Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Global change biology

Forests play an important role in both regional and global C cycles. However, the spatial patterns of biomass C density and underlying factors in Northeast Asia remain unclear. Here, we characterized spatial patterns and important drivers of biomass C density for Northeast Asia, based on multisource data from in-situ forest inventories, as well as remote sensing, bioclimatic, topographic, and human footprint data. We derived, for the first time, high-resolution (1 km × 1 km) maps of the current and future forest biomass C density for this region. Based on these maps, we estimated that current biomass C stock in northeastern China, the Democratic People's Republic of Korea, and Republic of Korea to be 2.53, 0.40, and 0.35 Pg C, respectively. Biomass C stock in Northeast Asia has increased by 20-46% over the past 20 years, of which 40-76% was contributed by planted forests. We estimated the biomass C stock in 2080 to be 6.13 and 6.50 Pg C under RCP4.5 and RCP8.5 scenarios, respectively, which exceeded the present region-wide C stock value by 2.85-3.22 Pg C, and were 8-14% higher than the baseline C stock value (5.70 Pg C). The spatial patterns of biomass C densities were found to vary greatly across the Northeast Asia, and largely decided by mean diameter at breast height, dominant height, elevation, and human footprint. Our results suggest that reforestation and forest conservation in Northeast Asia have effectively expanded the size of the carbon sink in the region, and sustainable forest management practices such as precision forestry and close forest monitoring for fire and insect outbreaks would be important to maintain and improve this critical carbon sink for Northeast Asia.

Luo Weixue, Kim Hyun Seok, Zhao Xiuhai, Ryu Daun, Jung Ilbin, Cho Hyunkook, Harris Nancy, Ghosh Sayon, Zhang Chunyu, Liang Jingjing

2020-Oct-02

Carbon stock; Carbon density, Climate change, Korean Peninsula, Machine learning, Spatial variations, human influences, northeastern China