中国西北半干旱区净初级生产力驱动因子空间计算分析

净初级生产力(Net Primary Productivity,NPP)驱动因子分析对区域生态系统生产力监控与预测及生态承载力评估有重要意义。NPP驱动力系统研究近年成果已有很多,但前人研究中未能将因子空间属性在原数据和分析中得到体现。实际上,这不利于获得更科学的分析结果,甚至阻碍了数据本身空间信息的表达。在前人研究分析基础上,以新疆伊犁河谷为试验研究区,使用一种全新的数据预处理方法(C.V计算,C.V=SD/Mean),通过该方法使数据固有的空间属性或空间关系信息得以表达。将该数据集与原始数据集统计分析结果对比分析,得出结论:(1)半干旱区环境下NDVI、积温和海拔与NPP相关性相对较强。经自变量因子重要性排序分析,发现以上因子对因变量(NPP)有较高重要性(P0.01);(2)C.V数据集较原数据集在模型建立中拥有更高的拟合度,r值平均高出0.1左右,结果更优(P0.01),在环境因子建模分析中具有较好的应用前景;(3)原始数据仅能表达因子在空间中的数值分布,经C.V计算处理后,能直观的表达因子的空间变异性和数组单元的空间紊乱程度。(4)在半干旱区海拔因子对NPP的作用力相对较强,Duncan分析发现,各因子在海拔尺度下存在显著的分异特征(P0.01),说明了海拔因子对NPP的作用力。综上,研究尝试使用C.V计算数据预处理,令原始数据集附带空间属性,使得研究分析结果不单纯依赖于数值关系,结果表达的关系更加全面。研究结果对生态环境因子分析及NPP驱动力分析研究都有重要的试验价值与科学意义。

Spatial calculation and analysis of Net Primary Productivity drivers in a semi-arid region of Northwest China

Analysis of Net Primary Productivity (NPP) driving factors can provide a scientific basis for the ecological environment monitoring and prediction, and assessment of ecological carrying capacity. The NPP drivers'' research has become a hot topic in recent years, but previous studies failed to reflect the spatial attributes of factor in the original data and analysis. Actually, it was unfavorable with achieving more scientific consequence, because it put a brake on expression of the spatial information of the data itself. Therefore, we selected the Xinjiang Yili Valley as our study area and analyzed the environmental drive factors of semi-arid region of NPP by attempting to use a new spatial pre-treated method (C.V computation, C.V=SD/Mean) for data, which can help to express the spatial information of the ecological data itself. By a comparative analysis between normal data sets results and C.V data set results, we obtained some conclusions:(1) in semi-arid region, NDVI, accumulated temperature, and altitude were the most drive factors of NPP (P<0.01). It is mainly reflected in the positive promoting effect of the accumulated temperature on vegetation growth and the control of elevation change to the local precipitation condition. We also found that these factors manifested superiority on the importance rankings. (2) The C.V data set had higher fitting degree (P<0.01) in the model establishment, and had a certain application prospect. The fitting degree of the ordinary data sets is about 0.10 lower than that of C.V data sets. (3) After the original image was processed by C.V pre-treated method, it could directly express the spatial fluctuation of the factor, and the C.V computing process highlighted the fluctuation of the data between small and nearby domains. (4) After Duncan analysis, we found that each factor had significant differentiation characteristics at elevation level (P<0.01). Furthermore, the results confirmed the effect of altitude factor on NPP. From all above, C.V computing process allows the original data set to have spatial attributes. The statistical analysis results are not dependent on the numerical relationship, the relationship of our results can express more comprehensively. This analysis process and results have important experimental value and the scientific significance for the analysis of ecological environmental factors and the driving force analysis of NPP.