Analysis of spatio-temporal changes in forest biomass in China

Weiyi Xu,Xiaobin Jin,Jie Ren,Jing Liu,Xuhong Yang,Yinkang Zhou

doi:10.1007/s11676-021-01299-8

Weiyi Xu, Xiaobin Jin + Show 4 more

Open Access

https://doi.org/10.1007/s11676-021-01299-8

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Abstract

Forests play a central role in the global carbon cycle. China's forests have a high carbon sequestration potential owing to their wide distribution, young age and relatively low carbon density. Forest biomass is an essential variable for assessing carbon sequestration capacity, thus determining the spatio-temporal changes of forest biomass is critical to the national carbon budget and to contribute to sustainable forest management. Based on Chinese forest inventory data (1999–2013), this study explored spatial patterns of forest biomass at a grid resolution of 1 km by applying a downscaling method and further analyzed spatio-temporal changes of biomass at different spatial scales. The main findings are: (1) the regression relationship between forest biomass and the associated influencing factors at a provincial scale can be applied to estimate biomass at a pixel scale by employing a downscaling method; (2) forest biomass had a distinct spatial pattern with the greatest biomass occurring in the major mountain ranges; (3) forest biomass changes had a notable spatial distribution pattern; increase (i.e., carbon sinks) occurred in east and southeast China, decreases (i.e., carbon sources) were observed in the northeast to southwest, with the largest biomass losses in the Hengduan Mountains, Southern Hainan and Northern Da Hinggan Mountains; and, (4) forest vegetation functioned as a carbon sink during 1999–2013 with a net increase in biomass of 3.71 Pg.

Highlights

Climate change characterized by global warming has been an international concern for many years (Bonan 2008; Fang et al 2011, 2018; Austin et al 2020)
To eliminate the impact of differences across data units, a greatest-value standardized method was employed before the multiple regression analysis to process data of both provincial impact factors and provincial forest vegetation biomass as shown in Eq 3: rj = xj∕xj max where, rj is the normalized value; xj is the actual value of the jth variable; xjmax is the maximum value of the jth variable
Once all coefficient estimates are acquired, this regression model can be applied to pixels, as shown in Eq 5, and all impact factors can be weighted along with their corresponding coefficients to obtain the weight of biomass at the pixel scale

Summary

Introduction

Climate change characterized by global warming has been an international concern for many years (Bonan 2008; Fang et al 2011, 2018; Austin et al 2020). It is commonly accepted that greenhouse gas emissions, especially carbon dioxide (CO2), are the major cause of global warming (Fang 2000; Quere et al 2016; Chen et al 2018; Xu et al 2018a). Atmospheric CO2 levels have been increasing significantly as a result of urbanization, the increasing use of fossil fuels, and land- use changes (Lal 2008; Fang et al 2011). Terrestrial ecosystems are significant carbon sinks, fixing atmospheric CO2 in vegetation and soil through photosynthesis. Sequestering CO2 into terrestrial ecosystems is considered to be one of the most cost-effective and environment-friendly way to reduce atmospheric CO2 concentrations (Piao et al 2010; Tang et al 2018).

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