Assessment of Species Composition and Community Structure of the Suburban Forest in Hangzhou, Eastern China

Environmental Filtering and Dispersal Limitations Driving the Beta Diversity Patterns at Different Scales of Secondary Evergreen Broadleaved Forests in the Suburbs of Hangzhou

Subtropical suburban secondary evergreen broadleaved forests are essential in regulating the ecological environment's quality and promoting urban sustainable development. In the suburbs of Hangzhou City, well-preserved secondary evergreen broadleaved forest communities were selected to establish a 6 ha forest dynamic monitoring plot. Community surveys and environmental factor measurements were conducted in this area. This study investigated the beta diversity patterns at different scales by considering the environmental and spatial factors to explore the driving beta diversity. Using a similar paired-site beta diversity decomposition method, the study aimed to investigate the differences in species composition and the mechanisms of multiple species coexistence within the secondary evergreen broadleaved forest communities. The results showed that the beta diversity of the suburban secondary evergreen broadleaved forest communities decreased with the increasing spatial scale. Both the dispersal limitation and the environmental filtering were found to drive the formation of beta diversity patterns in these subtropical suburban forests. At relatively smaller scales (<100 m), species turnover was found to determine the beta diversity patterns of the suburban secondary evergreen broadleaved forests. Dispersal limitation had a dominant influence at more minor scales, while the effect of environmental filtering gradually increased with scale, and the impact of the dispersal limitation decreased. The partitioning of the beta diversity in subtropical secondary evergreen broadleaved forests in China provides critical scientific insights into the spatial distribution patterns and changes in biodiversity. It offers valuable knowledge for the conservation and understanding of biodiversity maintenance in the region.

Negative Density Restricts the Coexistence and Spatial Distribution of Dominant Species in Subtropical Evergreen Broad-Leaved Forests in China

Negative densification affects the spatial distribution of species in secondary evergreen broad-leaved forests and is a key mechanism governing species coexistence. We investigated the effects of habitat heterogeneity and density on the spatial distribution of populations of dominant woody species in a secondary evergreen broad-leaved forest in Wuchaoshan using spatial univariate point pattern analyses. This 6 ha forest dynamic monitoring sample area in Hangzhou, China is a typical secondary subtropical evergreen broad-leaved forest. We found (1) a strong effect of habitat heterogeneity that led to the spatial aggregation of dominant species in the plot. Habitat heterogeneity had a strong impact on mature individuals at different life history stages and of different species on a large scale. (2) Negative density dependence (NDD) generally affected spatial distributions of most dominant species and decreased in magnitude with age class. Therefore, different species of subtropical evergreen broad-leaved forests in China have formed unique spatial structures due to their habitat preferences but are generally subjected to density-dependent effects.

Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.