Citation: | Wang Xin, Liu Xinyue, Mu Yanmei, Liu Peng, Jia Xin. Changes in vegetation phenology and its responses to climatic factors in the Mu Us Desert[J]. Journal of Beijing Forestry University, 2023, 45(7): 61-75. DOI: 10.12171/j.1000-1522.20220443 |
[1] |
Piao S L, Liu Q, Chen A P, et al. Plant phenology and global climate change: current progresses and challenges[J]. Global Change Biology, 2019, 25(6): 1922−1940. doi: 10.1111/gcb.14619
|
[2] |
朴世龙, 岳超, 丁金枝, 等. 试论陆地生态系统碳汇在“碳中和”目标中的作用[J]. 中国科学: 地球科学, 2022, 52(7): 1419−1426.
Piao S L, Yue C, Ding J Z, et al. Perspectives on the role of terrestrial ecosystems in the ‘carbon neutrality’ strategy[J]. Science China Earth Sciences, 2022, 52(7): 1419−1426.
|
[3] |
Piao S L, Wang X H, Park T, et al. Characteristics, drivers and feedbacks of global greening[J]. Nature Reviews Earth & Environment, 2020, 1(1): 14−27.
|
[4] |
Piao S L, Yin G D, Tan J G, et al. Detection and attribution of vegetation greening trend in China over the last 30 years[J]. Global Change Biology, 2015, 21(4): 1601−1609. doi: 10.1111/gcb.12795
|
[5] |
Li X H, Zha T S, Liu P, et al. Multi-year trend and interannual variability in soil respiration measurements collected in an urban forest ecosystem in Beijing, China[J/OL]. Agricultural and Forest Meteorology, 2022, 316: 108877[2023−01−23]. https://doi.org/10.1016/j.agrformet.2022.108877.
|
[6] |
Chen Z, Wang B, Xu C, et al. Interannual variabilities, long-term trends, and regulating factors of low-oxygen conditions in the coastal waters off Hong Kong[J]. Biogeosciences, 2022, 19(14): 3469−3490. doi: 10.5194/bg-19-3469-2022
|
[7] |
何玉杰, 孔泽, 户晓, 等. 水热条件分别控制了中国温带草地NDVI的年际变化和增长趋势[J]. 生态学报, 2022, 42(2): 766−777.
He Y J, Kong Z, Hu X, et al. Water and heat conditions seperately controlled inter-annual variation and growth trend of NDVI in the temperate grasslands in China[J]. Acta Ecologica Sinica, 2022, 42(2): 766−777.
|
[8] |
Liu P, Black T A, Jassal R S, et al. Divergent long-term trends and interannual variation in ecosystem resource use efficiencies of a southern boreal old black spruce forest 1999–2017[J]. Global Change Biology, 2019, 25(9): 3056−3069. doi: 10.1111/gcb.14674
|
[9] |
王向涛, 陈懂懂. 三江源草地GNDVI年际波动及其沿海拔梯度敏感性分析[J]. 生态环境学报, 2018, 27(8): 1411−1416.
Wang X T, Chen D D. Interannual variability of GNDVI and it’s relationship with altitudinal in the Three-River Headwater Region[J]. Ecology and Environmental Sciences, 2018, 27(8): 1411−1416.
|
[10] |
Piao S L, Fang J Y, Zhou L M, et al. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999[J/OL]. Journal of Geophysical Research, 2003, 108(D14): 4401[2022−10−02]. https://doi.org/10.1029/2002JD002848.
|
[11] |
Zhang L, Ren X L, Wang J B, et al. Interannual variability of terrestrial net ecosystem productivity over China: regional contributions and climate attribution[J/OL]. Environmental Research Letters, 2019, 14(1): 014003[2022−10−07]. https://doi.org/10.1088/1748-9326/aaec95.
|
[12] |
Shen M G, Wang S P, Jiang N, et al. Plant phenology changes and drivers on the Qinghai-Tibetan Plateau[J/OL]. Nature Reviews Earth & Environment, 2022[2022−10−07]. https://www.nature.com/articles/s43017-022-00317-5.
|
[13] |
Zhong L, Ma Y M, Xue Y K, et al. Climate change trends and impacts on vegetation greening over the Tibetan Plateau[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(14): 7540−7552. doi: 10.1029/2019JD030481
|
[14] |
Tang J W, Körner C, Muraoka H, et al. Emerging opportunities and challenges in phenology: a review[J/OL]. Ecosphere, 2016, 7(8)[2022−10−02]. https://onlinelibrary.wiley.com/doi/10.1002/ecs2.1436.
|
[15] |
Liu Q, Fu Y H, Zhu Z C, et al. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology[J]. Global Change Biology, 2016, 22(11): 3702−3711. doi: 10.1111/gcb.13311
|
[16] |
Fu Y H, Piao S L, Beeck Op de M, et al. Recent spring phenology shifts in western Central Europe based on multiscale observations: multiscale observation of spring phenology[J]. Global Ecology and Biogeography, 2014, 23(11): 1255−1263. doi: 10.1111/geb.12210
|
[17] |
Iler A M, Høye T T, Inouye D W, et al. Long-term trends mask variation in the direction and magnitude of short-term phenological shifts[J]. American Journal of Botany, 2013, 100(7): 1398−1406. doi: 10.3732/ajb.1200490
|
[18] |
CaraDonna P J, Iler A M, Inouye D W. Shifts in flowering phenology reshape a subalpine plant community[J]. Proceedings of the National Academy of Sciences, 2014, 111(13): 4916−4921. doi: 10.1073/pnas.1323073111
|
[19] |
Iler A M, Inouye D W, Schmidt N M, et al. Detrending phenological time series improves climate–phenology analyses and reveals evidence of plasticity[J]. Ecology, 2017, 98(3): 647−655. doi: 10.1002/ecy.1690
|
[20] |
Anderson J T, Inouye D W, McKinney A M, et al. Phenotypic plasticity and adaptive evolution contribute to advancing flowering phenology in response to climate change[J]. Proceedings of the Royal Society B: Biological Sciences, 2012, 279(1743): 3843−3852. doi: 10.1098/rspb.2012.1051
|
[21] |
Fujisawa M, Kobayashi K. Apple (Malus pumila var. domestica) phenology is advancing due to rising air temperature in northern Japan[J]. Global Change Biology, 2010, 16(10): 2651−2660. doi: 10.1111/j.1365-2486.2009.02126.x
|
[22] |
Estrella N, Sparks T H, Menzel A. Trends and temperature response in the phenology of crops in Germany[J]. Global Change Biology, 2007, 13(8): 1737−1747. doi: 10.1111/j.1365-2486.2007.01374.x
|
[23] |
Yang C, Lei H M. Climate and management impacts on crop growth and evapotranspiration in the North China Plain based on long-term eddy covariance observation[J/OL]. Agricultural and Forest Meteorology, 2022, 325: 109147[2023−01−04]. https://doi.org/10.1016/j.agrformet.2022.109147.
|
[24] |
He L, Jin N, Yu Q. Impacts of climate change and crop management practices on soybean phenology changes in China[J/OL]. Science of The Total Environment, 2020, 707: 135638[2022−12−23]. https://doi.org/10.1016/j.scitotenv.2019.135638.
|
[25] |
Liu Q, Fu Y H, Zeng Z Z, et al. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China[J]. Global Change Biology, 2016, 22(2): 644−655. doi: 10.1111/gcb.13081
|
[26] |
Ma R, Shen X J, Zhang J Q, et al. Variation of vegetation autumn phenology and its climatic drivers in temperate grasslands of China[J/OL]. International Journal of Applied Earth Observation and Geoinformation, 2022, 114: 103064[2023−02−12]. https://doi.org/10.1016/j.jag.2022.103064.
|
[27] |
Liu X G, Chen Y N, Li Z, et al. Driving forces of the changes in vegetation phenology in the Qinghai-Tibet Plateau[J/OL]. Remote Sensing, 2021, 13(23): 4952[2023−02−12]. https://doi.org/10.3390/rs13234952.
|
[28] |
Li X T, Guo W, Li S H, et al. The different impacts of the daytime and nighttime land surface temperatures on the alpine grassland phenology[J/OL]. Ecosphere, 2021, 12(6)[2022−10−02]. https://onlinelibrary.wiley.com/doi/10.1002/ecs2.3578.
|
[29] |
Wang H J, Wu C Y, Ciais P, et al. Overestimation of the effect of climatic warming on spring phenology due to misrepresentation of chilling[J/OL]. Nature Communications, 2020, 11(1): 4945[2022−11−22]. https://www.nature.com/articles/s41467-020-18743-8.
|
[30] |
Montgomery R A, Rice K E, Stefanski A, et al. Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range[J]. Proceedings of the National Academy of Sciences, 2020, 117(19): 10397−10405. doi: 10.1073/pnas.1917508117
|
[31] |
Gao M D, Wang X H, Meng F D, et al. Three-dimensional change in temperature sensitivity of northern vegetation phenology[J]. Global Change Biology, 2020, 26(9): 5189−5201. doi: 10.1111/gcb.15200
|
[32] |
Shen M G, Piao S L, Cong N, et al. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau[J]. Global Change Biology, 2015, 21(10): 3647−3656. doi: 10.1111/gcb.12961
|
[33] |
Shen M G, Tang Y H, Chen J, et al. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau[J]. Agricultural and Forest Meteorology, 2011, 151(12): 1711−1722. doi: 10.1016/j.agrformet.2011.07.003
|
[34] |
Fu Y H, Zhou X C, Li X X, et al. Decreasing control of precipitation on grassland spring phenology in temperate China[J]. Global Ecology and Biogeography, 2021, 30(2): 490−499. doi: 10.1111/geb.13234
|
[35] |
Wu C Y, Peng J, Ciais P, et al. Increased drought effects on the phenology of autumn leaf senescence[J/OL]. Nature Climate Change, 2022[2022−10−02]. https://www.nature.com/articles/s41558-022-01464-9.
|
[36] |
Yuan M X, Zhao L, Lin A W, et al. Impacts of preseason drought on vegetation spring phenology across the Northeast China Transect[J/OL]. Science of The Total Environment, 2020, 738: 140297[2022−11−22]. https://doi.org/10.1016/j.scitotenv.2020.140297.
|
[37] |
Ren P X, Liu Z L, Zhou X L, et al. Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China[J/OL]. Forest Ecosystems, 2021, 8(1): 31[2022−12−21]. https://forestecosyst.springeropen.com/articles/10.1186/s40663-021-00309-9.
|
[38] |
Delpierre N, Vitasse Y, Chuine I, et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models[J]. Annals of Forest Science, 2016, 73(1): 5−25. doi: 10.1007/s13595-015-0477-6
|
[39] |
王静璞, 刘连友, 贾凯, 等. 毛乌素沙地植被物候时空变化特征及其影响因素[J]. 中国沙漠, 2015, 35(3): 624−631. doi: 10.7522/j.issn.1000-694X.2015.00021
Wang J P, Liu L Y, Jia K, et al. Spatiotemporal variation of vegetation phenology and its affecting factors in the Mu Us Sandy Land[J]. Journal of Desert Research, 2015, 35(3): 624−631. doi: 10.7522/j.issn.1000-694X.2015.00021
|
[40] |
杨梅焕, 靳小燕, 王涛. 毛乌素沙地植被物候变化及其对气候变化的响应[J]. 水土保持通报, 2022, 42(2): 242−249.
Yang M H, Jin X Y, Wang T. Vegetation phenology change of Mu Us Sandy Land and its response to climate change[J]. Bulletin of Soil and Water Conservation, 2022, 42(2): 242−249.
|
[41] |
朱娅坤, 秦树高, 张宇清, 等. 毛乌素沙地植被物候动态及其对气象因子变化的响应[J]. 北京林业大学学报, 2018, 40(9): 98−106.
Zhu Y K, Qin S G, Zhang Y Q, et al. Vegetation phenology dynamic and its responses to meteorological factor changes in the Mu Us Desert of northern China[J]. Journal of Beijing Forestry University, 2018, 40(9): 98−106.
|
[42] |
穆家伟, 查天山, 贾昕, 等. 毛乌素沙地典型沙生灌木对土壤蒸发的影响[J]. 北京林业大学学报, 2016, 38(12): 39−45.
Mu J W, Zha T S, Jia X, et al. Influence of typical sandy shrubs on soil evaporation in Mu Us Sandland, northwestern China[J]. Journal of Beijing Forestry University, 2016, 38(12): 39−45.
|
[43] |
Peng S G, Gang C C, Cao Y, et al. Assessment of climate change trends over the Loess Plateau in China from 1901 to 2100[J/OL]. International Journal of Climatology, 2018, 38[2022−11−22]. https://doi.org/10.1002/joc.5331.
|
[44] |
Krivoruchko K, Gribov A. Evaluation of empirical Bayesian kriging[J/OL]. Spatial Statistics, 2019, 32: 100368[2022−11−22]. https://doi.org/10.1016/j.spasta.2019.100368.
|
[45] |
Krivoruchko K, Gribov A. Pragmatic Bayesian kriging for non-stationary and moderately non-Gaussian data[C]// Pardo-Igúzquiza E, Guardiola-Albert C, Heredia J, et al.Mathematics of planet earth. Berlin: Springer, 2014: 61−64.
|
[46] |
吉珍霞, 裴婷婷, 陈英, 等. 黄土高原植被物候变化及其对季节性气候变化的响应[J]. 生态学报, 2021, 41(16): 6600−6612.
Ji Z X, Pei T T, Chen Y, et al. Vegetation phenology change and its response to seasonal climate changes on the Loess Plateau[J]. Acta Ecologica Sinica, 2021, 41(16): 6600−6612.
|
[47] |
Jönsson P, Eklundh L. TIMESAT: a program for analyzing time-series of satellite sensor data[J]. Computers & Geosciences, 2004, 30(8): 833−845.
|
[48] |
Eklundh L, Jönsson P. TIMESAT for processing time-series data from satellite sensors for land surface monitoring[M/OL]// Ban Y. Multitemporal remote sensing. Cham: Springer International Publishing, 2016: 177−194[2022−10−02]. http://link.springer.com/10.1007/978-3-319-47037-59.
|
[49] |
宋春桥, 柯灵红, 游松财, 等. 基于TIMESAT的3种时序NDVI拟合方法比较研究: 以藏北草地为例[J]. 遥感技术与应用, 2011, 26(2): 147−155.
Song C Q, Ke L H, You S C, et al. Comparison of three NDVI timeseries fitting methods based on TIMESAT: taking the grassland in northern Tibet as case[J]. Remote Sensing Technology and Application, 2011, 26(2): 147−155.
|
[50] |
程琳琳, 李玉虎, 孙海元, 等. 京津冀MODIS长时序增强型植被指数拟合重建方法适用性研究[J]. 农业工程学报, 2019, 35(11): 148−158. doi: 10.11975/j.issn.1002-6819.2019.11.017
Cheng L L, Li Y H, Sun H Y, et al. Applicability of fitting and reconstruction method of MODIS long-time enhanced vegetation index in Beijing-Tianjin-Hebei[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(11): 148−158. doi: 10.11975/j.issn.1002-6819.2019.11.017
|
[51] |
Cao R Y, Chen Y, Shen M G, et al. A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2018, 217: 244−257. doi: 10.1016/j.rse.2018.08.022
|
[52] |
Lara B, Gandini M. Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland[J]. International Journal of Remote Sensing, 2016, 37(8): 1801−1813. doi: 10.1080/2150704X.2016.1168945
|
[53] |
Shen M G, Tang Y H, Chen J, et al. Specification of thermal growing season in temperate China from 1960 to 2009[J]. Climatic Change, 2012, 114(3−4): 783−798. doi: 10.1007/s10584-012-0434-4
|
[54] |
赵心睿, 刘冀, 杨少康, 等. 北方地区典型林草地物候时空变化特征及其对气象因子的响应[J]. 生态学报, 2023, 43(9): 1−12.
Zhao X R, Liu J, Yang S K, et al. Spatio-temporal variations of typical woodland and grassland phenology and its response to meteorological factors in northern China[J]. Acta Ecologica Sinica, 2023, 43(9): 1−12.
|
[55] |
秦格霞, 吴静, 李纯斌, 等. 中国北方草地植被物候变化及其对气候变化的响应[J]. 应用生态学报, 2019, 30(12): 4099−4107.
Qin G X, Wu J, Li C B, et al. Grassland vegetation phenology change and its response to climate changes in North China[J]. Chinese Journal of Applied Ecology, 2019, 30(12): 4099−4107.
|
[56] |
Zu J X, Zhang Y J, Huang K, et al. Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan Plateau[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 69: 198−205. doi: 10.1016/j.jag.2018.03.006
|
[57] |
Liu X Y, Tian Y, Liu S Q, et al. Time-lag effect of climate conditions on vegetation productivity in a temperate forest-grassland ecotone[J/OL]. Forests, 2022, 13(7): 1024[2023−01−23]. https://doi.org/10.3390/f13071024.
|
[58] |
原媛, 母艳梅, 邓钰洁, 等. 植被覆盖度和物候变化对典型黑沙蒿灌丛生态系统总初级生产力的影响[J]. 植物生态学报, 2022, 46(2): 162−175.
Yuan Y, Mu Y M, Deng Y J, et al. Effects of land cover and phenology changes on the gross primary productivity in an Artemisia ordosica shrubland[J]. Chinese Journal of Plant Ecology, 2022, 46(2): 162−175.
|
[59] |
Richardson A D, Andy B T, Ciais P, et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2010, 365(1555): 3227−3246. doi: 10.1098/rstb.2010.0102
|
[60] |
王思琪, 周广胜, 周梦子, 等. 增温背景下克氏针茅枯黄期物候对降水响应的光合生理机制[J]. 应用生态学报, 2021, 32(3): 845−852.
Wang S Q, Zhou G S, Zhou M Z, et al. Photosynthetically physiological mechanism of Stipa krylovii withered and yellow phenology response to precipitation under the background of warming[J]. Chinese Journal of Applied Ecology, 2021, 32(3): 845−852.
|
[61] |
Forkel M, Migliavacca M, Thonicke K, et al. Codominant water control on global interannual variability and trends in land surface phenology and greenness[J]. Global Change Biology, 2015, 21(9): 3414−3435. doi: 10.1111/gcb.12950
|
[62] |
Yao J Y, Yuan W P, Gao Z M, et al. Impact of shifts in vegetation phenology on the carbon balance of a semiarid sagebrush ecosystem[J/OL]. Remote Sensing, 2022, 14(23): 5924[2023−02−12]. https://doi.org/10.3390/rs14235924.
|
[63] |
廉泓林, 韩雪莹, 刘雅莉, 等. 基于标准化降水蒸散指数(SPEI)的毛乌素沙地1981—2020年干旱特征研究[J]. 中国沙漠, 2022, 42(4): 71−80.
Lian H L, Han X Y, Liu Y L, et al. Study on spatiotemporal characteristics of atmospheric drought from 1981 to 2020 in the Mu Us Sandy Land of China based on SPEI index[J]. Journal of Desert Research, 2022, 42(4): 71−80.
|
[64] |
Li D. Assessing the impact of interannual variability of precipitation and potential evaporation on evapotranspiration[J]. Advances in Water Resources, 2014, 70: 1−11. doi: 10.1016/j.advwatres.2014.04.012
|
[65] |
Way D A, Montgomery R A. Photoperiod constraints on tree phenology, performance and migration in a warming world[J]. Plant, Cell & Environment, 2015, 38(9): 1725−1736.
|
[66] |
Fu Y H, Piao S L, Delpierre N, et al. Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates[J]. Tree Physiology, 2019, 39(8): 1277−1284. doi: 10.1093/treephys/tpz041
|
[67] |
Liu Y J, Chen Q M, Ge Q S, et al. Modelling the impacts of climate change and crop management on phenological trends of spring and winter wheat in China[J]. Agricultural and Forest Meteorology, 2018, 248: 518−526. doi: 10.1016/j.agrformet.2017.09.008
|
[68] |
Chen J, Liu Y J, Zhou W M, et al. Effects of climate change and crop management on changes in rice phenology in China from 1981 to 2010[J]. Journal of the Science of Food and Agriculture, 2021, 101(15): 6311−6319. doi: 10.1002/jsfa.11300
|
[69] |
Ni M, Zhang X Y, Jiang C, et al. Responses of vegetation to extreme climate events in southwestern China[J]. Chinese Journal of Plant Ecology, 2021, 45(6): 626−640. doi: 10.17521/cjpe.2021.0042
|
[70] |
Li P, Liu Z L, Zhou X L, et al. Combined control of multiple extreme climate stressors on autumn vegetation phenology on the Tibetan Plateau under past and future climate change[J]. Agricultural and Forest Meteorology, 2021, 308−309: 108571[2022−10−02]. https://www.sciencedirect.com/science/article/pii/S0168192321002550.
|
1. |
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