Rapid greening response of China’s 2020 spring vegetation to COVID-19 restrictions: Implications for climate change

Su, Fenzhen; Fu, Dongjie; Yan, Fengqin; Xiao, Han; Pan, Tingting; Xiao, Yang; Kang, Lu; Zhou, Chenghu; Meadows, Michael E.; Lyne, Vincent; Wilson, John P.; Zhao, Na; Yang, Xiaomei; Liu, Gaohuan

doi:10.1126/sciadv.abe8044

Cited by 38 publications

(30 citation statements)

References 45 publications

(53 reference statements)

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“…Based on the concept of deviations from month-on-month growth, monthly chain growth [40] was used to determine monthly departures from an underlying linear yearly trend. Monthly changes of the target year were obtained from the monthly difference of the adjacent years before the target year.…”

Section: Monthly Chain Growthmentioning

confidence: 99%

Global Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020

Yan

et al. 2021

Remote Sensing

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Global Fishing Watch (GFW) provides global open-source data collected via automated monitoring of vessels to help with sustainable management of fisheries. Limited previous global fishing effort analyses, based on Automatic Identification System (AIS) data (2017–2020), suggest economic and environmental factors have less influence on fisheries than cultural and political events, such as holidays and closures, respectively. As such, restrictions from COVID-19 during 2020 provided an unprecedented opportunity to explore added impacts from COVID-19 restrictions on fishing effort. We analyzed global fishing effort and fishing gear changes (2017–2019) for policy and cultural impacts, and then compared impacts of COVID-19 lockdowns across several countries (i.e., China, Spain, the US, and Japan) in 2020. Our findings showed global fishing effort increased from 2017 to 2019 but decreased by 5.2% in 2020. We found policy had a greater impact on monthly global fishing effort than culture, with Chinese longlines decreasing annually. During the lockdown in 2020, trawling activities dropped sharply, particularly in the coastal areas of China and Spain. Although Japan did not implement an official lockdown, its fishing effort in the coastal areas also decreased sharply. In contrast, fishing in the Gulf of Mexico, not subject to lockdown, reduced its scope of fishing activities, but fishing effort was higher. Our study demonstrates, by including the dimensions of policy and culture in fisheries, that large data may materially assist decision-makers to understand factors influencing fisheries’ efforts, and encourage further marine interdisciplinary research. We recommend the lack of data for small-scale Southeast Asian fisheries be addressed to enable future studies of fishing drivers and impacts in this region.

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Section: Monthly Chain Growthmentioning

confidence: 99%

Global Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020

Yan

et al. 2021

Remote Sensing

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“…In the NWR, SOS shifted from an advanced trend (À1.05 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: 92%

“…Study area Study period Satellite data SOS EOS (Qiao and Wang, 2019) Inner Mongolia, China 1982-2015 AVH09C1 À0.30 (Li et al, 2021a) Xinjiang, China 1982-2014 GIMMS3g À0.19 (Ni et al, 2017) Northeastern China 1982-2011 GIMMS LAI3g À0.26 0.11 (Yu et al, 2017) Northeast China 1982-2015 GIMMS NDVI À0.13 0.25 (Luo and Yu, 2017) Northern China 2001-2014 MOD13A2 À0.34 0.20 China's temperate 1982-1999 AVHRR À0.79 0.37 (Liu et al, 2016a) Temperate China 1982-2011 GIMMS3g 0.12 ± 0.01 (Zhou et al, 2020) Temperate China 1982-2015 GIMMS3g À0.12 ± 0.03 (Shen et al, 2018) Temperate grasslands of China 1982-2015 GIMMS3g À0.18 (Piao et al, 2011) Tibetan Plateau 1982-1999 GIMMS À0.88 (Zhang et al, 2013) Tibetan Plateau 1982-1998 GIMMS À1.02 (Zhang et al, 2013) Tibetan Plateau 1998-2006 GIMMS 2.33 (Zhang et al, 2013) Tibetan Plateau 2000-2011 SPOT-VGT À1.36 (Zhang et al, 2013) Tibetan Plateau 2000-2011 MOD13A2 À0.78 (Zu et al, 2018) Tibetan Plateau 2000-2015 MOD13A2 À0.45 À0.05 (Peng et al, 2021b) Tibetan Plateau 1982-2018 AVHRR LTDR NDVI 0.01 (Luo and Yu, 2017) Southern China 2001-2014 MOD13A2 0.79 (Li et al, 2021b) Subtropical forest of China 2002-2017 GOSIF À0.68 (Wang et al, 2018) China's Grasslands The VIPPHEN dataset À0.23 ± 0.47 0.17 ± 0.46 vegetation phenology (Fu et al, 2021;Ge et al, 2015;Su et al, 2021), for example, on the Qinghai-Tibet Plateau (An et al, 2020;Shen et al, 2014Shen et al, , 2016Yang, 2021;Zhang et al, 2018;Zhu et al, 2017). China's phenological research accounted for 3124 out of 39,268 papers on the subject of "phenology" between 1980 and 2020 according to the Web of Science platform (Figure 1 (Chen et al, 2017;Dai et al, 2019;Ge et al, 2015;Wang et al, 2015a).…”

Section: Referencesmentioning

confidence: 99%

“…There has been a strong focus on geographical location and climate conditions in China in studies of vegetation phenology (Fu et al, 2021; Ge et al, 2015; Su et al, 2021), for example, on the Qinghai–Tibet Plateau (An et al, 2020; Shen et al, 2014, 2016; Yang, 2021; Zhang et al, 2018; Zhu et al, 2017). China’s phenological research accounted for 3124 out of 39,268 papers on the subject of “phenology” between 1980 and 2020 according to the Web of Science platform (Figure 1(a)).…”

Section: Development Of Vegetation Phenology Research In Chinamentioning

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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“…The phenology and greenness of vegetation growth were usually investigated using leaf area index (LAI) (Su et al, 2021). Normalized difference vegetation index (NDVI) was used for forest quality (e.g., vegetation type, cover form, and growth status) assessment (Li, Zheng, et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Dynamics arising from the impact of large‐scale afforestation on ecosystem services

Wang

2022

Land Degrad Dev

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Understanding how multiple ecosystem services interact, how trade-offs emerge, and how interactions among ecosystem services may change after the Grain-for-Green-Program (GFGP) and is critical for managing multiple ecosystem services.Here, we conducted a spatiotemporal approach to quantify the impact of large-scale afforestation on four key ecosystem services (i.e., crop production, soil conservation, carbon sequestration, and water conservation). We observed that three satellite datasets (i.e., GIMMS-LAI3g, GIMMS-NDVI3g, and NDVI-ESTARFM) time-series show a slightly improved and significantly improved trends, and crop production, carbon sequestration, soil conservation, and water conservation have improved after the implementation of GFGP. Simultaneously, almost 49% of the low levels and 58% of the high levels for all four of the ecosystem services considered changed after the implementation of GFGP. The results also show that relationships among ecosystem services have shifted from a trade-off in 1988 and 1999 to an apparent synergistic relationship in 2011.We conclude that GFGP is a successful programme for addressing trade-offs between services. Our study provides experiential support for largescale restoration programme in humid area around the World.

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Rapid greening response of China’s 2020 spring vegetation to COVID-19 restrictions: Implications for climate change

Cited by 38 publications

References 45 publications

Global Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020

Global Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020

Review of vegetation phenology trends in China in a changing climate

Dynamics arising from the impact of large‐scale afforestation on ecosystem services

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