Response of forest vegetation phenology to climate change in Xiaoxing’an Mountains of northeastern China
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摘要:目的 采用遥感提取植被物候的方法，以小兴安岭为研究区，构建森林植被物候时空变化，分析森林植被物候变化对气候变化的响应。方法 基于GIMMS NDVI 3g影像，运用一元六次多项式拟合植被生长曲线，并结合逐像元动态阈值法提取小兴安岭1982—2015年森林植被生长开始期（SOS）、生长结束期（EOS）和生长季长度（LOS）共3种物候参数；利用ArcGIS软件，将气温、降水以及日照时数数据与植被物候参数逐像元分析，得到物候参数与气象因子偏相关系数的空间分布特征。结果 （1）植被物候多年平均值空间分布特征呈现由西北向东南方向，植被SOS逐渐提前，植被EOS逐渐推迟，植被LOS逐渐延长的规律。（2）小兴安岭森林植被SOS集中在日序第112.1~128.3天，年际变化在1998年前后出现转折，1998年前呈显著提前趋势（R2 = 0.284，P = 0.028），1998年后呈不显著推迟趋势（R2 = 0.002，P = 0.86），导致整个时间段（1982—2015年）变化不显著，变化幅度为每10年提前0.12 d（R2 = 0.001，P = 0.872）；森林植被EOS集中在第277.3~294.8天，年际变化呈现显著推迟趋势，变化幅度为每10年推迟2.33 d（R2 = 0.294，P < 0.01）；森林植被LOS集中在149.5 ~ 167.5 d，年际变化呈现显著延长趋势，变化幅度为每10年延长2.45 d（R2 = 0.231，P < 0.01）。（3）小兴安岭森林植被SOS对当年4月温度的响应最明显，其次是当年2月温度；植被EOS对当年8月降水响应最明显，其次是当年6月温度。结论 （1）小兴安岭森林植被物候多年平均值与水热条件多年平均值呈现出比较一致的空间规律特征。（2）研究期植被EOS的变化主要受8月降水的变化驱动，8月降水的下降是导致植被EOS显著推迟的主要原因。（3）20世纪末出现的全球变暖停滞引起2月温度在1998年前后呈现由显著上升转变为不显著下降，引起植被SOS变化趋势在1998年前后发生突变，导致整个时间段植被SOS变化不显著。
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关键词:
- 小兴安岭 /
- 植被物候 /
- GIMMS 3g NDVI /
- 变暖停滞
Abstract:Objective Using the method of extracting vegetation phenology by remote sensing,taking Xiaoxing’an Mountains of northeastern China as the study area, this paper analyzes the temporal and spatial changing characteristics of forest vegetation phenology and the response of forest vegetation phenology change to climate change.Method Based on GIMMS NDVI 3g image, the forest vegetation growth curve was fitted by univariate sixth order polynomial, and the phenological parameters of forest vegetation SOS, vegetation EOS and vegetation LOS in Xiaoxing’an Mountains from 1982 to 2015 were extracted by pixel dynamic threshold method. Using ArcGIS software, the temperature, precipitation and sunshine duration data were analyzed pixel by pixel with vegetation phenological parameters to obtain partial correlation coefficients between phenological parameters and meteorological factors.Result (1) The characters in spatial distribution of multi-year mean value of vegetation phenology showed a kind of gradual change from northwest to southeast, including the advance of vegetation SOS, the delay of vegetation EOS and the extension of vegetation LOS. (2) The SOS of forest vegetation in Xiaoxing’an Mountains was concentrated among 112.1th−128.3th day of the year, with an abrupt change around 1998. Before 1998, it showed a significant advancing trend (R2 = 0.284, P = 0.028), and after 1998, it showed a non-significant delaying trend (R2 = 0.002, P = 0.86), resulting in a non-significant change during the whole analysis period (1982−2015), with a rate of change at 0.12 d per ten years (R2 = 0.001, P = 0.872). The forest vegetation EOS was concentrated among 277.3th−294.8th day of the year, and showed a significant delaying trend during the whole analysis period, with a rate range of 2.33 d per ten years (R2 = 0.294, P < 0.01). The LOS of forest vegetation was concentrated among 149.5−167.5 days, and showed a significant extension trend during the whole analysis period, with a rate of change at 2.45 d per ten years (R2 = 0.231, P < 0.01). (3) The forest vegetation SOS was correlated with the April temperature of current year, followed by the February temperature of current year. The vegetation EOS was correlated with the precipitation in August of current year, followed by the June temperature in current year.Conclusion (1) The multi-year average value of forest vegetation phenology and the multi-year average value of hydrothermal conditions in Xiaoxing’an Mountains show relatively consistent spatial characteristics. (2) The change of vegetation EOS during the study period is mainly driven by the change of precipitation in August. The decline of precipitation in August is the main reason for the significant delay of vegetation EOS. (3) The stagnation of global warming at the end of the 20th century causes the temperature in February to change from a significant increase to a non-significant decrease around 1998, causing a sudden change in the trend of vegetation SOS around 1998, leading to a non-significant change in vegetation SOS during the whole period.-
Keywords:
- Xiaoxing’an Mountains /
- vegetation phenology /
- GIMMS 3g NDVI /
- stagnation of warming
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图 1 1982—2015年多年平均物候指数生长季开始（SOS）、生长季结束（EOS）、生长季长度（LOS）空间分布
Figure 1. Spatial distribution of multi-year average phenological index at the beginning of growing season (SOS), end of growing season (EOS) and length of growing season (LOS) from 1982 to 2015
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图 2 1982—2015年小兴安岭森林区域生长季多年平均温度、降水量空间分布
Figure 2. Spatial distribution of annual average temperature and precipitation in the growing season of Xiaoxing’an Mountain forest area from 1982 to 2015
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图 3 1982—2015年物候参数变化趋势空间分布及显著性像元占比
Figure 3. Spatial distribution of phenological parameter changing trend and proportion of significant pixels from 1982 to 2015
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图 4 1982—2015年小兴安岭森林植被平均物候参数变化趋势图
Figure 4. Variation trend of average phenological parameters of forest vegetation in Xiaoxing’an Mountains from 1982 to 2015
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图 5 1982—2015年植被物候开始期（SOS）与气候因子的偏相关分析
Figure 5. Partial correlation analysis between vegetation phenological onset (SOS) and climate factors from 1982 to 2015
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图 6 植被SOS与月份气候的平均相关系数及主导因子相对贡献率分布
*表示有显著差异（P < 0.05）。* indicates significant difference (P < 0.05).
Figure 6. Distribution of average correlation coefficients and relative contribution rates of dominant factors between vegetation SOS and monthly climate
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图 7 1982—2015年小兴安岭2月和4月温度年际变化趋势
Figure 7. Interannual variation trend of temperature in February and April of Xiaoxing’an Mountains from 1982 to 2015
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图 8 1982—2015年植被生长结束期（EOS）与气候因子的偏相关分析
Figure 8. Partial correlation analysis between the end of vegetation growing season (EOS) and climate factors from 1982 to 2015
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图 9 植被EOS与月份气候的平均相关系数及主导因子相对贡献率分布
Figure 9. Distribution of average correlation coefficients and relative contribution rates of dominant factors between vegetation EOS and monthly climate
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