高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

植被归一化植被指数对气候因子的空间敏感性

王丹云 曾晓东 宋翔

王丹云, 曾晓东, 宋翔. 2023. 植被归一化植被指数对气候因子的空间敏感性[J]. 气候与环境研究, 28(1): 30−44 doi: 10.3878/j.issn.1006-9585.2022.21178
引用本文: 王丹云, 曾晓东, 宋翔. 2023. 植被归一化植被指数对气候因子的空间敏感性[J]. 气候与环境研究, 28(1): 30−44 doi: 10.3878/j.issn.1006-9585.2022.21178
WANG Danyun, ZENG Xiaodong, SONG Xiang. 2023. Spatial Sensitivity of NDVI Index to Climate Factors [J]. Climatic and Environmental Research (in Chinese), 28 (1): 30−44 doi: 10.3878/j.issn.1006-9585.2022.21178
Citation: WANG Danyun, ZENG Xiaodong, SONG Xiang. 2023. Spatial Sensitivity of NDVI Index to Climate Factors [J]. Climatic and Environmental Research (in Chinese), 28 (1): 30−44 doi: 10.3878/j.issn.1006-9585.2022.21178

植被归一化植被指数对气候因子的空间敏感性

doi: 10.3878/j.issn.1006-9585.2022.21178
基金项目: 国家重点研发计划项目2017YFA0604804
详细信息
    作者简介:

    王丹云,女,1991 年出生,博士,工程师,主要从事气候变化及生态系统研究。E-mail: dywang19@yeah.net

    通讯作者:

    曾晓东,E-mail: xdzeng@mail.iap.ac.cn

  • 中图分类号: P461+.7

Spatial Sensitivity of NDVI Index to Climate Factors

Funds: National Key Research and Development Project (Grant 2017YFA0604804)
  • 摘要: 基于全球土地利用类型和覆盖度,利用生长季多年平均(1982~2015年)归一化植被指数(Normalized Difference Vegetation Index,NDVI)和气候平均态(气温、降水量)数据,讨论了全球植被格局与气候因子之间的关系,建立了两者之间的多元回归模型,并分析了植被对气温和降水气候态敏感性的特征。植被与气候因子在气候梯度上存在明显的对应关系,回归模型可较好拟合气候态NDVI的全球分布格局,拟合与观测NDVI的相关系数达0.90。其中,常绿阔叶林、混交林、常绿针叶林、落叶阔叶林、农田和木本稀树草原空间分布的拟合能力较好(r>0.8)。不同土地覆盖类型的NDVI对气温、降水气候态的空间敏感性特征不同。整体而言,植被对气温和降水的敏感性呈现反相关关系(r=−0.6)。不同土地覆盖类型对气温表现出正/负敏感性,寒带灌木对气温的敏感性最强,而农作物、草原、裸地对气温负敏感性较大;植被对降水的敏感性均表现出正敏感性,其中落叶针叶林、草原和稀树草原对降水的空间敏感性较强。
  • 图  1  本文使用的全球13种土地利用类型(见表1)空间分布

    Figure  1.  Global spatial distribution of 13 land cover types (shown in Table 1) used in this work

    图  2  全球不同土地覆盖类型的覆盖度分布

    Figure  2.  Global coverage distribution of different land cover types

    图  3  生长季多年平均NDVI随气候要素(a)气温、(b)降水量的变化规律(气温和降水量选取间隔分别为0.5°C和10 mm,点线:间隔内平均值,蓝色虚线:间隔内25%分位数,红色虚线:间隔内75%分位数)

    Figure  3.  Change in mean annual NDVI (Normalized Difference Vegetation Index) depending on climate factors (a) temperature and (b) precipitation in the growing season (bin interval: 0.5°C and 100 mm; Dot line: mean value within the interval; blue line: 25% quantile within the interval; red line: 75% quantile within the interval)

    图  4  生长季多年平均NDVI与生长季平均气温和月降水量之间的关系(选取温度和降水间隔分别是0.5°C和10 mm)

    Figure  4.  Mean average NDVI compared to temperature and precipitation (resampled into bins with a temperature interval of 0.5°C and a precipitation interval of 10 mm)

    图  5  不同土地覆盖类型对应的生长季多年平均(a)气温、(b)降水量、(c)NDVI的箱线图(上下限为5%和95%分位数)

    Figure  5.  Box plots of different land cover types to climatological (a) temperature, (b) precipitation, and (c) NDVI (the top and bottom limitations are 5% and 95%, respectively)

    图  6  全球观测与拟合NDVI的比较

    Figure  6.  Comparison between the observed and simulated NDVI

    图  7  全球(a)观测NDVI、(b)拟合NDVI、(c)拟合与观测NDVI的差异以及(d)差异的概率密度分布

    Figure  7.  Global spatial pattern of (a) observed NDVI, (b) simulated NDVI, and (c) difference between simulations and observations, (d) the probability density function (PDF) of simulation bias

    图  8  拟合NDVI随气候要素(a)气温、(b)降水量的变化规律(气温和降水选取间隔分别为0.5°C和10 mm;点线:间隔内平均值,蓝色虚线:间隔内25%分位数,红色虚线:间隔内75%分位数)

    Figure  8.  Simulated NDVI in climate factors (a) temperature (b) precipitation in the growing season (bin interval: 0.5°C and 100 mm; dot line: mean value within the interval; blue line: 25% quantile within the interval; red line: 75% quantile within the interval)

    图  9  拟合NDVI与生长季多年平均气温和降水量之间的关系(选取温度和降水间隔分别是0.5°C和10mm)

    Figure  9.  Simulated NDVI compared to temperature and precipitation (resampled into bins with a temperature interval of 0.5°C and a precipitation interval of 10 mm)

    图  10  生长季多年平均NDVI对气温和降水的空间敏感性(*表示p<0.1)

    Figure  10.  Sensitivity of NDVI to temperature and precipitation (* indicates p < 0.1)

    图  11  NDVI对气候态(a)气温、(b)降水敏感性系数(βTβP)空间分布

    Figure  11.  Spatial distribution of vegetation sensitivity indexes to climate state (a) temperature ( βT) and (b) precipitation (βP)

    图  12  敏感性系数βTβP随生长季多年平均(a、b)气温、(c、d)月降水量、(e、f)NDVI的变化特征(格点温度、降水、NDVI的变化区间分别为0.5°C、10 mm和0.05

    Figure  12.  Variation in sensitivity coefficients (βT, βP) with (a, b) temperature, (c, d) precipitation, and (e, f) NDVI (resampled into bins with a temperature interval of 0.5°C, a precipitation interval of 10 mm, an NDVI interval of 0.05, respectively)

    图  13  (a)植被对温度敏感性(βT)、(b)植被对降水敏感性(βP)对生长季多年平均气温和月降水量变化的分布 (格点温度和降水的变化区间分别为0.5°C和10 mm)

    Figure  13.  Mean average sensitivity coefficients compared to temperature and precipitation (a) NDVI sensitivity to temperature, (b) NDVI sensitivity to precipitation (resampled into bins with a temperature interval of 0.5°C and a precipitation interval of 10 mm)

    表  1  本文及国际地圈生物圈计划(IGBP)对土地覆盖类型的分类

    Table  1.   Reclassification of land cover types in this article and the International Geosphere–Biosphere Programme (IGBP) classification

    本文分类国际地圈生物圈计划(IGBP)分类
    裸地(Bare)Barren
    Urban and Built-up lands
    常绿针叶林(NEF)Needleleaf Evergreen Forests
    落叶针叶林(NDF)Needleleaf Deciduous Forests
    常绿阔叶林(BEF)Broadleaf Evergreen Forests
    落叶阔叶林(BDF)Broadleaf Deciduous Forests
    混交林(MixF)Mixed Forests
    寒带灌木(BorS)Open Shrublands/ Closed Shrublands
    温带灌木(TemS)Closed Shrublands/ Open Shrublands
    木本稀树草原(wSav)Woody Savanna
    稀树草原(Sav)Savanna
    草原(Grass)Grass
    农田(Crop)Cropland
    Cropland/Natural Vegetation Mosaics
    雪(Snow)Snow and Ice
    /Water Bodies
    /Permanent Wetlands
    下载: 导出CSV

    表  2  不同土地覆盖类型观测与拟合NDVI的空间相关系数以及拟合NDVI均方根误差

    Table  2.   The spatial correlation coefficient between observed and simulated NDVI, and root mean square error (RMSE) of observed NDVI of different land cover types

    土地覆盖类型观测NDVI拟合NDVI格点数(n观测与拟合NDVI空间相关系数拟合NDVI均方根误差
    裸地(Bare)0.2330.2369900.530.092
    常绿针叶林(NEF)0.4570.42739890.850.075
    落叶针叶林(NDF)0.4930.4627540.570.058
    常绿阔叶林(BEF)0.7410.69960390.900.101
    落叶阔叶林(BDF)0.6020.5485330.810.091
    混交林(MixF)0.5310.50539070.870.065
    寒带灌木(BorS)0.3380.319101400.730.089
    温带灌木(TemS)0.2710.27345080.750.057
    木本稀树草原(wSav)0.5700.55031360.800.077
    稀树草原(Sav)0.5320.53039000.750.079
    草原
    (Grass)
    0.3220.33247370.760.074
    农田
    (Crop)
    0.4580.44482010.810.070
    下载: 导出CSV
  • [1] Bachelet D, Neilson R P, Lenihan J M, et al. 2001. Climate change effects on vegetation distribution and carbon budget in the United States [J]. Ecosystems, 4(3): 164−185. doi: 10.1007/s10021-001-0002-7
    [2] Baldocchi D, Falge E, Gu L H, et al. 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem–scale carbon dioxide, water vapor, and energy flux densities [J]. Bull. Amer. Meteor. Soc., 82(11): 2415−2434. doi:10.1175/1520-0477(2001)0829<2415:FANTTS9>2.3.CO;2
    [3] Belward A S, Estes J E, Kline K D. 1999. The IGBP–DIS global 1–km land-cover data set DISCover: A project overview [J]. Photogrammetric Engineering and Remote Sensing, 65(9): 1013−1020.
    [4] Box E O. 1981. Macroclimate and Plant Forms: An Introduction to Predictive Modeling in Phytogeography [M]. Hague: Dr. W. Junk Publishers.
    [5] Chen C, He B, Yuan W P, et al. 2019. Increasing interannual variability of global vegetation greenness [J]. Environmental Research Letters, 14(12): 124005. doi: 10.1088/1748-9326/ab4ffc
    [6] De jong R, Schaepman M E, Furrer R, et al. 2013. Spatial relationship between climatologies and changes in global vegetation activity [J]. Global Change Biology, 19(6): 1953−1964. doi: 10.1111/gcb.12193
    [7] Feng Q S, Liang T G, Huang X D, et al. 2013. Characteristics of global potential natural vegetation distribution from 1911 to 2000 based on comprehensive sequential classification system approach [J]. Grassland and Science, 59(2): 87−99. doi: 10.1111/grs.12016
    [8] Friedl M A, Sulla-Menashe D, Tan B, et al. 2010. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets [J]. Remote Sensing of Environment, 114(1): 168−182. doi: 10.1016/j.rse.2009.08.016
    [9] 高冬冬, 丹利, 范广洲, 等. 2019. 地球系统模式中植被净初级生产力百年尺度时空变化及其与气候的关系 [J]. 气候与环境研究, 24(6): 663−677. doi: 10.3878/j.issn.1006-9585.2018.18052

    Gao Dongdong, Dan Li, Fan Guangzhou, et al. 2019. Spatial and temporal variations of net primary productivity at century scale in earth system models and its relationship with climate [J]. Climatic and Environmental Research (in Chinese), 24(6): 663−677. doi: 10.3878/j.issn.1006-9585.2018.18052
    [10] García-Mora T J, Mas J F, Hinkley E A. 2012. Land cover mapping applications with MODIS: A literature review [J]. International Journal of Digital Earth, 5(1): 63−87. doi: 10.1080/17538947.2011.565080
    [11] Georganos S, Abdi A M, Tenenbaum D E, et al. 2017. Examining the NDVI–rainfall relationship in the semi-arid Sahel using geographically weighted regression [J]. Journal of Arid Environments, 146: 64−74. doi: 10.1016/j.jaridenv.2017.06.004
    [12] Harris I, Jones P D, Osborn T J, et al. 2014. Updated high–resolution grids of monthly climatic observations–the CRU TS3.10 dataset [J]. International Journal of Climatology, 34(3): 623−642. doi: 10.1002/joc.3711
    [13] Haxeltine A, Prentice I C. 1996. A general model for the light–use efficiency of primary production [J]. Functional Ecology, 10(5): 551−561. doi: 10.2307/2390165
    [14] Holben B N. 1986. Characteristics of maximum–value composite images from temporal AVHRR data [J]. Int. J. Remote Sens., 7(11): 1417−1434. doi: 10.1080/01431168608948945
    [15] Holdridge L R. 1967. Life Zone Ecology [M]. San Jose, Costa Rica: Tropical Science Center.
    [16] 侯美亭, 赵海燕, 王筝, 等. 2013. 基于卫星遥感的植被NDVI对气候变化响应的研究进展 [J]. 气候与环境研究, 18(3): 353−364. doi: 10.3878/j.issn.1006-9585.2012.11137

    Hou Meiting, Zhao Haiyan, Wang Zheng, et al. 2013. Vegetation responses to climate change by using the satellite–derived normalized difference vegetation index: A review [J]. Climatic and Environmental Research (in Chinese), 18(3): 353−364. doi: 10.3878/j.issn.1006-9585.2012.11137
    [17] Iio A, Hikosaka K, Anten N P R, et al. 2014. Global dependence of field-observed leaf area index in woody species on climate: A systematic review [J]. Global Ecology and Biogeography, 23(3): 274−285. doi: 10.1111/geb.12133
    [18] Ji L, Peters A J. 2004. A spatial regression procedure for evaluating the relationship between AVHRR–NDVI and climate in the northern Great Plains [J]. Int. J. Remote Sens., 25(2): 297−311. doi: 10.1080/0143116031000102548
    [19] 贾坤, 姚云军, 魏香琴, 等. 2013. 植被覆盖度遥感估算研究进展 [J]. 地球科学进展, 28(7): 774−782. doi: 10.11867/j.issn.1001-8166.2013.07.0774

    Jia Kun, Yao Yunjun, Wei Xiangqin, et al. 2013. A review on fractional vegetation cover estimation using remote sensing [J]. Advanced in Earth Science (in Chinese), 28(7): 774−782. doi: 10.11867/j.issn.1001-8166.2013.07.0774
    [20] Julien Y, Sobrino J A. 2019. Optimizing and comparing gap–filling techniques using simulated NDVI time series from remotely sensed global data [J]. International Journal of Applied Earth Observation and Geoinformation, 76: 93−111. doi: 10.1016/j.jag.2018.11.008
    [21] Köppen W. 1900. Versuch einer Klassifikation der Klimate, vorzugsweise nach ihren Beziehungen zur Pflanzenwelt [J]. Geographische Zeitschrift, 6(11): 593−611.
    [22] Kottek M, Grieser J, Beck C, et al. 2006. World Map of the Köppen–Geiger climate classification updated [J]. Meteorologische Zeitschrift, 15(3): 259−263. doi: 10.1127/0941-2948/2006/0130
    [23] 李广东. 2022. 全球土地覆被时空变化与中国贡献 [J]. 地理学报, 77(2): 353−368. doi: 10.11821/dlxb202202006

    Li Guangdong. 2022. Spatio−temporal change of global land cover and China’s contribution [J]. Acta Geographica Sinica (in Chinese), 77(2): 353−368. doi: 10.11821/dlxb202202006
    [24] 刘国华, 傅伯杰. 2001. 全球气候变化对森林生态系统的影响 [J]. 自然资源学报, 16(1): 71−78. doi: 10.3321/j.issn:1000-3037.2001.01.013

    Liu Guohua, Fu Bojie. 2001. Effects of global climate change on forest ecosystems [J]. Journal of Natural Resources (in Chinese), 16(1): 71−78. doi: 10.3321/j.issn:1000-3037.2001.01.013
    [25] 刘海, 刘凤, 郑粮. 2021. 气候变化及人类活动对黄河流域植被覆盖变化的影响 [J]. 水土保持学报, 35(4): 143−151. doi: 10.13870/j.cnki.stbcsb.2021.04.020

    Liu Hai, Liu Feng, Zheng Liang. 2021. Effects of climate change and human activities on vegetation cover change in the Yellow River basin [J]. Journal of Soil and Water Conservation (in Chinese), 35(4): 143−151. doi: 10.13870/j.cnki.stbcsb.2021.04.020
    [26] 刘宪锋, 朱秀芳, 潘耀忠, 等. 2015. 1982~2012年中国植被覆盖时空变化特征 [J]. 生态学报, 35(16): 5331−5342. doi: 10.5846/stxb201404150731

    Liu Xianfeng, Zhu Xiufang, Pan Yaozhong, et al. 2015. Spatiotemporal changes in vegetation coverage in China during 1982–2012 [J]. Acta Ecologica Sinica (in Chinese), 35(16): 5331−5342. doi: 10.5846/stxb201404150731
    [27] Loveland T R, Reed B C, Brown J F, et al. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data [J]. Int. J. Remote Sens. , 21(6–7): 1303–1330. doi: 10.1080/014311600210191
    [28] 马明国, 王建, 王雪梅. 2006. 基于遥感的植被年际变化及其与气候关系研究进展 [J]. 遥感学报, 10(3): 421−431. doi: 10.11834/jrs.20060363

    Ma Mingguo, Wang Jian, Wang Xuemei. 2006. Advance in the inter-annual variability of vegetation and its relation to climate based on remote sensing [J]. Journal of Remote Sensing (in Chinese), 10(3): 421−431. doi: 10.11834/jrs.20060363
    [29] Mohammad A G, Adam M A. 2010. The impact of vegetative cover type on runoff and soil erosion under different land uses [J]. Catena, 81(2): 97−103. doi: 10.1016/j.catena.2010.01.008
    [30] Paruelo J M, Epstein H E, Lauenroth W K, et al. 1997. ANPP estimates from NDVI for the central grassland region of the United States [J]. Ecology, 78(3): 953−958. doi: 10.1890/0012-9658(1997)078[0953:AEFNFT]2.0.CO;2
    [31] Peel M C, Finlayson B L, McMahon T A. 2007. Updated world map of the Köppen–Geiger climate classification [J]. Hydrology & Earth System Sciences, 11(5): 1633−1644. doi: 10.5194/hess-11-1633-2007
    [32] Piao S L, Fang J Y, Zhou L M, et al. 2006. Variations in satellite–derived phenology in China's temperate vegetation [J]. Global Change Biology, 12(4): 672−685. doi: 10.1111/j.1365-2486.2006.01123.x
    [33] Pinzon J E, Tucker C J. 2014. A Non–Stationary 1981–2012 AVHRR NDVI3g time series [J]. Remote Sensing, 6(8): 6929−6960. doi: 10.3390/rs6086929
    [34] Richard Y, Poccard I. 1998. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in southern Africa [J]. Int. J. Remote Sens., 19(15): 2907−2920. doi: 10.1080/014311698214343
    [35] Salim H A, Chen X L, Gong J Y. 2008. Analysis of Sudan vegetation dynamics using NOAA-AVHRR NDVI data from 1982–1993 [J]. Asian Journal of Earth Sciences, 1(1): 1−15. doi: 10.3923/ajes.2008.1.15
    [36] 邵璞, 曾晓东. 2012. 土地利用和土地覆盖变化对气候系统影响的研究进展 [J]. 气候与环境研究, 17(1): 103−111. doi: 10.3878/j.issn.1006-9585.2011.10029

    Shao Pu, Zeng Xiaodong. 2012. Progress in the study of the effects of land use and land cover change on the climate system [J]. Climatic and Environmental Research (in Chinese), 17(1): 103−111. doi: 10.3878/j.issn.1006-9585.2011.10029
    [37] Song X P, Hansen M C, Stehman S V, et al. 2018. Global land change from 1982 to 2016 [J]. Nature, 560(7720): 639−643. doi: 10.1038/s41586-018-0411-9
    [38] Suzuki R, Tanaka S, Yasunari T. 2000. Relationships between meridional profiles of satellite–derived vegetation index (NDVI) and climate over Siberia [J]. International Journal of Climatology, 20(9): 955−967. doi:10.1002/1097-0088(200007)20:9<955::AID-JOC5129>3.0.CO;2-1
    [39] Taylor C A, Rising J. 2021. Tipping point dynamics in global land use [J]. Environmental Research Letters, 16(12): 125012. doi: 10.1088/1748-9326/ac3c6d
    [40] Thornthwaite C W. 1948. An approach toward a rational classification of climate [J]. Geographical Review, 38(1): 55−94. doi: 10.2307/210739
    [41] Tucker C J, Pinzon J E, Brown M E, et al. 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data [J]. Int. J. Remote Sens., 26(20): 4485−4498. doi: 10.1080/01431160500168686
    [42] Wang Q, Adiku S, Tenhunen J, et al. 2005. On the relationship of NDVI with leaf area index in a deciduous forest site [J]. Remote Sensing of Environment, 94(2): 244−255. doi: 10.1016/j.rse.2004.10.006
    [43] Wang T M, Kou X J, Xiong Y C, et al. 2010. Temporal and spatial patterns of NDVI and their relationship to precipitation in the Loess Plateau of China [J]. Int. J. Remote Sens., 31(7): 1943−1958. doi: 10.1080/01431160902929263
    [44] 王正兴, 刘闯, Huete A. 2003. 植被指数研究进展: 从AVHRR-NDVI到MODIS-EVI [J]. 生态学报, 23(5): 979−987. doi: 10.3321/j.issn:1000-0933.2003.05.020

    Wang Zhengxing, Liu Chuang, Huete A. 2003. From AVHRR–NDVI to MODIS–EVI: Advances in vegetation index research [J]. Acta Ecologica Sinica (in Chinese), 23(5): 979−987. doi: 10.3321/j.issn:1000-0933.2003.05.020
    [45] Wen Y Y, Liu X P, Yang J, et al. 2019. NDVI indicated inter-seasonal non-uniform time-lag responses of terrestrial vegetation growth to daily maximum and minimum temperature [J]. Global and Planetary Change, 177: 27−38. doi: 10.1016/j.gloplacha.2019.03.010
    [46] Whittaker R H. 1975. Communities and Ecosystems [M]. 2nd Revise Edision, New York: MacMillan.
    [47] Woodward F I. 1987. Climate and Plant Distribution [M]. Cambridge: Cambridge University Press.
    [48] Wu D H, Zhao X, Liang S L, et al. 2015. Time-lag effects of global vegetation responses to climate change [J]. Global Change Biology, 21(9): 3520−3531. doi: 10.1111/gcb.12945
    [49] Yang Y J, Wang S J, Bai X Y, et al. 2019. Factors affecting long–term trends in global NDVI [J]. Forests, 10(5): 372. doi: 10.3390/f10050372
    [50] 于贵瑞, 伏玉玲, 孙晓敏, 等. 2006. 中国陆地生态系统通量观测研究网络(ChinaFLUX)的研究进展及其发展思路 [J]. 中国科学D辑:地球科学, (S1): 1−21.

    Yu Guirui, Fu Yuling, Sun Xiaomin, et al. 2006. Research progress and development ideas of China terrestrial ecosystem flux observation and research network (ChinaFLUX) [J]. Science in China Ser. D Earth Sciences (in Chinese), (S1): 1−21.
    [51] Yuan Q Z, Wu S H, Dai E F, et al. 2017. NPP vulnerability of the potential vegetation of China to climate change in the past and future [J]. Journal of Geographical Sciences, 27(2): 131−142. doi: 10.1007/s11442-017-1368-6
    [52] 岳东霞, 牟鑫亮, 周妍妍, 等. 2021. 疏勒河流域净初级生产力与土壤含水量耦合关系研究 [J]. 兰州大学学报(自然科学版), 57(4): 518−527. doi: 10.13885/j.issn.0455-2059.2021.04.012

    Yue Dongxia, Mou Xinliang, Zhou Yanyan, et al. 2021. Coupling relationship between net primary productivity and soil moisture content in the Shule River basin [J]. Journal of Lanzhou University (Natural Sciences) (in Chinese), 57(4): 518−527. doi: 10.13885/j.issn.0455-2059.2021.04.012
    [53] Zeng X D. 2010. Evaluating the dependence of vegetation on climate in an improved dynamic global vegetation model [J]. Advances in Atmospheric Sciences, 27(5): 977−991. doi: 10.1007/s00376-009-9186-0
    [54] 张宝庆, 吴普特, 赵西宁. 2011. 近30a黄土高原植被覆盖时空演变监测与分析 [J]. 农业工程学报, 27(4): 287−293. doi: 10.3969/j.issn.1002-6819.2011.04.051

    Zhang Baoqing, Wu Pute, Zhao Xining. 2011. Detecting and analysis of spatial and temporal variation of vegetation cover in the Loess Plateau during 1982–2009 [J]. Transactions of the CSAE (in Chinese), 27(4): 287−293. doi: 10.3969/j.issn.1002-6819.2011.04.051
    [55] Zhang X L, Wu S, Yan X D, et al. 2017. A global classification of vegetation based on NDVI, rainfall and temperature [J]. International Journal of Climatology, 37(5): 2318−2324. doi: 10.1002/joc.4847
    [56] 张新时. 1993. 研究全球变化的植被—气候分类系统 [J]. 第四纪研究, (2): 157−169. doi: 10.3321/j.issn:1001-7410.1993.02.006

    Zhang Xinshi. 1993. A vegetation–climate classification system for global change studies in China [J]. Quaternary Sciences (in Chinese), (2): 157−169. doi: 10.3321/j.issn:1001-7410.1993.02.006
    [57] Zhao D S, Wu S H. 2014. Vulnerability of natural ecosystem in China under regional climate scenarios: An analysis based on eco-geographical regions [J]. Journal of Geographical Sciences, 24(2): 237−248. doi: 10.1007/s11442-014-1085-3
    [58] 赵威, 李亚鸽, 王艳杰. 2016. 植物补偿性光合作用的发生模式及生理机制分析 [J]. 植物生理学报, 52(12): 1811−1818. doi: 10.13592/j.cnki.ppj.2016.0394

    Zhao Wei, Li Yage, Wang Yanjie. 2016. The occurrence patterns and physiological mechanism analysis of plant compensatory photosynthesis [J]. Plant Physiology Journal (in Chinese), 52(12): 1811−1818. doi: 10.13592/j.cnki.ppj.2016.0394
    [59] Zhu Z C, Piao S L, Myneni R B, et al. 2016. Greening of the earth and its drivers [J]. Nature Climate Change, 6(8): 791−795. doi: 10.1038/NCLIMATE3004
  • 加载中
图(13) / 表(2)
计量
  • 文章访问数:  108
  • HTML全文浏览量:  29
  • PDF下载量:  22
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-11-16
  • 网络出版日期:  2022-05-15
  • 刊出日期:  2023-01-25

目录

    /

    返回文章
    返回