The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80–120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

Large-diameter trees have mainly been used for timber production in forestry practices. Recently, their critical roles played in biodiversity conservation and maintenance of ecosystem functions have been recognized. However, current forestry policy on the management of large-diameter trees is weak. As China is the biggest consumer of large-diameter timbers, how to maintain sustainable large-diameter timber resources as well as maximize ecological functions of the forests is a critical question to address. Here we summarize historical uses, distribution patterns, and management strategies of large-diameter trees in China. We found that large-diameter trees are mainly distributed in old-growth forests. Although China’s forest cover has increased rapidly in the past decades, large-diameter trees are rarely found in plantation forests and secondary forests. We suggest that knowledge of large-diameter trees should be widely disseminated in local forestry departments, especially their irreplaceable value in terms of biodiversity conservation and ecosystem functions. Protection of large-diameter trees, especially those in old-growth forests, is critical for sustainable forestry. To meet the increasing demand of large-diameter timbers, plantation forests and secondary forests should apply forest density management with thinning to cultivate more large-diameter trees.

Biomass is a key biophysical parameter used to estimate carbon storage and forest productivity. Spatially-explicit estimation of biomass provides invaluable information for carbon stock calculation and scientific forest management. Nevertheless, there still exists large uncertainty concerning the relationship between biomass and influential factors. In this study, aboveground biomass (AGB) was estimated using the random forest algorithm based on remote sensing imagery (Landsat) and field data for three regions with different topographic conditions in Zhejiang Province, China. AGB distribution and change combined with stratified terrain classifications were analyzed to investigate the relations between AGB and topography conditions. The results indicated that AGB in three regions increased from 2010 to 2015 and the magnitude of growth varied with elevation, slope, and aspect. In the basin region, slope had a greater influence on AGB, and we attributed this negative AGB-elevation relationship to ecological forest construction. In the mountain area, terrain features, especially elevation, showed significant relations with AGB. Moreover, AGB and its growth showed positive relations with elevation and slope. In the island region, slope also played a relatively more important role in explaining the relationship. These results demonstrate that AGB varies with terrain conditions and its change is a consequence of interactions between the natural environment and anthropogenic behavior, implying that biomass retrieval based on Landsat imagery could provide considerable important information related to regional heterogeneity investigations.

The forest community structure and dynamic structure provide basic data for in-depth study of the community maintenance mechanisms and succession processes of urban forest ecosystems. Moreover, the study on the dynamics of suburban forest communities can provide a reference for the succession process of forest communities under severe human disturbance. In this context, this study analyzes the species composition and community structure of a 6 ha subtropical secondary evergreen broad-leaved forest plot in Wuchaoshan National Forest Park, Hangzhou, Eastern China. There are 36,757 existing woody plants (with DBH ≥ 1 cm), belonging to 94 species (45 evergreen species, and 48 deciduous species) in 63 genera of 34 families, with a plant density of 6126 plants/ha. The overall size–class distribution shows an obvious inverted “J” shape. Species with the top-five important values in the sample are Schima superba, Camellia fraternal, Symplocos anomala, Cyclobalanopsis glauca, Eurya rubiginosa, accounting for 57.3% of all individuals within the sample. In addition, different species have different habitat preferences and different distribution areas. The Wuchaoshan forest is in the middle and late stages of the succession. Different interference histories in the community succession process are demonstrated through studies of secondary evergreen broad-leaved forests. Long-term community monitoring contributes to indicating the direction and potential of the secondary forest succession.

Investigating functional plant traits is essential for understanding plant responses and adaptations to global climate change and ecosystem functions. Synchronous observations of multiple plant traits across multiple sites are rare. Here, we obtained community, functional trait, and environmental data for different forest vegetation types in China’s tropical, subtropical, warm-temperate, and cold-temperate zones. Using principal component (PC) analysis, we constructed a composite–habitat gradient axis of biotic and abiotic factors across different climatic zones, differentiated functional trait compositions along the axis and their driving factors, and reached three main conclusions. (1) At the community level, the community-weighted mean and variance of each trait level differed across the climatic zones. Specific leaf areas and leaf phosphorus contents decreased with increasing PC1 values, whereas community-weighted means and variances of leaf dry-matter contents, leaf nitrogen-to-phosphorus ratios, and wood densities significantly increased with increasing PC1 values. However, the leaf nitrogen content varied along the PC1 axis. Both the community-weighted skewness and kurtosis of functional traits increased significantly with increasing PC1 values for the composite habitat gradient across the climatic zones. (2) The weighted mean, variance, skewness, and kurtosis of each functional trait exhibited different patterns on the composite habitat gradient PC2 axis than on the PC1 axis across the climatic zones, and the correlation was weak. (3) Most functional traits correlated significantly with the community-weighted mean and variance, kurtosis, and skewness. Therefore, the different functional trait compositions of forest communities in different climatic zones reflected trait convergence caused by the environment and trait differences caused by species competition in response to local-scale filtering.