbial biomass N and water-soluble organic N were consistent with the higher litter and root quality (C/N) of graminoid vegetation compared to Dicranopteris. Changes in soil C and N cycles might be impacted by understory vegetation types via differences in litter or root quality.

In this study, an individual tree crown ratio (CR) model was developed with a data set from a total of 3134 Mongolian oak (Quercus mongolica) trees within 112 sample plots allocated in Wangqing Forest Bureau of northeast China. Because of high correlation among the observations taken from the same sampling plots, the random effects at levels of both blocks defined as stands that have different site conditions and plots were taken into account to develop a nested two-level nonlinear mixed-effect model. Various stand and tree characteristics were assessed to explore their contributions to improvement of model prediction. Diameter at breast height, plot dominant tree height and plot dominant tree diameter were found to be significant predictors. Exponential model with plot dominant tree height as a predictor had a stronger ability to account for the heteroskedasticity. When random effects were modeled at block level alone, the correlations among the residuals remained significant. These correlations were successfully reduced when random effects were modeled at both block and plot levels. The random effects from the interaction of blocks and sample plots on tree CR were substantially large. The model that took into account both the block effect and the interaction of blocks and sample plots had higher prediction accuracy than the one with the block effect and population average considered alone. Introducing stand density into the model through dummy variables could further improve its prediction. This implied that the developed method for developing tree CR models of Mongolian oak is promising and can be applied to similar studies for other tree species.

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Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the stem diameter from TLS data. The methodology of stem diameter measurement by diameter tape was adopted. The stem cross-section at a given height along the stem was determined. Stem points for stem diameter retrieval were extracted according to the stem cross-section. Convex hull points of the extracted stem points were calculated in a projection plane. Then, a closed smooth curve was interpolated onto the convex hull points to simulate the path of the diameter tape, and stem diameter was calculated based on the length of the simulated path. The stems of different tree species with different properties were selected to verify the presented method. Compared with the field-measured diameter, the RMSE of the method was 0.0909 cm, which satisfies the accuracy requirement for forest inventory. This study provided a method for determining the stem cross-section and an efficient and precise curve fitting method for deriving stem diameter from TLS data. The importance of the stem cross-section and convex hull points in stem diameter retrieval was demonstrated.

Abstract:The relationship of stand top and stand mean height is important for forest growth and yield modeling, but it has not been explored for natural mixed forests. Observations of stand top and stand mean height can present spatial dependence or autocorrelation, which should be considered in modeling. Simultaneous autoregressive (SAR) models, including spatial lag model (SLM), spatial Durbin model (SDM) and spatial error model (SEM), within nine spatial weight matrices were utilized to model the stand top and stand mean height relationship in the mixed Quercus mongolica Fisch. ex Ledeb. broadleaved natural stands of Northeast China, using ordinary least squares (OLS) as a benchmark model. The results showed that there was a high linear relationship between stand top and stand mean height and that there was a positive spatial autocorrelation pattern in model residuals of OLS. Moreover, SEM and SDM performed better than OLS in terms of reducing the spatial dependence of model residuals and model fitting, regardless of which spatial weight matrix was used. SEM was better than SDM. SLM scarcely reduced the spatial autocorrelation of model residuals. Among nine spatial matrices in SEM, rook contiguous matrix performed best in model fitting, followed by inverse distances raised to the second power (1/d 2 ) and local statistics model matrix (LSM).

Abstract:Determining the response of dominant height growth to climate change is important for understanding adaption strategies. Based on 550 permanent plots from a national forest inventory and climate data across seven provinces and three climate zones, we developed a climate-sensitive dominant height growth model under a mixed-effects model framework. The mean temperature of the wettest quarter and precipitation of the wettest month were found to be statistically significant explanatory variables that markedly improved model performance. Generally, future climate change had a positive effect on stand dominant height in northern and northeastern China, but the effect showed high spatial variability linked to local climatic conditions. The range in dominant height difference between the current climate and three future BC-RCP scenarios would change froḿ 0.61 m to 1.75 m (´6.9% to 13.5%) during the period 2041-2060 and from´1.17 m to 3.28 m (´9.1% to 41.0%) during the period 2061-2080 across provinces. The impacts of climate change on stand dominant height decreased as stand age increased. Forests in cold and warm temperate zones had a smaller decrease in dominant height, owing to climate change, compared with those in the mid temperate zone. Overall, future climate change could impact dominant height growth in northern and northeastern China. As spatial heterogeneity of climate change affects dominant height growth, locally specific mitigation measures should be considered in forest management.

Carbon density is an important indicator of carbon sequestration capacity in forest ecosystems. We investigated the vegetation carbon density of Pinus massoniana Lamb. forest in the Jiangxi Province. Based on plots investigation and measurement of the carbon content of the samples, the influencing factors and spatial variation of vegetation carbon density (including the tree layer, understory vegetation layer and litter layer) were analysed. The results showed that the average vegetation carbon density value of P. massoniana forest was 52 Mg·ha−1. The vegetation carbon density was significantly (p < 0.01) and positively correlated with the stand age, mean annual precipitation, elevation and stand density and negatively correlated with the slope and mean annual temperature. Forest management had a significant impact on vegetation carbon density. To manage P. massoniana forest for carbon sequestration as the primary objective, near-natural forest management theory should be followed, e.g., replanting broadleaf trees. These measures would promote positive succession and improve the vegetation carbon sequestration capacity of forests. The results from the global Moran’s I showed that the vegetation carbon density of P. massoniana forest had significant positive spatial autocorrelation. The results of local Moran’s I showed that the high-high spatial clusters were mainly distributed in the southern, western and eastern parts of the province. The low-low spatial clusters were distributed in the Yushan Mountains and in the northern part of the province. The fitting results of the semivariogram models showed that the spherical model was the best fitting model for vegetation carbon density. The ratio of nugget to sill was 0.45, indicating a moderate spatial correlation of carbon density. The vegetation carbon density based on kriging spatial interpolation was mainly concentrated in the range of 32.5–69.8 Mg·ha−1. The spatial distribution of vegetation carbon density regularity was generally low in the middle region and high in the peripheral region, which was consistent with the terrain characteristics of the study area.