Background: Urban green space (UGS) has been shown to play an important role in mitigating urban heat island (UHI) effects. In the context of accelerating urbanization, a better understanding of the landscape pattern mechanisms affecting the thermal environment is important for the improvement of the urban ecological environment. Methods: In this study, the relationship between land surface temperature (LST) and the spatial patterns of green space was analyzed using a bivariate spatial autocorrelation and spatial autoregression model in three seasons (summer, transition season (spring), and winter) with different grid scales in Fuzhou city. Results: Our results indicated that the LST in Fuzhou City has a significant spatial autocorrelation. The percentage of landscape and patch density area were negatively correlated with surface temperature. The results of our indicators differed according to the season, with population density and distance to the water indicators not being significant in the winter. The coefficient of determination was higher at the 510 m grid scale on this study’s scale. Conclusion: This study extends our understanding on the influence of UHI effects after accounting for different seasonal and spatial scale factors. It also provides a reference for urban planners to mitigate heat islands in the future.

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.

The expansion of Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau) has triggered native forest retreat and a range of ecological issues, especially for the Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) forests with similar growing conditions. In order to reveal the stoichiometric characteristics of Moso bamboo succession and scientifically control the forest retreat caused by the expansion of Moso bamboo into Chinese fir, mixed forests including 0%, 30%, 50%, 60%, and 80% of Moso bamboo expanded into Chinese fir forests were used to simulate the expansion stages I, II, III, IV, and V, respectively. In addition, by measuring the C, N, and P contents in Moso bamboo leaves and soils and calculating the correlation stoichiometric ratios, the correlation and coupling of which were explored and combined with an ecological homeostasis model at different stages of Moso bamboo expansion. The results demonstrated that P was a key element for the high utilization of Moso bamboo growth, and the expansion principle was influenced by N limitation. The conclusion was that the anthropogenic regulation of C content in soil could achieve the purpose of expansion control and exploit the carbon sequestration capacity in the mixed forest with half Moso bamboo and half Chinese fir, which should discourage the expansion.

Chinese fir (Cunninghamia lanceolata) is the main fast-growing timber species in China, and studies of its stand volume are important for evaluating the effectiveness of forest management. However, it is difficult to accurately estimate stand volume from the perspective of a single tree due to the mutual concealment among Chinese fir trees. Therefore, in this study, we propose a method for identifying different forms of Chinese fir. The specific idea is to realize the accurate identification of a single Chinese fir tree, two Chinese fir trees, and three Chinese fir trees, and construct their respective stand volume estimation models to obtain an estimate of the forest stand volume. The key results are as follows: (1) the overall accuracy of recognition of different forms of Chinese fir is 79%, and the construction of different forms of Chinese fir units is beneficial for identifying forest trees; (2) a multiunit volume equation for different forms of Chinese fir is constructed; (3) based on predictions obtained with the constructed stand volume model, the difference between the estimated stand volume and the measured stand volume is small, and the average accuracy reaches 89.19%; and (4) compared to traditional volume estimation methods based on individual tree scale, the research method in this study shows a significant improvement (about 9.96%) in overall accuracy. In summary, this method can weaken the influence of erroneous individual tree segmentation on the accuracy of stand volume estimation, and can greatly reduce the working time of single tree segmentation to achieve the fast and accurate estimation of fir plantation stand volume.

Introduction: Chinese fir (Cunninghamia lanceolata) is the fastest-growing timber species in China. investigating its spatial structure and influence on aboveground biomass allocation is crucial for understanding its adaptability to environmental conditions, enhancing carbon sequestration, and maintaining forest ecosystem stability.Methods: In this study, airborne LiDAR technology was used to derive forest structural metrics, and weighted Voronoi diagrams were constructed to extract spatial configuration metrics. Biomass models for different components of Chinese fir were developed using 20 harvested trees, and stem mass fraction (SMF), branch mass fraction (BMF), and leaf mass fraction (FMF) were calculated. Path analysis quantified the effects of stand structure variables on biomass allocation among different organs.Results: The openness ratio (OP), angle competition index (UCI), forest layer index (S), and openness (K) were identified as the primary spatial structural factors influencing aboveground biomass allocation. Stem biomass accumulation is maximized when 0.75 < OP ≤ 1, 0 < UCI ≤ 0.25, 0 < S ≤ 0.25, and 0.4 < K ≤ 0.5, with SMF reaching its highest value. Branch biomass peaks when 0.5 < OP ≤ 0.75, 0 < UCI ≤ 0.25, 0.75 < S ≤ 1, and 0.4 < K ≤ 0.5, maximizing BMF. Leaf biomass is highest when 0 < OP ≤ 0.25, 0.5 < UCI ≤ 0.75, 0.5 < S ≤ 0.75, and 0.2 < K ≤ 0.3, leading to the maximum FMF.Discussion: The results of this study not only reveal the survival strategy of Chinese fir in environmental change, but also provide a theoretical basis for understanding ecosystem carbon sequestration and sustainable management of Chinese fir plantations.