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Summary of the estimates for the "best model" accounting for the effect years, richness, and proportion of forest cover at explaining light variables, according to the AIC value, computed for an area of 5.0 m radius around each sampling point. Only the most significant values representing interactions between year, canopy richness, and proportion of forest cover are shown. Significant values (p < 0.05) are in bold.

Summary of the estimates for the "best model" accounting for the effect years, richness, and proportion of forest cover at explaining light variables, according to the AIC value, computed for an area of 5.0 m radius around each sampling point. Only the most significant values representing interactions between year, canopy richness, and proportion of forest cover are shown. Significant values (p < 0.05) are in bold.

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Natural and anthropogenic factors affect forest structure worldwide, primarily affecting forest canopy and its light properties. However, not only stand-replacing events modify canopy structure, but disturbances of lower intensity can also have important ecological implications. To study such effects, we analyzed long-term changes in light properti...

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... each dependent variable and buffer area, multiple independent models were generated with all possible combinations of variables. We used Spearman's correlation to test wheter canopy cover and richness were strongly correlated (r s > 0.7) only at the smallest buffer (1.0 m radius) (Table S3, Supplementary Materials). ...
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... for Max.Sunflecks (Figure 2e) only a few years showed significant contrast in the period 2009 to 2011. Overall, we found a higher number of significant pairwise contrasts between plots, which suggests that the observed temporal patterns were more related to the intrinsic particularities to each plot than to thinning treatments (see Table S3, Supplementary Materials, for detailed results on pairwise contrast between consecutive years). ...
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... for Max.Sunflecks, only a few years showed significant contrast in the period 2009 to 2011. Overall, we found a higher number of significant pairwise contrasts between plots, which suggests that the observed temporal patterns were more related to intrinsic plot features than to thinning treatments (see Table S3, Supplementary Materials, for detailed results on pairwise contrast between consecutive years). ...
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... accounting for light variables explained by forest canopy variables, we found that an area of 4.0 m radius around each sampling point had the highest performance for CanOpen, DirectBelowYr, N.Sunflecks, and Max.Sunflecks, whereas an area of 2.0 m radius had the highest performance for LAI (see Figure S10, Supplementary Materials). When evaluating the best models to explain each light variable, the proportion of broadleaf canopy cover negatively affected the majority of the light variables (except for N.Sunflecks, which occurred in the best model, but it was not significant), whereas canopy richness only explained DirectBelow.Yr positively (Table 3). In addition, we found that the effect of broadleaf canopy cover and richness at explaining light variables covaried with years, but only in N.Sunflecks. ...
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... addition, we found that the effect of broadleaf canopy cover and richness at explaining light variables covaried with years, but only in N.Sunflecks. In particular, we found a negative effect of broadleaf canopy cover in 2008, whereas a positive effect of canopy richness in 2016 (Table 3). ...

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... The vegetation with higher sensitivity to precipitation seems to be more influenced by sunshine hours than temperature. The increase in sunshine hours facilitates photosynthesis in plants, but also enhances transpiration thus reduces soil moisture availability (He et al., 2006;Ruiz de la Cuesta et al., 2021). We speculate that temperature and sunshine duration affect vegetation sensitivity to precipitation mainly by affecting the vapor pressure deficit (VPD), that is, increasing temperature and sunshine hours would increase the difference between the vegetation's demand for water and water supply, thus increasing sensitivity. ...
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... Natural mixed forest (NMX) is defined as forest in which broadleaved species account for 25-75% of the coniferous canopies [66]. Plantation and natural forests differ at the leaf scale (i.e., in leaf inclination, morphology, and clumping) [9] and canopy structure scale (i.e., in crown morphology and canopy cover) [67], as well as in spectral characteristics [68][69][70][71]. They also differ at the forest stand scale (i.e., in forest composition and diversity and tree distributions and interactions within the forest). ...
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The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and conifer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan.
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Canopy structure and understory light have important effects on forest productivity and the growth and distribution of the understory. However, the effects of stand composition and season on canopy structure and understory light environment (ULE) in the subtropical mountain Pinus massoniana forest system are poorly understood. In this study, the natural secondary P. massoniana — Castanopsis eyrei mixed forest (MF) and P. massoniana plantation forest (PF) were investigated. The study utilized Gap Light Analyzer 2.0 software to process photographs, extracting two key canopy parameters, canopy openness (CO) and leaf area index (LAI). Additionally, data on the transmitted direct (Tdir), diffuse (Tdif), and total (Ttot) radiation in the light environment were obtained. Seasonal variations in canopy structure, the ULE, and spatial heterogeneity were analyzed in the two P. massoniana forest stands. The results showed highly significant ( P < 0.01) differences in canopy structure and ULE indices among different P. massoniana forest types and seasons. CO and ULE indices (Tdir, Tdif, and Ttot) were significantly lower in the MF than in the PF, while LAI was notably higher in the MF than in the PF. CO was lower in summer than in winter, and both LAI and ULE indices were markedly higher in summer than in winter. In addition, canopy structure and ULE indices varied significantly among different types of P. massoniana stands. The LAI heterogeneity was lower in the MF than in the PF, and Tdir heterogeneity was higher in summer than in winter. Meanwhile, canopy structure and ULE indices were predominantly influenced by structural factors, with spatial correlations at the 10 m scale. Our results revealed that forest type and season were important factors affecting canopy structure, ULE characteristics, and heterogeneity of P. massoniana forests in subtropical mountains.
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