Spatial analysis of carbon storage density of mid-subtropical forests using geostatistics: a case study in Jiangle County, southeast China

The mid-subtropical forest is one of the biggest sections of subtropical forest in China and plays a vital role in mitigating climate change by sequestering carbon. Studies have examined carbon storage density (CSD) distribution in temperate forests. However, our knowledge of CSD in subtropical forests is limited. In this study, Jiangle County was selected as a study case to explore geographic variation in CSD. A spatial heterogeneity analysis by semi-variogram revealed that CSD varied at less than the mesoscale (approximately 2000–3000 m). CSD distribution mapped using Kriging regression revealed an increasing trend in CSD from west to east of the study area. Global spatial autocorrelation analysis indicated that CSD was clustered at the village level (at 5% significance). Some areas with local spatial autocorrelation were detected by Anselin Local Moran’s I and Getis-Ord G*. A geographically weighted regression model showed different impacts on the different areas for each determinant. Generally, diameter at breast height, tree height, and stand density had positive correlation with CSD in Jiangle County, but varied substantially in magnitude by location. In contrast, coefficients of elevation and slope ranged from negative to positive. Based on these results, we propose certain measures to increase forest carbon storage, including increasing forested area, improving the quality of the current forests, and promoting reasonable forest management decisions and harvesting strategies. The established CSD model emphasizes the important role of mid-subtropical forest in carbon sequestration and provides useful information for quantifying mid-subtropical forest carbon storage.