As a critical index for monitoring the response of terrestrial ecosystems to climate change, net primary productivity （NPP） is widely used to reflect the vegetation productivity under natural conditions. However, li...
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As a critical index for monitoring the response of terrestrial ecosystems to climate change, net primary productivity （NPP） is widely used to reflect the vegetation productivity under natural conditions. However, little is known about vegetation NPP in Buryatiya Republic, Russia. To fully understand temporal and spatial variations in vegetation NPP and its response to climate change, satellite images of Moderate-resolution Imaging Spectroradiometer （MODIS） Normalized Difference Vegetation Index （NDVI） products in conjunction with ground-based observations of climatology were used to estimate vegetation NPP in Buryatiya Republic from 2000 to 2008. Observed values of vegetation NPP and MODIS NPP products were jointly utilized to verify simulations of vegetation NPP in this area. The Carnegie Ames Stanford Approach （CASA） model was improved by adjusting its parameters with bio-temperature instead of monthly average temperature in a sub-model of soil water content. Model reliability was tested with observed vegetation NPP. Variation in vegetation NPP, and its relationship with climatic factors, was analyzed in detail. Results showed that the modified CASA model can be used to estimate vegetation NPP in Buryatiya Republic, and that the accuracy of the model was generally high with a correlation coefficient of 0.91 （P0.01）. The vegetation average annual NPP was 542.45 g C m^-2 y^ -1 and the total NPP was estimated as 1.91E＋14 g C y^ -1 from 2000 to 2008. Monthly vegetation NPP increased significantly from April to July, decreased rapidly from August to November, and remained steady from December to March of the next year. Vegetation NPP increased with increasing longitude and decreased with increasing latitude. Different vegetation types differ in total NPP, with the highest NPP in forests, followed by forest-steppe, high mountain vegetation, steppe and meadow-swamp. Temperature and precipitation were the primary factors limiting vegetation NPP in this area.
目的探讨建立一种高效、自动确定三维数据颜面解剖标志点(简称标志点)的方法,并对该方法的定点效果进行初步评价。方法选取2021年6至8月于北京大学口腔医学院·口腔医院修复科就诊的牙体缺损或缺失男性患者(面部对称性良好)30例,年龄18~45岁;采集患者三维颜面数据,基于普氏分析算法进行尺寸归一化和重叠对齐。在Geomagic Studio 2013软件中构建三维人脸平均模型,并通过参数化处理构建三维人脸模板,使用MeshLab 2020软件在其上确定32个标志点序号(10个中线标志点和22个双侧标志点)。选取2021年6至8月于北京大学口腔医学院·口腔医院正畸科或口腔颌面外科就诊的下颌无偏斜和下颌轻度偏斜男性患者各5例,获取患者三维颜面数据作为测试数据,基于三维人脸模板及其标志点序号,借助MeshMonk非刚性配准算法程序自动确定测试数据32个标志点坐标,作为模板法定点数据。由同一名主治医师人工确定测试数据32个标志点位置,记录标志点坐标作为专家法定点数据。计算模板法与专家法标志点距离,作为定点误差,评价模板法的定点效果。结果对于5例下颌无偏斜患者,模板法所有标志点的定点误差为(1.65±1.19)mm,其中中线标志点的定点误差为(1.19±0.45)mm,双侧标志点的定点误差为(1.85±1.33)mm。对于5例下颌轻度偏斜患者,模板法所有标志点的定点误差为(2.55±2.22)mm,其中中线标志点的定点误差为(1.85±1.13)mm,双侧标志点的定点误差为(2.87±2.45)mm。结论本项研究提出的通过三维人脸模板自动确定标志点的方法具有一定的可行性,其对中线标志点的定点效果优于双侧标志点,对下颌无偏斜患者标志点的定点效果优于下颌轻度偏斜患者。
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