Analysis and prediction of global vegetation dynamics: past variations and future perspectives

Abstract

Spatiotemporal dynamic vegetation changes affect global climate change, energy balances and the hydrological cycle. Predicting these dynamics over a long time series is important for the study and analysis of global environmental change. Based on leaf area index (LAI), climate, and radiation flux data of past and future scenarios, this study looked at historical dynamic changes in global vegetation LAI, and proposed a coupled multiple linear regression and improved gray model (CMLRIGM) to predict future global LAI. The results show that CMLRIGM predictions are more accurate than results predicted by the multiple linear regression (MLR) model or the improved gray model (IGM) alone. This coupled model can effectively resolve the problem posed by the underestimation of annual average of global vegetation LAI predicted by MLR and the overestimate predicted by IGM. From 1981 to 2018, the annual average of LAI in most areas covered by global vegetation (71.4%) showed an increase with a growth rate of 0.0028 a^–1; of this area, significant increases occurred in 34.42% of the total area. From 2016 to 2060, the CMLRIGM model has predicted that the annual average global vegetation LAI will increase, accounting for approximately 68.5% of the global vegetation coverage, with a growth rate of 0.004 a⁻¹. The growth rate will increase in the future scenario, and it may be related to the driving factors of the high emission scenario used in this study. This research may provide a basis for simulating spatiotemporal dynamic changes in global vegetation conditions over a long time series.

Keywords

Vegetation dynamics / LAI / CMLRIGM / Prediction / Climate / Radiation