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    小兴安岭南部臭冷杉红松林树轮指数记录的7月NDVI变化

    Variation of July NDVI recorded by tree-ring index of Pinus koraiensis and Abies nephrolepis forests in the southern Xiaoxing'an Mountains of northeastern China

      摘要
    • 摘要:
      目的归一化植被指数(Normalized difference vegetation index, NDVI)是应用最广的遥感数据之一，已被广泛应用于森林生态系统净初级生产力(Net primary productivity, NPP)的估算。但其缺点是实测数据时间较短，而基于树轮资料重建NDVI序列可在一定程度上弥补NDVI时间序列短的问题。
      方法本文研究了小兴安岭南部黑龙江省依兰县丹青河林场内红松、臭冷杉和蒙古栎的树轮宽度数据与NDVI的潜在关系，重建了小兴安岭南部地区过去的NDVI变化。
      结果红松、臭冷杉和蒙古栎树轮指数与当年7月的NDVI显著正相关，3个树种合成的样地年表与7月NDVI的相关性高于单个树种年表。样地年表和7月NDVI对气候因子(温度和降水)的响应一致；均与上一年10月至当年3月温度呈正相关，与当年4—7月温度呈负相关，与当年3月降水呈负相关，与4—7月降水呈正相关。因此，基于样地年表与7月NDVI的关系，利用一元线性回归模型建立二者之间的拟合方程，重建了过去115年的NDVI数据，方程的方差解释量达42.6% (Radj2=0.401, F=17.08, P < 0.000 1)。重建结果显示，自1900年以来NDVI序列存在2个高值时期(1967—1978、1986—1995)和3个低值时期(1920—1925、1949—1954、1999—2004)，NDVI的低值时期与该区域旱灾历史记录相吻合，因此干旱可能是导致该区域NDVI低值的主要原因；此外，砍伐、林火等因素也会导致NDVI值下降。
      结论建立树木年轮与NDVI的关系时应综合考虑NDVI投影范围和样地多树种组合以减少重建误差。

       

      Abstract:
      ObjectiveThe normalized difference vegetation index (NDVI) is one of the most widely used remote sensing data and has been widely used in the estimation of the net primary productivity (NPP). But the shortcoming of NDVI is that the actual measured data are relatively short. Reconstruction of NDVI sequences based on tree-ring data can compensate for the shortcomings of NDVI time series to some extent.
      MethodThis study analyzed the potential relationship between NDVI and tree ring width data of Pinus koraiensis, Abies nephrolepis and Quercus mongolica in the Danqinghe Forest Farm in the southern Xiaoxing'an Mountains of northeastern China, and attempted to reconstruct the past NDVI changes in Xiaoxing'an Mountains.
      ResultThe results showed that the standard chronologies of Pinus koraiensis, Abies nephrolepis and Quercus mongolica were significantly positively correlated with NDVI in July of current year. The correlation between the synthesized chronology of multiple species in the plot and the NDVI in July was higher than that of each single species. The synthesized chronology of multiple species and the NDVI in July have a consistent correlation with climatic factors (temperature and precipitation), they are positively correlated with temperature from October of the previous year to March of the growing year and negatively correlated with temperature from April to July of growing year. Therefore, we reconstructed the past 115 years NDVI in July according to the relationship between synthesized chronology and NDVI in July and using the linear regression model to fit the equation. The linear regression model can explain a variance amounted to 42.6% (Radj2=0.401, F=17.08, P < 0.000 1). The NDVI reconstruction showed that there were two high-value periods (1967-1978, 1986-1995) and three low-value periods (1920-1925, 1949-1954, 1999-2004) since 1900. The low value period of NDVI coincided with the historical record of drought, so drought may be the main cause of low NDVI values. In addition, factors such as deforestation and forest fire may also lead to the decrease of NDVI value.
      ConclusionIt is worth noting that the NDVI projection range and multi-species combinations should be considered to reduce reconstruction errors when establishing the relationship between tree rings and NDVI.

       

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