影响黄土高原地物光谱反射率的非均匀因子及反照率参数化研究

通过应用高光谱反射仪进行各种植被覆盖度地物的同期观测,分析不同地物光谱反射率和宽波段反照率的差异,得出:除太阳高度角的影响外,植被的不同生育期及生长状况决定的叶绿素、细胞构造和含水量等要素都会影响植物光谱反射率;基于归一化植被指数( NDVI)、归一化植被水分指数(NDWI)、土壤体积含水量以及参考对象的光谱曲线建立了植物光谱反射率的估算模型,能较好地反映地物光谱反射率特征;基于地物波谱反射率估算得到的全波段反照率误差在0.02范围内,可以作为反照率遥感反演和转换的依据;该方法也为高光谱遥感在反照率等陆面过程参数尺度耦合和转换过程中应用奠定了基础.     

遥感技术在鸟类生态学研究中的应用

获取鸟类活动及生境信息是鸟类生态学研究的基础,而遥感技术弥补了传统野外调查方法的缺陷,提供了获取多种信息的新途径。应用遥感技术的鸟类生态学研究热点从最初的种群行为观察,到栖息地选择,再到生境适宜性、破碎化及人为干扰探究等,随着技术的不断发展也在扩展和变化。不同波段或组合下的遥感技术各有所长。光学遥感应用广泛,尤其是信息量较大的红外波段图像和作为野外鸟巢及物种活动监测常用工具的红外相机;多光谱图像常用于栖息地制图以及地物识别,高空间分辨率的数据甚至可对鸟类种群进行直接计数;高光谱数据则可对光谱特征相似的地物进行更为精确的区分和反演;激光雷达遥感主要用于栖息地植被结构的三维探测,为了解鸟类栖息地选择提供更好的依据。微波遥感在飞鸟探测上应用颇多,近年来多极化数据在复杂栖息地精确制图上也具有优势,但成本较高、解译复杂且推广度较低。在实际应用中,遥感数据时空尺度的选择会影响研究结果,部分遥感反演参数也缺乏生态学意义。多源遥感数据的结合应用能够提升制图分类的精度,实现数据的时空分辨率互补,优化鸟类生态研究所需参数。未来的遥感技术在鸟类生态学中的应用应致力于提供更加明确的光谱信息、相对简便的解译方法,以及更为合理的多源数据组合方式等。     

大型沉水植物苦草的光谱特征识别

地物特征与其光谱特征的关系是解译遥感影像的关键.利用地物光谱仪在实验室和上海“梦清园”人工湿地中,分别实测了不同盖度沉水植物苦草的反射光谱特征.结果表明随着苦草盖度的增加,其光谱反射率也相应增加,不同盖度苦草的光谱反射率差异主要表现在500～650 nm和700～900 nm 波段范围.受水体环境影响,实验室模拟试验与室外控制试验测得的苦草光谱反射率差异主要表现在近红外波段（700～900 nm）.分别将苦草的不同盖度与其在QuickBird多光谱遥感影像4个波段与盖度相关性最大波段处的光谱反射率进行回归分析,得到了较好的线性关系.应用回归分析得到的线性方程,可以根据测定的光谱反射率定量反演水体中的苦草盖度.研究结果可为监测沉水植物的高光谱遥感影像判读和解译分类提供技术支撑,为大尺度遥感监测沉水植物的分布和动态变化提供科学依据.     

生物组织拉曼光谱数据的统计分析

拉曼光谱技术在生命科学领域已取得较为广泛的研究与应用,如何从低信噪比、质量差的拉曼光谱信号提取并充分利用谱图中所含信息,对光谱后续分析、样品的归类等至关重要.本文首先明确了生物组织拉曼光谱统计分析中几个重要的概念,进而对拉曼光谱数据的预处理,光谱分析研究中较为常用的几种多元统计方法进行了归纳、对比与分析.     

Monte Carlo模拟肠道组织的非接触式漫反射光谱

本文采用Monte Carlo模拟人体肠道组织的非接触式漫反射光谱,并分别研究了聚焦光束的聚焦深度、组织表面入射光斑与出射光斑(Source-Detector,S-D)之间的距离、探测面积和探测深度对光谱测量的影响,为设计非接触式光谱检测系统提供理论依据。结果表明在利用光谱技术对肠道疾病如早期肠癌进行诊断时,非接触式光谱检测系统的聚焦深度应小于0.1 cm;在漫反射光谱检测时,应根据探测信号的强弱以及探测器的灵敏度选择S-D距离;0.06 cm的探测面积半径能有效地反映组织中氧合血红蛋白和脱氧合血红蛋白含量的变化情况;为反映不同深度组织光学特性,在改变探测深度时,应保持探测光锥顶角不变。     

植物反射光谱对水分生理变化响应的研究进展

实时、无损伤地探测植物的水分及生理变化是高光谱遥感的深层次应用。由水分胁迫引发的植物一系列反射光谱响应体现了碳-氮-水耦合作用的结果。以往的研究大多集中于单一因素的响应,而忽略了多因素交互作用。该文综述和分析了植物水分状况变化引起的直接和间接光谱响应机制,包括植物水分含量、色素、养分状况、光合作用和叶绿素荧光指标的光谱响应及其内在的关联,探讨了反射光谱在探测植物水分生理活动应用中的主要方法与最新技术,并指出碳-氮-水多指标、多时空尺度的综合分析对于估测植被生产力及其对气候变化的响应具有重要意义。     

纳米生物传感研究进展

纳米生物传感利用了新型纳米材料极佳的反应活性、催化效率、光谱效应、导电性、强度、韧性和超顺磁性等特有性质,因此具有灵敏度高、体积小、速度快、功耗小、多功能集成等诸多优势.近年来,纳米生物传感技术发展迅猛,应用广阔.本文归纳总结了纳米生物传感技术研究的历史和最新进展,为纳米生物传感技术的深入研究与应用提供了资料.     

崇明东滩盐沼土壤重金属含量的高光谱估算模型

高光谱遥感技术现已被广泛应用于地表矿物成份的识别以及它的含量、植物化学成份的估测,土壤调查等方面的研究。利用地物光谱仪测定了长江口崇明东滩盐沼土壤的光谱信息,根据EO-1卫星Hyperion高光谱仪的波段设置,提取各波段平均波长的光谱反射率,并结合实验室实测的土壤重金属含量,应用偏最小二乘法(PLS),预测盐沼土壤重金属含量。结果表明,盐沼土壤重金属Zn、Cr、Cu预测值与实测值相关系数分别达到0.822**、0.761**和0.775**,均为极显著相关,其实测值与预测值的平均相对误差为4%、3%和4%。研究结果可以为高光谱遥感技术反演盐沼土壤重金属含量,进一步应用空间或航空遥感进行大尺度环境污染遥感、遥测信息提取和反演提供技术支撑。     

功能红细胞研究的拉曼光谱技术与进展

红细胞在人体新陈代谢和物质运输方面起着非常重要的作用。作为一种无损、快速而灵敏的光谱学检测方法,拉曼光谱技术在生物医学领域的应用日渐广泛。本文综述了近年来拉曼光谱技术在红细胞及血红蛋白研究中的应用进展,详尽评述当前研究功能红细胞中较有优势的拉曼技术—显微拉曼光谱技术和光镊拉曼光谱技术,并对其技术的改进做出总结,以期为利用激光拉曼光谱研究红细胞提供较系统的参考。     

近红外光谱技术在水果成熟期预测中的应用(综述)

近红外光谱技术以快速、准确和多组分同步分析等优势,近年来在水果果实发育、成熟期预测和品质检测等方面应用广泛,并在果实品质无损检测分析技术研究方面取得重要进展。本文综述近红外光谱技术的基本原理和特点,分析近红外光谱技术在果实成熟期预测中的研究现状和存在问题,并提出今后研究方向。     

亚热带典型常绿阔叶树种叶片叶绿素含量与其高光谱特征间的关系

叶绿素在植物的生理生态过程中非常重要.利用高光谱数据,揭示光谱反射率上特征光谱与叶绿素含量间的关系将有助于理解叶绿素光谱反射特征的规律.选取了6种亚热带常绿阔叶树种,抽取一定数量的叶片样品,分别测量了叶片的光谱反射率及其叶绿素含量,并建立了光谱反射率曲线上的红边位置附近的光谱反射率和叶绿素总量间的回归模型.结果显示:对所研究的树种而言,红边位置处一窄波长范围内的平均光谱反射率与叶绿素总量间的相关性普遍较高,回归效果显著.从高光谱遥感的角度结合植物的生理生态特点分析了其机理.建立的有关红边位置处的光谱反射率与叶绿素总量间的回归模型普遍具有较高的拟合度,其应用前景较好.     

Distinguishing natural from anthropogenic stress in plants: physiology, fluorescence and hyperspectral reflectance

Background and Aims

Explosives released into the environment from munitions production, processing facilities, or buried unexploded ordnances can be absorbed by surrounding roots and induce toxic effects in leaves and stems. Research into the mechanisms with which explosives disrupt physiological processes could provide methods for discrimination of anthropogenic and natural stresses. Our objectives were to experimentally evaluate the effects of natural stress and explosives on plant physiology and to link differences among treatments to changes in hyperspectral reflectance for possible remote detection.

Methods

Photosynthesis, water relations, chlorophyll fluorescence, and hyperspectral reflectance were measured following four experimental treatments (drought, salinity, trinitrotoluene and hexahydro-1,3,5-trinitro-l,3,5-triazine) on two woody species. Principal Components Analyses of physiological and hyperspectral results were used to evaluate the differences among treatments.

Results

Explosives induced different physiological responses compared to natural stress responses. Stomatal regulation over photosynthesis occurred due to natural stress, influencing energy dissipation pathways of excess light. Photosynthetic declines in explosives were likely the result of metabolic dysfunction. Select hyperspectral indices could discriminate natural stressors from explosives using changes in the red and near-infrared spectral region.

Conclusions

These results show the possibility of using variations in energy dissipation and hyperspectral reflectance to detect plants exposed to explosives in a laboratory setting and are promising for field application using plants as phytosensors to detect explosives contamination in soil.     

The fluorescence lidar technique for the remote sensing of photoautotrophic biodeteriogens in the outdoor cultural heritage: A decade of in situ experiments

Fluorescence lidar is a non-invasive, remote sensing technique that makes it possible to extend the application of the laser-induced fluorescence technique to the outdoor environment, where uncontrolled external conditions must be met. Although initially developed for the investigation of marine environment and vegetation, in the past decade this technique has been successfully applied to the field of the cultural heritage. Among other applications, the detection and characterisation of photoautotrophic biodeteriogens has become very promising: the method is based on the detection of chlorophyll a fluorescence, while fluorescent accessory pigments can be exploited for a rough classification of the biodeteriogens present on the surface. Early experiments on monuments date back to the mid 1990s, when fluorescence lidar point measurements were conducted on the Cathedral and Baptistery of Parma, Italy. Subsequently, the technique has taken further advantage of the introduction of imaging capabilities in lidar instrumentation and this has led to the acquisition of hyperspectral fluorescence maps over extended areas of several monuments from distances as great as 80 m. Here, we present the main achievements obtained in the outdoor cultural heritage so far and the latest developments in the technique.     

Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Tea plant stresses threaten the quality of tea seriously. The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation. In recent years, hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases, pests and some other stresses at the leaf level. However, the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale. In this study, based on the canopy-level hyperspectral imaging data, the methods for identifying and differentiating the three commonly occurred tea stresses (i.e., the tea leafhopper, anthrax and sun burn) were studied. To account for the complexity of the canopy scenario, a stepwise detecting strategy was proposed that includes the process of background removal, identification of damaged areas and discrimination of stresses. Firstly, combining the successive projection algorithm (SPA) spectral analysis and K-means cluster analysis, the background and overexposed non-plant regions were removed from the image. Then, a rigorous sensitivity analysis and optimization were performed on various forms of spectral features, which yielded optimal features for detecting damaged areas (i.e., YSV, Area, GI, CARI and NBNDVI) and optimal features for stresses discrimination (i.e., MCARI, CI, LCI, RARS, TCI and VOG). Based on this information, the models for identifying damaged areas and those models for discriminating different stresses were established using K-nearest neighbor (KNN), Random Forest (RF) and Fisher discriminant analysis. The identification model achieved an accuracy over 95%, and the discrimination model achieved an accuracy over 93% for all stresses. The results suggested the feasibility of stress detection and differentiation using canopy-level hyperspectral imaging techniques, and indicated the potential for its extension over large areas.     

Parallel-scan based microarray imager capable of simultaneous surface plasmon resonance and hyperspectral fluorescence imaging

With the development of the microarray technology, demands for array detection techniques become higher and higher. For many microarrays, several biomolecular interactions occur simultaneously and the interplay of various factors that affect these interactions remains poorly understood. Detecting such interactions with a single technique can often be a difficult and complicated process. In this work we propose a combined technique which enables simultaneous angle-interrogation surface plasmon resonance (SPR) sensing and hyperspectral fluorescence imaging. This tandem technique offers two-dimensional imaging of the whole array plane. The refractive index information obtained from SPR sensing and the physicochemical properties obtained from fluorescence imaging provide a comprehensive analysis of biological events on the array-chip. In addition, SPR and fluorescence detection techniques confirm each other in experimental results to exclude false-positive or false-negative cases. In terms of SPR sensing performance, the refractive index resolution is 3.86 × 10⁻⁶ refractive index units (RIU), and the detection limit is 10⁴ cfu/ml of Escherichia coli bacteria. The resolving power and detection sensitivity of fluorescence imaging are approximately 20 μm and 0.61 fluors/μm², respectively. Finally, two model experiments, detecting the DNA hybridization and biotin–avidin interactions respectively, demonstrate the biomedical application of this system.     

Classification of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on Self-Organizing Maps

Rotylenchulus reniformis is one of the major nematode pests capable of reducing cotton yields by more than 60%, causing estimated losses that may exceed millions of dollars U.S. Therefore, early detection of nematode numbers is necessary to reduce these losses. This study investigates the feasibility of using remotely sensed hyperspectral data (reflectances) of cotton plants affected with different nematode population numbers with self-organizing maps (SOM) in correlating and classifying nematode population numbers extant in a plant's rhizosphere. The hyperspectral reflectances were classified into three classes based on R. renifomis population numbers present in plant's rhizosphere. Hyperspectral data (350-2500 nm) were also sub-divided into Visible, Red Edge + Near Infrared (NIR) and Mid-IR region to determine the sub-region most effective in spectrally classifying the nematode population numbers. Various combinations of different feature extraction and dimensionality reduction methods were applied in different regions to extract reduced sets of features. These features were then classified using a supervised-SOM classification method. Our results suggest that the overall classification accuracies, in general, for most methods in most regions (except visible region) varied from 60% to 80%, thereby, indicating a positive correlation between the nematode numbers present in plant's rhizosphere and the corresponding plant's hyperspectral signatures. Results showed that classification accuracies in the Mid-IR region were comparable to the accuracies obtained in other sub-regions. Finally, based on our findings, the use of remotely-sensed hyperspectral data with SOM could prove to be extremely time efficient in detecting nematode numbers present in the soil.     

Enhanced spatial resolution for snapshot hyperspectral imaging of blood perfusion and melanin information within human tissue

We report a reconstruction method to achieve high spatial resolution for hyperspectral imaging of chromophore features in skin in vivo. The method utilizes an established structure‐adaptive normalized convolution algorithm to reconstruct high spatial resolution of hyperspectral images from snapshot low‐resolution hyperspectral image sequences captured by a snapshot spectral camera. The reconstructed images at chromophore‐sensitive wavebands are used to map the skin features of interest. We demonstrate the method experimentally by mapping the blood perfusion and melanin features (moles) on the facial skin. The method relaxes the constrains of the relatively low spatial resolution in the snapshot hyperspectral camera, making it more usable in imaging applications.     

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust and aphid with a few specific bands. Through a comprehensive spectral analysis, only three bands at 570, 680 and 750 nm were selected. A novel vegetation index namely Ratio Triangular Vegetation Index (RTVI) was developed for detecting anomalous areas on leaves. Then, the Support Vector Machine (SVM) method was applied to construct the discrimination model based on the spectral ratio analysis. The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy (OA) of 83%. With three bands from the hyperspectral imaging data, the three wheat diseases and pests were successfully detected and discriminated. A stepwise strategy including background removal, damage lesions recognition and stresses discrimination was proposed. The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.     

Intracellular Localized Surface Plasmonic Sensing for Subcellular Diagnosis

This paper proposes a method for diagnosing intracellular conditions and organelles of cells with localized surface plasmonic resonance (LSPR) by directly internalizing the gold nanoparticles (AuNPs) into the cells and measuring their plasmonic properties through hyperspectral imaging. This technique will be useful for direct diagnosis of cellular organelles, which have potential for cellular biology, proteomics, pharmaceuticals, drug discovery etc. Furthermore, localization and characterization of citrate-capped gold nanoparticles in HeLa cells were studied, by hyperspectral microscopy and other imaging techniques. Here, we present the method of internalizing the gold nanoparticles into the cells and subcellular organelles to facilitate subcellular plasmonic measurements. An advanced label-free visualization technique, namely hyperspectral microscopy providing images and spectral data simultaneously, was used to confirm the internalization of gold nanoparticles and to reveal their optical properties for possible intracellular plasmonic detection. Hyperspectral technology has proved to be effective in the analysis of the spectral profile of gold nanoparticles, internalized under different conditions. Using this relatively novel technique, it is possible to study the plasmonic properties of particles, localized in different parts of the cell. The position of the plasmon bands reflects the interactions of gold nanoparticles with different subcellular systems, including particle-nucleus interactions. Our results revealed the effect of the different intracellular interactions on the aggregation pattern of gold nanoparticles, inside the cells. This novel technique opens the door to intracellular plasmonics, an entirely new field, with important potential applications in life sciences. Similarly, the characterization of AuNP inside the cell was validated using traditional methods such as light microscopy and scanning electron microscopy. Under the conditions studied in this work, gold nanoparticles were found to be non-toxic to HeLa (cervical cancer) cells.     

生物过程化学机制的单分子荧光探测

单分子荧光检测越来越广泛地被应用于生命科学领域。这项技术可以对生物过程的化学机制进行定量、仔细的探究，与传统系综实验形成很好的互补。本文简介近几年单分子荧光检测研究的若干典型实例，以此展示这项技术的特点、优势及其可能的应用。它们涉及从简单的生化反应到复杂的蛋白表达调控等重要的生物过程。