Spatial pattern and driving factors of biomass carbon density for natural and planted coniferous forests in mountainous terrain, eastern Loess Plateau of China

Abstract

Background: Understanding the spatial pattern and driving factors of forest carbon density in mountainous terrain is of great importance for monitoring forest carbon in support of sustainable forest management for mitigating climate change. Methods: We collected the forest inventory data in 2015 in Shanxi Province, eastern Loess Plateau of China, to explore the spatial pattern and driving factors of biomass carbon density (BCD) for natural and planted coniferous forests using Anselin Local Moran's I, Local Getis-Ord G* and semivariogram analyses, and multi-group structural equation modeling, respectively. Results: The result of spatial pattern of BCDs for natural forests showed that the BCD was generally higher in the north but lower in the south of Shanxi. The spatial pattern for planted forests was substantially different from that for natural forests. The results of multi-group SEM suggested that elevation (or temperature as the alternative factor of elevation) and stand age were important driving factors of BCD for these two forest types. Compared with other factors, the effects of latitude and elevation on BCD showed much greater difference between these two forest types. The difference in indirect effect of latitude (mainly through affecting elevation and stand age) between natural and planted forests was to some extent a reflection of the difference between the spatial patterns of BCDs for natural and planted forests in Shanxi. Conclusions: The natural coniferous forests had a higher biomass carbon density, a stronger spatial dependency of biomass carbon density relative to planted coniferous forests in Shanxi. Elevation was the most important driving factor, and the effect on biomass carbon density was stronger for natural than planted coniferous forests. Besides, latitude presented only indirect effect on it for the two forest types.

Species Diversity, Growing Stock Variables and Carbon Mitigation Potential in the Phytocoenosis of Monotheca buxifolia Forests along Altitudinal Gradient across Pakistan

“…However, in contrast, lower biomass at high elevation ranges was also documented by several workers [69][70][71]. Sun et al [72] reported a strong positive relation (R 2 = 0.67) of ABG biomass with elevation in the central highland, Vietnam. Furthermore, the findings of Li et al [19] exposes no significant interaction of altitude with vegetation carbon stock in Chitteri reserve forest.…”

An Integrative Approach to Study How Driving Factors Control Biomass Carbon Density for Natural Mountain Forests, in China’s Loess Plateau

“…The shrub and herb characteristics were measured from three randomly selected 2 m × 2 m quadrats in each plot. The biomass carbon density (BCD) (Mg/ha) of individual living trees for each forest plot was estimated using the biomass expansion factor (BEF) method [32,37]. The BCD of the shrub, herb, or litter layer for the forest-present plot was obtained by multiplying the biomass (Mg/m) by carbon content, which was 0.4627, 0.3270, and 0.4700 for the shrub, herb, and litter layers, respectively [32].…”

“…We collected the dataset of annual mean temperature (TEMP) and annual mean precipitation (PRCP) for the period 1981-2015 at all these stations from the meteorological database of the scientific data platform of Shanxi Province. We then used the regression-kriging interpolation method [37][38][39] to derive the climatic data for each plot from that for each meteorological station, which combined regression of the climatic variable on topography variables (slope degree, slope aspect, and slope position) with kriging of the regression residuals [40]. We finally further corrected the derived TEMP for each sample plot according to the differences between the plot elevation and the interpolated elevation using the temperature lapse rate of 4.89 • C km −1 as the correction factor [33,37].…”

Differences in spatial patterns and driving factors of biomass carbon density between natural coniferous and broad-leaved forests in mountainous terrain, eastern Loess Plateau of China

“…The biomass carbon density (BCD) (Mg/ha) of individual living trees for each forest plot was estimated using the biomass expansion factor (BEF) method (Sun et al, 2020;Wang et al, 2018). The BCD of the shrub, herb, or litter layer for the forest-present plot was obtained by multiplying the biomass (Mg/m) by carbon content, which was 0.4627, 0.3270, and 0.4700 for the shrub, herb, and litter layers, respectively (Wang et al, 2018).…”

“…We collected the dataset of annual mean temperature (TEMP) and annual mean precipitation (PRCP) for the period 1981-2015 at all these stations from the meteorological database of the scienti c data platform of Shanxi Province. We then used the regression-kriging interpolation method (Lamsal et al, 2011;Plouffe et al, 2015;Sun et al, 2020) to derive the climatic data for each plot from that for each meteorological station, which combined regression of the climatic variable on topography variables (slope degree, slope aspect, and slope position) with kriging of the regression residuals (Eldeiry and Garcia, n.d.). We nally further corrected the derived TEMP for each sample plot according to the differences between the plot elevation and the interpolated elevation using the temperature lapse rate of 4.89°C km −1 as the correction factor (Sun et al, 2020;Wang, 2014).…”