地表水中甲醛的光谱吸收曲线-线性拟合斜率-乙酰丙酮分光光度法检测

光谱吸收曲线-线性拟合斜率-分光光度法(SAC-LFS-S)用于检测地表水中甲醛尚存准确度不足问题,通过筛选合适的参比波长段,使SAC-LFS-S法准确度进一步提高。以试剂空白和浊度梯度地表水样品的检测结果及方法检出限为主要筛选对象,得到425～435、420～435、420～440、415～440、415～445、410～445、415～450、410～450、455～495、410～455、405～455、450～500 nm等共12个合适的测定波长段;再选择试剂空白结果较低、批间重复性较好的450～500 nm测定波长段,筛选参比波长段(455～465、480～495、450～490 nm共3个)。结果表明:优化后SAC-LFS-S法线性范围为0. 100～3. 00 mg/L,方法检出限为0. 025～0. 033 mg/L;优化前、后方法测得部分地表水样品结果分别为有检出和未检出,回收率为73. 4%～108. 8%,后者与无亮黄色化合物产生的实验现象更吻合。经参比波长段校正的SAC-LFS-S法具有更高的准确度,非常适用于地表水中甲醛的直接测定。     

基于时序MODIS-NDVI的油菜种植面积变化趋势分析——以江汉平原为例

油菜是我国主要油料作物,其种植与监测对于保障食油生产安全具有重要意义。本文以江汉平原作为研究区,利用2002~2014年250 m分辨率的MODIS-NDVI时序数据,采用经验模态分解(EMD)方法,结合地面调查数据以及作物物候数据等辅助数据,获取主要地物覆盖类型的端元信息,然后利用线性光谱混合模型(LSMM)进行混合像元分解,得到油菜种植面积丰度的空间分布。结果表明,EMD方法较好地过滤了时序数据的噪声;经年鉴统计数据验证,油菜提取面积精度达到了92.5%以上,R2大于0.9。油菜种植面积的时空变化结果很好地反映出江汉平原油菜种植面积总体呈增长态势。此外,政策导向和市场价格对油菜种植面积的影响明显。     

石油石化企业泄漏检测技术现状及前景

石油石化站场设备繁多,挥发性介质泄漏易造成安全事故。目前站场点式、线式等固定式检测设备存在泄漏检测反应滞后、泄漏点定位困难等缺点,便携式检测设备人工巡检工作量大。文章对可调谐二极管激光吸收光谱技术、红外热成像技术、红外多光谱技术和红外高光谱技术进行了分析,提出红外成像检测技术在泄漏检测方面优势明显,具有视频成像、反应迅速、定位准确、连续监控等优点,有望在石油石化行业普及。     

汤逊湖水体叶绿素浓度遥感估测研究

内陆水体叶绿素浓度遥感监测的各种经验模型在模型的方法和参数上都有一定的时空限制。为了实现武汉汤逊湖叶绿素浓度的遥感监测,利用同步实测的光谱数据和水质数据进行了该湖泊叶绿素浓度的遥感定量反演研究。首先分析了汤逊湖水体的反射率光谱曲线;其次通过相关分析的方法,发现了汤逊湖水体反射率在单波段法、一阶微分法和比值法建模中的最优波段或组合,并进行了一元线性建模;最后对3种模型的反演精度进行了比较分析。结果表明,对于汤逊湖叶绿素浓度遥感反演,一阶微分法和比值法优于单波段法,一阶微分法的最优波段在446.9 nm附近,比值法的最优波段组合为R861.1/R865.7,二者回归R2都在0.86以上。本项研究可为汤逊湖叶绿素浓度的遥感估测提供了参考。     

谱元法数值模拟地震波传播

谱元法基于弹性力学方程弱形式基础之上,在有限单元上进行谱展开,从而使该方法具备了有限元适应任意复杂介质模型的韧性和伪谱法的精度,该方法又被称为高阶有限元方法或伪谱法的域分解,同时为地震波传播的数值模拟提供一种方法。本文阐述了基于Legendre多项式的谱元法的理论和推导过程,相对于Chebychev谱元法,Legendre谱元法可以形成对角的全局质量矩阵,这样可以形成显式的时间差分算法,提高运算效率。最后,进行了复杂介质的各向同性地震波数值模拟,结果表明这是数值模拟地震波传播的有效方法。     

基于实测光谱的海河悬浮物浓度反演研究

悬浮物浓度是评价水质优劣的重要指标之一。以天津滨海新区海河为研究区域,进行光谱数据测量和水体样本采集,并在实验室进行水质参数提取,得到归一化光谱反射率和光谱一阶微分数据。然后对光谱测量数据和悬浮物浓度实测值进行相关性分析,发现896 nm归一化光谱反射率和光谱反射率比值(R_(896)/R_(546))与悬浮物浓度相关性较好。最后,分别建立单波段、波段比值和一阶微分函数拟合模型,进行对比分析。结果表明,基于R_(896)/R_(546)的二次多项式模型拟合效果最好,方差齐性检验(F)值也是最高的,可用于该地区水体的悬浮物浓度反演和预测。     

Audiences for Science Communication in the United States

ABSTRACT

The US National Science Foundation collects biennial data on views about science and technology (S&T). Analyses have typically focused on the degree to which demographics, knowledge, and attitudes predict S&T support or views about specific technologies or issues. Published efforts do not appear to have focused on identifying latent classes within the data. Initial analyses using latent profile analysis suggests that Americans can be categorized into six substantive audience groups when it comes to views about S&T. These groups vary by demographics, including scientific background and ideology, as well as in views about S&T. Post hoc analyses explore how “class-focused” analyses may provide additional insight beyond what can be obtained from “variable-focused” analyses.     

基于彩色图像的林火图像火焰分割研究

结合实际林火图像的特点,提出了一种基于彩色图像分割策略的林火图像的火焰分割算法。该算法首先将图像转化为HSI彩色模型,然后根据各个分量的特点进行分割,并且进行了仿真实验,实验结果表明此算法能够很好地将火焰从复杂的背景中分割出来,克服了基于灰度图像分割不能准确分割烟雾和火焰的弊端,为今后对林火图像进一步分析提供了良好的基础。     

Detecting Invasive Sericea Lespedeza (<Emphasis Type="Italic">Lespedeza cuneata</Emphasis>) in Mid-Missouri Pastureland Using Hyperspectral Imagery

Missouri ranks second in cow-calf numbers in the United States and its pastureland has experienced invasion of various plant species. As one of the invasive weeds, sericea lespedeza is becoming a serious threat to pasturelands in this state. The in-situ field survey in these vast pastures is time consuming and often impossible because of accessibility. Typical aerial survey is also difficult to detect sericea because the plant is of similar size and color as natural grass and, thus, cannot be effectively discriminated in broadband aerial color photos. This study used an airborne hyperspectral image to map sericea and its invasiveness in a public grass field in Mid-Missouri. The maximal 1st-order derivative in red-near infrared region (650–800nm) was derived to separate sericea from fescue, the dominant grass in pastures in Missouri. With a simple threshold approach, sericea of various sizes were identified in the study area. It was also found that the maximal 1st-order derivatives of sericea patches were log-linearly related to sericea “volume,” a quasi 3-dimensional biophysical variable as an approximate measure of sericea invasiveness. The squared correlation coefficient (r ²) of the regression was 0.65 and the estimation error of sericea “volume” estimation was 11% based on ground measurements at 27 sample sites. With this empirical regression model, the quantitative distribution of sericea volume was mapped, which could serve as a first step in alerting landowners and the general public about the seriousness of sericea invasion in Missouri pasturelands.     

Remote sensing water observation for supporting Lake Victoria weed management

This paper aims to assess the suitability of remote sensing for enhancing the management of water body resources and for providing an inexpensive way to gather, on a wide area, weed infestation extent and optical parameter linked to the water body status. Remotely sensed satellite images and ancillary ground true data were used to produce land cover maps, trough classification techniques, and water compounds maps, applying radiative transfer models. The study proposed within the framework of the cooperation between Italian Foreign Affair Ministry (through the University of Rome) and Kenyan Authorities has been carried out on the Kenyan part of the Lake Victoria. This lake is one of the largest freshwater bodies of the world where, over the last few years environmental challenges and human impact have perturbed the ecological balance affecting the biodiversity. The objective of this research study is to define the thematic products, retrievable from satellite images, like weed abundance maps and water compound concentrations. These products, if provided with an appropriate time frequency, are useful to identify the preconditions for the occurrence of hazard events like abnormal macrophyte proliferation and to develop an up-to-date decision support system devoted to an apprised territory, environment and resource management.