As human living environments face increasing challenges with resilience, the concept of nature-based solutions (NBS) was proposed in recent years as a way to promote sustainable living in urban environments. Urban forests and trees play important roles in urban ecosystems, while their potential as an NBS is promising. A bibliometric analysis was first conducted to explore the research pattern of NBS in urban environments. Studies of urban forest and tree-based green infrastructure in NBS research were further investigated using a systematic literature review method. The initial studies on NBS have increased since 2015 with 493 documents published from 142 sources in over 70 countries and regions. Keyword analysis showed green infrastructure had a rather high frequency of utility and received considerable attention. As for urban forests as nature-based solutions (UF-NBS) research, the most prominent study approaches used at different scales and the main benefits and typologies of urban forest studied in the articles were identified. UF-NBS research is still relatively scarce at present. Despite the role of urban forest and trees in addressing environmental challenges being well recognized, UF-NBS studies still need to be conducted in a more comprehensive context, taking social and economic aspects into account.

Tree thinning affects the light environment, which in turn affects the growth and survival of understory vegetation, thus improving species diversity and nutrient cycling, as well as the ecological habitat factors. However, the response of understory vegetation to the thinning intensity and short-time effects in the temperate broadleaf-conifer mixed forest is not completely clear. In this study, four permanent plots with a total area of 4 hm² were established in a mixed broadleaf-conifer forest in northeast China, with thinning intensities of 20% (light thinning, LT), 35% (medium thinning, MT), 55% (heavy thinning, HT) and the unthinned plot (CK), respectively, in accordance with the basal area. The responses of species diversity to changes in understory vegetation were conducted by a structural equation model (SEM). The results showed that compared with CK, thinning significantly increased the photosynthetically active radiation (PAR) and the light quality (R/FR) (p < 0.05), while decreased the contents of soil total nitrogen (TN), total phosphorous (TP), organic matter (OM), nitrate nitrogen (NN), ammonia nitrogen (AN) and pH. The degree of fragmentation of light factors among the treatment plots gradually decreased as thinning intensity increased. Among all the thinning treatments, PAR and R/FR were found to be the optimal light condition when the forest thinning intensity was 55%. The light condition was found to have a significant negative correlation with soil TN, TP, OM, and AN. While the soil nutrients were positively correlated with herbaceous layer diversity but negatively correlated with shrub layer diversity. The soil nutrients were lost after thinning in a short time and herb diversity decreased, but shrub diversity increased significantly compared with unthinned plots. For the understory vegetation, the species diversity of shrub and herb layer were showed to be more sensitive to soil nutrients than light environment.

Despite growing recognition of nature-based solutions (NBS), there remains a research gap in understanding their implementation in urban areas, which poses a significant challenge for urban forest development. Therefore, our paper aims to explore the intersection of NBS with urban forests (UF), identify current barriers, propose strategies to maximize the potential of urban forests as nature-based solutions (UF-NBS) in effectively improving the resilience of urban forests, and enhance the service capacity of urban forest ecosystems. To achieve our objective, we conducted a comprehensive analysis that included a bibliometric review to summarize the evolution of the UF-NBS literature and classify UF-NBS types for the first time. Subsequently, we identified and organized current challenges faced by UF-NBS. Additionally, we proposed an original technological framework system for urban forest development based on NBS principles. The results show the significance of UF-NBS for enhancing urban resilience and human wellbeing, with multiple successful implementations in both China and Europe, validating their effectiveness. However, the implementation of UF-NBS faces several challenges, including inadequate financing, the gap between scientific knowledge and practical implementation, the absence of region-specific information, and the need for interdisciplinary collaboration. This study contributes to establishing a scientific theoretical basis for integrating UF and NBS and provides a systematic approach for decision-makers in urban forest management. Future research should focus on exploring the integration of UF within the NBS framework and prioritize knowledge sharing, international cooperation, and education initiatives to promote the global adoption of UF-NBS and address pressing urban challenges.

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R² of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R² = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.

Sexual dimorphism in dioecious plants serves as a critical adaptive strategy in complex environments. This study systematically investigated the effects of topographic factors (elevation, slope, aspect, and convexity), soil nutrients (C, N, P), and interspecific competition intensity on the reproductive strategies and vegetative growth of the clonal dioecious plant, Acer barbinerve. Using Spearman’s correlation analysis, multiple regression models, and PLS-PM path models, key findings include the following: (1) female sexual reproduction biomass showed a significant positive correlation with the topography principal component (topo_PC1), with a notable gender–topography interaction, whereas male sexual reproduction was negatively regulated by elevation; (2) clonal reproduction in both sexes was significantly suppressed by interspecific competition, but females additionally exhibited positive topographic responses; and (3) male vegetative growth was significantly impacted by environmental stress, while females maintained relative stability. These results demonstrate that females optimize reproductive investment through topography-mediated resource acquisition, whereas males are more susceptible to resource competition constraints. This sex-specific adaptive strategy corroborates the dimorphic niche hypothesis, highlighting how environmental heterogeneity drives divergent life history allocations in dioecious species. The findings provide novel insights into the ecological mechanisms underlying sexual dimorphism and inform gender ratio management in ecological restoration practices.