Factors Affecting Spatial Variation in Vegetation Carbon Density in Pinus massoniana Lamb. Forest in Subtropical China

Abstract

Carbon density is an important indicator of carbon sequestration capacity in forest ecosystems. We investigated the vegetation carbon density of Pinus massoniana Lamb. forest in the Jiangxi Province. Based on plots investigation and measurement of the carbon content of the samples, the influencing factors and spatial variation of vegetation carbon density (including the tree layer, understory vegetation layer and litter layer) were analysed. The results showed that the average vegetation carbon density value of P. massoniana forest was 52 Mg·ha−1. The vegetation carbon density was significantly (p < 0.01) and positively correlated with the stand age, mean annual precipitation, elevation and stand density and negatively correlated with the slope and mean annual temperature. Forest management had a significant impact on vegetation carbon density. To manage P. massoniana forest for carbon sequestration as the primary objective, near-natural forest management theory should be followed, e.g., replanting broadleaf trees. These measures would promote positive succession and improve the vegetation carbon sequestration capacity of forests. The results from the global Moran’s I showed that the vegetation carbon density of P. massoniana forest had significant positive spatial autocorrelation. The results of local Moran’s I showed that the high-high spatial clusters were mainly distributed in the southern, western and eastern parts of the province. The low-low spatial clusters were distributed in the Yushan Mountains and in the northern part of the province. The fitting results of the semivariogram models showed that the spherical model was the best fitting model for vegetation carbon density. The ratio of nugget to sill was 0.45, indicating a moderate spatial correlation of carbon density. The vegetation carbon density based on kriging spatial interpolation was mainly concentrated in the range of 32.5–69.8 Mg·ha−1. The spatial distribution of vegetation carbon density regularity was generally low in the middle region and high in the peripheral region, which was consistent with the terrain characteristics of the study area.

Estimated biomass carbon in thinned Cunninghamia lanceolate plantations at different stand-ages

“…Global warming increases the frequency of extreme hightemperature events which further increases atmospheric carbon (C) concentration (Zhou et al 2016). Forests mitigate atmospheric carbon dioxide (CO 2 ) concentration by sequestering nearly half of the terrestrial C in forest vegetations (Beer et al 2010;Pan et al 2019). Forest biomass largely accounts for the mitigation of climate change through pooling atmosphere CO 2 , around 50% of tree biomass is derived from C (Tang et al 2017).…”

“…The first-rotation density declines as the age increases over a long term, up to 50 (Xie et al 2016) or 90 years (Selvaraj et al 2017). Stand density is an influential factor that determines biomass C sequestration and consequently physiological activities in trees (Devi and Yadava 2015;Xie et al 2016;Li et al 2019;Pan et al 2019). Older stands resulted in greater tree C storage and higher partition of biomass to the root (Xie et al 2016).…”

Application of mathematical modeling in forest ecological protection

“…There are many factors affecting forest carbon sequestration, which can be divided into human factors and natural factors. Among the natural factors, forest age, slope, elevation, temperature, precipitation and forest density occupy the main position [8] . Human activities also have a great impact on forest ecosystems [9] , mainly including all kinds of commercial development and wood products manufacturing processes, such as tree felling, forest burning, reclamation of woodland, etc.…”

Expansion and Evolution of a Typical Resource-Based Mining City in Transition Using the Google Earth Engine: A Case Study of Datong, China

“…Specifically, two spatial autocorrelation indicators are used in this research: one is a global measure (the Moran's I) and the other is a local measure (the local Moran's I), both of which are common approaches for spatial cluster analysis. While the former can detect the global spatial clustering pattern, the latter can identify the locations of local clusters [43,44]. The mathematical formulations of the two statistics are expressed as in ( 5)- (7).…”