基于三维荧光光谱-平行因子技术联用的浮游藻化学分类学技术研究

An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis(PARAFAC) and CHEMTAX. The PARAFAC model was applied to fluorescence excitation-emission matrix(EEM) of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly at the division level using Bayesian discriminant analysis(BDA). Then the reference fluorescent component ratio matrix was constructed for CHEMTAX, and the EEM–PARAFAC–CHEMTAX method was developed to differentiate algae taxonomic groups. The correct discrimination ratios(CDRs) when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacillariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.     

长江口北支异常强盐水入侵观测与分析

长江口北支由于径流分流比很小,盐水入侵较强,特别是枯季大潮期盐水甚至倒灌进入南支,影响上海市和江苏省的水源地水质。为了进一步研究北支盐水入侵的规律及影响因素,2014年1月1-9日在北支进行了大小潮同步水文观测。本次观测到了北支异常强盐水入侵:小潮期发生强盐水入侵,且强度大于大潮期。小潮期强盐水入侵导致中下游河段被高浓度盐水控制,盐度从下游B01到中游B02几乎没有变化,且盐度的涨落潮变化几乎消失。分析认为,小潮观测期间长江入河口流量较小、河口潮差不是太小,加上强偏北风,三者的叠加是导致强盐水入侵的主要原因。     

因子分析在海河流域石油类污染综合评价中的应用(英文)

基于因子分析对2012~2013年海河流域pH、DO、COD、BOD5、氨态氮和总石油烃等6项水质指标的监测数据进行统计,并对其中四项指标进行水污染综合指数评估,旨在对海河水质进行较为系统的评价。结果显示,BJ1和HB2两个站位水质属于Ⅳ类标准,其余各站位均为Ⅴ类;各站位综合评价结果得到海河流域指数为1.44,说明海河流域处于污染状态,其污染程度超过该流域功能区的标准。因子分析发现,COD、DO和NH3-N之间差异显著(P0.05);主成分分析显示,除pH和BOD5外,其余指标都在0.70以上;COD、DO、NH3-N和TPH的贡献率较高,其中总石油烃的贡献率为100%,因此可以认为该海域的污染类型属于有机污染,且石油烃污染较为突出。     

相控阵雷达扫描方式对回波强度测量的影响

有源数字阵列雷达的波束设计非常灵活,可变的波束宽度和多波束模式不仅可以满足不同任务的观测需求,还可有效节约扫描时间,但阵列天线参数随波位改变的特性对相控阵天气雷达回波强度的精确定标提出了挑战。2013年4—6月,中国气象科学研究院与安徽四创电子股份有限公司联合研发的X波段一维有源相控阵天气雷达 (X-PAR) 进行了装配后的首次测试观测。对比该雷达与相同位置的C波段双线偏振雷达 (C-POL) 观测资料发现,X-PAR不同宽度的扫描波束均能取得较合理的观测资料。但由于各模式的雷达方程及其标定方法不尽相同,并受到阵列天线照射体积、展宽波束和发射增益的影响,造成X-PAR回波强度存在一定的测量偏差。结合天线参数分析和实际数据统计将测量偏差订正至±1 dB内,为雷达的进一步调试和改进提供了依据。     

Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification

Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing.Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline.     

The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation

Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally.     

区域Gamma混合模型的SAR图像分割

针对传统Gamma混合模型用于SAR图像分割时忽略像素间空间相关性,导致分割结果不连续并产生大量误分割的现象,提出了区域Gamma混合模型的SAR图像分割算法。首先对图像进行分水岭分割,得到过分割区域块,然后将其作为输入样本进行基于Gamma混合模型的聚类,在模型的参数估计过程中进一步考虑区域间的空间相关性,设计邻域因子融入到迭代过程,得到邻域加权类分布概率。该算法充分利用像素间的空间相关性,能够降低噪声对分割结果的影响。通过合成图像和真实SAR图像的实验表明,本文算法能够实现SAR图像的准确分割。     

电离层闪烁对GNSS接收机性能影响比较分析

电离层闪烁是引起GNSS接收机性能降低甚至失锁的重要环境干扰因素。利用实测数据,比较分析了不同电离层闪烁活动强度下,不同GNSS系统（BD和GPS）接收机的定位性能。结果表明：电离层闪烁较弱时（S4〈0．3）,两种接收机均可以实现基本的定位功能;电离层闪烁较强（S4〉0.7）,且持续时间较长时,不同GNSS接收机将出现定位结果的抖动、跳变或失去定位能力;GNSS接收机应对电离层闪烁影响的能力与接收机设计相关。研究结果可作为抗闪烁接收机开发或闪烁影响分级的参考。     

结合地形因子与分层策略的喀斯特地区无人机影像分类

西南喀斯特山区地形起伏较大,地物分布较为破碎,致使传统的光谱特征一次分类方法的精度较低。本文基于高分辨率无人机正射影像和地形指标,充分利用无人机遥感影像空间特征、光谱特征、纹理特征及地形特征,采取面向对象CART决策树算法与分层策略提取了研究区土地覆盖类型。研究表明,结合空间地形因子和分层策略的方法减少了破碎区地物间的相干扰,故具有较高的分类精度,总体分类精度达91.2%,Kappa系数为0.87,较传统一次分类精度提高了9.8%,Kappa系数提高了0.13。该方法对西南喀斯特地区土地覆盖解译精度较好,可为土地利用监测提供参考。