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Canopy structural features for selected years: 1999 (after 1st thinning), 2009 (before 2nd Table 2018. (last available inventory). Average tree height measured as the average of 50 trees randomly selected in each plot.
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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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... initially more than 90% of the stems in this forest were pine trees, the initial dominance of Scots pine has been steadily decreasing, particularly since the year 2009, through its replacement by European beech, a common process where these two species coexist [27], accelerated by pine commercial thinning (see Table 1). By 2017, annual beech leaf litter biomass already exceeded that of pine [28]. ...Context 2
... case of multiple matches, we retained those variables that showed fewer correlations. Overall, we retained CanOpen, LAI, DirectBelow.Yr, N.sunflecks, and Max.Sunflecks for further analyses (Table S1, Supplementary Materials). ...Context 3
... the coincidence in time of thinning and windstorms in the same year (2009), their individual effects could not be separated, but we found that changes in light properties varied inconsistently between thinning treatments and were more dependent on the temporal and spatial dynamics of each plot. In particular, we found that plot 1 (20% thinning), plot 3 (control) and, notably, plot 9 (also control) had strong differences in the light properties, probably due to their different stand structure compared to other plots at our study site ( Table 1). All of those more-affected plots were located in the northern area of our study site (Figure 1). ...Citations
... 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. ...
Recognizing the sensitivity of vegetation to precipitation is fundamental to monitoring and assessing ecosystem health and predicting ecosystem response to climate change. However, there is a lack of detailed, large-scale assessment of the sensitivity of temperate vegetation to climate variability. We analyzed the spatiotemporal trends of NDVI in Inner Mongolia (IM) for the period from 2000 to 2020, evaluated the sensitivity of NDVI to precipitation by constructing a linear mixed-effects model, and assessed the relative importance of an array of biotic and abiotic factors in affecting the vegetation sensitivity to precipitation using random forest models. We found that (1) the NDVI in IM had a significant or slight increase in 80.86% of the region during the studied two decades, though it showed a large inter-annual fluctuation, and the areas with stable or decreasing NDVI were mainly in the desert areas. Precipitation and wind speed were the two major drivers for the NDVI changes. (2) The typical steppes demonstrated the highest sensitivity to precipitation, followed by desert steppes and forest steppes, broad-leaved forests, and deserts and coniferous forests. (3) The major factor affecting the vegetation sensitivity to precipitation varied with vegetation types; it is temperature in the coniferous forests, forest steppes and steppe deserts, but it is the sunshine hours in the deciduous broad-leaved forests, the typical steppes, and the desert steppes. The high sensitivity of the typical steppe vegetation to precipitation is related with its location on climate gradient, it has a high production potential but limited by precipitation under a semiarid climate. Our analysis deepens the understanding of the NDVI sensitivity to precipitation and its influencing factors in different vegetation regions, and is valuable for predicting vegetation changes and developing vegetation management strategies to adapt to climate changes
... 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). ...
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.
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.
Forest growth models are increasingly being used in forestry and ecology research as predictive tools to help developing practical guidelines and to improve understanding of the drivers of forest ecosystem functioning. Models are usually calibrated using parameters directly obtained or estimated from empirical field observation, and hence are subject to uncertainty. Thus, output accuracy depends on input parameters precision and on how influential is each parameter on model behaviour. Hence, it is important to analyse parameter-related uncertainty and its effects on model outputs. This can be done by performing sensitivity analyses, which allow to explore the influence of one or several calibration parameters on model outputs. As studies on tree root parameters are particularly scarce, the aim of the present work was to evaluate the influence of parameters related to fine roots on estimations of long-term forest growth patterns in pure and mixed forests, using FORECAST (a hybrid forest growth model) as a virtual lab. The fine root parameters assessed were biomass, turnover rate, and nitrogen content. The analysis was performed by simulating monospecific stands of two contrasting species (Pinus sylvestris L. and Fagus sylvatica L.), and mixed stands formed by both species. In all cases, FORECAST showed good capability to contain uncertainty propagation during the first and middle stages of stand development (<40 years). After that moment, model output uncertainty steadily increased, but it reached different maximum uncertainty levels depending on stand type. Simulations of the less nutrient demanding P. sylvestris manifested very little sensitivity when growing in monospecific stands. However, F. sylvatica monospecific stands showed intermediate sensitivity, but significant species interactions occurred in mixed stands that determined the biggest impact detected of uncertainty related to fine root parameters over model outputs. All things considered, FORECAST displayed an interesting capability to capture some of the interspecific interactions that are key in mixed forests functioning. Our results suggest an acceptable model performance under uncertain parameterization but also caution against expecting accurate quantitative estimations of forest growth, especially when considering long-term scenarios in complex mixed stands.
Thinning in forest management primarily reduces the density of trees and alters the patchiness and spatial complexity of environmental factors and individual interactions between plant recruits. At fine spatial scales, little is known about the relative weight of ecological processes affecting tree regeneration before and after thinning events. Here we studied the density and aggregation of tree recruits in fully-mapped plots located in mixed forests in Northern Iberian Peninsula (Southern Europe) for over four years, which comprises one year before and three years after a thinning event. We applied spatial point-pattern analyses to examine (a) the aggregation of recruits, and their association with trees and (b) the relative effect of both environmental ( i.e. , the patchiness of the local environment) and density-dependent factors ( i.e. , the aggregation of trees and/or recruits) to predict the density, aggregation, and survival of recruits. We found, in thinning plots, that recruits were less dense, their aggregation pattern was more heterogeneous, were distributed randomly in respect of trees and their survival was almost unaffected by the tree proximity. By contrast, recruits in control plots were denser, were only aggregated at distances lower than 1.0 m, were closer to trees, and such closer distance to trees affected negatively in their survival. Independently of the treatment, the aggregation of recruits was chiefly determined by the density-dependent factors at less than 1.0 m and environmental factors at distances beyond that proximity. Overall, our results suggest that thinning affected the aggregation of recruits at two spatial scales: (a) by favoring the tree-recruit and recruit-recruit facilitation at less than 1.0 m and (b) by modifying spatial heterogeneity of the environment at distances beyond that proximity.
