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The urban heat island (UHI) phenomenon caused by rapid urbanization has become an important global ecological and environmental problem that cannot be ignored. In this study, the UHI effect was quantified using Landsat 8 image inversion land surface temperatures (LSTs). With the spatial scale of street units in Fuzhou City, China, using ordinary least squares (OLS) regression, geographically weighted regression (GWR) models, and multi-scale geographically weighted regression (MGWR), we explored the spatial heterogeneities of the influencing factors and LST. The results indicated that, compared with traditional OLS models, GWR improved the model fit by considering spatial heterogeneity, whereas MGWR outperformed OLS and GWR in terms of goodness of fit by considering the effects of different bandwidths on LST. Building density (BD), normalized difference impervious surface index (NDISI), and the sky view factor (SVF) were important influences on elevated LST, while building height (BH), forest land percentage (Forest_per), and waterbody percentage (Water_per) were negatively correlated with LST. In addition, built-up percentage (Built_per) and population density (Pop_Den) showed significant spatial non-stationary characteristics. These findings suggest the need to consider spatial heterogeneity in analyses of impact factors. This study can be used to provide guidance on mitigation strategies for UHIs in different regions.

Background: High-speed urbanization has brought about a number of ecological and environmental problems, as well as the use of remote sensing to monitor the urban ecological environment and explore the main factors affecting its changes. It is important to promote the sustainable development of cities. Methods: In this study, we quantify the ecological quality of the study area from 2000 to 2020 based on the remote sensing ecological index (RSEI) and analyze its drivers through Geodetector and geographically weighted regression. Results: The RSEI of Fuzhou City from 2000 to 2020 showed an increasing followed by a decreasing trend, with obvious spatial autocorrelation. The main driving factors causing the spatial divergence of the RSEI were elevation (q = 0.48–0.63), slope (0.42–0.59), and GDP (0.3–0.42), and the driving effect and range of each factor changed with time. Conclusion: In this paper, we explore changes in the ecological environment in Fuzhou City over the past 20 years, as well as the scope and magnitude of the drivers, providing an important reference basis to improve the ecological environment quality of the city.

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.

Ecological corridor construction is an important support of the current pursuit of high-quality urbanization. Fuzhou is a mountain–water city characterized by a unique spatial structure. However, rapid urbanization has exacerbated the rate of ecosystem fragmentation, negatively impacting the livable living environment. The construction of ecological corridors is of great significance for efforts to restore the broken landscape and form the urban ecosystem as an organic whole in Fuzhou. In the present study, Fuzhou was considered as the study area, and the water, green, and ventilation corridors, as well as surface temperature data, were analyzed using the kernel density analysis method to generate surface-temperature-based ecological nodes. The impacts of various corridors and surface temperatures on the construction of the Fuzhou ecological corridors were assessed using ecological theory, and the ecological resistance surfaces of the influencing factors were obtained. We constructed ecological corridors for the mitigation of the urban heat island in Fuzhou using the MCR model with four levels and then evaluated the network connectivity of the corridors. The results revealed the following findings: (1) The study area comprises 32 ecological nodes, including nine in Minhou County and Changle District, four in Mawei and Cangshan Districts, and two in Gulou, Taijiang, and Jin’an Districts. (2) Fuzhou contains 63 ecological corridors with a total length of approximately 494.65 km. These include 31 first-level (201.16 km), 11 second-level (98.56 km), 14 third-level (129.12 km), and 7 fourth-level (65.81 km) corridors. (3) The degree of closure (α), the point rate of lines (β), the degree of connectivity (γ), and the degree of connectivity (Cr) indexes of the network structure for the ecological corridors were 0.27, 2.03, 0.72, and 0.87, respectively. They indicate that the overall ecological effectiveness of the network is high and can provide a theoretical basis for the construction of ecological corridors in the future.

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the LM and MCWS algorithms. The LM algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for LM algorithm, F1 score = 85.92% for MCWS algorithm). The LM and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the information from multi-layer images, but an additional training set is needed for model training, robust computer resources are required, and a large number of accurate training samples are necessary. This study provides valuable information for forestry practitioners to select an optimal approach for detecting individual trees.

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.

Accurate, cost-effective monitoring and management of young forests is important for future stand quality. There is a critical need for a rapid assessment tool for forest monitoring and management. This study uses a low-cost unmanned aerial vehicle (UAV) to complete a tree height and tree density assessment in a newly forested Chinese fir (Cunninghamia lanceolata (Lamb) Hook) planting (15 sample plots), Shunchang County, Fujian, China (1.12 ha). Images obtained from a Phantom4-Multispectral UAV were used to generate a digital surface model (DSM) with DJI Terra software (0.02 m spatial resolution). Based on the DSM, the individual trees were identified and the height of each corresponding tree was determined. The impacts of factors related to individual tree detection (ITD) and tree height accuracy were also analyzed. For the tree-level, the highest accuracy of ITD for Chinese fir was 98.93% (F-score = 98.93%). Remotely sensed individual tree heights produced an R2 value of 0.89, RMSE value of 0.22 m when compared to a field survey. At the stand-level, tree height assessment yielded R2 = 0.95, RMSE = 0.12 m, and tree density assessment yielded R2 = 0.99, RMSE = 48 tree ha−1. The results highlight that UAVs can successfully monitor forest parameters and hold great potential as a supplement or substitute tool in field inventory.

Constructing and optimising ecological networks in high-density cities plays an important role in mitigating urban ecological problems. Our study uses comprehensive evaluation methods such as Morphological Spatial Pattern Analysis (MSPA), the Remote Sensing Ecological Index (RSEI), and Connectivity to identify ecological source areas in Fuzhou City, and constructs and optimises the network using the Minimum Cumulative Resistance (MCR) model, current theory, and other methods. Meanwhile, the changes in urban landscape connectivity under different development scenarios were explored. The results show that the following: (1) the identification of ecological source sites based on the integrated approach is better than the single MSPA method; (2) the ecological network of Fuzhou City consists of 44 ecological source sites and 92 corridors; and (3) among the various development modes, transforming the top 30% of the bare land patches in Fuzhou City into green spaces improves the overall connectivity of the ecological network the most. The results can provide auxiliary decision-making for ecological construction in high-density cities.

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.

Chinese fir ( ) 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. 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. 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. 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.