Responses of vegetation spring phenology to climatic factors in Xinjiang, China

Li, Cheng; Wang, Ranghui; Cui, Xuefeng; Wu, Fang; Yan, Yu; Qing, Peng; Qian, Zhang; Yang, Xu

doi:10.1016/j.ecolind.2020.107286

Cited by 39 publications

(31 citation statements)

References 35 publications

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“…This result is consistent with other studies around the globe [37]. Additionally, Li et al [77] extracted SGS using time series of NDVI, and found that warmer spring temperatures led to advanced SGS, whereas declined spring warming after 2005 reversed the SGS trends. Noticeably, this study also identified two distinct periods around 2005 with opposite SGS trends.…”

Section: Discussionsupporting

confidence: 90%

Spatial and Temporal Variability of Key Bio-Temperature Indicators and Their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Gao

Zhao

2022

Remote Sensing

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Dryland ecosystems are fragile to climate change due to harsh environmental conditions. Climate change affects vegetation growth primarily by altering some key bio-temperature thresholds. Key bio-temperatures are closely related to vegetation growth, and slight changes could produce substantial effects on ecosystem structure and function. Therefore, this study selected the number of days with daily mean temperature above 0 °C (DT0), 5 °C (DT5), 10 °C (DT10), 20 °C (DT20), the start of growing season (SGS), the end of growing season (EGS), and the length of growing season (LGS) as bio-temperature indicators to analyze the response of vegetation dynamics to climate change in the Great Lakes Region of Central Asia (GLRCA) for the period 1982–2014. On the regional scale, DT0, DT5, DT10, and DT20 exhibited an overall increasing trend. Spatially, most of the study area showed that the negative correlation between DT0, DT5, DT10, DT20 with the annual Normalized Difference Vegetation Index (NDVI) increased with increasing bio-temperature thresholds. In particular, more than 88.3% of the study area showed a negative correlation between annual NDVI and DT20, as increased DT20 exacerbated ecosystem drought. Moreover, SGS exhibited a significantly advanced trend at a rate of −0.261 days/year for the regional scale, while EGS experienced a significantly delayed trend at a rate of 0.164 days/year. Because of changes in SGS and EGS, LGS across the GLRCA was extended at a rate of 0.425 days/year, which was mainly attributed to advanced SGS. In addition, our study revealed that about 53.6% of the study area showed a negative correlation between annual NDVI and LGS, especially in the north, indicating a negative effect of climate warming on vegetation growth in the drylands. Overall, the results of this study will help predict the response of vegetation to future climate change in the GLRCA, and support decision-making for implementing effective ecosystem management in arid and semi-arid regions.

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Section: Discussionsupporting

confidence: 90%

Spatial and Temporal Variability of Key Bio-Temperature Indicators and Their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Gao

Zhao

2022

Remote Sensing

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“…In the Southern region (SR), the vegetation index of tropical evergreen plants showed small interannual changes, and it was difficult to retrieve phenological parameters using vegetation indices for tropical evergreen plants. The quantity of vegetation phenological research in the Qinghai–Tibet region (QTR) and in the Northeast region (NR) was lower compared with that on phenology in the Northwest region (NWR) and SR (Qiao and Wang, 2019; Li et al, 2021a). In the QTR, the SOS contributed the most to the interannual variations of the EOS compared with that of climate variables including temperature, precipitation, and radiation (Peng et al, 2021b).…”

Section: Response Of Vegetation Phenology To Climate Changementioning

confidence: 97%

“…In the NR, Yu et al (2013) demonstrated that the SOS showed a significantly advanced trend (0.68 days/year) from 1982 to 1998, and a non-significant delayed trend (2.13 days/year) from 1998 to 2005 in temperate China. In the NWR, SOS shifted from an advanced trend (−1.05 days/year, p < .05) to a delayed trend (0.43 days/year, p > .05) after 2005 in Xinjiang, China (Li et al, 2021a). The interannual variability of spring phenology was 8.8 ± 1.1 days and 10.3 ± 1.1 days during 1982–1998 and 1999–2015, respectively, in temperate semi-dry grasslands in China (Fu et al, 2021).…”

Section: Response Of Vegetation Phenology To Climate Changementioning

confidence: 99%

“…The study found that the SOS became earlier by 4.93 days/°C of rising temperature during the period 1982–2006 in China (Ma and Zhou, 2012), whereas a short photoperiod associating with earlier SOS may significantly reduce the temperature sensitivity of SOS, mainly because of increased heat requirement for SOS to avoid premature leaf and protect the leaves from potential frost damage (Fu et al, 2014a, 2019b). Radiation could compensate chilling deficient to avoid late SOS, and increasing photosynthetic active radiation can promote earlier SOS (Guo et al, 2021; Li et al, 2021a). In China, due to the success in air pollution prevention and control, the sky became clearer in recent years, that is, higher radiation sum, and an associated earlier SOS was found (Su et al, 2021).…”

Section: Climate Driving Factorsmentioning

confidence: 99%

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Review of vegetation phenology trends in China in a changing climate

Zhang

Chen

et al. 2022

Progress in Physical Geography: Earth and Environment

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Vegetation phenology is sensitive to climate change and has been defined as the footprint of ongoing climate change. Previous studies have shown that the spatial difference in China’s vegetation phenology varies substantially in both spring and autumn. Here, we reviewed phenological dynamics at the national and the regional scale of China over the period 1982−2020 using a remote sensing-based dataset and meta-analysis from phenological studies in China. We also explored the underlying mechanisms of both spring and autumn phenology and discussed potential phenological studies under future climate conditions. We found that, over the past four decades, the spring phenology advanced at a rate of 0.23 ± 0.47 days/year, while the autumn phenology was delayed at a rate of 0.17 ± 0.46 days/year. This led to an extended vegetation growth season of approximately 5 days per decade. The trends in the spring and autumn phenology were spatially specific in the Northern region, Northwest region, Qinghai–Tibet region, and Southern region: the change in spring phenology was −0.16, −0.46, −0.18, and −0.13 days/year, respectively, while the change in autumn phenology was 0.02, 0.32, 0.09, and 0.28 days/year, respectively. We also explored the dominant climatic drivers of regional phenological changes. We found that temperature was the dominant factor for spring phenology in cold regions, while precipitation, radiation, and temperature co-determined spring phenology in warm regions. The autumn phenology was affected by all three environmental cues but the effect of temperature was larger than that of radiation and precipitation across all regions. In future climate warming conditions, we recommend that studies focus on the phenological feedback mechanisms, such as the climatic and hydrological effects of vegetation changes, and agricultural phenology to investigate its fundamental role in crop productivity, especially under extreme climate events, to ensure national food security and ecological security.

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“…With the rapid acquisition of multi-source satellite data in the past few decades, monitoring EOS has made great progress through the use of spectral indices-e.g., the Normalize Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), etc.-at the larger spatial scales [8][9][10]. However, previous work found that the inconsistent trends of EOS, e.g., advanced, delayed, and unchanged, have been detected across different regions over the Northern Hemisphere [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Responses of Vegetation Autumn Phenology to Climatic Factors in Northern China

Wang

Liu

et al. 2022

Sustainability

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Understanding the dynamics of vegetation autumn phenology (i.e., the end of growing season, EOS) is crucial for evaluating impacts of climate change on vegetation growth. Nevertheless, responses of the EOS to climatic factors were unclear at the regional scale. In this study, northern China was chosen for our analysis, which is a typical ecologically fragile area. Using the Enhanced Vegetation Index (EVI) and climatic data from 1982 to 2016, we extracted the EOS and analyzed its trends in northern China by using the linear least-squares regression and the Bayesian change-point detection method. Furthermore, the partial correlation analysis and multivariate regression analysis were used to determine which climatic factor was more influential on EOS. The main findings were as follows: (1) multi-year average of EOS mainly varied between 275 and 305 day of year (DOY) and had complicated spatial differences for different vegetation types; (2) the percentage of the pixel showing delaying EOS (65.50%) was larger than that showing advancing EOS (34.50%), with a significant delaying trend of 0.21 days/year at the regional scale during the study period. As for different vegetation types, their EOS trends were similar in sign but different in magnitude; (3) temperature showed a dominant role in governing EOS trends from 1982 to 2016. The increase in minimum temperature led to the delayed EOS, whereas the increase in maximum temperature reversed the EOS trends. In addition to temperature, the impacts of precipitation and radiation on EOS trends were more complex and largely depended on the vegetation types. These findings can provide a crucial support for developing vegetation dynamics models in northern China.

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Responses of vegetation spring phenology to climatic factors in Xinjiang, China

Cited by 39 publications

References 35 publications

Spatial and Temporal Variability of Key Bio-Temperature Indicators and Their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Spatial and Temporal Variability of Key Bio-Temperature Indicators and Their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Review of vegetation phenology trends in China in a changing climate

Responses of Vegetation Autumn Phenology to Climatic Factors in Northern China

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