基于多时相遥感数据的徐州市植被净初级生产力估算及相关性分析

以遥感数据和气象数据为主要数据源,应用改进的光能利用率模型估算徐州市2006、2008和2010年3年间6月份的植被净初级生产力(Net Primary Productivity,NPP),研究了该区域6月份NPP的时空变化及其与气象因子的相关性。结果表明：时间上,受气候和环境等因素综合变化的影响,研究区域6月份NPP呈逐年下降趋势;空间上,NPP的分布表现为在林地、草地和农田相对集中的区域偏高,且不同植被类型6月份的NPP大小关系在不同年份可能不同,其中在2006和2008年为农田>草地>林地,而2010年为农田>林地>草地。通过分析与气象因子的相关性和偏相关性,限制NPP的主要气象因子不是固定不变的,其中2006和2008年,限制NPP的主要气象因子为太阳辐射,而2010年为降雨量和温度。不同植被类型下NPP与气象因子相关性和偏相关性差异反映了不同类型植被生长对光、热、水条件要求的差异。
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Net Primary Productivity of Vegetation Estimation and Correlation Analysis based on Multi-Temporal Remote Sensing Data in Xuzhou

An improved Carnegie Ames Stanford Approach model due to remote sensing data and climate variables were used to estimate the Net Primary Productivity (NPP) of Xuzhou in the June of 2006,2008,2010.The results of analysis variations and the relationship with climatic factors were as the follow:The mean NPP of terrestrial vegetation in Xuzhou showed decreasing trend in recent years because of the changes in climate and environment;The NPP values were higher in most of places where woodland,lawn and farmland were aggregation,the variations of NPP might be significant in different vegetation types,the NPP of farmland was more than lawn and woodland in 2006 and 2008,and the NPP of farmland was more than farmland and lawn in 2010.Based on the analysis of the correlation and partial correlation between NPP and climatic factors (temperature,precipitation and sunshine duration),it could be concluded that the influences of climate were complicated with NPP,and the dominant factor was not constant in this area.Because water and heat was in a good condition for vegetation growth in 2006 and 2008,the relationship between sunshine duration and NPP was more stronger;The temperature and precipitation became the limiting factors in 2010 because of lower in themselves and longer in sunshine duration.The different requirements of the different vegetation types in light,heat and water could be reflected by the variations of correlation and partial correlation between NPP and climatic factors in different vegetation types.