长白山地森林植被物候对气候变化的响应研究
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摘要
目前,越来越多的证据表明全球气候正逐步变暖,而植物物候现象是全球变化的积分仪和自然环境变化的综合指示器。有关物候对气候变化的响应研究正成为全球变化的热点问题。森林是全球生态系统的重要组成部分,森林物候是反映短期或长期气候变化对森林生长阶段影响的综合性生物指标。遥感技术的发展,为在景观层面进行物候观测提供了技术手段,有效地实现了物候监测从点向面的空间尺度转换。
本文主要运用GIS和遥感技术,使用10天合成的SPOT/NDVI多时相遥感数据、气象数据以及各种相关的图表和统计资料等,构建了长白山地森林生长季遥感监测模型(Double Logistic模型),并利用该模型计算了1999~2008年长白山地森林植被的各物候参数;分析了1999~2008年长白山森林生长季始期、生长季末期、生长季长度、DN年最大值、DN年振幅和DN生长季积分值的时空分布格局及年际变化趋势;探讨了长白山地的气候变化特征;并在此基础上进一步深入分析了森林生长季变化与气候因素的关系,揭示了长白山森林生长季对区域降水和气温变化的响应方式和反馈机制。论文的主要结论如下:
1 Double logistic模型的构建
基于遥感和地理信息系统技术,本文结合logistic模型和一个全局函数的优点,构建了double logistic模型。该模型不仅可以同时提取多年的森林物候期,而且可以消除时间序列的边界效应,它能更灵活地拟合复杂的曲线。同时,由于森林开始生长和结束生长是两个不同的生物物理过程,对于拟合曲线的两侧采用动态的比例更符合森林的生长过程;再者,该模型是在没有设置预定的阈值或者经验参数前提下而逐个像元运算的,所以它能够更加稳定地表征不同像元之间物理意义,具有更好的通用性。野外观测数据和先前学者的研究结果都证明了利用double logistic模型动态监测长白山地植被物候期是一种可行的方法。
2长白山地森林植被物候参数时空格局
(1)在1999~2008年间,长白山地大部分地区森林生长季始期发生在第100~140天,其中在第100~110天和在第110~120天开始生长的地区所占面积比较大。提前的区域仅占研究区面积的32.46%,平均提前速率约0.71d/a;延迟的区域占总面积的67.54%,平均延迟速率约为0.43d/a。
(2)大部分地区森林生长季末期发生在第270~290天,其中在第280~290天开始生长的地区所占面积比较大。长白山地森林的生长季末期中南部表现为延迟趋势,南端和北部表现为一定的提前趋势。延迟区域占有较大比例,约为65.3%,平均延迟速率为0.53d/a;提前的区域占总面积的34.7%,平均提前速率为0.57d/a。
(3)大部分地区森林生长季长度140~180天,其中长度在160~180天的地区所占面积比较大。长白山地森林的生长季长度中东部表现为延长趋势,南端和西北部表现为一定的缩短趋势,整体上呈现东南—西北向的空间分异格局。生长季延长的区域占研究区面积的59.69%,平均延长率为0.67d/a;生长季缩短的区域占总面积的40.31%,平均缩短率约为1d/a。
(4)生长季DN积分值、DN年最大值和DN值年振幅整体上都有增加的趋势,且空间格局具有相似性,是因为三者之间是相互关联的。如果生长季长度不变、DN年最大值或者振幅的增加必然会导致生长季DN积分值的增加;如果生长季长度和生长季的DN基准值不变,那么DN年最大值和振幅的含义就是一样的了。
3长白山地的气候变化特征
综合年平均气温和年降水量的时间变化来看,可以认为长白山地近50年来来气候的年代际变化经历了一个“冷湿—冷干—暖湿—暖干”的过程,并且暖干的过程仍将持续。从地域分布上看,年均增温趋势分布与春季的增温空间格局较相似,增温幅度与春季也相差无几,与其它几个季节的空间分布存在明显的差异。秋季降水量变化趋势空间分布特征与夏季相似,但是降水量增加的区域略有增加。年降水量的空间分布主要受夏季和秋季降水量的影响,故它与夏、秋季二者叠加后的分布趋势一致。
4森林植被生长季变化与气候的关系
长白山地大部分地区的NDVI与旬降水量、旬均气温的年内相关系数和偏相关系数都较高,呈强显著相关,但温度对森林植被的生长过程的影响大于降水。各种森林植被类型,无论是针叶林还是阔叶林,它们的生长季始期大都与其之前各月月均温呈显著负相关,而生长季末期则与其之前各月月均温呈显著正相关;而它们的生长季始末期与降水的关系就较复杂一些,针叶林的始期与其之前的各月降水量呈正相关,阔叶林则呈大都呈负相关。除蒙古栎外,其它森林类型的生长季末期与其前各月的降水量呈负相关。
Recently, more and more evidences have shown that global climate is becoming warmer and warmer, and vegetation phenology is an integraph of global changes and a comprehensive indicator of landscape and environment changes. The responses of vegetation phenology to global environment changes have become a focus field of global changes. Forest is an important part in global ecological system, and the forest phenology is the comprehensive biological index which reflect the influences of the short- and long-term climate changes on the forest growth stage. Remote sensing technology offer the technical means for monitoring the vegetation phenology on landscape scale, and realize the spatial transition of phenological data from points to coverage.
Combing with ten-day SPOT/NDVI data, meteorological data and varities of statistic data using GIS and remote sensing technology, the paper constructs a double logistic model, and calculate forest phenology metrics based on it, then analysize the spatio-temporal patterns and change trends of the start of growing season (SOS), the end of growing season (EOS), the length of growing season (LOS), annual DN (digital number) max, annual DN amplitude and DN integral of growing period in Changbai Mountains during 1999~2008. Subsequently, the paper evaluates the characteristics of climate change in Changbai Mountains, and analysize the relationship between growing season variation and climate factors, then discuss the response and feedback mechanism of vegetation phenology metrics to regional air temperature and precipitation in different vegetation types. From these researches, some basic conclusions are drawn as follows:
1 Double logistic model construction
Based on remote sensing and GIS, the paper develop a double logistic model that combines the merits of logistic model and global function. The model can not only extract vegetation phenophases for several years simultaneously, but also avoid the boundary effects of time-series curve. With this feature, the data obtained are more flexible and complicated for fitting curves. It is more proper to fit the both sides of curve using dynamic ratios for the SOS and EOS being different biophysical processes. Moreover, the model treats each pixel individually without setting absolute thresholds or empirical constants, in which way, the model can characterize the physical meaning of different pixels more accurately. Validation through comparing with the field data and previous research results demonstrate that double logistic model used in this paper is appropriate for the study of vegetation phenology in Changbai Mountains.
2 Temporal-spatial patterns of forest phenology metrics in Changbai Mountains
(1) During the year 1999~2008, the SOS of forest in Changbai Mountains commences on the 100th to 140th day of year, which is consistent with the period of leaf unfolding for forest in late April and early May. Among of the period the day 100~110 plus the day 110~120 take a greater proportion. The advance areas of SOS are small from the whole aspects, only accounting for 32.46% over the study area, and the average advance ratio is about 0.71 d/yr; The delay areas of SOS take up to 67.54%, and the average delay ratio is about 0.43 d/yr.
(2) The EOS concentrate on the day 270~290, of which the day 280~290 takes a greater proportion, it is corresponding to the period of forest defoliation in the fall. The delay areas of EOS take a greater proportion, about 65.3%, which chiefly centralize at the middle-southern area, and the average delay ratio is about 0.53 d/yr. Meanwhile, the advance areas of EOS distribute in the southern extrem and northern part, which occupy 34.7% of whole study area, and the average advance ratio is about 0.57 d/yr.
(3) The LOS of forest mainly range from 140 to 180 days, of which the period 160~180 takes a greater proportion. The LOS prolong in middle-eastern areas and shorten in northwestern areas, and the spatial pattern present as Southeast-Northwest discrepancy. The prolonged areas and shortened areas occupy 59.69% and 40.31%, and the prolonged ratio and the shortened ratio are 0.67 d/yr and 1 d/yr, respectively.
(4) DN integral of growing period, annual DN max and annual DN amplitude all express increasing trend, and their spatial patterns are similar, because the three metrics are correlated. If LOS keeps invariant, the increase of annual DN max or annual amplitude will result in the increase of DN integral of growing period. If both LOS and base DN keep invariant, annual DN max and annual DN amplitude will have the same meaning.
3 Characteristic of climate change in Changbai Mountains
Considering the annual mean temperature and annual precipitation variation, the decadal climate variation have gone through one process:“cold and wet– cold and dry– warm and wet– warm and dry”, and the last period will persist in the future. From the perspective of spatial pattern, the distribution and change ampltude of annual mean temperature and mean temperature in spring are similar, but obvious differences exist between other seasons. The precipitation distribution in spring and in autumn is similar, nothing but the rainfall increasing areas in autumn are little larger than that in sping. The spatial pattern of annual precipitation is consistent with the overlay of precipitation distribution in spring and in autumn.
4 Relationship beween climate and the variations of forest growing season
The correlation coefficient and partial correlation coefficient are high between decad NDVI and decad precipitation/temperature in most regions of Changbai Mountains, and the influnence of temperature on forest growing process are more significant than that of precipitation. There are a significant negative correlation between the SOS and the average monthly temperature before SOS in majority of various vegetation types, and the EOS have a significant positive correlation with the average monthly temperature before EOS and after SOS. The relationship display relatively complex, the SOS of coniferous forest have a positive correlation with average monthly precipitation before SOS, and it is opposite to broadleave forest. The EOS of each forest types express negative correlation with annual precipitation before EOS and after SOS, except Quercus mongolica.
本文主要运用GIS和遥感技术,使用10天合成的SPOT/NDVI多时相遥感数据、气象数据以及各种相关的图表和统计资料等,构建了长白山地森林生长季遥感监测模型(Double Logistic模型),并利用该模型计算了1999~2008年长白山地森林植被的各物候参数;分析了1999~2008年长白山森林生长季始期、生长季末期、生长季长度、DN年最大值、DN年振幅和DN生长季积分值的时空分布格局及年际变化趋势;探讨了长白山地的气候变化特征;并在此基础上进一步深入分析了森林生长季变化与气候因素的关系,揭示了长白山森林生长季对区域降水和气温变化的响应方式和反馈机制。论文的主要结论如下:
1 Double logistic模型的构建
基于遥感和地理信息系统技术,本文结合logistic模型和一个全局函数的优点,构建了double logistic模型。该模型不仅可以同时提取多年的森林物候期,而且可以消除时间序列的边界效应,它能更灵活地拟合复杂的曲线。同时,由于森林开始生长和结束生长是两个不同的生物物理过程,对于拟合曲线的两侧采用动态的比例更符合森林的生长过程;再者,该模型是在没有设置预定的阈值或者经验参数前提下而逐个像元运算的,所以它能够更加稳定地表征不同像元之间物理意义,具有更好的通用性。野外观测数据和先前学者的研究结果都证明了利用double logistic模型动态监测长白山地植被物候期是一种可行的方法。
2长白山地森林植被物候参数时空格局
(1)在1999~2008年间,长白山地大部分地区森林生长季始期发生在第100~140天,其中在第100~110天和在第110~120天开始生长的地区所占面积比较大。提前的区域仅占研究区面积的32.46%,平均提前速率约0.71d/a;延迟的区域占总面积的67.54%,平均延迟速率约为0.43d/a。
(2)大部分地区森林生长季末期发生在第270~290天,其中在第280~290天开始生长的地区所占面积比较大。长白山地森林的生长季末期中南部表现为延迟趋势,南端和北部表现为一定的提前趋势。延迟区域占有较大比例,约为65.3%,平均延迟速率为0.53d/a;提前的区域占总面积的34.7%,平均提前速率为0.57d/a。
(3)大部分地区森林生长季长度140~180天,其中长度在160~180天的地区所占面积比较大。长白山地森林的生长季长度中东部表现为延长趋势,南端和西北部表现为一定的缩短趋势,整体上呈现东南—西北向的空间分异格局。生长季延长的区域占研究区面积的59.69%,平均延长率为0.67d/a;生长季缩短的区域占总面积的40.31%,平均缩短率约为1d/a。
(4)生长季DN积分值、DN年最大值和DN值年振幅整体上都有增加的趋势,且空间格局具有相似性,是因为三者之间是相互关联的。如果生长季长度不变、DN年最大值或者振幅的增加必然会导致生长季DN积分值的增加;如果生长季长度和生长季的DN基准值不变,那么DN年最大值和振幅的含义就是一样的了。
3长白山地的气候变化特征
综合年平均气温和年降水量的时间变化来看,可以认为长白山地近50年来来气候的年代际变化经历了一个“冷湿—冷干—暖湿—暖干”的过程,并且暖干的过程仍将持续。从地域分布上看,年均增温趋势分布与春季的增温空间格局较相似,增温幅度与春季也相差无几,与其它几个季节的空间分布存在明显的差异。秋季降水量变化趋势空间分布特征与夏季相似,但是降水量增加的区域略有增加。年降水量的空间分布主要受夏季和秋季降水量的影响,故它与夏、秋季二者叠加后的分布趋势一致。
4森林植被生长季变化与气候的关系
长白山地大部分地区的NDVI与旬降水量、旬均气温的年内相关系数和偏相关系数都较高,呈强显著相关,但温度对森林植被的生长过程的影响大于降水。各种森林植被类型,无论是针叶林还是阔叶林,它们的生长季始期大都与其之前各月月均温呈显著负相关,而生长季末期则与其之前各月月均温呈显著正相关;而它们的生长季始末期与降水的关系就较复杂一些,针叶林的始期与其之前的各月降水量呈正相关,阔叶林则呈大都呈负相关。除蒙古栎外,其它森林类型的生长季末期与其前各月的降水量呈负相关。
Recently, more and more evidences have shown that global climate is becoming warmer and warmer, and vegetation phenology is an integraph of global changes and a comprehensive indicator of landscape and environment changes. The responses of vegetation phenology to global environment changes have become a focus field of global changes. Forest is an important part in global ecological system, and the forest phenology is the comprehensive biological index which reflect the influences of the short- and long-term climate changes on the forest growth stage. Remote sensing technology offer the technical means for monitoring the vegetation phenology on landscape scale, and realize the spatial transition of phenological data from points to coverage.
Combing with ten-day SPOT/NDVI data, meteorological data and varities of statistic data using GIS and remote sensing technology, the paper constructs a double logistic model, and calculate forest phenology metrics based on it, then analysize the spatio-temporal patterns and change trends of the start of growing season (SOS), the end of growing season (EOS), the length of growing season (LOS), annual DN (digital number) max, annual DN amplitude and DN integral of growing period in Changbai Mountains during 1999~2008. Subsequently, the paper evaluates the characteristics of climate change in Changbai Mountains, and analysize the relationship between growing season variation and climate factors, then discuss the response and feedback mechanism of vegetation phenology metrics to regional air temperature and precipitation in different vegetation types. From these researches, some basic conclusions are drawn as follows:
1 Double logistic model construction
Based on remote sensing and GIS, the paper develop a double logistic model that combines the merits of logistic model and global function. The model can not only extract vegetation phenophases for several years simultaneously, but also avoid the boundary effects of time-series curve. With this feature, the data obtained are more flexible and complicated for fitting curves. It is more proper to fit the both sides of curve using dynamic ratios for the SOS and EOS being different biophysical processes. Moreover, the model treats each pixel individually without setting absolute thresholds or empirical constants, in which way, the model can characterize the physical meaning of different pixels more accurately. Validation through comparing with the field data and previous research results demonstrate that double logistic model used in this paper is appropriate for the study of vegetation phenology in Changbai Mountains.
2 Temporal-spatial patterns of forest phenology metrics in Changbai Mountains
(1) During the year 1999~2008, the SOS of forest in Changbai Mountains commences on the 100th to 140th day of year, which is consistent with the period of leaf unfolding for forest in late April and early May. Among of the period the day 100~110 plus the day 110~120 take a greater proportion. The advance areas of SOS are small from the whole aspects, only accounting for 32.46% over the study area, and the average advance ratio is about 0.71 d/yr; The delay areas of SOS take up to 67.54%, and the average delay ratio is about 0.43 d/yr.
(2) The EOS concentrate on the day 270~290, of which the day 280~290 takes a greater proportion, it is corresponding to the period of forest defoliation in the fall. The delay areas of EOS take a greater proportion, about 65.3%, which chiefly centralize at the middle-southern area, and the average delay ratio is about 0.53 d/yr. Meanwhile, the advance areas of EOS distribute in the southern extrem and northern part, which occupy 34.7% of whole study area, and the average advance ratio is about 0.57 d/yr.
(3) The LOS of forest mainly range from 140 to 180 days, of which the period 160~180 takes a greater proportion. The LOS prolong in middle-eastern areas and shorten in northwestern areas, and the spatial pattern present as Southeast-Northwest discrepancy. The prolonged areas and shortened areas occupy 59.69% and 40.31%, and the prolonged ratio and the shortened ratio are 0.67 d/yr and 1 d/yr, respectively.
(4) DN integral of growing period, annual DN max and annual DN amplitude all express increasing trend, and their spatial patterns are similar, because the three metrics are correlated. If LOS keeps invariant, the increase of annual DN max or annual amplitude will result in the increase of DN integral of growing period. If both LOS and base DN keep invariant, annual DN max and annual DN amplitude will have the same meaning.
3 Characteristic of climate change in Changbai Mountains
Considering the annual mean temperature and annual precipitation variation, the decadal climate variation have gone through one process:“cold and wet– cold and dry– warm and wet– warm and dry”, and the last period will persist in the future. From the perspective of spatial pattern, the distribution and change ampltude of annual mean temperature and mean temperature in spring are similar, but obvious differences exist between other seasons. The precipitation distribution in spring and in autumn is similar, nothing but the rainfall increasing areas in autumn are little larger than that in sping. The spatial pattern of annual precipitation is consistent with the overlay of precipitation distribution in spring and in autumn.
4 Relationship beween climate and the variations of forest growing season
The correlation coefficient and partial correlation coefficient are high between decad NDVI and decad precipitation/temperature in most regions of Changbai Mountains, and the influnence of temperature on forest growing process are more significant than that of precipitation. There are a significant negative correlation between the SOS and the average monthly temperature before SOS in majority of various vegetation types, and the EOS have a significant positive correlation with the average monthly temperature before EOS and after SOS. The relationship display relatively complex, the SOS of coniferous forest have a positive correlation with average monthly precipitation before SOS, and it is opposite to broadleave forest. The EOS of each forest types express negative correlation with annual precipitation before EOS and after SOS, except Quercus mongolica.
引文
[1] Solomon S.,D.H.Qin,Manning M., et al. Climate Change 2007:The Physical Science Basis[M]. United Kingdom and New York: Cambridge University Press, 2007.
[2] Running S.W.,Nemani R.R. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates[J]. Remote Sensing of Environment, 1988, 24(2):347~367.
[3] Kutzbach J.,Bonan G.,Foley J., et al. Vegetation and soil feedbacks on the response of the African monsoon to orbital forcing in the early to middle Holocene[J]. Nature, 1996, 384(6610):623~626.
[4] Barford C.C.,Wofsy S.C.,Goulden M.L., et al. Factors controlling long-and short-term sequestration of atmospheric CO2 in a mid-latitude forest[J]. Science, 2001, 294(5547):1688.
[5] Aerts R.,Cornelissen J.H.C.,Dorrepaal E., Plant performance in a warmer world: general responses of plants from cold, northern biomes and the importance of winter and spring events[J], in Plants and Climate Change, J. Rozema, R. Aerts.H. Cornelissen, Editors. Springer Netherlands. 2006, 65~78.
[6] Keeling C.D.,Chin J.F.S.,Whorf T.P. Increased activity of northern vegetation inferred from atmospheric CO2 measurements[J]. Nature, 1996, 382(6587):146~149.
[7] Chapin Iii F.S.,Mcguire A.D.,Ruess R.W., et al. Resilience of Alaska's boreal forest to climatic change[J]. Canadian Journal of Forest Research, 2010, 40(7):1360~1370.
[8] Myneni R.,Tucker C.,Asrar G., et al. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991[J]. Journal of Geophysical Research, 1998, 103(D6):6145~6160.
[9] Nemani R.R.,Keeling C.D.,Hashimoto H., et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999[J]. Science, 2003, 300(5625):1560.
[10] Braswell B.,Schimel D.,Linder E., et al. The response of global terrestrial ecosystems to interannual temperature variability[J]. Science, 1997, 278(5339):870.
[11] Lucht W.,Prentice I.C.,Myneni R.B., et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect[J]. Science, 2002, 296(5573):1687.
[12] Nezlin N.,Kostianoy A.,Li B.L. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region[J]. Journal of Arid Environments, 2005, 62(4):677~700.
[13]施奈勒.植物物候学[M].北京:科学出版社, 1965.
[14]竺可桢,宛敏渭.物候学[M]. (第二版).长沙:湖南教育出版社, 1999.
[15] K?rner C.,Basler D. Phenology under global warming[J]. Science, 2010, 327(5972):1461~1462.
[16] Spiecker H. Growth trends in European forests: studies from 12 countries[M]. Berlin: Springer, 1996.
[17] Menzel A.,Sparks T.H.,Estrella N., et al. European phenological response to climate change matches the warming pattern[J]. Global Change Biology, 2006, 12(10):1969~1976.
[18]张福春.气候变化对中国木本植物物候的可能影响[J].地理学报, 1995, 50(5):402~410.
[19]郑景云,葛全胜,郝志新.气候增暖对我国近40年植物物候变化的影响[J].科学通报, 2002, 47(20):1582~1587.
[20] Walkovszky A. Changes in phenology of the locust tree (Robinia pseudoacacia L.) in Hungary[J]. International Journal of Biometeorology, 1998, 41(4):155~160.
[21] Beaubien E.,Freeland H. Spring phenology trends in Alberta, Canada: links to ocean temperature[J].International Journal of Biometeorology, 2000, 44(2):53~59.
[22]武永峰,李茂松,宋吉青.植物物候遥感监测研究进展[J].气象与环境学报, 2008, 24(3):51~58.
[23] Xin J.,Yu Z.,Van Leeuwen L., et al. Mapping crop key phenological stages in the North China Plain using NOAA time series images[J]. International Journal of Applied Earth Observation and Geoinformation, 2002, 4(2):109~117.
[24] Maignan F.,Bréon F.M.,Bacour C., et al. Interannual vegetation phenology estimates from global AVHRR measurements: Comparison with in situ data and applications[J]. Remote Sensing of Environment, 2008, 112(2):496~505.
[25] Zhou L.M.,Shabanov N.V.,Myneni R. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999[J]. Journal of Geophysical Research, 2001, 106(D17):20,069~20,083.
[26] Tucker C.J.,Slayback D.A.,Pinzon J.E., et al. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999[J]. International Journal of Biometeorology, 2001, 45(4):184~190.
[27]崔海亭,颜琼雪.利用NOAA AVHRR资料进行草场植被季节动态研究[J].环境监测与作物估产的遥感论文集[C].北京:北京大学出版社, 1991. 148~156.
[28]温刚.利用AVHRR植被指数数据集分析中国东部季风区的物候季节特征[J].遥感学报, 1998, 2(4):270~274.
[29]陈效逑,谭仲军,徐成新.利用植物物候和遥感资料确定中国北方的生长季节[J].地学前缘, 2000, 7(增刊):196.
[30] Chen X.,Hu B.,Yu R. Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China[J]. Global Change Biology, 2005, 11(7):1118~1130.
[31]王长富.地球环境与森林[M].哈尔滨:东北林业大学出版社, 1992.
[32]周广胜,王玉辉.全球生态学[M].北京:气象出版社, 2003.
[33]徐德应.气候变化对中国森林影响研究[M].北京:中国科学技术出版社, 1997.
[34] Townshend J.,Justice C.,Li W., et al. Global land cover classification by remote sensing: present capabilities and future possibilities[J]. Remote Sensing of Environment, 1991, 35(2~3):243~255.
[35] Townshend J.R.G.,Justice C.,Kalb V. Characterization and classification of South American land cover types using satellite data[J]. International Journal of Remote Sensing, 1987, 8(8):1189~1207.
[36] Loveland T.,Merchant J.,Brown J., et al. Development of a land-cover characteristics database for the conterminous U. S[J]. Photogrammetric Engineering and Remote Sensing, 1991, 57(11):1453~1463.
[37] Running S.W.,Loveland T.R.,Pierce L.L., et al. A remote sensing based vegetation classification logic for global land cover analysis* 1[J]. Remote Sensing of Environment, 1995, 51(1):39~48.
[38]史培军,宫鹏.土地利用/变化研究的方法与实践[M].北京:科学出版社, 2000.
[39] Dixon R.,Solomon A.,Brown S., et al. Carbon pools and flux of global forest ecosystems[J]. Science, 1994, 263(5144):185.
[40]于贵瑞.全球变化与陆地生态系统碳循环和碳蓄积[M].北京:气象出版社, 2003.
[41] Goulden M.L.,Munger J.W.,Fan S.M., et al. Exchange of carbon dioxide by a deciduous forest: response to interannual climate variability[J]. Science, 1996, 271(5255):1576.
[42] Randerson J.T.,Thompson M.V.,Conway T.J., et al. The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide[J]. Global Biogeochemical Cycles, 1997, 11(4):535~560.
[43] White M.,Running S.,Thornton P. The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest[J]. International Journal of Biometeorology, 1999, 42(3):139~145.
[44] Xiao X.,Hollinger D.,Aber J., et al. Satellite-based modeling of gross primary production in anevergreen needleleaf forest[J]. Remote Sensing of Environment, 2004, 89(4):519~534.
[45] Churkina G.,Schimel D.,Braswell B.H., et al. Spatial analysis of growing season length control over net ecosystem exchange[J]. Global Change Biology, 2005, 11(10):1777~1787.
[46]陈效逑,王林海.遥感物候学研究进展[J].地理科学进展, 2009, 28(1):33~40.
[47]承继成,郭华东,史文中等.遥感数据的不确定性问题[M].北京:科学出版社, 2004.
[48] Malthus T.J.,Mumby P.J. Remote sensing of the coastal zone: an overview and priorities for future research[J]. International Journal of Remote Sensing, 2003, 24(13):2805~2815.
[49]史文中.空间数据与空间分析不确定性原理[M].北京:科学出版社, 2005.
[50] Ernst-Dottavio C.L.,Hoffer R.M.,Mroczynski R.P. Spectral characteristics of wetland habitats[J]. Photogrammetric Engineering and Remote Sensing, 1981, 47:223~227.
[51] Best R.G.,Wehde M.,Linder R. Spectral reflectance of hydrophytes[J]. Remote Sensing of Environment, 1981, 11:27~35.
[52] Ullah A.,Rundquist D.C.,Derry D.P. Characterizing spectral signatures for three selected emergent aquatic macrophytes: a controlled experiment[J]. Geocarto International, 2000, 15(4):29~40.
[53] Becker B.L.,Lusch D.P.,Qi J. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis[J]. Remote Sensing of Environment, 2005, 97(2):238~248.
[54] Coppin P.,Jonckheere I.,Nackaerts K., et al. Review ArticleDigital change detection methods in ecosystem monitoring: a review[J]. International Journal of Remote Sensing, 2004, 25(9):1565 ~ 1596.
[55]武永峰.中国植被绿度期遥感监测及其环境因子关系的研究[D]:[博士学位论文].北京:北京师范大学资源学院, 2006.
[56] Chen X.,Tan Z.,Schwartz M.D., et al. Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China[J]. International Journal of Biometeorology, 2000, 44(2):97~101.
[57] S M.,L K.,N V. Global - scale assessment of vegetation phenology using NOAA/AVHRR atellite measurements[J]. Journal of Climate, 1997, 10(6):1154~1170.
[58] Zhang X.,Friedl M.A.,Schaaf C.B., et al. Monitoring vegetation phenology using MODIS[J]. Remote Sensing of Environment, 2003, 84(3):471~475.
[59] Duchemin B. Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data[J]. Remote Sensing of Environment, 1999, 67(1):68~82.
[60] Birky A.K. NDVI and a simple model of deciduous forest seasonal dynamics[J]. Ecological Modelling, 2001, 143(1~2):43~58.
[61] St?ckli R.,Vidale P.L. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset[J]. International Journal of Remote Sensing, 2004, 25(17):3303 ~ 3330.
[62] Suzuki R.,Nomaki T.,Yasunari T. West-east contrast of phenology and climate in northern Asia revealed using a remotely sensed vegetation index[J]. International Journal of Biometeorology, 2003, 47(3):126~138.
[63] Kimball J.S.,Mcdonald K.C.,Running S.W., et al. Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests[J]. Remote Sensing of Environment, 2004, 90(2):243~258.
[64] Delbart N.,Kergoat L.,Le Toan T., et al. Determination of phenological dates in boreal regions using normalized difference water index[J]. Remote Sensing of Environment, 2005, 97(1):26~38.
[65] Delbart N.,Le Toan T.,Kergoat L., et al. Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004)[J]. Remote Sensingof Environment, 2006, 101(1):52~62.
[66]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.
[67]王宏,李晓兵,李霞, et al.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.
[68]丁登,陈效逑.我国遥感植被生长季节的地面检验研究——以温带草原和暖温带落叶阔叶林区为例[J].遥感技术与应用, 2007, 22(3):382~388.
[69] Shabanov N.V.,Myneni R. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999[J]. Journal of Geophysical Research, 2001, 106(D17):20,069~20,083.
[70] Ebata M.,Tateishi R. Phenological stage monitoring in Siberia by using NOAA/AVHRR data[J]. in Proceedings of the ACRS 2001– 22nd Asian Conference on Remote Sensing, Singapore. 2001, 1:412~417.
[71] Runtunuwu E.,Kondoh A.,Wikantika K., et al. NDVI-derived length of the growth period estimations for different vegetation types in Monsoon Asia[J]. IECI Chapter Japan Series, 2001, 3(1):106~109.
[72] Tateishi R.,Ebata M. Analysis of phenological change patterns using 1982~2000 Advanced Very High Resolution Radiometer AVHRR data[J]. International Journal of Remote Sensing, 2004, 25(12):2287~2300.
[73] Weiss J.L.,Gutzler D.S.,Coonrod J.E.A., et al. Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico, USA[J]. Journal of Arid Environments, 2004, 58(2):249~272.
[74] Xiao X.,Hagen S.,Zhang Q., et al. Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images[J]. Remote Sensing of Environment, 2006, 103(4):465~473.
[75]齐晔.北半球高纬度地区气候变化对植被的影响途径和机制[J].生态学报, 1999, 19(4):474~477.
[76]郑景云,葛全胜,赵会霞.近40年中国植物物候对气候变化的响应研究[J].中国农业气象, 2003, 24(1):28~32.
[77]陈效逑,张福春.近50年北京春季物候的变化及其对气候变化的响应[J].中国农业气象, 2001, 22(1):1~5.
[78]徐雨晴,陆佩玲,于强.近50年北京树木物候对气候变化的响应[J].地理研究, 2005, 24(3):412~420.
[79] Piao S.,Fang J.,Zhou L., et al. Variations in satellite-derived phenology in China's temperate vegetation[J]. Global Change Biology, 2006, 12(4):672~685.
[80]张峰,周广胜,王玉辉.内蒙古克氏针茅草原植物物候及其与气候因子关系[J].植物生态学报, 2008, 32(6):1312~1322.
[81]郭江勇,林纾.气候变化对西峰黄土高原地温与物候期的影响[J].干旱地区农业研究, 2008, 26(2):235~239.
[82] White M.A.,Nemani R.R.,Thornton P.E., et al. Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest[J]. Ecosystems, 2002, 5(3):260~273.
[83] De Beurs K.M.,Henebry G.M. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan[J]. Remote Sensing of Environment, 2004, 89(4):497~509.
[84] Souza C.M. Mapping land use of tropical regions from space[J]. Proceedings of the National Academy of Sciences, 2006, 103(39):14261.
[85] Zhang X.,Friedl M.A.,Schaaf C.B., et al. The footprint of urban climates on vegetation phenology[J]. Geophysical Research Letters, 2004, 31(12):L12209.
[86] Xu X.,Levy,Kevin J., et al. Monitoring anthropogenic effects on land-surface phenologies in China from AVHRR using the discrete fourier transform[M]. Kyoto, JAPON: Kyoto University, 2004.
[87]韩贵锋,徐建华,袁兴中.城市化对长三角地区主要城市植被物候的影响[J].应用生态学报, 2008, 19(8):1803~1809.
[88] Hudson I.L.,Keatley M.R. Phenological research: methods for environmental and climate change analysis[M]. Berlin: Springer Verlag, 2010.
[89] Lloyd D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery[J]. International Journal of Remote Sensing, 1990, 11(12):2269 ~ 2279.
[90] Jonsson P.,Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2002, 40(8):1824~1832.
[91] Karlsen S.R.,Elvebakk A.,H?gda K.A., et al. Satellite-based mapping of the growing season and bioclimatic zones in Fennoscandia[J]. Global Ecology and Biogeography, 2006, 15(4):416~430.
[92] Karlsen S.,Solheim I.,Beck P., et al. Variability of the start of the growing season in Fennoscandia, 1982–2002[J]. International Journal of Biometeorology, 2007, 51(6):513~524.
[93] Moulin S.,Kergoat L.,Viovy N., et al. Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements[J]. Journal of Climate, 1997, 10(6):1154~1170.
[94] Balzter H.,Gerard F.,George C., et al. Coupling of vegetation growing season anomalies and fire activity with hemispheric and regional-scale climate patterns in central and east Siberia[J]. Journal of Climate, 2007, 20(15):3713~3729.
[95] Reed B.C.,Brown J.F.,Vanderzee D., et al. Measuring Phenological Variability from Satellite Imagery[J]. Journal of Vegetation Science, 1994, 5(5):703~714.
[96] Archibald S.,Scholes R.J. Leaf green-up in a semi-arid African savanna -separating tree and grass responses to environmental cues[J]. Journal of Vegetation Science, 2007, 18(4):583~594.
[97] Moody A.,Johnson D.M. Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform[J]. Remote Sensing of Environment, 2001, 75(3):305~323.
[98] Hermance J.F. Stabilizing high-order, non-classical harmonic analysis of NDVI data for average annual models by damping model roughness[J]. International Journal of Remote Sensing, 2007, 28(12):2801 ~ 2819.
[99] Badhwar G.D. Use of LANDSAT-derived profile features for spring small-grains classification[J]. International Journal of Remote Sensing, 1984, 5(5):783~797.
[100] Zhang X.,Friedl M.A.,Schaaf C.B., et al. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data[J]. Global Change Biology, 2004, 10(7):1133~1145.
[101] Fisher J.I.,Mustard J.F.,Vadeboncoeur M.A. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite[J]. Remote Sensing of Environment, 2006, 100(2):265~279.
[102] Fisher J.I.,Mustard J.F. Cross-scalar satellite phenology from ground, Landsat, and MODIS data[J]. Remote Sensing of Environment, 2007, 109(3):261~273.
[103] Goodin D.G.,Henebry G.M. A technique for monitoring ecological disturbance in tallgrass prairie using seasonal NDVI trajectories and a discriminant function mixture model[J]. Remote Sensing of Environment, 1997, 61(2):270~278.
[104] Mitchell R.,Fritz J.,Moore K., et al. Predicting forage quality in switchgrass and big bluestem[J]. Agronomy journal, 2001, 93(1):118~124.
[105] Smart A.J.S.,Moser W.H.,Lowell E. Predicting leaf/stem ratio and nutritive value in grazed and nongrazed big bluestem[J]. Agronomy Journal, 2001, 93(6):1243.
[106] Davidson A.,Csillag F. A comparison of three approaches for predicting C4 species cover of northern mixed grass prairie[J]. Remote Sensing of Environment, 2003, 86(1):70~82.
[107]李祯,祁承留,孙文昌.东北地区自然地理[M].北京:高等教育出版社, 1993.
[108]于信芳.基于MODIS数据的森林物候期遥感监测及森林类型识别研究[D]:[博士学位论文].北京:中国科学院地理科学与资源研究所, 2005.
[109] Malingreau J.P. Global vegetation dynamics: Satellite observations over Asia[J]. International Journal of Remote Sensing, 1986, 7(9):1121~1146.
[110] Kogan F.,Sullivan J. Development of global drought-watch system using NOAA/AVHRR data[J]. Advances in Space Research, 1993, 13(5):219~222.
[111]辛景峰,宇振荣,P.M.Driessen.利用NOAA NDVI数据集监测冬小麦生育期的研究[J].遥感学报, 2001, 5(6):442~447+483.
[112]侯英雨,王石立.基于作物植被指数和温度的产量估算模型研究[J].地理学与国土研究, 2002, 18(3):105~107.
[113] Van Dijk A.,Callis S.,Sakamoto C., et al. Smoothing vegetation index profiles: An alternative method for reducing radiometric disturbance in NOAA/AVHRR data[J]. Photogrammetric engineering and remote sensing 1987, 53(8):1059~1067.
[114] Roerink G.,Menenti M.,Verhoef W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series[J]. International Journal of Remote Sensing, 2000, 21(9):1911~1918.
[115] Azzali S.,Menenti M. Mapping isogrowth zones on continental scale using temporal Fourier analysis of AVHRR-NDVI data[J]. International Journal of Applied Earth Observation and Geoinformation, 1999, 1(1):9~20.
[116] Jakubauskas M.E.,Legates D.R.,Kastens J.H. Harmonic analysis of time-series AVHRR NDVI data[J]. Photogrammetric Engineering and Remote Sensing, 2001, 67(4):461~470.
[117] Jakubauskas M.E.,Legates D.R.,Kastens J.H. Crop identification using harmonic analysis of time-series AVHRR NDVI data[J]. Computers and Electronics in Agriculture, 2002, 37(1~3):127~139.
[118]王丹,姜小光.利用NOAA数据分析中国地区植被覆盖变化周期[J].中国图像图形学报, 2006, 11(4):516~520.
[119]左丽君,张增祥,董婷婷, et al. MOD I S/NDV I和MOD I S/EV I在耕地信息提取中的应用及对比分析[J].农业工程学报, 2008, 24(3):167~172.
[120]赵伟,李召良.利用MODIS/EVI时间序列数据分析干旱对植被的影响[J].地理科学进展, 2007, 26(6):40~47.
[121]魏淑秋.农业气象统计[M].福州:福建科学技术出版社, 1985.
[122]武永峰,李茂松,李京.中国植被绿度期遥感监测方法研究[J].遥感学报, 2008, 12(1):92~103.
[123] Wagenseil H.,Samimi C. Assessing spatio-temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia[J]. International Journal of Remote Sensing, 2006, 27(16):3455~3471.
[124]魏凤英.现代气候统计诊断与预测技术[M].北京:气象出版社, 2007.
[125] Solomon S. Climate Change 2007: the physical science basis: contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[M]. British: Cambridge University Press, 2007.
[126]孙凤华,袁健,路爽.东北地区近百年气候变化及突变检测[J].气候与环境研究, 2006, 11(1):101~108.
[127]孙凤华,袁健,关颖.东北地区最高、最低温度非对称变化的季节演变特征[J].地理科学, 2008, 28(4):532~536.
[128]孙凤华,杨素英,陈鹏狮.东北地区近44年的气候暖干化趋势分析及可能影响[J].生态学杂志,2005, 24(7):751~755+762.
[129]孙凤华,袁健.东北地区1959~2002年最高、最低气温时空变化特征[J].气候变化研究进展, 2005, 1(4):168~171.
[130]董满宇,吴正方.近50年来东北地区气温变化时空特征分析[J].资源科学, 2008, 30(7):1093~1099.
[131]孙莹,江静,杨青, et al.东北夏季气温分区变化特征[J].气象科学, 2008, 28(1):50~55.
[132]廉毅,安刚,王琪, et al.吉林省40年来气温和降水的变化[J].应用气象学报, 1997, 8(2):197~204.
[133]董满宇,吴正方.近50年来吉林省气温和降水变化趋势分析[J].东北师大学报(自然科学版), 2007, 39(3):114~119.
[134]李平,卢文喜,辛欣, et al.挠力河流域降水量序列的功率谱分析和最大熵谱分析[J].世界地质, 2008, 27(1):63~67.
[135]黄嘉佑.气象统计分析与预报方法[M].北京:气象出版社, 2000.
[136]李珍,姜逢清. 1961—2004年新疆气候突变分析[J].冰川冻土, 2007, 29(3):351~359.
[137]闫敏华,邓伟,马学慧.大面积开荒扰动下的三江平原近45年气候变化[J].地理学报, 2001, 56(2):159~170.
[138]闫敏华,邓伟,陈泮勤.三江平原气候突变分析[J].地理科学, 2003, 23(6):661~667.
[139] Yue S.,Hashino M. Temperature trends in Japan: 1900~1996[J]. Theoretical and Applied Climatology, 2003, 75(1):15~27.
[140]尤卫红,段旭,邓自旺, et al.全球、中国及云南近百年气温变化的层次结构和突变特征[J].热带气象学报, 1998, 14(2):173~180.
[141]于秀晶,李栋梁,胡靖彪.吉林近50a来气候的年代际变化特征及其突变分析[J].冰川冻土, 2004, 26(6):779~783.
[142]任国玉,郭军,徐铭志, et al.近50年中国地面气候变化基本特征[J].气象学报, 2005, 6(36):942~956.
[143]徐建华.现代地理学中的数学方法[M]. (第二版).北京:高等教育出版社, 2002.
[2] Running S.W.,Nemani R.R. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates[J]. Remote Sensing of Environment, 1988, 24(2):347~367.
[3] Kutzbach J.,Bonan G.,Foley J., et al. Vegetation and soil feedbacks on the response of the African monsoon to orbital forcing in the early to middle Holocene[J]. Nature, 1996, 384(6610):623~626.
[4] Barford C.C.,Wofsy S.C.,Goulden M.L., et al. Factors controlling long-and short-term sequestration of atmospheric CO2 in a mid-latitude forest[J]. Science, 2001, 294(5547):1688.
[5] Aerts R.,Cornelissen J.H.C.,Dorrepaal E., Plant performance in a warmer world: general responses of plants from cold, northern biomes and the importance of winter and spring events[J], in Plants and Climate Change, J. Rozema, R. Aerts.H. Cornelissen, Editors. Springer Netherlands. 2006, 65~78.
[6] Keeling C.D.,Chin J.F.S.,Whorf T.P. Increased activity of northern vegetation inferred from atmospheric CO2 measurements[J]. Nature, 1996, 382(6587):146~149.
[7] Chapin Iii F.S.,Mcguire A.D.,Ruess R.W., et al. Resilience of Alaska's boreal forest to climatic change[J]. Canadian Journal of Forest Research, 2010, 40(7):1360~1370.
[8] Myneni R.,Tucker C.,Asrar G., et al. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991[J]. Journal of Geophysical Research, 1998, 103(D6):6145~6160.
[9] Nemani R.R.,Keeling C.D.,Hashimoto H., et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999[J]. Science, 2003, 300(5625):1560.
[10] Braswell B.,Schimel D.,Linder E., et al. The response of global terrestrial ecosystems to interannual temperature variability[J]. Science, 1997, 278(5339):870.
[11] Lucht W.,Prentice I.C.,Myneni R.B., et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect[J]. Science, 2002, 296(5573):1687.
[12] Nezlin N.,Kostianoy A.,Li B.L. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region[J]. Journal of Arid Environments, 2005, 62(4):677~700.
[13]施奈勒.植物物候学[M].北京:科学出版社, 1965.
[14]竺可桢,宛敏渭.物候学[M]. (第二版).长沙:湖南教育出版社, 1999.
[15] K?rner C.,Basler D. Phenology under global warming[J]. Science, 2010, 327(5972):1461~1462.
[16] Spiecker H. Growth trends in European forests: studies from 12 countries[M]. Berlin: Springer, 1996.
[17] Menzel A.,Sparks T.H.,Estrella N., et al. European phenological response to climate change matches the warming pattern[J]. Global Change Biology, 2006, 12(10):1969~1976.
[18]张福春.气候变化对中国木本植物物候的可能影响[J].地理学报, 1995, 50(5):402~410.
[19]郑景云,葛全胜,郝志新.气候增暖对我国近40年植物物候变化的影响[J].科学通报, 2002, 47(20):1582~1587.
[20] Walkovszky A. Changes in phenology of the locust tree (Robinia pseudoacacia L.) in Hungary[J]. International Journal of Biometeorology, 1998, 41(4):155~160.
[21] Beaubien E.,Freeland H. Spring phenology trends in Alberta, Canada: links to ocean temperature[J].International Journal of Biometeorology, 2000, 44(2):53~59.
[22]武永峰,李茂松,宋吉青.植物物候遥感监测研究进展[J].气象与环境学报, 2008, 24(3):51~58.
[23] Xin J.,Yu Z.,Van Leeuwen L., et al. Mapping crop key phenological stages in the North China Plain using NOAA time series images[J]. International Journal of Applied Earth Observation and Geoinformation, 2002, 4(2):109~117.
[24] Maignan F.,Bréon F.M.,Bacour C., et al. Interannual vegetation phenology estimates from global AVHRR measurements: Comparison with in situ data and applications[J]. Remote Sensing of Environment, 2008, 112(2):496~505.
[25] Zhou L.M.,Shabanov N.V.,Myneni R. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999[J]. Journal of Geophysical Research, 2001, 106(D17):20,069~20,083.
[26] Tucker C.J.,Slayback D.A.,Pinzon J.E., et al. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999[J]. International Journal of Biometeorology, 2001, 45(4):184~190.
[27]崔海亭,颜琼雪.利用NOAA AVHRR资料进行草场植被季节动态研究[J].环境监测与作物估产的遥感论文集[C].北京:北京大学出版社, 1991. 148~156.
[28]温刚.利用AVHRR植被指数数据集分析中国东部季风区的物候季节特征[J].遥感学报, 1998, 2(4):270~274.
[29]陈效逑,谭仲军,徐成新.利用植物物候和遥感资料确定中国北方的生长季节[J].地学前缘, 2000, 7(增刊):196.
[30] Chen X.,Hu B.,Yu R. Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China[J]. Global Change Biology, 2005, 11(7):1118~1130.
[31]王长富.地球环境与森林[M].哈尔滨:东北林业大学出版社, 1992.
[32]周广胜,王玉辉.全球生态学[M].北京:气象出版社, 2003.
[33]徐德应.气候变化对中国森林影响研究[M].北京:中国科学技术出版社, 1997.
[34] Townshend J.,Justice C.,Li W., et al. Global land cover classification by remote sensing: present capabilities and future possibilities[J]. Remote Sensing of Environment, 1991, 35(2~3):243~255.
[35] Townshend J.R.G.,Justice C.,Kalb V. Characterization and classification of South American land cover types using satellite data[J]. International Journal of Remote Sensing, 1987, 8(8):1189~1207.
[36] Loveland T.,Merchant J.,Brown J., et al. Development of a land-cover characteristics database for the conterminous U. S[J]. Photogrammetric Engineering and Remote Sensing, 1991, 57(11):1453~1463.
[37] Running S.W.,Loveland T.R.,Pierce L.L., et al. A remote sensing based vegetation classification logic for global land cover analysis* 1[J]. Remote Sensing of Environment, 1995, 51(1):39~48.
[38]史培军,宫鹏.土地利用/变化研究的方法与实践[M].北京:科学出版社, 2000.
[39] Dixon R.,Solomon A.,Brown S., et al. Carbon pools and flux of global forest ecosystems[J]. Science, 1994, 263(5144):185.
[40]于贵瑞.全球变化与陆地生态系统碳循环和碳蓄积[M].北京:气象出版社, 2003.
[41] Goulden M.L.,Munger J.W.,Fan S.M., et al. Exchange of carbon dioxide by a deciduous forest: response to interannual climate variability[J]. Science, 1996, 271(5255):1576.
[42] Randerson J.T.,Thompson M.V.,Conway T.J., et al. The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide[J]. Global Biogeochemical Cycles, 1997, 11(4):535~560.
[43] White M.,Running S.,Thornton P. The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest[J]. International Journal of Biometeorology, 1999, 42(3):139~145.
[44] Xiao X.,Hollinger D.,Aber J., et al. Satellite-based modeling of gross primary production in anevergreen needleleaf forest[J]. Remote Sensing of Environment, 2004, 89(4):519~534.
[45] Churkina G.,Schimel D.,Braswell B.H., et al. Spatial analysis of growing season length control over net ecosystem exchange[J]. Global Change Biology, 2005, 11(10):1777~1787.
[46]陈效逑,王林海.遥感物候学研究进展[J].地理科学进展, 2009, 28(1):33~40.
[47]承继成,郭华东,史文中等.遥感数据的不确定性问题[M].北京:科学出版社, 2004.
[48] Malthus T.J.,Mumby P.J. Remote sensing of the coastal zone: an overview and priorities for future research[J]. International Journal of Remote Sensing, 2003, 24(13):2805~2815.
[49]史文中.空间数据与空间分析不确定性原理[M].北京:科学出版社, 2005.
[50] Ernst-Dottavio C.L.,Hoffer R.M.,Mroczynski R.P. Spectral characteristics of wetland habitats[J]. Photogrammetric Engineering and Remote Sensing, 1981, 47:223~227.
[51] Best R.G.,Wehde M.,Linder R. Spectral reflectance of hydrophytes[J]. Remote Sensing of Environment, 1981, 11:27~35.
[52] Ullah A.,Rundquist D.C.,Derry D.P. Characterizing spectral signatures for three selected emergent aquatic macrophytes: a controlled experiment[J]. Geocarto International, 2000, 15(4):29~40.
[53] Becker B.L.,Lusch D.P.,Qi J. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis[J]. Remote Sensing of Environment, 2005, 97(2):238~248.
[54] Coppin P.,Jonckheere I.,Nackaerts K., et al. Review ArticleDigital change detection methods in ecosystem monitoring: a review[J]. International Journal of Remote Sensing, 2004, 25(9):1565 ~ 1596.
[55]武永峰.中国植被绿度期遥感监测及其环境因子关系的研究[D]:[博士学位论文].北京:北京师范大学资源学院, 2006.
[56] Chen X.,Tan Z.,Schwartz M.D., et al. Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China[J]. International Journal of Biometeorology, 2000, 44(2):97~101.
[57] S M.,L K.,N V. Global - scale assessment of vegetation phenology using NOAA/AVHRR atellite measurements[J]. Journal of Climate, 1997, 10(6):1154~1170.
[58] Zhang X.,Friedl M.A.,Schaaf C.B., et al. Monitoring vegetation phenology using MODIS[J]. Remote Sensing of Environment, 2003, 84(3):471~475.
[59] Duchemin B. Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data[J]. Remote Sensing of Environment, 1999, 67(1):68~82.
[60] Birky A.K. NDVI and a simple model of deciduous forest seasonal dynamics[J]. Ecological Modelling, 2001, 143(1~2):43~58.
[61] St?ckli R.,Vidale P.L. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset[J]. International Journal of Remote Sensing, 2004, 25(17):3303 ~ 3330.
[62] Suzuki R.,Nomaki T.,Yasunari T. West-east contrast of phenology and climate in northern Asia revealed using a remotely sensed vegetation index[J]. International Journal of Biometeorology, 2003, 47(3):126~138.
[63] Kimball J.S.,Mcdonald K.C.,Running S.W., et al. Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests[J]. Remote Sensing of Environment, 2004, 90(2):243~258.
[64] Delbart N.,Kergoat L.,Le Toan T., et al. Determination of phenological dates in boreal regions using normalized difference water index[J]. Remote Sensing of Environment, 2005, 97(1):26~38.
[65] Delbart N.,Le Toan T.,Kergoat L., et al. Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004)[J]. Remote Sensingof Environment, 2006, 101(1):52~62.
[66]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.
[67]王宏,李晓兵,李霞, et al.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.
[68]丁登,陈效逑.我国遥感植被生长季节的地面检验研究——以温带草原和暖温带落叶阔叶林区为例[J].遥感技术与应用, 2007, 22(3):382~388.
[69] Shabanov N.V.,Myneni R. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999[J]. Journal of Geophysical Research, 2001, 106(D17):20,069~20,083.
[70] Ebata M.,Tateishi R. Phenological stage monitoring in Siberia by using NOAA/AVHRR data[J]. in Proceedings of the ACRS 2001– 22nd Asian Conference on Remote Sensing, Singapore. 2001, 1:412~417.
[71] Runtunuwu E.,Kondoh A.,Wikantika K., et al. NDVI-derived length of the growth period estimations for different vegetation types in Monsoon Asia[J]. IECI Chapter Japan Series, 2001, 3(1):106~109.
[72] Tateishi R.,Ebata M. Analysis of phenological change patterns using 1982~2000 Advanced Very High Resolution Radiometer AVHRR data[J]. International Journal of Remote Sensing, 2004, 25(12):2287~2300.
[73] Weiss J.L.,Gutzler D.S.,Coonrod J.E.A., et al. Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico, USA[J]. Journal of Arid Environments, 2004, 58(2):249~272.
[74] Xiao X.,Hagen S.,Zhang Q., et al. Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images[J]. Remote Sensing of Environment, 2006, 103(4):465~473.
[75]齐晔.北半球高纬度地区气候变化对植被的影响途径和机制[J].生态学报, 1999, 19(4):474~477.
[76]郑景云,葛全胜,赵会霞.近40年中国植物物候对气候变化的响应研究[J].中国农业气象, 2003, 24(1):28~32.
[77]陈效逑,张福春.近50年北京春季物候的变化及其对气候变化的响应[J].中国农业气象, 2001, 22(1):1~5.
[78]徐雨晴,陆佩玲,于强.近50年北京树木物候对气候变化的响应[J].地理研究, 2005, 24(3):412~420.
[79] Piao S.,Fang J.,Zhou L., et al. Variations in satellite-derived phenology in China's temperate vegetation[J]. Global Change Biology, 2006, 12(4):672~685.
[80]张峰,周广胜,王玉辉.内蒙古克氏针茅草原植物物候及其与气候因子关系[J].植物生态学报, 2008, 32(6):1312~1322.
[81]郭江勇,林纾.气候变化对西峰黄土高原地温与物候期的影响[J].干旱地区农业研究, 2008, 26(2):235~239.
[82] White M.A.,Nemani R.R.,Thornton P.E., et al. Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest[J]. Ecosystems, 2002, 5(3):260~273.
[83] De Beurs K.M.,Henebry G.M. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan[J]. Remote Sensing of Environment, 2004, 89(4):497~509.
[84] Souza C.M. Mapping land use of tropical regions from space[J]. Proceedings of the National Academy of Sciences, 2006, 103(39):14261.
[85] Zhang X.,Friedl M.A.,Schaaf C.B., et al. The footprint of urban climates on vegetation phenology[J]. Geophysical Research Letters, 2004, 31(12):L12209.
[86] Xu X.,Levy,Kevin J., et al. Monitoring anthropogenic effects on land-surface phenologies in China from AVHRR using the discrete fourier transform[M]. Kyoto, JAPON: Kyoto University, 2004.
[87]韩贵锋,徐建华,袁兴中.城市化对长三角地区主要城市植被物候的影响[J].应用生态学报, 2008, 19(8):1803~1809.
[88] Hudson I.L.,Keatley M.R. Phenological research: methods for environmental and climate change analysis[M]. Berlin: Springer Verlag, 2010.
[89] Lloyd D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery[J]. International Journal of Remote Sensing, 1990, 11(12):2269 ~ 2279.
[90] Jonsson P.,Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2002, 40(8):1824~1832.
[91] Karlsen S.R.,Elvebakk A.,H?gda K.A., et al. Satellite-based mapping of the growing season and bioclimatic zones in Fennoscandia[J]. Global Ecology and Biogeography, 2006, 15(4):416~430.
[92] Karlsen S.,Solheim I.,Beck P., et al. Variability of the start of the growing season in Fennoscandia, 1982–2002[J]. International Journal of Biometeorology, 2007, 51(6):513~524.
[93] Moulin S.,Kergoat L.,Viovy N., et al. Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements[J]. Journal of Climate, 1997, 10(6):1154~1170.
[94] Balzter H.,Gerard F.,George C., et al. Coupling of vegetation growing season anomalies and fire activity with hemispheric and regional-scale climate patterns in central and east Siberia[J]. Journal of Climate, 2007, 20(15):3713~3729.
[95] Reed B.C.,Brown J.F.,Vanderzee D., et al. Measuring Phenological Variability from Satellite Imagery[J]. Journal of Vegetation Science, 1994, 5(5):703~714.
[96] Archibald S.,Scholes R.J. Leaf green-up in a semi-arid African savanna -separating tree and grass responses to environmental cues[J]. Journal of Vegetation Science, 2007, 18(4):583~594.
[97] Moody A.,Johnson D.M. Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform[J]. Remote Sensing of Environment, 2001, 75(3):305~323.
[98] Hermance J.F. Stabilizing high-order, non-classical harmonic analysis of NDVI data for average annual models by damping model roughness[J]. International Journal of Remote Sensing, 2007, 28(12):2801 ~ 2819.
[99] Badhwar G.D. Use of LANDSAT-derived profile features for spring small-grains classification[J]. International Journal of Remote Sensing, 1984, 5(5):783~797.
[100] Zhang X.,Friedl M.A.,Schaaf C.B., et al. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data[J]. Global Change Biology, 2004, 10(7):1133~1145.
[101] Fisher J.I.,Mustard J.F.,Vadeboncoeur M.A. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite[J]. Remote Sensing of Environment, 2006, 100(2):265~279.
[102] Fisher J.I.,Mustard J.F. Cross-scalar satellite phenology from ground, Landsat, and MODIS data[J]. Remote Sensing of Environment, 2007, 109(3):261~273.
[103] Goodin D.G.,Henebry G.M. A technique for monitoring ecological disturbance in tallgrass prairie using seasonal NDVI trajectories and a discriminant function mixture model[J]. Remote Sensing of Environment, 1997, 61(2):270~278.
[104] Mitchell R.,Fritz J.,Moore K., et al. Predicting forage quality in switchgrass and big bluestem[J]. Agronomy journal, 2001, 93(1):118~124.
[105] Smart A.J.S.,Moser W.H.,Lowell E. Predicting leaf/stem ratio and nutritive value in grazed and nongrazed big bluestem[J]. Agronomy Journal, 2001, 93(6):1243.
[106] Davidson A.,Csillag F. A comparison of three approaches for predicting C4 species cover of northern mixed grass prairie[J]. Remote Sensing of Environment, 2003, 86(1):70~82.
[107]李祯,祁承留,孙文昌.东北地区自然地理[M].北京:高等教育出版社, 1993.
[108]于信芳.基于MODIS数据的森林物候期遥感监测及森林类型识别研究[D]:[博士学位论文].北京:中国科学院地理科学与资源研究所, 2005.
[109] Malingreau J.P. Global vegetation dynamics: Satellite observations over Asia[J]. International Journal of Remote Sensing, 1986, 7(9):1121~1146.
[110] Kogan F.,Sullivan J. Development of global drought-watch system using NOAA/AVHRR data[J]. Advances in Space Research, 1993, 13(5):219~222.
[111]辛景峰,宇振荣,P.M.Driessen.利用NOAA NDVI数据集监测冬小麦生育期的研究[J].遥感学报, 2001, 5(6):442~447+483.
[112]侯英雨,王石立.基于作物植被指数和温度的产量估算模型研究[J].地理学与国土研究, 2002, 18(3):105~107.
[113] Van Dijk A.,Callis S.,Sakamoto C., et al. Smoothing vegetation index profiles: An alternative method for reducing radiometric disturbance in NOAA/AVHRR data[J]. Photogrammetric engineering and remote sensing 1987, 53(8):1059~1067.
[114] Roerink G.,Menenti M.,Verhoef W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series[J]. International Journal of Remote Sensing, 2000, 21(9):1911~1918.
[115] Azzali S.,Menenti M. Mapping isogrowth zones on continental scale using temporal Fourier analysis of AVHRR-NDVI data[J]. International Journal of Applied Earth Observation and Geoinformation, 1999, 1(1):9~20.
[116] Jakubauskas M.E.,Legates D.R.,Kastens J.H. Harmonic analysis of time-series AVHRR NDVI data[J]. Photogrammetric Engineering and Remote Sensing, 2001, 67(4):461~470.
[117] Jakubauskas M.E.,Legates D.R.,Kastens J.H. Crop identification using harmonic analysis of time-series AVHRR NDVI data[J]. Computers and Electronics in Agriculture, 2002, 37(1~3):127~139.
[118]王丹,姜小光.利用NOAA数据分析中国地区植被覆盖变化周期[J].中国图像图形学报, 2006, 11(4):516~520.
[119]左丽君,张增祥,董婷婷, et al. MOD I S/NDV I和MOD I S/EV I在耕地信息提取中的应用及对比分析[J].农业工程学报, 2008, 24(3):167~172.
[120]赵伟,李召良.利用MODIS/EVI时间序列数据分析干旱对植被的影响[J].地理科学进展, 2007, 26(6):40~47.
[121]魏淑秋.农业气象统计[M].福州:福建科学技术出版社, 1985.
[122]武永峰,李茂松,李京.中国植被绿度期遥感监测方法研究[J].遥感学报, 2008, 12(1):92~103.
[123] Wagenseil H.,Samimi C. Assessing spatio-temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia[J]. International Journal of Remote Sensing, 2006, 27(16):3455~3471.
[124]魏凤英.现代气候统计诊断与预测技术[M].北京:气象出版社, 2007.
[125] Solomon S. Climate Change 2007: the physical science basis: contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[M]. British: Cambridge University Press, 2007.
[126]孙凤华,袁健,路爽.东北地区近百年气候变化及突变检测[J].气候与环境研究, 2006, 11(1):101~108.
[127]孙凤华,袁健,关颖.东北地区最高、最低温度非对称变化的季节演变特征[J].地理科学, 2008, 28(4):532~536.
[128]孙凤华,杨素英,陈鹏狮.东北地区近44年的气候暖干化趋势分析及可能影响[J].生态学杂志,2005, 24(7):751~755+762.
[129]孙凤华,袁健.东北地区1959~2002年最高、最低气温时空变化特征[J].气候变化研究进展, 2005, 1(4):168~171.
[130]董满宇,吴正方.近50年来东北地区气温变化时空特征分析[J].资源科学, 2008, 30(7):1093~1099.
[131]孙莹,江静,杨青, et al.东北夏季气温分区变化特征[J].气象科学, 2008, 28(1):50~55.
[132]廉毅,安刚,王琪, et al.吉林省40年来气温和降水的变化[J].应用气象学报, 1997, 8(2):197~204.
[133]董满宇,吴正方.近50年来吉林省气温和降水变化趋势分析[J].东北师大学报(自然科学版), 2007, 39(3):114~119.
[134]李平,卢文喜,辛欣, et al.挠力河流域降水量序列的功率谱分析和最大熵谱分析[J].世界地质, 2008, 27(1):63~67.
[135]黄嘉佑.气象统计分析与预报方法[M].北京:气象出版社, 2000.
[136]李珍,姜逢清. 1961—2004年新疆气候突变分析[J].冰川冻土, 2007, 29(3):351~359.
[137]闫敏华,邓伟,马学慧.大面积开荒扰动下的三江平原近45年气候变化[J].地理学报, 2001, 56(2):159~170.
[138]闫敏华,邓伟,陈泮勤.三江平原气候突变分析[J].地理科学, 2003, 23(6):661~667.
[139] Yue S.,Hashino M. Temperature trends in Japan: 1900~1996[J]. Theoretical and Applied Climatology, 2003, 75(1):15~27.
[140]尤卫红,段旭,邓自旺, et al.全球、中国及云南近百年气温变化的层次结构和突变特征[J].热带气象学报, 1998, 14(2):173~180.
[141]于秀晶,李栋梁,胡靖彪.吉林近50a来气候的年代际变化特征及其突变分析[J].冰川冻土, 2004, 26(6):779~783.
[142]任国玉,郭军,徐铭志, et al.近50年中国地面气候变化基本特征[J].气象学报, 2005, 6(36):942~956.
[143]徐建华.现代地理学中的数学方法[M]. (第二版).北京:高等教育出版社, 2002.
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