Implementation of the Natural Forest Protection Project and Grain for Green Programme in China has promoted forest restoration, increased productivity, and enhanced the carbon stocks. However, few studies have characterized temporal and spatial variation in productivity and ecological stability in planted and natural forests and evaluated the factors driving such variation. In this study, we used 1399 permanent forest plots to identify change patters in the productivity and temporal stability of above-ground biomass (AGB) and evaluated the factors driving these changes in planted and natural forests in Sichuan Province, China. The mean temporal stability of AGB was higher for natural forest than for planted forest from 1979 to 2017; While, the productivity of planted forest was higher. The stability decreased at a rate of -0.013 yr-1 in entire natural forest and -0.011 yr-1 in planted forests, and the productivity of natural forest decreased significantly over time, with a slope of -0.0065 Mg ha-1 yr-1 per calendar year. Altitude, latitude, annual precipitation, and stand age dominated variability in the productivity and AGB stability of natural forest. Richness, tree density, and stand age were the determinants of productivity and stability in planted forest. Our results suggest that selective thinning and enriching species richness and forest stand age can effectively balance the productivity and biomass temporal stability of planted forests. Older natural forests still need to be strictly protected under climate change.

Understanding the long-term characteristics of vegetation variations and their relationship to climate and human activities is important for regional sustainable development and ecological construction. Herein, the normalized difference vegetation index (NDVI) was selected as a proxy, related method and algorithm were applied to obtain the nonlinear characteristics of long-term interannual NDVI in China. Partial least squares-structural equation modeling was employed to separate the effects of climate and human activities on vegetation greening. Further, geographically weighted regression was applied to explore the spatial correlations among comprehensive forces and vegetation growth and achieve the partitioning of driving forces. The results suggested that vegetation growth in China experienced an abrupt change in 1995, there was obvious vegetation browning during 1990–1995, and noticeable vegetation recovery from 1996 to 2018. Climate was a directly main driving force for vegetation increasing in China. The positive effect of climate was the most obvious in south China, with a path coefficient of 0.348. However, climate was significantly negative to vegetation growth in northwest China (-0.049). Improving socio-economic conditions had a slightly negative impact on vegetation greening, while ecological policy played a direct and obvious role in promoting vegetation growth, especially in northwest China, with a path coefficient of 0.295. Furthermore, ecological policy would directly affect the microclimate in northwest China, strengthen the restraint effect of water resources on vegetation, and then indirectly hinder vegetation increasing. Therefore, the implementation of ecological policies should be adjusted according to regional climatic conditions, to avoid the traditional way of increasing forest (grassland) area, and reduce the contradiction between water, soil and vegetation. Actually, the indirect effect of socio-economic conditions and ecological policy on vegetation growth was far greater than its direct impact in some cases; therefore, research attention should be paid to the indirect effects of driving forces on vegetation growth

The complexity and uncertainty of land use and environmental factors pose challenges to the management decisions of ecological restoration and conservation.We integrated the mixed-cell CA model and Bayesian belief networks to develop an innovative method for optimizing ecosystem services under different land development scenarios, including consideration of the uncertainty and variability of factors.The southern region of Sichuan Province, China, was selected as an example.We first established three development scenarios between 2015 and 2035, namely, natural development scenario (NDS), ecological protection scenario (EPS), and cultivated land protection scenario (CLPS).The MCCA model was utilized to simulate the land use pattern in 2035 under different scenarios.We then construced a BBN-based model to investigate the carbon sequestration, grain supply, soil conservation, habitat quality, and water yield in 2035 under uncertain scenarios.After the sensitivity analysis and evaluation of the model, we determined the state combination of influential factors at various ecosystem service levels and the ecological restoration and conservation key areas.The obtained result showed that the key influencing factors impacting the ecosystem services level included NPP, Slope, forestland and ET, and the state combination corresponding to the highest level of ecosystem services was predominantly distributed in regions with the highest NPP, the highest Slope, the highest forestland area and low ET.Based on this finding, we proposed some suggestions for ecological restoration and conservation of key areas.This model considers uncertainties and is capable of providing scientific recommendations on restoration and conservation; therefore, it can serve as an effective tool to assist stakeholders in making decisions.