Shifted Global Vegetation Phenology in Response to Climate Changes and Its Feedback on Vegetation Carbon Uptake

Fang, Husheng; Sha, Moquan; Xie, Yichun; Wang, Lin; Qiu, Dai; Tu, Jiangguang; Tan, Xicheng; Li, Xiaolei; Sha, Zongyao

doi:10.3390/rs15092288

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…Shifts of vegetation phenology due to climate changes can affect vegetation productivity by affecting the process of carbon uptake [14][15][16][17]. Most of previous studies on vegetation phenology have demonstrated that the start of the growing season (SOS) and the end of the growing season (EOS) have, respectively, advanced and delayed trends under global warming [18][19][20]. Cheng et al [21] found that earlier SOS and later EOS led to an extension of the length of growing season (LOS) in the Tibetan Plateau, resulting in an increase of the gross primary productivity of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Yang,

Liu,

Chen

et al. 2024

Remote Sensing

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Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming.

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Section: Introductionmentioning

confidence: 99%

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Yang,

Liu,

Chen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review

Matyukira,

Mhangara

2024

European Journal of Remote Sensing

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Impact of Extreme Climate Indices on Vegetation Dynamics in the Qinghai–Tibet Plateau: A Comprehensive Analysis Utilizing Long-Term Dataset

Duan,

Huang,

Liu

et al. 2024

IJGI

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The Qinghai–Tibet Plateau (QTP) is crucial for global climate regulation and ecological equilibrium. However, the phenomenon of global climate warming has increased the frequency of extreme weather events on the QTP, exerting substantial effects on both regional and global ecological systems. This study utilized long-term series NDVI and extreme climate indices to comprehensively evaluate the impact of extreme climatic changes on diverse vegetation types within the QTP. A variety of analytical methodologies, including trend analysis, a Mann–Kendall test, correlation analysis, and random forest importance ranking, were employed in this study. These methodologies were applied to investigate the distribution patterns and variation trends of diverse vegetation types and extreme climate indices. This comprehensive approach facilitated a detailed analysis of the responses of different vegetation types to interannual variability under extreme climatic conditions and enabled the assessment of the impact of extreme climate indices on these vegetation types. The findings have the following implications: (1) Except for forests, the annual NDVI for overall vegetation, meadows, steppes, deserts, and alpine vegetation in the QTP exhibits a significant upward trend (p < 0.01). Notably, meadows and deserts demonstrate the highest growth rates at 0.007/10y, whereas the annual NDVI of forests is not statistically significant (p > 0.05). Substantial increases in vegetation were predominantly detected in the central and northeastern regions of the QTP, while significant decreases were mostly observed in the southeastern and western regions. The area exhibiting significant vegetation increase (38.71%) considerably surpasses that of the area with a significant decrease (14.24%). (2) There was a statistically significant reduction (p < 0.05) in the number of days associated with extreme cold temperature indices, including CSDI, DTR, FD, ID, TN10p, and TX10p. In contrast, indices related to extremely warm temperatures, such as GSL, WSDI, SU25, TN90p, TNn, TNx, TX90p, and TXx, exhibited a statistically significant increase (p < 0.01). The pronounced rise in minimum temperatures, reflected by fewer cold days, has notably contributed to climate warming. Although extreme precipitation events have become less frequent, their intensity has increased. Notable spatial variations in extreme precipitation were observed, although no consistent changing pattern emerged. (3) The annual NDVI for non-forest vegetation types showed a significant negative correlation with most extreme cold temperature indices and a significant positive correlation with extreme warm temperature indices. A significant positive correlation (p < 0.05) between annual NDVI and extreme precipitation indices is found only in steppe and desert ecosystems, with no such correlation observed in other vegetation types. Both correlation analysis and random forest methodologies underscore the impact of extreme climate indices on vegetation variations, with the random forest model exhibiting superior capability in capturing nonlinear relationships. In conclusion, global climate change is projected to result in a heightened frequency of extreme warm events. Although these conditions might temporarily enhance vegetation growth, they are also associated with numerous detrimental impacts. Therefore, it is imperative to enhance awareness and take proactive measures for early warning and prevention.

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Shifted Global Vegetation Phenology in Response to Climate Changes and Its Feedback on Vegetation Carbon Uptake

Cited by 3 publications

References 55 publications

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review

Impact of Extreme Climate Indices on Vegetation Dynamics in the Qinghai–Tibet Plateau: A Comprehensive Analysis Utilizing Long-Term Dataset

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