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

Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period

GUO Meng LI Jing HE Hongshi XU Jiawei JIN Yinghua

GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. 中国地理科学, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2
引用本文: GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. 中国地理科学, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2
GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. Chinese Geographical Science, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2
Citation: GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. Chinese Geographical Science, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2

Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period

doi: 10.1007/s11769-018-1002-2
基金项目: Under the auspices of National Natural Science Foundation of China (No. 41771179, 41871103, 41771138), the National Key Research and Development Project (No. 2016YFA0602301)
详细信息
    通讯作者:

    LI Jing.E-mail:lijingsara@iga.ac.cn

Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period

Funds: Under the auspices of National Natural Science Foundation of China (No. 41771179, 41871103, 41771138), the National Key Research and Development Project (No. 2016YFA0602301)
More Information
    Corresponding author: LI Jing.E-mail:lijingsara@iga.ac.cn
  • 摘要: Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982-2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.
  • [1] Alcaraz-Segura D, Chuvieco E, Epstein H E et al., 2010a. Debating the greening vs. browning of the North American boreal forest:differences between satellite datasets. Global Change Biology, 16(2):760-770. doi:10.1111/j.1365-2486.2009. 01956.x
    [2] Alcaraz-Segura D, Liras E, Tabik S et al., 2010b. Evaluating the consistency of the 1982-1999 NDVI Trends in the Iberian peninsula across four time-series derived from the AVHRR sensor:LTDR, GIMMS, FASIR, and PAL-Ⅱ. Sensors, 10(2):1291-1314. doi: 10.3390/s100201291
    [3] Aldakheel Y, 2011. Assessing NDVI spatial pattern as related to irrigation and soil salinity management in al-hassa oasis, Saudi Arabia. Journal of the Indian Society of Remote Sensing, 39(2):171-180. doi: 10.1007/s12524-010-0057-z
    [4] Anyamba A, Tucker C J, 2005. Analysis of sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981-2003. Journal of Arid Environments, 63(3):596-614. doi:10.1016/j. jaridenv.2005.03.007
    [5] Bai Z G, Dent D L, Olsson L et al., 2008. Proxy global assessment of land degradation. Soil Use and Management, 24(3):223-234. doi: 10.1111/j.1475-2743.2008.00169.x
    [6] Barrow C J, 2009. Desertification. In:Kitchin R, Thrift N (eds). International Encyclopedia of Human Geography. Oxford:Elsevier, 96-101.
    [7] Boelman N T, Stieglitz M, Rueth H M et al., 2003. Response of NDVI, biomass, and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra. Oecologia, 135(3):414-421. doi: 10.1007/S00442-003-1198-3
    [8] Brown M E, Pinzon J E, Didan K et al., 2006. Evaluation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors. IEEE Transactions on Geoscience and Remote Sensing, 44(7):1787-1793. doi: 10.1109/TGRS.2005.860205
    [9] Chen Z J, Li J B, Fang K Y et al., 2012. Seasonal dynamics of vegetation over the past 100 years inferred from tree rings and climate in Hulunbei'er steppe, northern China. Journal of Arid Environments, 83:86-93. doi: 10.1016/j.jaridenv.2012.03.013
    [10] Dardel C, Kergoat L, Hiernaux P et al., 2014. Re-greening Sahel:30 years of remote sensing data and field observations (Mali, Niger). Remote Sensing of Environment, 140:350-364. doi: 10.1016/j.rse.2013.09.011
    [11] de Beurs K M, Henebry G M, 2004. Trend analysis of the Path-finder AVHRR Land (PAL) NDVI data for the deserts of cen-tral Asia. IEEE Geoscience and Remote Sensing Letters, 1(4):282-286. doi: 10.1109/LGRS.2004.834805
    [12] de Jong R, de Bruin S, de Wit A et al., 2011. Analysis of mono-tonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2):692-702. doi: 10.1016/j.rse.2010.10.011
    [13] Dietz E J, Killeen T J, 1981. A nonparametric multivariate test for monotone trend with pharmaceutical applications. Journal of the American Statistical Association, 76(373):169-174. doi: 10.1080/01621459.1981.10477624
    [14] Douglas E M, Vogel R M, Kroll C N, 2000. Trends in floods and low flows in the United States:impact of spatial correlation. Journal of Hydrology, 240(1-2):90-105. doi: 10.1016/S0022-1694(00)00336-X
    [15] El Hassan I M, 2004. Desertification monitoring using remote sensing technology. In:Proceeding of the International Con-ference on Water Resources and Arid Environment. Saudi Arabia:King Saud University.
    [16] Erasmi S, Schucknecht A, Barbosa M et al., 2014. Vegetation greenness in northeastern brazil and its relation to ENSO warm events. Remote Sensing, 6(4):3041-3058. doi: 10.3390/rs6043041
    [17] Fensholt R, Sandholt I, Stisen S et al., 2006. Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor. Remote Sensing of Environment, 101(2):212-229. doi: 10.1016/j.rse.2005.11.013
    [18] Giannini A, Biasutti M, Verstraete M M, 2008. A climate mod-el-based review of drought in the Sahel:Desertification, the re-greening and climate change. Global Planet Change, 64:119-128. doi: 10.1016/j.gloplacha.2008.05.004
    [19] Gu Y X, Wylie B K, Howard D M et al., 2013. NDVI saturation adjustment:a new approach for improving cropland perfor-mance estimates in the Greater Platte River Basin, USA. Eco-logical Indicators, 30:1-6. doi: 10.1016/j.ecolind.2013.01.041
    [20] Guo M, Wang X F, Li J et al., 2013. Spatial distribution of greenhouse gas concentrations in arid and semi-arid regions:a case study in East Asia. Journal of Arid Environments, 91:119-128. doi: 10.1016/j.jaridenv.2013.01.001
    [21] Herrmann S M, Anyamba A, Tucker C J, 2005. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change, 15(4):394-404. doi: 10.1016/j.gloenvcha.2005.08.004
    [22] Hirsch R M, Slack J R, Smith R A, 1982. Techniques of trend analysis for monthly water quality data. Water Resources Re-search, 18(1):107-121. doi: 10.1029/WR018i001p00107
    [23] Holben B N, 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11):1417-1434. doi:10.1080/0143116 8608948945
    [24] Huete A, Didan K, Miura T et al., 2002. Overview of the radio-metric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2):195-213. doi: 10.1016/S0034-4257(02)00096-2
    [25] Huete A R, Liu H Q, van Leeuwen W J D, 1997. The use of veg-etation indices in forested regions:issues of linearity and satu-ration. Proceedings of the IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing-A Scientific Vision for Sustainable De-velopment. Singapore, Singapore:IEEE, 1964:1966-1968. doi: 10.1109/IGARSS.1997.609169
    [26] Jiang N, Zhu W Q, Zheng Z T et al., 2013. A comparative analysis between GIMSS NDVIg and NDVI3g for monitoring veg-etation activity change in the northern hemisphere during 1982-2008. Remote Sensing, 5(8):4031-4044. doi:10. 3390/rs5084031
    [27] Kaplan S, Blumberg D G, Mamedov E et al., 2014. Land-use change and land degradation in Turkmenistan in the post- Soviet era. Journal of Arid Environments, 103:96-106. doi: 10.1016/j.jaridenv.2013.12.004
    [28] Kendall M G, 1938. A new measure of rank correlation. Bio-metrika, 30(1-2):81-93. doi: 10.1093/biomet/30.1-2.81
    [29] La Puma I P, Philippi T E, Oberbauer S F, 2007. Relating NDVI to ecosystem CO2 exchange patterns in response to season length and soil warming manipulations in arctic Alaska. Remote Sensing of Environment, 109(2):225-236. doi:10. 1016/j.rse.2007.01.001
    [30] Lanzante J R, 1996. Resistant, robust and non-parametric tech-niques for the analysis of climate data:theory and examples, including applications to historical radiosonde station data. In-ternational Journal of Climatology, 16(11):1197-1226. doi: 10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L
    [31] Luo X Z, Chen X Q, Xu L et al., 2013. Assessing performance of NDVI and NDVI3g in monitoring leafUnfolding dates of the deciduous broadleaf forest in Northern China. Remote Sensing, 5(2):845-861. doi: 10.3390/rs5020845
    [32] Miura T, Turner J P, Huete A R, 2013. Spectral compatibility of the NDVI Across VⅡRS, MODIS, and AVHRR:an analysis of atmospheric effects using EO-1 Hyperion. IEEE Transac-tions on Geoscience and Remote Sensing, 51(3):1349-1359. doi: 10.1109/TGRS.2012.2224118
    [33] Mohsin T, Gough W A, 2010. Trend analysis of long-term tem-perature time series in the Greater Toronto Area (GTA). The-oretical and Applied Climatology, 101(3-4):311-327. doi: 10.1007/s00704-009-0214-x
    [34] Na Xiaodong, Zhang Shuqing, Li Xiaofeng et al., 2007. Applica-tion of MODIS NDVI time series to extracting wetland vege-tation information in the Sanjiang plain. Wetland Science, 5(3):227-236. (in Chinese)
    [35] Neeti N, Eastman J R, 2011. A contextual Mann-Kendall approach for the assessment of trend significance in image time series. Transactions in Gis, 15(5):599-611. doi:10.1111/j. 1467-9671.2011.01280.x
    [36] Neeti N, Rogan J, Christman Z, et al., 2012. Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico. Remote Sensing Letters, 3(5):433-442. doi: 10.1080/01431161.2011.616238
    [37] Pinzon J E, Tucker C J, 2014. A non-stationary 1981-2012 AVHRR NDVI3g time series. Remote Sensing, 6(8):6929-6960. doi: 10.3390/rs6086929
    [38] Pouliot D, Latifovic R, Olthof I, 2009. Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985-2006. International Journal of Remote Sensing, 30(1):149-168. doi: 10.1080/01431160802302090
    [39] Prince S D, Beckerreshef I, Rishmawi K, 2009. Detection and mapping of long-term land degradation using local net produc-tion scaling:application to Zimbabwe. Remote Sensing of En-vironment, 113(5):1046-1057. doi: 10.1016/j.rse.2009.01.016
    [40] Sen P K, 1968. Estimates of the regression coefficient based on Kendall's Tau. Journal of the American Statistical Association, 63(324):1379-1389.
    [41] Sobrino J A, Julien Y, 2011. Global trends in NDVI-derived pa-rameters obtained from GIMMS data. International Journal of Remote Sensing, 32(15):4267-4279. doi:10.1080/01431161. 2010.486414
    [42] Some'e B S, Ezani A, Tabari H, 2012. Spatiotemporal trends and change point of precipitation in Iran. Atmospheric Research, 113:1-12. doi: 10.1016/j.atmosres.2012.04.016
    [43] Stellmes M, Udelhoven T, Röder A et al., 2010. Dryland observa-tion at local and regional scale:Comparison of Landsat TM/ETM+ and NOAA AVHRR time series. Remote Sensing of Environment, 114(10):2111-2125. doi:10.1016/j.rse.2010. 04.016
    [44] Sternberg T, Rueff H, Middleton N, 2015. Contraction of the Gobi desert, 2000-2012. Remote Sensing, 7(2):1346-1358. doi: 10.3390/rs70201346
    [45] Suzuki R, Nomaki T, Yasunari T, 2001. Spatial distribution and its seasonality of satellite-derived vegetation index (NDVI) and climate in Siberia. International Journal of Climatology, 21(11):1321-1335. doi: 10.1002/joc.653
    [46] Theil H, 1992. A rank-invariant method of linear and polynomial regression analysis. In:Raj B, Koerts J (eds). Henri Theil's Contributions to Economics and Econometrics. Houten, the Netherlands:Springer, 345-381.
    [47] Tottrup C, Rasmussen M S, 2004. Mapping long-term changes in savannah crop productivity in Senegal through trend analysis of time series of remote sensing data. Agriculture, Ecosystems & Environment, 103(3):545-560. doi: 10.1016/j.agee.2003.11.009
    [48] Tucker C, 1979. Red and photographic infrared linear combina-tions for monitoring vegetation. Remote Sensing of Environ-ment, 8(2):127-150. doi: 10.1016/0034-4257(79)90013-0
    [49] Valz P D, McLeod A I, 1990. A simplified derivation of the var-iance of Kendall's rank correlation coefficient. The American Statistician, 44(1):39-40. doi: 10.2307/2684956
    [50] Vrieling A, de Leeuw J, Said M Y, 2013. Length of growing pe-riod over Africa:variability and trends from 30 years of NDVI time series. Remote Sensing, 5(2):982-1000. doi:10.3390/rs 5020982
    [51] Wang J B, Dong J W, Liu J Y, et al., 2014. Comparison of gross primary productivity derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia. Remote Sensing, 6(3):2108-2133. doi: 10.3390/rs6032108
    [52] Yang J, Sun J, Ge Q S et al., 2017. Assessing the impacts of urbanization-associated green space on urban land surface temperature:a case study of Dalian, China. Urban Forestry & Urban Greening. 22:1-10. doi:10.1016/j.ufug.2017.01. 002.
    [53] Yang J, Guan Y Y, Xia J H et al., 2018. Spatiotemporal variations characteristics of green space ecosystem service value at urban fringes:a case study on Ganjingzi District in Dalian, China. Science of the Total Environment. 639:1453-1461. doi:10. 1016/j.scitotenv.2018.05.253.
    [54] Zeng F W, Collatz G J, Pinzon J E et al., 2013. Evaluating and quantifying the climate-driven interannual variability in global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) at global scales. Remote Sensing, 5(8):3918-3950. doi: 10.3390/rs5083918
    [55] Zhu Z C, Bi J, Pan Y Z et al., 2013. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sensing, 5(2):927-948. doi: 10.3390/rs5020927
  • [1] Bo CAO, Xiaole KONG, Yixuan WANG, Hang LIU, Hongwei PEI, Yan-Jun SHEN.  Response of Vegetation Cover Change to Drought at Different Time-scales in the Beijing-Tianjin Sandstorm Source Region, China . Chinese Geographical Science, 2021, 31(3): 491-505. doi: 10.1007/s11769-021-1206-8
    [2] LIU Qiuyu, ZHANG Tinglong, LI Yizhe, LI Ying, BU Chongfeng, ZHANG Qingfeng.  Comparative Analysis of Fractional Vegetation Cover Estimation Based on Multi-sensor Data in a Semi-arid Sandy Area . Chinese Geographical Science, 2019, 20(1): 166-180. doi: 10.1007/s11769-018-1010-2
    [3] GUO Peipei, SU Yuebo, WAN Wuxing, LIU Weiwei, ZHANG Hongxing, SUN Xu, OUYANG Zhiyun, WANG Xiaoke.  Urban Plant Diversity in Relation to Land Use Types in Built-up Areas of Beijing . Chinese Geographical Science, 2018, 28(1): 100-110. doi: 10.1007/s11769-018-0934-x
    [4] TIAN Yichao, BAI Xiaoyong, WANG Shijie, QIN Luoyi, LI Yue.  Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China . Chinese Geographical Science, 2017, 27(1): 25-38. doi: 10.1007/s11769-017-0844-3
    [5] LI Xianju, CHEN Gang, LIU Jingyi, CHEN Weitao, CHENG Xinwen, LIAO Yiwei.  Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region . Chinese Geographical Science, 2017, 27(5): 827-835. doi: 10.1007/s11769-017-0894-6
    [6] YU Lingxue, ZHANG Shuwen, LIU Tingxiang, TANG Junmei, BU Kun, YANG Jiuchun.  Spatio-temporal Pattern and Spatial Heterogeneity of Ecotones Based on Land Use Types of Southeastern Da Hinggan Mountains in China . Chinese Geographical Science, 2015, 25(2): 184-197. doi: 10.1007/s11769-014-0671-8
    [7] DU Peijun, YUAN Linshan, XIA Junshi, et al..  Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area . Chinese Geographical Science, 2011, 21(6): 656-665.
    [8] LIU Dianwei, WANG Zongming, SONG Kaishan, ZHANG Bai, HU Liangjun, HUANG Ni, ZHANG Sumei, LUO Ling, ZHANG Chunhua, JIANG Guangjia.  Land Use/Cover Changes and Environmental Consequences in Songnen Plain, Northeast China . Chinese Geographical Science, 2009, 19(4): 299-305. doi: 10.1007/s11769-009-0299-2
    [9] XU Jianhua, CHEN Yaning, LI Weihong, DONG Shan.  Long-term Trend and Fractal of Annual Runoff Process in Mainstream of Tarim River . Chinese Geographical Science, 2008, 18(1): 77-84. doi: 10.1007/s11769-008-0077-6
    [10] CHEN Zheng-hong, QIN Jun.  TREND OF PRECIPITATION VARIATION IN HUBEI PROVINCE SINCE THE 1960S . Chinese Geographical Science, 2003, 13(4): 322-327.
    [11] ZHUANG Da-fang, LIU Ming-liang, DENG Xiang-zheng.  SPATIALIZATION MODEL OF POPULATION BASED ON DATASET OF LAND USE AND LAND COVER CHANGE IN CHINA . Chinese Geographical Science, 2002, 12(2): 114-119.
    [12] LAN Yong-chao, KANG Er-si, MA Quan-jie, ZHANG Ji-shi.  STUDY ON TREND PREDICTION AND VARIATION ON THE FLOW INTO THE LONGYANGXIA RESERVOIR . Chinese Geographical Science, 2001, 11(1): 35-41.
    [13] SUN Gen-nian.  FOUNDATION AND APPLICATION OF BACKGROUND TREND LINE OF CHINA'S INBOUND TOURISM . Chinese Geographical Science, 2000, 10(3): 231-237.
    [14] 田卫, 俞穆清, 王国平, 郭传新.  POLLUTION TREND IN THE TUMEN RIVER AND ITS INFLUENCE ON REGIONAL DEVELOPMENT . Chinese Geographical Science, 1999, 9(2): 146-150.
    [15] 黄铁青, 刘兆礼, 潘瑜春, 张养贞.  LAND COVER SURVEY IN NORTHEAST CHINA USING REMOTE SENSING AND GIS . Chinese Geographical Science, 1998, 8(3): 264-270.
    [16] 胡汝骥, 杨川德, 马虹, 姜逢清.  CLIMATIC TREND INDICATED BY VARIATIONS OF GLACIERS AND LAKES IN THE TIANSHAN MOUNTAINS . Chinese Geographical Science, 1996, 6(3): 239-246.
    [17] 董光荣, 董玉祥, 李森, 金炯, 靳鹤龄, 刘玉璋.  THE CAUSES AND DEVELOPMENTAL TREND OF DESERTIFICATION IN THE MIDDLE REACHES OF THE YARLUNG ZANGBO RIVER AND ITS TWO TRIBUTARIES IN XIZANG . Chinese Geographical Science, 1995, 5(4): 355-364.
    [18] 王景华.  BACKGROUND VALUES AND TREND DISTRIBUTION OF Cu, Zn AND Ni IN SOILS IN CHINA . Chinese Geographical Science, 1992, 2(4): 357-366.
    [19] 李为, 曲丽霞.  CHANGING FEATURES AND TREND OF LIGHT INDUSTRY DISTRIBUTION IN NORTHEAST CHINA . Chinese Geographical Science, 1991, 1(4): 359-369.
    [20] 蒋忠信.  TREND SURFACE ANALYSIS OF THE EXISTENT SNOWLINE IN WEST CHINA . Chinese Geographical Science, 1991, 1(1): 62-69.
  • 加载中
计量
  • 文章访问数:  228
  • HTML全文浏览量:  2
  • PDF下载量:  695
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-03-09
  • 修回日期:  2018-07-06
  • 刊出日期:  2018-12-27

Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period

doi: 10.1007/s11769-018-1002-2
    基金项目:  Under the auspices of National Natural Science Foundation of China (No. 41771179, 41871103, 41771138), the National Key Research and Development Project (No. 2016YFA0602301)
    通讯作者: LI Jing.E-mail:lijingsara@iga.ac.cn

摘要: Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982-2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.

English Abstract

GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. 中国地理科学, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2
引用本文: GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. 中国地理科学, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2
GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. Chinese Geographical Science, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2
Citation: GUO Meng, LI Jing, HE Hongshi, XU Jiawei, JIN Yinghua. Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982-2015 Time Period[J]. Chinese Geographical Science, 2018, 28(6): 907-919. doi: 10.1007/s11769-018-1002-2
参考文献 (55)

目录

    /

    返回文章
    返回