Vegetation Coverage Prediction for the Qinling Mountains Using the CA–Markov Model

The Qinling Mountains represent the dividing line of the natural landscape of north-south in China. The prediction on vegetation coverage is important for protecting the ecological environment of the Qinling Mountains. In this paper, the data accuracy and reliability of three vegetation index data (GIMMS NDVI, SPOT NDVI, and MODIS NDVI) were compared at first. SPOT, NDVI, and MODIS NDVI were used for calculating the vegetation coverage in the Qinling Mountains. Based on the CA–Markov model, the vegetation coverage grades in 2008, 2010, and 2013 were used to simulate the vegetation coverage grade in 2025. The results show that the grades of vegetation coverage of the Qinling Mountains calculated by SPOT, NDVI, and MODIS NDVI are highly similar. According to the prediction results, the grade of vegetation coverage in the Qinling Mountains has a rising trend under the guidance of the policy, particularly in urban areas. Most of the vegetation coverage transit from low vegetation coverage to middle and low vegetation coverage. The grades of the vegetation coverage, which were predicted by the CA–Markov model using SPOT, NDVI, and MODI NDVI, are consistent in spatial distribution and temporal variation.

Recent greening of grasslands in northern China driven by increasing precipitation

Aims Recent warmer and wetter climate in northern China remains a hot topic in recent years, yet its effect on vegetation growth has not been fully understood. This study investigated the temporal change of vegetation cover and its correlations with climatic variables from 1982 to 2018 for grasslands in northern China. Our aim is to clarify whether the warmer and wetter climate in recent years drives the greening of the vegetation in this region. Methods We investigated the temporal dynamic of vegetation Normalized Difference Vegetation Index (NDVI) and its driving forces based on long time-series data. Piecewise regression was used to examine whether there was a turning point of the trend of NDVI and climatic variables. Pearson correlation analyses were conducted to quantify the relationship between NDVI and climatic factors. Stepwise multivariable regression was used to quantify the contributions of climate variables to the temporal variations in NDVI. Important Findings We found a turning point of NDVI trend in 2008, with GIMMS NDVI indicating a slight increase of 0.00022 yr -1 during 1982-2008 to an increase of 0.002 yr -1 for GIMMS NDVI during 2008-2015 and 0.0018 yr -1 for MODIS NDVI during 2008-2018. Precipitation was the predominant driver, and air temperature and vapor pressure deficit (VPD) exerted a minor impact on the temporal dynamics of NDVI. Overall, our results suggest a turning point of NDVI trend, and that recent warmer and wetter climate has caused vegetation greening, which provides insights for better predicting the vegetation cover in this region under changing climate.