基于实测光谱的植被指数对水稻叶面积指数的响应特征分析

叶面积指数(LAI)是目前最常用的农业生态监测指标,可以为农作物的病虫害监测、作物长势监测、碳循环、生物量估算及作物估产提供依据。植被指数(VI)是卫星LAI产品生产的重要数据源,但不同VIs对植被LAI的响应特征具有一定的差异性。以江西省水稻为例,基于实测光谱提取了水稻实测VIs,结合实测LAI,讨论了归一化植被指数(NDVI)、增强型植被指数(EVI)、土壤调节植被指数(SAVI)和修正的土壤调节植被指数(MSAVI)四种常见VIs对LAI的响应特征,并与MODIS LAI备用算法的计算结果进行了对比分析,研究了不同VIs用于LAI产品反演的可行性及存在的问题。通过对不同实测VIs-LAI模型精度的评估,分析其应用于LAI反演的适应性,结果显示EVI,SAVI和MSAVI比NDVI有更好的适应性,其中EVI效果最优。此外,通过对比MODIS LAI备用算法查找表,发现针对MODIS LAI备用算法中草地与谷物作物这一地表覆盖大类,在LAI>4时,NDVI出现饱和;而实测水稻作物的NDVI在LAI>2时开始出现饱和;且当NDVI相同时,查找表LAI远大于实测LAI,MODIS备用算法中使用的地表覆盖产品分类过粗可能是造成这一结果的主要原因。因此MODIS LAI备用算法在该区域水稻LAI监测中可能产生较大误差,有必要改用其他VIs优化该备用算法。通过对比分析四种VIs模型对LAI的预测误差,发现EVI,SAVI和MSAVI精度明显优于NDVI,基于EVI的模型平均预测误差仅为MODIS LAI备用算法的1/6,基于实测NDVI反演算法的1/2,因此设计基于EVI的LAI算法对LAI的反演精度有一定的提升空间。     

几种常见树种叶片光谱秋季变化特征分析

叶片随着时间生长产生变化,其光谱特征也会发生变化。研究相同树种叶片不同时间条件下的光谱变化规律以及不同树种叶片相同时间条件下的光谱特征,不仅为植被叶片光谱随时间变化的规律研究提供理论基础,也是高光谱遥感精确识别植被信息的关键。选取北京市10种常见树种,利用地物光谱仪观测各树种不同时间叶片光谱,同时将观测的光谱进行一阶微分处理和典型波段分析,对比相同时间不同树种叶片光谱的差异,分析相同树种不同时间的光谱变化规律,探索不同时相条件下高光谱遥感识别树种的有效波段。结果表明：不同树种叶片光谱均随时间的改变而产生显著变化,但差异规律各不相同;不同树种相同时间叶片光谱在部分波段存在显著差异,为高精度树种识别提供了理论依据和叶片基础光谱数据。     

运用光谱参数冠层覆盖度建立作物长势及氮营养状态模型

为了探索运用数码照片中光谱(红、绿、蓝)的像素计算得到的冠层覆盖度(canopy cover, CC)对玉米长势及氮素营养状态进行非破坏性监测的技术。通过获取玉米冠层的数码照片图像,定量化数码照片色彩参数与作物叶面积指数(leaf area index, LAI)、冠层干重(shoot dry matter weight, DM)、叶片氮素含量(leaf nitrogen content percentage, N%)之间的关系。试验于2012年和2013年在中国农业科学院试验田进行,运用基于Visual Basic Version 6.0研发的玉米冠层图像分析系统,分析了玉米品种中单909在3个氮素水平条件下分别于9叶展时期、抽雄期和灌浆期的CC、11种色彩指数与植株LAI,DM,N%及产量之间的相关性,并对相关性显著的指标进行了拟合与建模。结果表明,CC与LAI(r=0.93, p＜0.01),DM(r=0.94, p＜0.01),N%(r=0.82, p＜0.01)之间均达到了极显著水平;用CC估算LAI,DM和N%的模型均为幂函数,方程式分别是y=3.281 2x0.763 9,y=283.658 1x0.553 6,y=3.064 5x0.932 9;用与建模相独立的数据对模型验证,结果表明,CC估算LAI模型的实测值与模拟值基于1∶1直线的R2,RMSE和RE分别是0.996,0.035和1.46%;CC估算DM模型的R2,RMSE和RE分别是0.978,5.408 g和2.43%;CC估算N%模型的R2,RMSE和RE分别是0.990,0.054和2.62%。综上所述,模型能够较准确的通过CC估算不同氮肥水平条件下玉米9叶展时期、抽雄期和灌浆期的LAI,DM与N%,表明应用数码相机的光谱信息可实现对玉米生长过程中的生长状况及氮素营养状态进行实时无损快速监测与预测。     

基于叶片光学属性的作物叶片水分含量反演模型研究

叶片含水量是反映作物生理特性的一个重要参数, 对生态环境的研究具有重要意义。采用小波分析方法, 分析叶片含水量对反射率的影响特征, 建立综合利用多波段信息的作物叶片水分含量反演模型。基于PROSPECT模型的辐射传输理论, 推导出由叶片反射率光谱的小波系数反演叶片水分含量CW的理论模型。利用六种常用的小波函数, 对叶片组分水、干物质和白化基本层的吸收光谱进行小波分解。选取对水分变化最敏感, 同时对其他组分不敏感的分解尺度和波段位置, 找到能稳定突出水的光谱特征的小波系数。结果表明: bior1.5小波函数在尺度为200 nm, 波段位置为1 405和1 488 nm的小波系数具有上述特征。建立由叶片反射率光谱的bior1.5小波系数反演叶片水分含量CW的反演模型, 模型有两个转换系数a和Δ都受叶片结构参数N的影响。利用PROSPECT模型生成模拟光谱数据集, 校正建立的叶片水分含量反演模型中的两个转换系数a和Δ, 并与LOPEX93实验光谱数据集结合验证反演模型。结果表明: 反演模型不仅比传统基于植被指数的统计模型在精度上有提高(反演值与实测值的R2最高达到0.987), 而且更加稳定, 普适性更高。研究表明, 小波分析方法在利用高光谱数据反演作物叶片水分含量方面具有独特的优势。     

Processing tactics for low-cost production of pure nuciferine from lotus leaf

Nuciferine is an important drug candidate for the treatment of many diseases. However, there is no general method for its low - cost production. In this work, a feasible method for the production of nuciferine from lotus leaf, using ultrasonic-assisted extraction-solid phase extraction (UAE-SPE) as extraction and cleanup procedure, was developed. Petroleum ether and silica gel have been successfully used as extraction solvent and adsorbent to integrate UAE with SPE, respectively. Except for filtration, no treatment (e.g. concentration and redissolution, etc) was needed on UAE extract before SPE and the effluents obtained in the loading process of SPE could be used as UAE extraction solvent without purification. No obvious decline in the extraction efficiency of UAE and adsorption capacity of SPE was observed at least for 5 runs, which provides a feasible way for the continuous production of nuciferine in industry, i.e. Cyclic UAE-SPE. Moreover, SPE column could be conveniently regenerated and reused without significant decline in its adsorption capacity at least for 5 cycles, which can be used to reduce the cost of the whole system further. In comparison with other cleanup procedures, Cyclic UAE-SPE showed apparent advantages in energy conservation and emission reduction. LLE and crystallization were applied to separate nuciferine from other impurities further. Under optimum conditions, the total recovery rate of nuciferine with a purity over 90.0% from lotus leaf reached 50.1%. All in all, the developed method has advantages in convenient operation, low cost, and high efficiency, thus, is fitting for the production of high purity nuciferine.     

固相萃取-高效液相色谱法测定烟叶中吡虫啉农药残留量

建立了固相萃取-高校液相色谱法测定烟叶中吡虫啉农药残留量的方法。样品用乙腈提取,弗罗里硅土固相萃取柱净化.梯度洗脱方式分离,DAD二极管阵列检测器检测。吡虫啉加标回收率为87.83%—94.80%,相对标准偏差为1.64%,检出限为0.01μg/g。该方法灵敏、准确,适用烟草中吡虫啉农药残留的分析。     

Contents of heavy metals in lotus seed from REEs mining area

At rare earth mining area, the pollution of heavy metals has been paid little attention. In lotus seeds and soil samples sampled from a typical rare earth elements mining area were detected the contents of eight kinds of heavy metals (Cr, Mn, Cu, Zn, As, Cd, Hg and Pb) to investigate the pollution from rare earth elements mining by ICP-MS (Inductively Coupled Plasma Mass Spectrometry). The results showed that the contents of the heavy metals Cr, Mn, Cu, Zn, As, Cd, Hg and Pb in lotus seeds were 0.25, 86.94, 8.32, 19.76, 0.13, 0.08, 0.01, and 0.20 μg/g in lotus seeds, respectively, and corresponding contents in soil were 27.68, 168.71, 20.23, 47.16, 1.83, 0.13, 0.04, and 23.15 μg/g. These data showed that the contents of heavy metals in lotus seeds from REEs mining area are no higher than reference area and meet national food safety standards of China.     

利用偏最小二乘回归从冬小麦冠层光谱提取叶片含水量

通过人为控制灌溉水平,在冬小麦3个发育期(孕穗、开花、乳熟)测定了冠层光谱和叶片含水量(leaf water content,LWC)。针对每期数据,结合偏最小二乘回归和迭代特征去除,建立了基于诊断波段的LWC回归模型。结果表明,叶片水分的光谱响应及反演精度受小麦生长状态的影响。在孕穗、开花和乳熟3个发育阶段,回归模型中光谱数据的最佳利用形式分别为对数光谱、导数光谱和反射率光谱;重要光谱区间为SWIR,NIR和SWIR;模型交叉验证决定系数(R2_CV)为0.750,0.889和0.696。研究结论对今后监测冬小麦旱情和开发作物水分遥感产品具有重要的指导作用。     

基于Notes群件技术的知识管理系统

讨论了知识管理系统的构成及其功能,并提出了基于Notes群件技术的知识管理系统,以及在知识管理运作中应当注重的几个问题。     

Numerical analysis of pattern formation on the surface of transpiring leaves

The process of photosynthesis is facilitated by pores on the leaf surface called stomata. When a particular stoma is open, CO₂ is absorbed through its aperture, but H₂O is also lost due to evaporation. Thus a plant will seek a stomatal aperture that balances its need for CO₂ with its aversion to H₂O loss. In order to visualize a particular leaf’s stomatal aperture distribution and how it changes with time, fluorescence data is collected at regular intervals as digital images, resulting in a video sequence. It has been observed that stomatal apertures are often synchronized into spatially extended patches. In order objectively to analyze this phenomenon we have developed a technique to isolate patches via a three-dimensional PDE-based segmentation method. The resulting segmented data is then collapsed to a vector valued time series of much smaller dimension with a hybrid PCA-Archetypal Analysis approach. This allows for a unique interpretation of the data in terms of statistical measures of the motions of representative patches. The technique is illustrated with a data-set from a particularly complicated regime collected by the Complexity and Stomatal Behavior research lab at Utah State University.