Simulation and prediction of changes in tree species composition in subtropical forests of China using a nonlinear difference equation system model.

Changes in tree species composition are one of the key aspects of forest succession. In recent decades, significant changes have occurred in the tree species composition of subtropical forests in China, with a decrease in coniferous trees and an increase in broad-leaved trees. This study focuses on Zhejiang Province, located in the subtropical region of China, and utilizes seven inventories from the National Continuous Forest Inventory (NCFI) System spanning 30 years (1989-2019) for modeling and analysis. We categorized tree species into three groups: pine, fir, and broadleaf. We used the proportion of biomass in a sample plot as a measure of the relative abundance of each tree species group. A novel nonlinear difference equation system (NDES) model was proposed. A NDES model was established based on two consecutive survey datasets. A total of six models were established in this study. The results indicated that during the first two re-examination periods (1989-1994, 1994-1999), there was significant fluctuation in the trend of tree species abundance, with no consistent pattern of change. During the latter four re-examination periods (1999-2004, 2004-2009, 2009-2014, 2014-2019), a consistent trend was observed, whereby the abundance of the pine group and the fir group decreased while the abundance of the broad-leaved group increased. Moreover, over time, this pattern became increasingly stable. Although the abundances of the pine group and the fir group have been steadily declining, neither group is expected to become extinct. The NDES model not only facilitates short-term, medium-term, and even long-term predictions but also employs limit analysis to reveal currently obscure changing trends in tree species composition.

Critical factors influencing visitor emotions: analysis of "restorativeness" in urban park visits in Fuzhou, China.

Objective: To date, a comprehensive analysis of urban green space (UGS) visitors' emotional remains largely unexplored. In this study, we focus on how UGS environmental preferences, restorativeness, other physical factors (sound, air, and thermal environments), and individual characteristics affecting visitor emotions. Such a comprehensive analysis would allow relevant practitioners to check the environmental quality of UGSs and improve certain conditions to promote visitor emotions. Methods: A total of 904 questionnaire responses with concurrently monitored physical factors were analyzed by independent sample t-tests, one-way ANOVA and path analysis. Results: The thermal evaluation had the largest impact on positive emotions (ß = 0.474), followed by perceived restorativeness (ß = 0.297), which had ß values of -0.120 and -0.158, respectively, on negative emotions. Air evaluation was more effective for increasing positive emotions (ß = 0.293) than reducing negative emotions (ß = -0.115). Sound evaluation also had similar results (ß = 0.330 vs. ß = -0.080). Environmental preference significantly influenced only positive emotions (ß = 0.181) but could still indirectly impact negative emotions. Moreover, objective physical factors can indirectly affect visitors' emotions by enhancing their evaluations.. Conclusion: The influence of different UGS environmental factors on visitors' emotions vary, as does their impacts on positive versus negative emotions. Positive emotions were generally more affected than negative emotions by UGS. Visitor emotions were mainly influenced by physical and psychological factors. Corresponding suggestions are proposed for UGS design and management in this study.

Exploring the Influencing Paths of Villagers' Participation in the Creation of Micro-Landscapes: An Integrative Model of Theory of Planned Behavior and Norm Activation Theory.

Villager participation has become a key breakthrough in rural landscape governance. Using the theory of planned behavior and the norm activation theory as frameworks, this study adopts the structural equation model to explore the influencing mechanism of villager participation in rural micro-landscapes based on data gathered from 414 villagers in a rural micro-landscape construction survey in Jinjiang, China. The results indicate that (1) integrated planned behavior theory and norm activation theory can better explain the influencing mechanism of villagers' participation in rural micro-landscape construction; (2) perception, norm, attitude, and control dimensions significantly influence villagers' participation behavior intention. The attitude dimension had the greatest influence, followed by the normative and control dimensions, while the perception dimension had the least influence on the procedure; and (3) according to the mediation results, natural environment perception, social environment perception, personal norm, social norm, participation attitude, result awareness, and self-efficacy all exert indirect effects on participation behavior based on villagers' participation behavioral intention. The largest median effect value was result awareness, followed by personal norm, participation attitude, natural environment awareness, self-efficacy, and social norm. This study expands the theoretical framework and research content of planned behavior and clarifies the mechanism of the influencing factors of villagers' participation in rural micro-landscapes, extending the theory of planned behavior to the research field of villagers' participation, which has a guiding role in promoting the co-construction, co-governance, and sharing of rural landscapes.

The Relationships between Perceived Design Intensity, Preference, Restorativeness and Eye Movements in Designed Urban Green Space.

Recent research has demonstrated that landscape design intensity impacts individuals' landscape preferences, which may influence their eye movement. Due to the close relationship between restorativeness and landscape preference, we further explore the relationships between design intensity, preference, restorativeness and eye movements. Specifically, using manipulated images as stimuli for 200 students as participants, the effect of urban green space (UGS) design intensity on landscapes' preference, restorativeness, and eye movement was examined. The results demonstrate that landscape design intensity could contribute to preference and restorativeness and that there is a significant positive relationship between design intensity and eye-tracking metrics, including dwell time percent, fixation percent, fixation count, and visited ranking. Additionally, preference was positively related to restorativeness, dwell time percent, fixation percent, and fixation count, and there is a significant positive relationship between restorativeness and fixation percent. We obtained the most feasible regression equations between design intensity and preference, restorativeness, and eye movement. These results provide a set of guidelines for improving UGS design to achieve its greatest restorative potential and shed new light on the use of eye-tracking technology in landscape perception studies.

Effects of soil erosion and reforestation on soil respiration, organic carbon and nitrogen stocks in an eroded area of Southern China.

Soil erosion and reforestation greatly affects the functionality of many terrestrial ecosystems. However, the effects of soil erosion and reforestation on soil respiration (SR), and soil organic carbon (SOC) and total nitrogen (TN) stocks remain unclear. Therefore, we investigated the changes in SR, and SOC and TN stocks at four different soil erosion levels (severely, moderately, lightly, and non-eroded) and two different aged Pinus massoniana plantations (8- and 36-year-old) in the hilly red soil regions of Southern China. Our results showed that soil erosion level and reforestation significantly influenced SR, and SOC and TN stocks. Meanwhile, the mean SR, and SOC and TN stocks all significantly decreased with erosion level but increased significantly with times since reforestation. Soil temperature (ST) could explain 70-92% of SR seasonal variation based on exponential models, whereas no significant relationship between SR and soil water content were found. Furthermore, the structural equation modeling indicated that SOC stocks at 0-20 cm had a much stronger effect on SR than ST. Meanwhile, the SOC stocks for 0-20 cm increased by 177% and 558% in the 8- and 36-year-old Pinus massoniana plantations in comparison with the severely eroded forestland, respectively. This study highlights that reforestation could be an effective strategy for restoring the carbon and nitrogen storage in eroded regions of Southern China and emphasizes the need to consider the effects of soil erosion and reforestation when assessing regional carbon budgets under different climate scenarios.

[Spatial Heterogeneity of Soil Respiration in the Soil Erosion Area of West Mountains in Fujian Province, China].

The spatial heterogeneity of soil respiration (Rs) is of great significance in accurately estimating the carbon budget in erosion areas. This study investigated the soil respiration (Rs), total nitrogen (TN), carbon-nitrogen ratio (C/N), soil organic carbon content (SOC), leaf area index (LAI), soil temperature (T10), and soil moisture (W) in 59 soil samples collected from Hetian Town in Fujian Province. Both classical statistics and geostatistics were used to analyze the spatial heterogeneity of soil respirations and other measured factors. The variability of Rs and other measured factors in the samples ranked from the largest to the smallest: LAI > SOC > TN > Rs > C/N > T10 > W. Rs was positively correlated with T10 (P<0.01) and with TN (P<0.05), but not significantly correlated with other factors (P>0.05); TN, SOC and T10 could be used to explain the spatial variation of soil respiration among the samples. The results from geo-statistical analysis showed that Rs was in a medium spatial autocorrelation, with 52.89% of spatial heterogeneity caused by structural factors and 47.11% of spatial heterogeneity resulted from random factor; the fractal dimensions of soil respiration and its interacted factors were ranked as: Rs > LAI > C/N > T10 > SOC > W > TN; the spatial distribution patterns of Rs were similar with those of TN and T10, but different from those of C/N, SOC or LAI. At the 95% confidence level and 90% estimation accuracy, the reasonable sampling number of Rs was 62.

[Construction of age group vegetation index and preliminary application].

In the present paper, one remote sensing index-age group vegetation index (AGVI) was put forward, and its feasibility was verified. Taking 518 groups of pine forest age group data collected in 13 counties (cities) of Sanming, Jiangle, Shaxian, Nanping, Huaan, Yunxiao, Nanping, Anxi, Putian, Changting, Jianyang, Ningde and Fuqing, Fujian Province and HJ-1 CCD multi-spectral image at the same time-phase as the basis, the spectrum differences of blue, green, red, near infrared and NDVI of each age group were analyzed, showing the characteristics of young forest>middle-aged forest>over-mature forest>mature forest>near mature forest at near infrared band and mature forest>near mature forest>over-mature forest>young forest>middle-aged forest at NDVI, thus the age group vegetation index (AGVI) was constructed; the index could increase the absolute and relative spectrum differences among age groups. For the pine forest AGVI, cluster analysis was conducted with K-mean method, showing that the division accuracy of pine forest age group was 80.45%, and the accurate rate was 90.41%. Therefore, the effectiveness of age group vegetation index constructed was confirmed.

[Spectral features analysis of Pinus massoniana with pest of Dendrolimus punctatus Walker and levels detection].

Taking 51 field measured hyperspectral data with different pest levels in Yanping, Fujian Province as objects, the spectral reflectance and first derivative features of 4 levels of healthy, mild, moderate and severe insect pest were analyzed. On the basis of 7 detecting parameters construction, the pest level detecting models were built. The results showed that (1) the spectral reflectance of Pinus massoniana with pests were significantly lower than that of healthy state, and the higher the pest level, the lower the reflectance; (2) with the increase in pest level, the spectral reflectance curves' "green peak" and "red valley" of Pinus massoniana gradually disappeared, and the red edge was leveleds (3) the pest led to spectral "green peak" red shift, red edge position blue shift, but the changes in "red valley" and near-infrared position were complicated; (4) CARI, RES, REA and REDVI were highly relevant to pest levels, and the correlations between REP, RERVI, RENDVI and pest level were weak; (5) the multiple linear regression model with the variables of the 7 detection parameters could effectively detect the pest levels of Dendrolimus punctatus Walker, with both the estimation rate and accuracy above 0.85.

[Construction of vegetation shadow index (SVI) and application effects in four remote sensing images].

Taking the images of Landsat TM, ALOS AVNIR-2, CBERS-02B CCD and HJ-1 CCD as the experimental data, for increasing the differences among shaded area, bright area and water further, the present paper construed a novel vegetation index-Shaded Vegetation Index(SVI), which can not only keep the absolute differences among bright area, shaded area and water area in the near-infrared band, but also can enlarge NDVI, eliminate the possible mixes, and change the histogram "skewed" phenomenon of NDVI, so the vegetation index value is closer to normal distribution, and more in line with the filed condition; this new index was applied to the surface features of large difference of the near-infrared radiation characteristics. Verified by accuracy assessment for the bright area, shaded area and water area recognition effects with SVI, it was showed that the overall classification accuracies of these images were up to 98. 89%, 100%, 97.78% and 97.78% respectively, with the overall Kappa statistics of 0.9833, 1, 0.9667, and 0.966 7, indicating that SVI has excellent detection effects for bright area, shaded area and water area; the statistical comparison of sub-images between SVI and NDVI also illustrated the reliability and effectiveness of SVI, which can be applied in the shadow removal for remote sensing images.