Analysis of Linkage between Long-Term Morphological Spatial Pattern Analysis and Vegetation Carbon Storage of Forests in Hunan, China

Abstract

The carbon sequestration of forest ecosystems plays a pivotal role in constraining global warming and mitigating climate change. The landscape pattern of forests is being altered due to the combined effects of climate change and human interference. Furthermore, the relationship between forest pattern changes and carbon storage distribution in a long time series remains unclear. Therefore, it is necessary to examine the relationship between forest patterns and carbon density, investigating the variations and similarities in the changes in carbon density across different modes of pattern change over time, and suggestions for forest planning were provided from a perspective focused on pattern change to enhance carbon storage. The Google Earth Engine (GEE) platform’s random forest model was used to map the spatial distribution of forests in Hunan Province for 1996 and 2020, followed by analyzing the correlation between the changes in forest patterns using the morphological spatial pattern analysis (MSPA) and carbon density simulated by the model. Results show that the net growth rate ((area in 2020-area in 1996)/area in 2020) of the forest in Hunan increased 26.76% between 1996 and 2020. The importance scores for the decade average temperature, short-wave length infrared band 1 (SWIR-1), and slope were the highest metrics in the model of carbon density, and were 0.127, 0.107 and 0.089, respectively. The vegetation carbon storage in Hunan Province increased by 31.02 Tg, from 545.91 Tg to 576.93 Tg in 25 years. This study demonstrates that vegetation carbon storage is influenced by the pattern type in both newly established and pre-existing forests (p < 0.05). The findings of this study offer empirical evidence to support forest management strategies targeted at enhancing carbon sequestration.