Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R 2 (0.63) and root-mean-square error (26.44 ton∕ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34 ton∕ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.

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Forest biomass is a significant indicator for substance accumulation and forest succession, and a spatiotemporal biomass map would provide valuable information for forest management and scientific planning. In this study, Landsat imagery and field data cooperated with a random forest regression approach were used to estimate spatiotemporal Above Ground Biomass (AGB) in Fuyang County, Zhejiang Province of East China. As a result, the AGB retrieval showed an increasing trend for the past decade, from 74.24 ton/ha in 2004 to 99.63 ton/ha in 2013. Topography and forest management were investigated to find their relationships with the spatial distribution change of biomass. In general, the simulated AGB increases with higher elevation, especially in the range of 80-200 m, wherein AGB acquires the highest increase rate. Moreover, the forest policy of ecological forest has a positive effect on the AGB increase, particularly within the national level ecological forest. The result in this study demonstrates that human activities have a great impact on biomass distribution and change tendency. Furthermore, Landsat image-based biomass estimates would provide illuminating information for forest policy-making and sustainable development.

Applying allometric equations in combination with forest inventory data is an effective approach to use when qualifying forest biomass and carbon storage on a regional scale. The objectives of this study were to (1) develop general allometric tree component biomass equations and (2) investigate tree biomass allocation patterns for Pinus massoniana, a principal tree species native to southern China, by applying 197 samples across 20 site locations. The additive allometric equations utilized to compute stem, branch, needle, root, aboveground, and total tree biomass were developed by nonlinear seemingly unrelated regression. Results show that the relative proportion of stem biomass to tree biomass increased while the contribution of canopy biomass to tree biomass decreased as trees continued to grow through time. Total root biomass was a large biomass pool in itself, and its relative proportion to tree biomass exhibited a slight increase with tree growth. Although equations employing stem diameter at breast height (dbh) alone as a predictor could accurately predict stem, aboveground, root, and total tree biomass, they were poorly fitted to predict the canopy biomass component. The inclusion of the tree height (H) variable either slightly improved or did not in any way increase model fitness. Validation results demonstrate that these equations are suitable to estimate stem, aboveground, and total tree biomass across a broad range of P. massoniana stands on a regional scale.

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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.

Non-commercial forests represent important habitats for the maintenance of biodiversity and ecosystem function in China, yet no studies have explored the patterns and determinants of plant biodiversity in these human dominated landscapes. Here we test the influence of (1) forest type (pine, mixed, and broad-leaved), (2) disturbance history, and (3) environmental factors, on tree species richness and composition in 600 study plots in eastern China. In total, we found 143 species in 53 families of woody plants, with a number of species rare and endemic in the study region. Species richness in mixed forest and broad-leaved forest was higher than that in pine forest, and was higher in forests with less disturbance. Species composition was influenced by environment factors in different ways in different forest types, with important variables including elevation, soil depth and aspect. Surprisingly, we found little effect of forest age after disturbance on species composition. Most non-commercial forests in this region are dominated by species poor pine forests and mixed young forests. As such, our results highlight the importance of broad-leaved forests for regional plant biodiversity conservation. To increase the representation of broad-leaved non-commercial forests, specific management practices such as thinning of pine trees could be undertaken.