Forests comprise indispensable components of terrestrial ecosystems while serving as primary global carbon sinks, which play a critical role in offsetting global warming.^1,2 Researchers have suggested that the carbon emissions from forests generated by deforestation and decomposition account for 17% of the global total. Therefore, the protection of forest carbon sinks is an essential measure toward dealing with global climate change.³ Due to the complexity and dynamic characteristics of forest ecosystems, assessments of multiscale forest carbon storage have typically been highly ambiguous. The estimation of forest carbon reserves typically relies on accurate data of aboveground biomass (AGB), which is generally believed to contain the majority of forest carbon reserves.^{4 – 6}

In order to overcome the problems of low accuracy, low recommendation efficiency, and low user satisfaction of educational resources recommendation algorithm, this paper proposes a personalized recommendation algorithm for online educational resources based on knowledge association. Firstly, online education resources are collected according to association rules. Secondly, firefly algorithm is used to classify online education resources. Then, the vector space function is constructed to filter the classified online education resources. Finally, the correlation between knowledge points is calculated by knowledge association theory, and the knowledge with the highest user interest is selected as the target recommendation resource to realize the personalized recommendation of online education resources. The resource recommendation accuracy of this method can reach 97%, the recommendation time is less than 5.0 s, and users are more satisfied with it, indicating that its recommendation effect is good.

Poplar plantations have the capacity to improve the properties of soils in muddy coastal areas; however, our understanding of the impacts of plantation development on soil arthropods remains limited. For this study, we determined the community dynamics of soil dwelling arthropods across poplar plantations of different ages (5-, 10-, and 21-years) over the course of one year in Eastern Coastal China. The total abundance of soil arthropods differed with stand development. Further, there were some interactions that involved the sampling date. On average, total abundance was highest in the 10-year-old stands and lowest in the 5-year-old stands. Total abundance exhibited strong age-dependent trends in June and September, but not in March or December. The abundance of Prostigmata and Oribatida increased in the 5- to 21-year-old stands, with the highest levels being in the 10-year-old stands. The abundance of Collembola increased with stand development; however, the stand age had no significant impact on the abundance of epedapic, hemiedaphic, and euedaphic Collembola. Order richness (Hill number q = 0) curve confidence intervals overlapped among three stand ages. Shannon and Simpson diversity (Hill numbers q = 1 and q = 2) differed between 10- and 21-year-old stand age. They showed almost similar trends, and the highest and lowest values were recorded in the 21- and 10-year-old stand ages, respectively. Permutational multivariate analysis of variance demonstrated that composition also varied significantly with the sampling date and stand age, and the 10-year-old stands that were sampled in June stood well-separated from the others. Indicator analysis revealed that Scolopendromorpha and Prostigmata were indicators in June for the 10-year-old stands, while Collembola were indicators for the 21-year-old stands sampled in September. Our results highlight that both stand development and climate seasonality can significantly impact soil arthropod community dynamics in the reclaimed coastal saline soils of managed poplar plantations.

The accurate estimation and inversion of stand characteristic parameters and biomass is the premise for the evaluation of forest productivity, forest ecosystem carbon storage, and ecological forest service capacity. The horizontal accuracies of structure from motion (SfM) and light detection and ranging (LiDAR) point clouds were identical, whereas the descriptions of the upper canopy indices were similar (R = 0.91 to 0.93). However, the descriptions of the lower canopy indices and vertical structure variables were different based on comparisons of the two point cloud types in the same region. Variables that described the vertical structure of the canopy in the SfM point clouds were much less important in the factor estimation models, exhibiting high correlations with basal area and volume. The factor estimation models with high correlations with tree height and diameter at breast height (DBH) exhibited similar importance for LiDAR variables. LiDAR (R² = 0.515 to 0.895) was superior to SfM (R² = 0.63 to 0.835) in estimating the same parameters, particularly those with high correlations with vertical structures. The combination of unmanned aerial vehicle aerial photogrammetry and LiDAR technology might enable the estimation of stand characteristic parameters and the inversion of biomass, as well as the estimation of characteristic parameters such as Lorey’s H and DBH, which are highly sensitive for large datasets. The LiDAR and SfM model outcomes were comparable. We found that the model outcomes exhibited the greatest differences for volume (ΔrRMSE % = 13.94 % ) and the lowest for average DBH (ΔrRMSE = 0.8 % )