澳门红树林植物组成及种群分布格局的研究

调查工作于１９９２年４月～１９９４年９月在路环及凼仔红树林区内进行．路环红树林面积约７．１１５ｈｍ２,凼仔红树林面积约５７．２０５ｈｍ２．在两地区的海滩中,红树林植物计有４科４种,分属于红树科（Ｒｈｉｚｏｐｈｏｒａｃｅａｅ）、紫金牛科（Ｍｙｒｏｉｎａｃｅａｅ）、马鞭草科（Ｖｅｒｂｅｎａｃｅａｅ）和爵床科（Ａｃａｎｔｈａｃｅａｅ）．海岸半红树植物计有１５科２７种．利用样带法研究澳门红树林种群的分布格局,除秋茄为随机分布外,桐花树、白骨壤及老鼠勒均趋于集群的分布格局．结合数据分析８个研究区域种群生态位序列,只有凼仔红树林研究区最具有显著的生态位序列．     

红树林生态系统遥感监测研究进展

随着现代遥感技术的迅速发展,遥感监测已经成为红树林生态系统变化监测的重要手段和方法。从遥感技术在生态系统变化监测应用领域入手,综述了国内外红树林遥感监测的发展历程,系统总结了遥感技术在红树林湿地动态、种间分类、群落结构(叶面积指数、冠幅、树高等)、生物量、灾害灾情(病虫害、风暴潮等)、景观格局动态、驱动力、红树林湿地保护与管理等领域应用现状,归纳了不同应用领域遥感监测的理论、方法及研究现状。指出我国在红树林遥感监测中存在的不足。提出红树林遥感监测应在分类标准体系规范化、分类精度提升、红树林生态学特征参数(物种多样性、优势度等)、生态系统环境空间演变过程及遥感监测的尺度效应方面加大研究力度。充分发挥区域综合监测模型在红树林生态系统变化遥感监测中的作用。     

基于多源遥感的红树林监测

红树林是生长在热带以及亚热带海岸潮间带上的生态群落, 其生产力高, 固碳能力强, 对保持海岸带生物多样性具有十分重要的价值。本文介绍了利用多源遥感数据监测红树林的一些主要研究内容, 分为3个方面: (1)在时空模式研究方面, 利用高空间分辨率影像像素和对象结合的方法对红树林树种进行分类以及利用Landsat影像对红树林进行动态变化监测并分析其驱动因素; (2)在结构参数研究方面, 利用无人机多光谱数据及地面激光雷达数据对红树林叶面积指数进行反演; (3)在生理生化参数研究方面, 探讨了红树林叶绿素含量对淹没状况的响应、互花米草(Spartina alterniflora)入侵是否影响红树林光能利用率, 以及光化学反射指数(photochemical reflectance index, PRI)与光能利用率(light use efficiency, LUE)的关系。上述系列研究为提取红树林相关信息要素时如何选择合适的分析方法提供了有力的参考, 强调了遥感在研究红树林时空模式, 提取结构参数和生物生化参数监测的有效性, 从而更好地促进红树林生态系统的生物多样性保育工作。     

支持向量机及组合预测在蛋白质四级结构分类中的应用

目的:基于支持向量机建立一个自动化识别新肽链四级结构的方法,提高现有方法的识别精度.方法:改进4种已有的蛋白质一级序列特征值提取方法,采用线性和非线性组合预测方法建立一个有效的组合预测模型.结果:以同源二聚体及非同源二聚体为例.对4种特征值提取方法进行改进后其分类精度均提升了2～3%;进一步实施线性与非线性组合预测后,其分类精度再次提高了2～3%,使独立测试集的分类精度达到了90%以上.结论:4种特征值提取方法均较好地反应出蛋白质一级序列包含四级结构信息,组合预测方法能有效地集多种特征值提取方法优势于一体.     

基于高分辨率与高光谱遥感影像的北亚热带马尾松及次生落叶树种的分类

利用遥感数据开展森林资源树种的分类对森林资源的监测、森林可持续经营及生物多样性研究都有重要意义。该文以江苏南部丘陵地区的北亚热带天然次生林为研究对象, 利用LiCHy (LiDAR、CCD、Hyperspectral)集成传感器同期获取的高分辨率和高光谱数据, 进行冠幅识别和多个层次的树种分类: 首先, 对高分辨率影像进行基于边缘检测的多尺度分割, 提取出单木冠幅; 其次, 对高光谱影像进行特征变量提取, 并对提取出的特征变量利用信息熵原理选取优化特征变量; 然后, 分别利用全部特征变量和经优化的重要特征变量对森林树种及森林类型进行预分类; 最后, 在预分类结果中加入单木冠幅信息对森林树种及森林类型进行重分类, 并分析分类结果的精度。研究表明: 1)利用全部特征变量进行4个典型树种分类时, 总体精度为64.6%, Kappa系数为0.493; 而针对森林类型的分类精度为81.1%, Kappa系数为0.584。2)利用选取的优化特征变量分类精度略低于利用全部特征变量的分类精度, 其中对4个典型树种分类时, 总体精度为62.9%, Kappa系数为0.459; 而针对森林类型的分类精度为77.7%, Kappa系数为0.525。通过集成传感器同期获取的高分辨率和高光谱数据可以有效地进行北亚热带森林的树种分类及森林类型的划分。     

辅助数据在面向对象分类方法中的应用——以密云水库上游为例

密云水库上游位于华北平原向蒙古高原的过渡带内,自然条件差异大,人类活动对该区域影响显著。因此,对该地区土地覆盖类型遥感监测方法的研究具有典型的指导意义。基于高分辨率遥感影像,着重探讨辅助数据在面向对象分类方法中的应用,对密云水库上游地区的土地覆盖进行分类提取。结果显示,研究区内共包含26类土地覆盖类型,其中落叶阔叶灌木林、落叶阔叶林、草丛以及旱地,这4种类型的面积占总面积的85%,是全区的主要土地覆盖类型。在分类时,采用多源异构辅助数据,研究了北方山区常绿针叶林、旱地等土地覆盖类型的识别方法,有效降低了"同物异谱"和"同谱异物"现象对分类精度的影响。     

洞庭湖洲滩速生杨树林变化信息提取方法

洞庭湖是我国第二大淡水湖,其湿地资源具有重要的生态功能和经济价值。近20年来,洞庭湖洲滩速生杨树林发展迅速,其中西洞庭湖杨树林的扩张最为明显,极大改变了湖区湿地植被分布格局,隐含极大的生态风险。以Landsat ETM+和HJ-1A/1B CCD影像为数据源,提出了洞庭湖速生杨树林变化信息提取的两种方法,并对这两种方法进行了比较研究。一种是分类的方法,即采用面向对象分层信息提取的方法先提取出树林滩地信息,再将距离大堤一定范围内的树林滩地归为防护林,速生杨树林变化的面积即为两个时相提取结果的差值。另一种是变化检测的方法,它是基于像元进行变化检测,先确定出总的变化区域,再从中筛选速生杨树林的变化信息。结果表明:(1)两种提取方法都是可行的,不同方法提取的速生林变化信息存在一定差异,但空间分布大体一致;(2)基于分类的方法总体精度和Kappa系数均略高于基于变化检测的方法:其中基于分类的方法总体精度达84.00%,Kappa系数为0.67,基于变化检测的方法总体精度达83.00%,Kappa系数为0.65;(3)基于分类的方法图斑较大、图斑数较少,基于变化检测的方法图斑较小且较破碎、图斑数多;(4)基于分类的方法漏分较少、错分较多,基于变化检测的方法漏分较多、错分较少。为洞庭湖洲滩杨树林的动态监测提供了研究方法,也为杨树林扩张原因及其生态效应分析提供研究基础。     

京族药用红树林民族植物学知识及现状

京族是我国唯一的海洋民族,在长期与红树林为伴的生存环境中,积累了丰富的利用红树林植物的民族植物学知识。然而,这些知识至今仍不为外界所知。为掌握京族药用红树林民族植物学知识和现状,该研究采用民族植物学和植物分类学方法,调查京族药用红树林知识中的红树林植物种类,记录其具体药用用途,并分析其生存现状及其中包含的可持续利用红树林植物资源经验。结果表明:共记录到京族药用红树林植物15种隶属于10科,具体药用用途共49种。其中,20种已有报道,29种未见报道;通过京族对红树林药用知识认知程度和依赖程度的调查还表明,京族历史上依赖本民族草医的情形已经基本消失,对红树林医药的依赖性和需求性也在消失。最后,剖析了京族在药用红树林植物选择与采集中的可持续利用经验。该研究结果不仅补充和拓宽了红树林民间药物的利用知识,而且为红树植物资源的管理与可持续利用提供了新视角。     

基于可见光植被指数的面向对象湿地水生植被提取方法

利用ESP分割工具确定最佳分割尺度,通过多尺度分割算法创建最优分割影像,基于微型无人机影像数据生成可见光植被指数,从一系列可见光植被指数中选取一组最优植被指数,建立决策树规则,利用隶属度函数对研究区自动分类,生成水生植被分布图.结果表明: 监督分类法的总体精度为53.7%,面向对象分类法总体精度为91.7%,与基于像元的监督分类法相比,面向对象分类法显著改善了影像分类结果,并大大提高了水生植被提取精度,监督分类法的Kappa系数为0.4,而面向对象分类法的Kappa系数为0.9.这表明利用微型无人机数据生成的可见光植被指数结合面向对象分类方法提取水生植被在该研究区是可行的,并能够应用到其他类似区域.     

面向对象的优势树种类型信息提取技术

森林植被优势树种类型信息的提取是遥感影像分类中的难点.面向对象分类方法是用高空间分辨率遥感数据实现精确类型信息提取的新方法.本文以2013年Quickbird影像作为基础数据,选择福建省三明市将乐林场为研究区,采用面向对象多尺度分割方法提取耕地、灌草地、未成林造林地、马尾松、杉木和阔叶树等类型信息.分类特征融合植被的光谱、纹理和多种植被指数3类特征信息,建立类层次结构,对不同层次分别用隶属度函数和决策树分类规则,最终完成分类,并与只用纹理与光谱特征相结合的方法进行对比.结果表明:融合纹理、光谱、多种植被指数的面向对象的分类方法提取研究区优势树种类型信息的精度为91.3％,比只用纹理和光谱的方法精度提高了5.7％.     

Mapping tree species in temperate deciduous woodland using time‐series multi‐spectral data

Questions: What is the optimum combination of image dates across a growing season for tree species differentiation in multi‐spectral data and how does species composition affect overstorey canopy density? Location: Monks Wood, Cambridgeshire, eastern England, UK. Methods: Six overstorey tree species were mapped using five Airborne Thematic Mapper images acquired across the 2003 growing season (17 March, 30 May, 16 July, 23 September, 27 October). After image pre‐processing, supervised maximum likelihood classification was performed on the images and on all two‐, three‐, four‐ and five‐date combinations. Relationships between tree species composition and canopy density were assessed using regression analyses. Results: The image with the greatest tree species discrimination was acquired on 27/10 when the overstorey species were in different stages of leaf tinting and fall. In this image, tree species were mapped with an overall classification accuracy (OCA) of 71% (kappa 0.63). A similar OCA was achieved from the other four images combined (OCA 72%, kappa 0.64). The highest classification accuracy was achieved by combining three images: 17 March, 16 July, 27 October. This achieved an OCA of 84% (kappa 0.79), increasing to 88% (kappa 0.85) after a post‐classification clump and sieve procedure. Canopy height and percentage cover of oak explained 72% of variance in canopy density. Conclusions: The ability to discriminate and map temperate deciduous tree species in airborne multi‐spectral imagery is increased using time‐series data. An autumn image supplemented with an image from both the green‐up and full‐leaf phases was optimum. The derived tree species map provides a more powerful ecological tool for determining woodland structural/compositional relationships than field‐based measures.     

Digitized photographs in vegetation analysis ‐ a comparison of cover estimates

Abstract. The use of digital images in measuring plant species cover and species number in boreal forest vegetation was studied. Plant cover was estimated by manual delineation on photographs and subsequent digitized measurement of the areas. This value was regarded as the reference and compared with cover obtained by visual estimate, point frequency and automatic image analysis methods. The automatic image analysis was based on scanned photographs. Supervised image classification with ERDAS software was then used to distinguish between the covers of different plant species. When comparing the ability of the methods to detect species, the visual estimate method gave values similar to the reference. The study material was collected from three sites along a heavy‐metal pollution transect in western Finland. All four methods detected, in a similar manner, differences between the plant species abundances along the transect. Compared with the reference, the digital images underestimated the cover of the lichens Cladina spp. and Cetraria islandica, but gave similar estimates for the dwarf shrubs Empetrum nigrum and Vaccinium vitis‐idaea. The point frequency method overestimated the cover of all the species studied. The visual estimates of lichens were close to the reference, while the dwarf shrub covers were overestimated. The number of species detected using supervised image analysis and the point frequency method was lower than that with visual estimation. Visual estimation was faster, and the estimate closer to the reference cover values than the others. Digital images may be useful in detecting changes in some selected species in vegetation with a simple vertical structure but with taller, multilayered vegetation and a higher species number, the reliability of the cover estimates is lower.     

A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features

Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.     

Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico

Predicting broad-scale patterns of biodiversity is challenging, particularly in ecosystems where traditional methods of quantifying habitat structure fail to capture subtle but potentially important variation within habitat types. With the unprecedented rate at which global biodiversity is declining, there is a strong need for improvement in methods for discerning broad-scale differences in habitat quality. Here, we test the importance of habitat structure (i.e. fine-scale spatial variability in plant growth forms) and plant productivity (i.e. amount of green biomass) for predicting avian biodiversity. We used image texture (i.e. a surrogate for habitat structure) and vegetation indices (i.e., surrogates for plant productivity) derived from Landsat Thematic Mapper (TM) data for predicting bird species richness patterns in the northern Chihuahuan Desert of New Mexico. Bird species richness was summarized for forty-two 108 ha plots in the McGregor Range of Fort Bliss Military Reserve between 1996 and 1998. Six Landsat TM bands and the normalized difference vegetation index (NDVI) were used to calculate first-order and second-order image textures measures. The relationship between bird species richness versus image texture and productivity (mean NDVI) was assessed using Bayesian model averaging. The predictive ability of the models was evaluated using leave-one-out cross-validation. Texture of NDVI predicted bird species richness better than texture of individual Landsat TM bands and accounted for up to 82.3% of the variability in species richness. Combining habitat structure and productivity measures accounted for up to 87.4% of the variability in bird species richness. Our results highlight that texture measures from Landsat TM imagery were useful for predicting patterns of bird species richness in semi-arid ecosystems and that image texture is a promising tool when assessing broad-scale patterns of biodiversity using remotely sensed data.     

Multicolour digital image analysis system for identification of bacteria and concurrent assessment of their respiratory activity

AIMS: To develop a rapid and simple multicolour digital image analysis system for simultaneous identification of bacteria and assessment of their metabolic activity. METHODS AND RESULTS: We developed an image analyser capable of distinguishing triple-stained bacterial cells. Bacteria were stained with a nucleic acid stain, a fluorescent antibody and a fluorescent metabolic indicator for enumeration, species identification and assessment of metabolic activity. This multicolour image analyser was used to simultaneously identify Escherichia coli O157:H7 in milk samples and assess their respiratory activity. The images of the triple-stained bacteria were captured using a combination of blue light and u.v. excitation and an epifluorescence microscope and were processed by our image analyser. We found a good correlation between the counts of actively respiring (r = 0.93) and total (r = 0.94) E. coli O157:H7 measured by digital image analysis and visual observation. CONCLUSION: The multicolour digital image analysis system described here was able to quantify active pathogenic micro-organisms within 2 h. SIGNIFICANCE AND IMPACT OF THE STUDY: This multicolour image analysis allows the rapid and simultaneous quantification of bacteria, identification of species and assessment of metabolic activity.     

植物染色体图象分析数据库在芸苔族分类研究中的应用

本文首次用电脑图象分析手段对芸苔族5属13种植物进行了核型分析。其中蓝花子Raphanus;sativus var．raphanistroides、白芥Sinapis alba等植物的核型分析结果,都是国内外首次报道的。笔者建立了十字花科芸苔族植物的染色体数据库,这是国内外第一个专科性的植物染色体图象分析数据库,并且首次将染色体图象分析数据库应用于分类学研究。例如,比较分析芸苔族植物种间的亲缘关系和分类地位,以及探索物种之间的演化关系,得到了若干新的核型论据。笔者认为,植物染色体图象分析数据库在植物分类学研究中具有应用前景。     

Quantitative Infrared Photoanalysis of Selected Bacteria

A technique to measure transmitted infrared radiation from minute biological systems is described. Infrared color film was exposed by radiation transmitted through bacterial colonies. The resultant photographic image was unique for each species of bacteria examined and spectral analysis of the image provided differential light emission patterns which could be quantitated. A formula for developing numerical comparisons among bacterial colonies was provided. The results of this numerical procedure gave quantitative relationships for the total infrared data from each microbial colony and made possible the differential identification of ten species of medically significant bacteria.     

First combined in vivo X-ray tomography and high-resolution molecular electron paramagnetic resonance (EPR) imaging of the mouse knee joint taking into account the disappearance kinetics of the EPR probe

Although laboratory data clearly suggest a role for oxidants (dioxygen and free radicals derived from dioxygen) in the pathogenesis of many age-related and degenerative diseases (such as arthrosis and arthritis), methods to image such species in vivo are still very limited. This methodological problem limits physiopathologic studies about the role of those species in vivo, the effects of their regulation using various drugs, and the evaluation of their levels for diagnosis of degenerative diseases. In vivo electron paramagnetic resonance (EPR) imaging and spectroscopy are unique, noninvasive methods used to specifically detect and quantify paramagnetic species. However, two problems limit their application: the anatomic location of the EPR image in the animal body and the relative instability of the EPR probes. Our aim is to use EPR imaging to obtain physiologic and pathologic information on the mouse knee joint. This article reports the first in vivo EPR image of a small tissue, the mouse knee joint, with good resolution (≈ 160 μm) after intra-articular injection of a triarylmethyl radical EPR probe. It was obtained by combining EPR and x-ray micro-computed tomography for the first time and by taking into account the disappearance kinetics of the EPR probe during image acquisition to reconstruct the image. This multidisciplinary approach opens the way to high-resolution EPR imaging and local metabolism studies of radical species in vivo in different physiologic and pathologic situations.     

Diffraction pattern applicability in the identification of Ceratium species

At present, a subject of great interest for the scientific community is to obtain automatic systems for counting and identifying organisms responsible for red tides. Nevertheless, there are key problems that affect the results in the correct identification and quantification, such as image background (detritus, lighting variation in the microscope), variation in cell sedimentation in the observation field, natural morphological variation of the species in a sample, intra- and interspecific problems, and organism fragmentation. These problems are quantified by means of digital analysis of the phytoplankton organisms' image diffraction patterns. Quantification was accomplished by analyzing the results of the image diffraction pattern correlations and the image correlations. The results showed that the use of diffraction patterns in the identification of six Ceratium species overcomes the numerous noise problems mentioned above.      

Multifractal analysis of the species structure of freshwater hydrobiocenoses

The principles and methods of fractal analysis of the species structure of freshwater phytoplankton, zooplankton, and macrozoobenthos communities of plain water reservoirs and urban waterbodies are discussed. The theoretical foundation and experimental verification are provided for the authors?? concept of self-similar (quasi-fractal) nature of the species structure of communities. According to this concept, the adequate mathematical image of species richness accumulation with growing sampling effort is quasi-monofractals, while the generalized geometric image of the species structure of the community is a multifractal spectrum.