Weakened connection between spring leaf-out and autumn senescence in the Northern Hemisphere.

Vegetation autumn phenology is critical in regulating the ecosystem carbon cycle and regional climate. However, the dominant drivers of autumn senescence and their temporal shifts under climate change remain poorly understood. Here, we conducted a multi-factor analysis considering both direct climatic controls and biological carryover effects from start-of-season (SOS) and seasonal peak vegetation activities on the end-of-season (EOS) to fill these knowledge gaps. Combining satellite and ground observations across the northern hemisphere, we found that carryover effects from early-to-peak vegetation activities exerted greater influence on EOS than the direct climatic controls on nearly half of the vegetated land. Unexpectedly, the carryover effects from SOS on EOS have significantly weakened over recent decades, accompanied by strengthened climatic controls. Such results indicate the weakened constraint of leaf longevity on senescence due to prolonged growing season in response to climate change. These findings underscore the important role of biological carryover effects in regulating vegetation autumn senescence under climate change, which should be incorporated into the formulation and enhancement of phenology modules utilized in land surface models.

Modeling the mechanisms of conifer mortality under seawater exposure.

Relative sea level rise (SLR) increasingly impacts coastal ecosystems through the formation of ghost forests. To predict the future of coastal ecosystems under SLR and changing climate, it is important to understand the physiological mechanisms underlying coastal tree mortality and to integrate this knowledge into dynamic vegetation models. We incorporate the physiological effect of salinity and hypoxia in a dynamic vegetation model in the Earth system land model, and used the model to investigate the mechanisms of mortality of conifer forests on the west and east coast sites of USA, where trees experience different form of sea water exposure. Simulations suggest similar physiological mechanisms can result in different mortality patterns. At the east coast site that experienced severe increases in seawater exposure, trees loose photosynthetic capacity and roots rapidly, and both storage carbon and hydraulic conductance decrease significantly within a year. Over time, further consumption of storage carbon that leads to carbon starvation dominates mortality. At the west coast site that gradually exposed to seawater through SLR, hydraulic failure dominates mortality because root loss impacts on conductance are greater than the degree of storage carbon depletion. Measurements and modeling focused on understanding the physiological mechanisms of mortality is critical to reducing predictive uncertainty.

Contrasting temperature effects on the velocity of early- versus late-stage vegetation green-up in the Northern Hemisphere.

Global vegetation greening has been widely confirmed in previous studies, yet the changes in the velocity of green-up in each month of green-up period (GUP) remains unclear. Here, we defined the velocity of vegetation green-up as VNDVI (the monthly increase of Normalized Difference Vegetation Index [NDVI] during GUP) and further explored its response to climate change in middle-high-latitude Northern Hemisphere. We found that in early GUP, VNDVI generally showed positive trends from 1982 to 2015, whereas in late GUP, it showed negative trends in most areas. Such contrasting trends were mainly due to a positive temperature effect on VNDVI in early GUP, but this effect turned negative in late GUP. The increase of soil moisture also in part explained the accelerated vegetation green-up, especially in the arid and semi-arid ecosystems of inland areas. Our analyses also indicate that the first month of the GUP was the key stage impacting vegetation greenness in summer. Future warming may continuously speed up the early growth of vegetation, altering the seasonal trajectory of vegetation and its feedbacks to the Earth system.

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Long-term occupation of coal gangue dumping sites (CGDS) may destroy ecological environment of nearby area. However, how the CGDS affects the distribution pattern of soil seed banks and vegetation in the nearby area is not clear. In this study, we investigated soil seed bank and vegetation at different distances from the second CGDS of Yangchangwan in Ningdong mining area, Lingwu, Ningxia. The results showed that soil seed bank was mainly distributed in 0-10 cm layer and decreased with increasing soil depth. Species richness of soil seed bank and vegetation first increased and then tended to be stable with increasing distance to the CGDS. The influence range of CGDS on soil seed banks was 300-500 m and was 100-300 m on aboveground vegetation. The CGDS did not affect the vertical distribution pattern of soil seed bank, but significantly affected the horizontal distribution pattern of soil seed banks and aboveground vegetation. The key area of vegetation restoration around the CGDS was between 100 m and 300 m.

Vegetation dynamic analysis based on multisource remote sensing data in the east margin of the Qinghai-Tibet Plateau, China.

This study focuses on the vegetation dynamic caused by global environmental change in the eastern margin of the Qinghai-Tibet Plateau (EMQTP). The Qinghai-Tibet Plateau (QTP) is one of the most sensitive areas responding to global environmental change, particularly global climate change, and has been recognized as a hotspot for coupled studies on changes in global terrestrial ecosystems and global climates. An important component of terrestrial ecosystems, vegetation dynamic has become a key issue in global environmental change, and numerous case studies have been conducted on vegetation dynamic trends using multi-source data and multi-scale methods across different study periods. The EMQTP is regarded as a transitional area located between the QTP and the Sichuan basin, and has special geographical and climatic conditions. Although this area is ecologically fragile and sensitive to climate change, few studies about vegetation dynamics have been carried out in this area. Thus, in this study, we used long-term series datasets of GIMMS 3g NDVI and VGT/PROBA-V NDVI to analyze the vegetation dynamics and phenological changes from 1982 to 2018. Validation was performed based on Landsat NDVI and Vegetation Index & Phenology (VIP) data. The results reveal that the year 1998 was a vital turning point in the start of growing season (SGS) in vegetation ecosystems. Before this turning point, the SGS had an average slope of 9.2 days/decade, and after, the average slope was 3.9 days/decade. The length of growing season (LGS) was slightly prolonged between 1982 to 2015. Additionally, the largest national alpine wetland grassland experienced significant vegetation degradation; in autumn, the degraded area accounted for 63.4%. Vegetation degradation had also appeared in the arid valleys of the Yalong River and the Jinsha River. Through validation analysis, we found that the main causes of vegetation degradation are the natural degradation of wetland grassland and human activities, specifically agricultural development and residential area expansion.

Understanding vegetation changes in northern China and Mongolia with change vector analysis.

In recent years, a close link between vegetation change and climate change has been established. Vegetation change can be detected with remotely sensed images, especially with normalized difference vegetation index time series records. We used change vector analysis, especially change vector magnitude (CV magnitude), as an indicator to better understand vegetation change. Twenty-one layers of CV magnitude for each 10-day period from April to October have been acquired. Maxima, range, standard deviation, mean, and minima of CV magnitude were obtained and analyzed, identifying 11 regions with different types of vegetation change during different 10-day periods. In addition, the months of maximum CV magnitude were determined to help predict future vegetation change. The following conclusions were drawn: (a) CV magnitude can serve as an indicator to compare vegetation conditions among different years; (b) 11 typical regions were identified in the study area that show vegetation changes between 1999 and 2006;