基于物候表征的中国东部南北样带上植被动态变化研究
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摘要
在各种自然要素中,植物及其群体对于其他要素所施加的影响,反应最灵敏,并具有最充分的表现能力。植被特有的年际和季节的变化即植物物候,是全球变化的敏感指示器。在全球变化遥感研究中,许多学者正是根据植被的这一特点,利用宏观遥感数据,来监测植被物候的变化,并用以反演气候的变化及影响。
基于植物物候遥感监测的原理和特点,本项研究应用野外物候观测数据和植物物候遥感监测相结合的研究方法,采用中国国家气象局355个站点从1951～2004年观测的相关资料,以及美国地球资源观测系统数据中心的探路者数据集中的NOAA/AVHRR NDVI数据,构建起Logistic模型。将其应用于中国东部南北样带上以物候为指示的植被格局动态与气候变化研究之中,分析了1982～2003年样带植被绿度始期、绿度末期、绿度期长度等绿度期参量的时空格局与演变趋势;分析了植被生长状况与气温和降水等气候因子的相关关系;重点探讨了不同植被类型和不同生物气候区植被绿度始期与绿度末期的时空变化特征及其对区域气温、降水变化的响应方式;初步揭示了全球变化下环境因子对植被生长期变化的驱动机制。论文研究工作主要结论如下:
(1)样带近22年来冬、春季升温现象明显,春季大部分地区降水减少,冬季降水增加,植被绿度始期提前趋势明显,特别是在20世纪90年代中后期,平均绿度始期提前8天左右。样带植被季相变化对气候的响应表明温度对植被的季相变化的驱动作用大于水分条件。温度每升高1℃,植被绿度始期平均提前7天左右,绿度期长度延长5-8天。植被生长与气候条件之间表现了一定的滞后性,其中以气温对植被生长的滞后效应较为显著。春季植被生长对前一个冬季的温度有滞后效应。
(2)冬季温度对植被绿度的影响远远超过降水冬季降水,且从南至北,这种影响趋势增加。
植物绿度期变化在大范围上受水热条件驱动。在东部湿润的季风区沿同一经线,温度是植被绿度期变化的主要驱动因子;在北部沿同一纬线,随着从湿润季风区过渡到半干旱的草原荒漠内陆,降水是这些地区植被绿度期变化的重要驱动因子。
(3)在中国东部南北样带不同生物气候区,植被绿度始期显著提前,而绿度末期呈不显著提前趋势,使得植被绿度期延长。这与欧洲和北美地区植被绿度始期显著提前而末期不显著推迟的变化趋势不同。
就整个样带而言,对温度变化的敏感区位于东北、华北、华中大部分地区以及内蒙区东部;对降水敏感区主要位于内蒙东部区以及华北北部地区,其他地区响应较为零散。由南向北,温度成为影响植被覆盖变化的主要驱动因子,而且影响程度随着纬度的升高而降低。
(4)在全球气候变暖的情况下,受气候变化干扰影响较大的植被类型是寒温带、温带针叶林和温带落叶阔叶林。它们将在地带性森林中所占比例缩小。
样带北部温度升高、降水减少,特别是春季降水减少,会使温带荒漠地区荒漠化趋势加重。
Phenology refers to seasonal biological life stages driven by environmental factors, and is considered to be a sensitive and precise indicator of climate change. Vegetation phenology detection methods based on remote sensing overcome conventional ground observation’s shortcomings, such as limited observation sites and missing data, and realize the spatial scale transition of observation methods from points to coverage. Remote sensing technology greatly promotes a study on vegetation ecosystem response to climate changes at regional, continental, even global scales.
Therefore, we developed a“bottom-up”method for first determining the phenological growing season at sample stations, and based on NOAA/AVVHRR, meteorological data, ground phenology observation data, vegetation category data, and so on, the essay build a Logistic fitting model on cumulative frequency of NDVI to determine turning green date(TGD)in spring, wilting date(WD) in autumn and length of greenness period (LGP) since 1982,then analyze the spatio-temporal pattern and change trends of TGD, WD, LGP,analyze correlation between NDVI and air temperature, precipitation, mainly discuss spatio-temporal dynamics of TD and WD and their response and feedback to regional air temperature and precipitation in different vegetation types and different bioclimatic regions, primarily reveal the dynamic mechanism of climate on vegetation..
Using phenological and NDV I data from 1982 to 2003 at seven sample stations in the North South Transect of Eastern China, we calculated the cumulative frequency of leaf unfolding and leaf coloration dates for deciduous species every five days throughout the study period. Then, we determined the growing season beginning and end dates by computing times when 50% of the species had undergone leaf unfolding and leaf coloration for each station 2 year. Next, we used these beginning and end dates of the growing season as time markers to determine corresponding threshold NDV I values on NDV I curves for the pixels overlaying phenological stations. Based on a cluster analysis, we determined extrapolation areas for each phenological station in every year, and then, implemented the spatial extrapolation of growing season parameters from the seven sample stations to all possible meteorological stations in the study area.
The results show: ( 1 ) the spatial pattern of average turning green and wilting dates of the growing season correlates significantly with the spatial pattern of average temperatures in sp ring and winter across the North South Transect of Eastern China during 1982 to 2003; the growing season extended on average by 5 to 8 days ;(2) On an interannual basis, correlation analysis shows that TGD were mainly influenced by mean air temperature from last winter to spring in all vegetation types. A negative correlation indicates that higher mean temperature in late winter and spring trigger an earlier onset of TGD. In contrast to TGD, the correlations of WD and seasonal mean air temperature before it are not significant in mostly vegetation types. It indicates that the delay or advance of WD in autumn mainly lied on a temperature threshold under which WD arise. Precipitation has a weak influence on TGD and WD In contrast to temperature.(3) a insignificant advance of wilting dates but a significant advance of turning green dates of the growing season were detected in different latitudinal zones and the whole area, which is different from findings in Europe and North America (where a significant advance of beginning dates and an insignificant delay of end dates of the growing season were observed) ; (4) An increase in air temperature in North China may tend to result in less temperate forest but more shrubs and grasses in the transect area.
基于植物物候遥感监测的原理和特点,本项研究应用野外物候观测数据和植物物候遥感监测相结合的研究方法,采用中国国家气象局355个站点从1951～2004年观测的相关资料,以及美国地球资源观测系统数据中心的探路者数据集中的NOAA/AVHRR NDVI数据,构建起Logistic模型。将其应用于中国东部南北样带上以物候为指示的植被格局动态与气候变化研究之中,分析了1982～2003年样带植被绿度始期、绿度末期、绿度期长度等绿度期参量的时空格局与演变趋势;分析了植被生长状况与气温和降水等气候因子的相关关系;重点探讨了不同植被类型和不同生物气候区植被绿度始期与绿度末期的时空变化特征及其对区域气温、降水变化的响应方式;初步揭示了全球变化下环境因子对植被生长期变化的驱动机制。论文研究工作主要结论如下:
(1)样带近22年来冬、春季升温现象明显,春季大部分地区降水减少,冬季降水增加,植被绿度始期提前趋势明显,特别是在20世纪90年代中后期,平均绿度始期提前8天左右。样带植被季相变化对气候的响应表明温度对植被的季相变化的驱动作用大于水分条件。温度每升高1℃,植被绿度始期平均提前7天左右,绿度期长度延长5-8天。植被生长与气候条件之间表现了一定的滞后性,其中以气温对植被生长的滞后效应较为显著。春季植被生长对前一个冬季的温度有滞后效应。
(2)冬季温度对植被绿度的影响远远超过降水冬季降水,且从南至北,这种影响趋势增加。
植物绿度期变化在大范围上受水热条件驱动。在东部湿润的季风区沿同一经线,温度是植被绿度期变化的主要驱动因子;在北部沿同一纬线,随着从湿润季风区过渡到半干旱的草原荒漠内陆,降水是这些地区植被绿度期变化的重要驱动因子。
(3)在中国东部南北样带不同生物气候区,植被绿度始期显著提前,而绿度末期呈不显著提前趋势,使得植被绿度期延长。这与欧洲和北美地区植被绿度始期显著提前而末期不显著推迟的变化趋势不同。
就整个样带而言,对温度变化的敏感区位于东北、华北、华中大部分地区以及内蒙区东部;对降水敏感区主要位于内蒙东部区以及华北北部地区,其他地区响应较为零散。由南向北,温度成为影响植被覆盖变化的主要驱动因子,而且影响程度随着纬度的升高而降低。
(4)在全球气候变暖的情况下,受气候变化干扰影响较大的植被类型是寒温带、温带针叶林和温带落叶阔叶林。它们将在地带性森林中所占比例缩小。
样带北部温度升高、降水减少,特别是春季降水减少,会使温带荒漠地区荒漠化趋势加重。
Phenology refers to seasonal biological life stages driven by environmental factors, and is considered to be a sensitive and precise indicator of climate change. Vegetation phenology detection methods based on remote sensing overcome conventional ground observation’s shortcomings, such as limited observation sites and missing data, and realize the spatial scale transition of observation methods from points to coverage. Remote sensing technology greatly promotes a study on vegetation ecosystem response to climate changes at regional, continental, even global scales.
Therefore, we developed a“bottom-up”method for first determining the phenological growing season at sample stations, and based on NOAA/AVVHRR, meteorological data, ground phenology observation data, vegetation category data, and so on, the essay build a Logistic fitting model on cumulative frequency of NDVI to determine turning green date(TGD)in spring, wilting date(WD) in autumn and length of greenness period (LGP) since 1982,then analyze the spatio-temporal pattern and change trends of TGD, WD, LGP,analyze correlation between NDVI and air temperature, precipitation, mainly discuss spatio-temporal dynamics of TD and WD and their response and feedback to regional air temperature and precipitation in different vegetation types and different bioclimatic regions, primarily reveal the dynamic mechanism of climate on vegetation..
Using phenological and NDV I data from 1982 to 2003 at seven sample stations in the North South Transect of Eastern China, we calculated the cumulative frequency of leaf unfolding and leaf coloration dates for deciduous species every five days throughout the study period. Then, we determined the growing season beginning and end dates by computing times when 50% of the species had undergone leaf unfolding and leaf coloration for each station 2 year. Next, we used these beginning and end dates of the growing season as time markers to determine corresponding threshold NDV I values on NDV I curves for the pixels overlaying phenological stations. Based on a cluster analysis, we determined extrapolation areas for each phenological station in every year, and then, implemented the spatial extrapolation of growing season parameters from the seven sample stations to all possible meteorological stations in the study area.
The results show: ( 1 ) the spatial pattern of average turning green and wilting dates of the growing season correlates significantly with the spatial pattern of average temperatures in sp ring and winter across the North South Transect of Eastern China during 1982 to 2003; the growing season extended on average by 5 to 8 days ;(2) On an interannual basis, correlation analysis shows that TGD were mainly influenced by mean air temperature from last winter to spring in all vegetation types. A negative correlation indicates that higher mean temperature in late winter and spring trigger an earlier onset of TGD. In contrast to TGD, the correlations of WD and seasonal mean air temperature before it are not significant in mostly vegetation types. It indicates that the delay or advance of WD in autumn mainly lied on a temperature threshold under which WD arise. Precipitation has a weak influence on TGD and WD In contrast to temperature.(3) a insignificant advance of wilting dates but a significant advance of turning green dates of the growing season were detected in different latitudinal zones and the whole area, which is different from findings in Europe and North America (where a significant advance of beginning dates and an insignificant delay of end dates of the growing season were observed) ; (4) An increase in air temperature in North China may tend to result in less temperate forest but more shrubs and grasses in the transect area.
引文
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