基于遥感的中国东北植被物候不对称特征分析
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摘要
植被物候是反映全球气候变化的重要生态指标,春季返青期物候已经得到广泛研究,但秋季物候及其与春季物候的不对称性仍然不明朗。基于GIMMS NDVI 3g遥感数据提取中国东北地区植被关键物候参数,利用春季和秋季中返青(衰落)速率、生长期长度、植被活动能力(以NDVI均值表示)3个物候指标的差异来刻画春秋物候的不对称性,定义为物候不对称指数AsyR、AsyL和AsyV(Asymmetry of growing Rate,Length,Vegetation index)。首先利用双逻辑斯蒂曲线拟合和曲率求导方法获取各植被像元的物候期和生长速率参数,其次在像素尺度上探索了3种春秋物候不对称性的时空分布特征。结果表明:3种不对称指数的年际变异显著,研究区整体上3种不对称指数均呈现大约10 a的周期性,AysV和AsyL同相位并与AsyR呈相反相位。3种指数可以从不同角度刻画植被春秋季生长形态不对称性,在时空表现上存在一定的不确定性。AsyR和AsyV在不同植被类型中的空间格局比较相似,并能一定程度上区分农作物和自然植被,AsyL的空间分布规律较差、区分度不高。不对称指数发现,针叶林和阔叶林区域主要为衰落期的植被活动占主要优势,形态上是春季快速成长、秋季缓慢衰落;农作物区表现为缓慢成长和快速衰落;草原区域不对称性不显著。生长形态不对称性可以反映春秋两季的植被活动对整个生长季植被生产力的控制作用,有助于更加细致地探索物候对植被生态系统固碳的影响。在实际应用方面,也可以根据不同植被的物候不对称特征进行植被分类,服务于农业普查和植被生态系统管理。
Vegetation phenology is an important ecological indicator for global climate change.Plant greenup phenology in the spring time has been well studied,whereas autumn phenology and its asymmetry with spring phenology still remain unclear.Here,the GIMMS NDVI3 g dataset for Northeast China was applied to extract the key phenological parameters during plant growth process,then three phenological asymmetry indices were defined according to the difference between greenup rate and senescence rate(AsyR),growth length in spring and autumn(AsyL),mean vegetation greenness index in spring and autumn(AsyV).First,plant growing curve was fitted with double logistic function and the phenological parameters was calculated.Second,the spatiotemporal pattern of asymmetry indices was explored.The results indicate that the three phenological asymmetry indices show a significant interannual variability and a time cycle of around ten years.The direction of amplitude for AsyV and AsyL was opposite with that of AsyR.Three indices could depict the phenological asymmetries from various perspectives and have a degree of uncertainty.The landscape pattern for AsyV and Asy R is similar.AsyV and AsyR show a capability of distinguishing cropland and natural vegetation cover.AsyL reflects a complex spatial distribution.Phenological asymmetries reveal that coniferous forest and broad-leaved forest present a dominant control of senescence vegetation activities.These natural vegetation commonly show a growth feature of rapid growth in spring and slow decrease in autumn.Cropland exhibits a slowly growing rate in spring and a rapid decrease in autumn.Phenological asymmetry is not significant in grassland area.Phenological asymmetry could enhance our knowledge on ecosystem carbon sink.In a practical way,phenological asymmetry could serve as a useful tools in vegetation type classification,agricultural investigation and plant ecosystem management.
Vegetation phenology is an important ecological indicator for global climate change.Plant greenup phenology in the spring time has been well studied,whereas autumn phenology and its asymmetry with spring phenology still remain unclear.Here,the GIMMS NDVI3 g dataset for Northeast China was applied to extract the key phenological parameters during plant growth process,then three phenological asymmetry indices were defined according to the difference between greenup rate and senescence rate(AsyR),growth length in spring and autumn(AsyL),mean vegetation greenness index in spring and autumn(AsyV).First,plant growing curve was fitted with double logistic function and the phenological parameters was calculated.Second,the spatiotemporal pattern of asymmetry indices was explored.The results indicate that the three phenological asymmetry indices show a significant interannual variability and a time cycle of around ten years.The direction of amplitude for AsyV and AsyL was opposite with that of AsyR.Three indices could depict the phenological asymmetries from various perspectives and have a degree of uncertainty.The landscape pattern for AsyV and Asy R is similar.AsyV and AsyR show a capability of distinguishing cropland and natural vegetation cover.AsyL reflects a complex spatial distribution.Phenological asymmetries reveal that coniferous forest and broad-leaved forest present a dominant control of senescence vegetation activities.These natural vegetation commonly show a growth feature of rapid growth in spring and slow decrease in autumn.Cropland exhibits a slowly growing rate in spring and a rapid decrease in autumn.Phenological asymmetry is not significant in grassland area.Phenological asymmetry could enhance our knowledge on ecosystem carbon sink.In a practical way,phenological asymmetry could serve as a useful tools in vegetation type classification,agricultural investigation and plant ecosystem management.
引文
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[20] Hirsch R M,Slack J R,Smith R A.Techniques of Trend Analysis for Monthly Water Quality Data[J].Water Resources Research,1982,18(1):107-121.
[21] Fu Y H,Cheng H F,Piao S L,et al.Declining Global Warming Effects on the Pphenology of Spring Leaf Unfolding[J].Nature,2015,526(7571):104-107.
[2] Piao S L,Philippe C,Pierre F,et al.Net Carbon Dioxide Losses of Northern Ecosystems in Response to Autumn Warming[J].Nature,2008,451(7174):49-52.
[3] Fan Deqin,Zhao Xuesheng,Zhu Wenquan,et al.Review of Influencing Factors of Accuracy of Plant Phenology Monitoring based on Remote Sensing Data[J].Progress in Geography,2016,35(3):304-319.[范德芹,赵学胜,朱文泉,等.植物物候遥感监测精度影响因素研究综述[J].地理科学进展,2016,35(3):304-319.]
[4] Zhou Yuke,Liu Jianwei.Spatio-temproal Analysis of Vegetation Phenology with Multiple Methods over the Tibetan Plateau based on MODIS NDVI Data[J].Remote Sensing Technology,2018,33(3):112-124.[周玉科,刘建文.基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析[J].遥感技术与应用,2018,33(3):112-124.]
[5] Zhu W,Tian H,Xu X,et al.Extension of the Growing Season Due to Delayed Autumn over Mid and High Latitudes in North America during 1982 & 2006[J].Global Ecology & Biogeography,2012,21(2):260-271.
[6] Zhu Z,Piao S,Myneni R B,et al.Greening of the Earth and its drivers[J].Environmental Pollution,2016,231(Pt 1):182.
[7] Fang Jingyun,Piao Shilong,He Jinsheng,et al.Advanced Vegetation Activity in China for Last Twenty Years[J].Science In China:Series C,2003,33(6):554-565.[方精云,朴世龙,贺金生,等.近20年来中国植被活动在增强[J].中国科学:C辑,2003,33(6):554-565.]
[8] Piao S L,Friedlingstein P,Ciais P,et al.Growing season Extension and Its Impact on Terrestrial Carbon Cycle in the Northern Hemisphere over the Past 2 Decades[J].Global Biogeochemical Cycles,2007,21(3):3018-3029.
[9] Richardson A D,T Andy B,Philippe C,et al.Influence of Spring and Autumn Phenological Transitions on Forest Ecosystem Productivity[J].Philosophical Transactions of the Royal Society of London,2010,365(1555):3227-3246.
[10] Yu Xinfang,Zang Dafang.Monitoring Forest Phenophases of Northest China based on MODIS DNVI Data[J].Resources Science,2006,28(4):111-117.[于信芳,庄大方.基于MODIS NDVI数据的东北森林物候期监测[J].资源科学,2006,28(4):111-117.]
[11] Qiu Yue,Fan Deqin,Zhao Xuesheng,et al.Spatio-temporal Changes of NPP and Its Responses to Phenology in Northeast China[J].Geography and Geo-Information Science,2017,33(5):21-27.[邱玥,范德芹,赵学胜,等.中国东北地区植被NPP时空变化及其对物候的响应研究[J].地理与地理信息科学,2017,33(5):21-27.]
[12] Wang Hong,Li Xiaobing,Li Xia,et al.The Variability of Vegetation Growing Season in the Northern China based on NOAA NDVI and MSAVI from 1982 to 1999[J].Acta Ecologica Sinica,2007,27(2):504-515[王宏,李晓兵,李霞,等.基于NOAA NDVI和MSAVI研究中国北方植被生长季变化[J].生态学报,2007,27(2):504-515.]
[13] 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.
[14] Fensholt,Rasmus,Proud,et al.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(3):131-147.
[15] Piao S,Fang J,Zhou L,et al.Interannual Variations of Monthly and Seasonal Normalized Difference Vegetation Index(NDVI) in China from 1982 to 1999[J].Journal of Geophysical Research,2003,108(D14):4401-4413.
[16] Beck P S A,Atzberger C,H?gda K A,et al.Improved Monitoring of Vegetation Dynamics at very High Latitudes:A New Method Using MODIS NDVI[J].Remote Sensing of Environment,2006,99(3):321-334.
[17] Beck,Hylke E,Mcvicar,et al.Global Evaluation of four AVHRR-NDVI Data Sets:Intercomparison and Ssessment Against Landsat Imagery[J].Remote Sensing of Environment,2011,115(10):2547-2563.
[18] Esterby S R.Review of Methods for the Detection and Estimation of Trends with Emphasis on Water Quality Applications[J].Hydrological Processes,2015,10(2):127-149.
[19] Cao Jieping,Chi Daocai,Wu Liqiang,et al.Mann-Kendall Examination and Application in the Analysis of Precipitation Trend[J].Agricultural Science & Technology and Equipment,2008,(5):35-37.[曹洁萍,迟道才,武立强,等.Mann-Kendall检验方法在降水趋势分析中的应用研究[J].农业科技与装备,2008,(5):35-37.]
[20] Hirsch R M,Slack J R,Smith R A.Techniques of Trend Analysis for Monthly Water Quality Data[J].Water Resources Research,1982,18(1):107-121.
[21] Fu Y H,Cheng H F,Piao S L,et al.Declining Global Warming Effects on the Pphenology of Spring Leaf Unfolding[J].Nature,2015,526(7571):104-107.