基于高光谱的冬油菜植株氮素积累量监测模型

为无损和定量研究高光谱技术在冬油菜植株氮素积累量(PNA,plant nitrogen accumulation)时空变化监测的适宜性及准确性,该文以两年田间氮肥水平试验为基础,采用单变量线性和非线性回归方法,建立基于特征光谱参数的冬油菜P NA高光谱估算模型。结果表明,采用比值光谱的方法可显著提高冬油菜冠层光谱反射率与PNA间的相关性,其最佳的波段组合为1 259 nm与492 nm处光谱反射率比值(R1259/R492),决定系数R2为0.85。高光谱参数间,以比值植被指数(RVI-5)、归一化光谱指数(NDSI)、线性内插法红边位置(REIP)、三角植被指数(TVI)、742 nm处一阶微分光谱值(FD742)和红边面积(SDR)等光谱参数与PNA相关性较好(平均R2和标准误SE分别为0.69和42.70),且以FD742表现最优(R2=0.79,SE=35.66)。精度分析结果显示,以光谱参数R1259/R492和FD742为自变量的指数方程模型作为高光谱监测油菜PNA的最佳模型,各生育期Noise Equivalent(NE)均较低且表现稳定,同时模型估测精度较高,R2分别为0.98和0.98,相对均方根误差RRMSE分别为0.73和0.72,相对误差MRE分别为14.42%和10.31%。该方法为快捷和精确评估冬油菜PNA提供了新的研究思路。     

基于反射光谱的山核桃幼苗氮素营养状况分析

山核桃(Carya cathayensis)是世界著名的干果之一,也是重要的木本油料作物,其油味清香、营养丰富,是优良的休闲保健食品.山核桃长期以来产量不稳、大小年现象明显,通过肥水管理、病虫害防治等有效管理措施,大部分产区大小年现象都明显趋缓,产量逐年上升(黎章矩,2003).我国山核桃研究起步较晚,在其营养方面的研究甚少.     

稻瘟病胁迫下水稻叶片叶绿素含量与光谱特征参数的相关性研究

为实现受稻瘟病侵染水稻叶片叶绿素含量的高光谱反演,以‘陵两优268’为研究对象,测定受稻瘟病侵染的85个水稻叶片样品的叶绿素含量和高光谱反射率,分析受稻瘟病侵染的水稻叶片高光谱反射率与叶绿素含量间的相关关系,使用线性与非线性回归技术建立叶绿素含量反演模型。结果显示:叶绿素含量与原始光谱及一阶导数光谱的敏感波段分别发生在700 nm和752 nm,基于光谱特征参数SDr的回归模型均方根误差为1.27,平均相对误差为10.2%。研究表明受稻瘟病侵染水稻叶片光谱反射率差异明显,基于光谱特征参数SDr的回归模型预测叶绿素含量具有较高的精度。     

精准施药中叶面积指数探测研究

精准施药技术的研究以提高农药的利用率为目的,是精准施药的发展方向。果树叶面积指数能为精准施药提供重要参考依据,通过叶面积指数及生物量探测可以进行大面积果树的长势监测并计算其药量的需求,进而对作物进行指导性施药。为此,结合国内外研究现状阐述了各类叶面积指数探测技术和计算方法,分析了精准施药技术中靶标探测的方法及叶面积指数探测在精准施药中的应用,并对精准变量施药中叶面积指数探测方法进行了展望。     

基于二维光谱特征空间的土壤线自动提取算法

土壤线是在二维光谱空间中土壤纯像元光谱反射率按照大小排列而成的直线,它综合反映了具有不同水分条件的土壤含水状况,对于了解土壤和植被的理化性质和生态特征有着重要的意义。由于土壤线采用手工描绘效率低,因人而异,致使涉及土壤线的模型在业务化应用和推广中受到限制。为此,提出了一种土壤线自动提取算法,以及算法实现的流程和步骤,利用该算法可以基于遥感影像构建的二维光谱特征空间自动提取土壤线参数。通过时间序列和实际估算2种方法分析了算法的性能,发现在提取土壤线参数上具有较好的稳定性和精度,时间序列内提取土壤线斜率的平均偏差小于0.1,与实际估算的土壤线斜率偏差也很小。该算法已被应用于农田干旱监测模型PDI、MPDI和SPSI的土壤线自动提取运算中。结果表明,在保证模型精度的前提下,自动算法有效地提高了模型运算的效率,对农业干旱模型的业务化推广有实际的应用价值。     

厌氧去除活性污泥中痕量有机污染物:15年实验研究

Knowledge of cellular metal homeostasis will provide a better understanding of the mechanisms involved in metal tolerance and hyperaccumulation in metal-hyperaccumulating plants. Energy dispersive X-ray spectrometry (EDS) was used to determine the localization of cadmium (Cd) in leaves of the Zn/Cd hyperaccumulator Picris divaricata which had a shoot Cd concentration of 565 mg kg 1 after 2 weeks of growth in solution culture supplying 10 μmol L-1 CdCl2 . The results indicated that Cd was distributed mainly in the trichomes, upper and lower epidermis and bundle sheath cells, with a relatively low level of Cd in mesophyll cells. Mesophyll protoplasts isolated from leaves remained viable after 24 h exposure to CdCl2 at a concentration up to 1 mmol L-1 , indicating their high tolerance to Cd. The intracellular Cd was visualized by staining with Leadmium Green dye, a cellular permeable Cd fluorescence probe. The results showed that the majority of protoplasts (> 82%) did not accumulate Cd, with only a minority (< 18%) showing Cd accumulation. In the Cd-accumulating protoplasts, Cd accumulation was depressed by the addition of Fe2+ , Mn2+ and the metabolic inhibitor carbonyl cyanide m-chlorophenylhydrazone (CCCP), but not by Ca 2+ or Zn2+ . Furthermore, the entire process of Cd uptake from external solution into the cytoplasm and subsequent sequestration into vacuoles was successfully recorded by confocal images. These results suggested that reduced cellular Cd accumulation and efficient Cd vacuolar sequestration in mesophyll cells might be responsible for cellular Cd tolerance and distribution in the leaves of P. divaricata.     

基于自适应全局阈值融合标记的遥感图像建筑群分割

针对分割遥感图像建筑群时,标记不完全所产生的过分割和欠分割并存问题,提出一种基于自适应全局阈值融合标记的图像分割算法.该算法根据建筑群的分布和纹理特点,利用小波变换提取图像梯度,通过形态学重构对梯度图像进行滤波;采用局部极小值法提取背景标记,并应用自适应全局阈值法提取建筑群标记.采用逻辑运算进行标记融合,用融合后的标记修改加权像素的Sobel梯度图实现精准分割.实验结果表明,该算法能够弥补形态学滤波梯度图的局部极值标记不足问题,抑制了建筑群的过分割和欠分割,准确地将建筑群从背景中提取出来,分割正确率达到90.7％.     

基于光谱分析的温室黄瓜营养状态的研究

黄瓜在温室栽培面积中占有很大的比重,监测温室黄瓜营养状态非常重要。因此,利用光谱分析技术对叶片所含的化学成分进行测量,主要包括建模与预测两步,采用基于BP算法的人工神经网络和支持向量机进行光谱的定量分析,探索基于光谱分析的整株光谱判断温室黄瓜的营养状态。研究结果表明,基于 BP 算法的人工神经网络和支持向量机所建立的回归模型,在一定程度上提高了模型的自预测能力和实际预测能力。     

Tracing Sediment Sources Using Mid‐infrared Spectroscopy in Arvorezinha Catchment,Southern Brazil

The widespread adoption of the sediment fingerprinting approach to guide catchment management has been limited by the cost and the difficulty to prepare and process samples for geochemical and radionuclide analyses. Spectral properties have recently been shown to provide a rapid and cost‐efficient alternative for this purpose. The current research objective was (i) to quantify the sediment source contributions in a 1∙19‐km² rural catchment of Southern Brazil by using mid‐infrared (MIR) spectroscopy and (ii) to compare these results with those obtained with geochemical approach and near‐infrared and ultraviolet–visible spectroscopy methods. The sediment sources to discriminate were cropland surface (n  = 20), unpaved roads (n  = 10) and stream channel banks (n  = 10). Twenty‐nine suspended sediment samples were collected at the catchment outlet during nine significant flood events. The sources could be distinguished by MIR spectroscopy. Cropland and channel bank sources mainly differed in their clay mineral contents, but their similar organic matter content complicated the MIR‐model predictions. Unpaved road contributions were discriminated from the other sources by their lower organic carbon content. When the results of the current research based on MIR spectroscopy are compared with those obtained using other sediment fingerprinting approaches, based on geochemistry and near‐infrared and ultraviolet–visible spectroscopy, an overestimation of channel banks contribution and an underestimation of cropland and unpaved road contributions is found. These results suggest that MIR spectroscopy can provide a useful tool that is non‐destructive, rapid and cheap for tracing sediment sources in rural catchments and for guiding the implementation of soil and water conservation measures. Copyright © 2016 John Wiley & Sons, Ltd.     

Mapping Ridolfia segetum patches in sunflower crop using remote sensing

Ridolfia segetum is a frequent umbelliferous weed in sunflower crops in the Mediterranean basin. Field and remote sensing research was conducted in 2003 and 2004 over two naturally infested fields to determine the potential of multispectral imagery for discrimination and mapping of R. segetum patches in sunflower crops. The efficiency of the four wavebands blue (B), green (G), red (R) and near‐infrared (NIR), selected vegetation indices and the spectral angle mapper (SAM) classification method were studied using aerial photographs taken in the late vegetative (mid‐May), flowering (mid‐June) and senescence (mid‐July) crop growth stages. Discrimination efficiency of R. segetum patches in sunflower crops is consistently affected by their phenological stages, in this order: flowering > senescence > vegetative. In both fields, R. segetum patches were efficiently discriminated in mid‐June, corresponding to the flowering phase, by using the waveband G, the ratio R/B or SAM with overall accuracies ranging from 85% to 98%. The application of the median‐filtering algorithm to any of the classified images improved the accuracy. Our results suggest that mapping R. segetum weed patches in sunflower to implement site‐specific weed management techniques is feasible with aerial photography when images are taken from 8 to 10 weeks before harvesting.