地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (9): 1910-1919.doi: 10.12082/dqxxkx.2020.190237
陈如如1,2,3(), 胡中民4, 李胜功1,2,3, 郭群1,2,3,*(
)
收稿日期:
2019-05-20
修回日期:
2019-11-17
出版日期:
2020-09-25
发布日期:
2020-11-25
通讯作者:
郭群
E-mail:chenrr.16s@igsnrr.ac.cn;guoq@igsnrr.ac.cn
作者简介:
陈如如(1992— ),女,浙江温州人,硕士生,主要从事全球变化生态学研究。E-mail:基金资助:
CHEN Ruru1,2,3(), HU Zhongmin4, LI Shenggong1,2,3, GUO Qun1,2,3,*(
)
Received:
2019-05-20
Revised:
2019-11-17
Online:
2020-09-25
Published:
2020-11-25
Contact:
GUO Qun
E-mail:chenrr.16s@igsnrr.ac.cn;guoq@igsnrr.ac.cn
Supported by:
摘要:
中国北方草原区生产力在区域碳水循环、农牧业发展中举足轻重。归一化植被指数(Normalized Difference Vegetation Index,NDVI)广泛应用于生产力的计算,然而目前来源众多的NDVI数据反映中国北方草原植被时空动态的一致性仍未可知。本研究利用2000—2015年3个来源NDVI数据集(MODIS NDVI、GIMMS NDVI和SPOT NDVI)并以国际上公认的数据准确性较高的MODIS NDVI为基准对比分析了中国北方草原区NDVI时空动态的一致性,并选取适宜的NDVI产品揭示研究区NDVI长期的时空格局。结果表明:整个中国北方草原区以及部分草原类型(高寒草甸、高寒草原、高寒荒漠、温带荒漠草原)GIMMS NDVI和MODIS NDVI 2套数据集无论是数值范围,还是年际波动和变化趋势具有较高一致性(二者在高寒草甸、高寒草原、高寒荒漠、温带荒漠草原的相关系数分别为0.60、0.47、0.51、0.74),而SPOT NDVI数值远高于其他2个数据集,尤其是在青藏高原草原区,SPOT NDVI数值每年较另外两套数据集约偏高0.15,表明该区域使用SPOT数据应慎重。部分温带草原类型(典型草原和草甸草原)GIMMS NDVI和SPOT NDVI数据集在年际波动以及变化趋势上具有较高的一致性(相关系数分别为0.85和0.60),但温带草原区3种数据集NDVI数值范围整体相差不大,小于0.06。基于上述结果,本研究进一步采用时间序列最长且与MODIS NDVI一致性最好的GIMMS NDVI分析了研究区NDVI的时空动态,发现1982—2015年中国北方草原区NDVI整体呈增加趋势,25%的区域达显著水平(p<0.05),主要集中在温带草原区;高寒草原区NDVI大部分区域变化不显著且有一定比例的区域NDVI呈显著下降趋势。本研究可以为模型数据集选择和预测中国北方草原区植被对未来气候变化的响应提供科学依据。
陈如如, 胡中民, 李胜功, 郭群. 不同数据源归一化植被指数在中国北方草原区的应用比较[J]. 地球信息科学学报, 2020, 22(9): 1910-1919.DOI:10.12082/dqxxkx.2020.190237
CHEN Ruru, HU Zhongmin, LI Shenggong, GUO Qun. Assessment of Normalized Difference Vegetation Index from Different Data Sources in Grassland of Northern China[J]. Journal of Geo-information Science, 2020, 22(9): 1910-1919.DOI:10.12082/dqxxkx.2020.190237
表2
2000—2015年不同草原类型归一化植被指数年际变化趋势统计"
变化趋势 | NDVI | 北方草原 | 高寒草甸 | 高寒草原 | 高寒荒漠 | 温性草甸 | 典型草原 | 荒漠草原 |
---|---|---|---|---|---|---|---|---|
不显著 | GIMMS | 73.8 | 83.9 | 82.8 | 77.3 | 76.2 | 65.8 | 71.4 |
MODIS | 79.4 | 82.8 | 88.2 | 75.6 | 77.9 | 68.8 | 82.1 | |
SPOT | 86.4 | 90.9 | 92.5 | 91.8 | 81.6 | 78.8 | 87.6 | |
显著减少 | GIMMS | 10.6 | 5.9 | 12.4 | 19.6 | 3.9 | 2.8 | 16.2 |
MODIS | 5.3 | 10.8 | 4.2 | 1.6 | 12.4 | 4.1 | 3.4 | |
SPOT | 2.1 | 2.8 | 2.7 | 2.6 | 2.3 | 1.3 | 1.3 | |
显著增加 | GIMMS | 15.6 | 10.2 | 4.7 | 3.0 | 19.9 | 31.4 | 12.5 |
MODIS | 15.3 | 6.4 | 7.7 | 22.8 | 9.7 | 27.1 | 14.5 | |
SPOT | 11.5 | 6.3 | 4.8 | 5.6 | 16.1 | 19.9 | 11.1 |
[1] | Parton W J, Scurlock J M O, Ojima D S, et al. Impact of climate change on grassland production and soil carbon worldwide[J]. Global Change Biology, 1995,1(1):13-22. |
[2] | Wang Y, Zhou G, Jia B. Modeling SOC and NPP responses of meadow steppe to different grazing intensities in Northeast China[J]. Ecological Modelling, 2008,217(1):72-78. |
[3] | O'Mara F P. The role of grasslands in food security and climate change[J]. Annals of Botany, 2012,110(6):1263-1270. |
[4] |
Hautier Y, Seabloom E W, Borer E T, et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands[J]. Nature, 2014,508(7497):521-525.
pmid: 24531763 |
[5] |
Poulter B, Frank D, Ciais P, et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle[J]. Nature, 2014,509(7502):600-603.
pmid: 24847888 |
[6] |
Bai Y, Wu J, Xing Q, et al. Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau[J]. Ecology, 2008,89(8):2140-2153.
doi: 10.1890/07-0992.1 pmid: 18724724 |
[7] | Lambin E F, Strahler A H. Indicators of land-cover change for change-vector analysis in multitemporal space at coarse spatial scales[J]. International Journal of Remote Sensing, 1994,15(10):2099-2119. |
[8] | Carlson T N, Ripley D A. On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote sensing of Environment, 1997,62(3):241-252. |
[9] | Brinkmann K, Dickhoefer U, Schlecht E, et al. Quantification of aboveground rangeland productivity and anthropogenic degradation on the Arabian Peninsula using Landsat imagery and field inventory data[J]. Remote Sensing of Environment, 2011,115(2):465-474. |
[10] |
Gang C, Zhang Y, Guo L, et al. Drought-Induced Carbon and Water Use Efficiency Responses in Dryland Vegetation of Northern China[J]. Frontiers in Plant Science, 2019,10:224.
doi: 10.3389/fpls.2019.00224 pmid: 30863421 |
[11] | Zhao J, Zhang H, Zhang Z, et al. Long-term time series of vegetation various and its relationship with climate factors by integrating AVHRR GIMMS and Terra MODIS data[J]. Fresenius Environ. Bull, 2015,24:4005-4018. |
[12] | Jong R D, Bruin S D, Wit A D, et al. Analysis of monotonic greening and browning trends from global NDVI time-series[J]. Remote Sensing of Environment, 2011,115(2):692-702. |
[13] | Jong R D, Verbesselt J, Schaepman M E, et al. Trend changes in global greening and browning: Contribution of short-term trends to longer-term change[J]. Global Change Biology, 2012,18(2):642-655. |
[14] | Piao S, Wang X, Park T, et al. Characteristics, drivers and feedbacks of global greening[J]. Nature Reviews Earth & Environment, 2019, doi. org/10.1038/ s43017-019-0001-x. |
[15] | Mao D, Wang Z, Luo L, et al. Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China[J]. International Journal of Applied Earth Observations & Geoinformation, 2012,18. |
[16] | Tucker C J, Pinzon J E, Brown M E, et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data[J]. International Journal of Remote Sensing, 2005,26(20):4485-4498. |
[17] | Song Y, Ma M, Veroustraete F. Comparison and conversion of AVHRR GIMMS and SPOT VEGETATION NDVI data in China[J]. International Journal of Remote Sensing, 2010,31(9):2377-2392. |
[18] | Van Leeuwen W J D, Orr B J, Marsh S E, et al. Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications[J]. Remote sensing of environment, 2006,100(1):67-81. |
[19] |
Guay K C, Beck P S A, Berner L T, et al. Vegetation productivity patterns at high northern latitudes: A multi-sensor satellite data assessment[J]. Global Change Biology, 2014,20(10):3147-3158.
doi: 10.1111/gcb.12647 pmid: 24890614 |
[20] | Fensholt R, Rasmussen K, Nielsen T T, et al. Evaluation of earth observation based long term vegetation trends: Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data[J]. Remote Sensing of Environment, 2009,113(9):1886-1898. |
[21] | Atzberger C, Klisch A, Mattiuzzi M, et al. Phenological metrics derived over the European continent from NDVI3g data and MODIS time series[J]. Remote Sensing, 2014,6(1):257-284. |
[22] | Brown M E, Pinzón J E, Didan K, et al. Evaluation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006,44(7):1787-1793. |
[23] | Tarnavsky E, Garrigues S, Brown M E. Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products[J]. Remote Sensing of Environment, 2008,112(2):535-549. |
[24] | 神祥金, 周道玮, 李飞 等. 中国草原区植被变化及其对气候变化的响应[J]. 地理科学, 2015,35(5):622-629. |
[ Shen X J, Zhou D W, Li F, et al. Vegetation change and its response to climate change in Grassland of China[J]. Geosciences, 2015,35(5):622-629. ] | |
[25] | 张镱锂, 刘林山, 王兆锋, 等. 青藏高原土地利用与覆被变化的时空特征[J]. 科学通报, 2019,64(27):2865-2875. |
[ Zhang Y L, Liu L S, Wang Z F, et al. Temporal and spatial characteristics of land use and cover change in the Tibetan Plateau[J] Science Bulletin, 2019,64(27):2865-2875. ] | |
[26] | GIMMS NDVI: https://ecocast.arc.nasa.gov/data/pub/gimms/. |
[27] | MODIS NDVI: Liu R G, Shang R, Liu Y, et al. Globally comparing approaches for gap filling of temporal data to generate the continuous vegetation parameters, submission to Remote Sensing of Environment, 2015. http://www.geodata.cn |
[28] | SPOT_NDVI V2.2 Global: http://land.copernicus.vgt.vito.be. |
[29] | Sen P K. Estimates of the regression coefficient based on Kendall's tau[J]. Journal of the American statistical association, 1968,63(324):1379-1389. |
[30] | Fensholt R, Proud S R. Evaluation of earth observation based global long term vegetation trends: Comparing GIMMS and MODIS global NDVI time series[J]. Remote Sensing of Environment, 2012,119:131-147. |
[31] | 杜加强, 舒俭民, 王跃辉, 等. 青藏高原MODIS NDVI与GIMMS NDVI的对比[J]. 应用生态学报, 2014,25(2):533-544. |
[ Du J Q, Shu J M, Wang Y H, et al. Comparison of MODIS NDVI and GIMMS NDVI in Qinghai-Tibet Plateau[J]. Journal of Applied Ecology, 2014,25(2):533-544. ] | |
[32] |
Pettorelli N, Vik J O, Mysterud A, et al. Using the satellite-derived NDVI to assess ecological responses to environmental change[J]. Trends in Ecology & Evolution, 2005,20(9):503-510.
doi: 10.1016/j.tree.2005.05.011 pmid: 16701427 |
[33] | IPCC, 2016. Intergovernmental Panel on Climate Change (IPCC). http://www.ipcc.ch/. |
[34] |
Zhao W, Hu Z, Guo Q, et al. Contributions of climatic factors to inter-annual variability of vegetation index in northern China grasslands[J]. Journal of Climate, 2019. DOI: 10.1175/JCLI-D-18-0587.1.
doi: 10.1175/JCLI-D-16-0704.1 pmid: 32742077 |
[35] |
Knapp, A K and M D Smith. Variation among biomes in temporal dynamics of aboveground primary production[J]. Science, 2001,291(5503):481-484.
doi: 10.1126/science.291.5503.481 pmid: 11161201 |
[36] | Piao S, Wang X, Ciais P, et al. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006[J]. Global Change Biology, 2011,17(10):3228-3239. |
[37] | 郭群, 李胜功, 胡中民, 等. 内蒙古温带草原典型草地生态系统生产力对水分在不同时间尺度上的响应[J]. 中国沙漠, 2015,35(3):616-623. |
[ Guo Q, Li S G, Hu Z M, et al. Response of typical grassland ecosystem productivity of temperate grassland in Inner Mongolia to water at different time scales[J]. Desert of China, 2015,35(3):616-623. ] | |
[38] | 陆晴, 吴绍洪, 赵东升. 1982-2013年青藏高原高寒草地覆盖变化及与气候之间的关系[J]. 地理科学, 2017,37(2):292-300. |
[ Lu Q, Wu S H, Zhao D S. The relationship between alpine grassland cover change and climate in Qinghai-Tibet Plateau from 1982 to 2013[J]. Geographic Science, 2017,37(2):292-300. ] | |
[39] | 王青霞, 吕世华, 鲍艳, 等. 青藏高原不同时间尺度植被变化特征及其与气候因子的关系分析[J]. 高原气象, 2014,33(2):301-312. |
[ Wang Q X, Lu S H, Bao Y, et al. Analysis of vegetation change characteristics at different time scales on the Qinghai-Tibet Plateau and its relationship with climate factors[J]. Plateau Meteorology, 2014,33(2):301-312. ] |
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