Rapid algal culture diagnostics for open ponds using multispectral image analysis

This article presents a multispectral image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel floating reference platform was placed in the culture, which enhanced the sensitivity of image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22 and 14%, respectively. Moreover, the difference in spectral signatures between the two strains was exploited to detect the invasion of Chlorella sp. cultures by A. variabilis. Invasion was successfully detected for A. variabilis to Chlorella sp. mass ratios as small as 0.08. Finally, a method was presented for using multispectral imaging to detect thermal stress in A. variabilis. These methods can be extended to field applications to provide delay free process control feedback for efficient operation of large scale algae cultivation systems. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:233–240, 2014     

遥感技术支持下的植被生产力与生物量研究进展

目前广泛应用于植被生产力与生物量估算的遥感模型主要有经验模型、物理模型、半经验模型和综合模型 ,它们的应用受到诸如大气、背景、地形、植被覆盖率与结构等因素的影响。遥感技术的迅速发展及其它技术的应用 ,包括热红外、微波和激光遥感仪器以及多角度、高光谱和高分辨率技术等 ,正逐步消除或降低影响因素 ,进一步提高植被生产力与生物量估算的范围和精度     

Spatial distribution and quantification of Fucus species and Ascophyllum nodosum beds in intertidal zones using spot imagery

Low tide SPOT images were selected from two French coast areas characterized by important Fucaceae populations (Pleubian-Bréhat site in Northern Brittany and Ré Island on the Atlantic coast).A specific data transformation yielding the theoretical algal cover was used. This index takes the radiometric properties of the intertidal zone and of the Fucaceae into account. Satellite data cover was validated by comparing it with selected field samples.Other field data indicate that a linear relation exists between cover and biomass. This relationship is quite independent of cover patchiness. However, it can vary according to species, season and location. Hence it was possible to estimate Fucus sp and Ascophyllum nodosum harvestable biomass using appropriate segments of the intertidal zone.     

Methods for the quantification ofFrankia cell biomass

Six methods for the estimation of microbial biomass were compared for determination ofFrankia cell concentrations. Six strains ofFrankia were cultivated in stationary culture, harvested by centrifugation, washed with saline buffer and diluted to five standardized concentrations. These cell suspensions were then used to assess reliability of each of the biomass determination methods. The destructive total protein determination methods were the most sensitive and reliable. Two non-destructive methods, packed cell volume and turbidity measurement, were also accurate, and because of their simplicity hold advantage for routine growth measurements and inoculum dilutions. Dry weight determinations were inconsistent for the small cell masses used in this study. An ELISA procedure demonstrated reliability but little sensitivity.     

黑龙江长白山森林生物量的时空变化分析

森林生物量碳储量的空间分布及其变化信息, 对揭示地表空间变化规律及驱动因子、分析评价森林生产力及生态功能具有重要意义。该文以20世纪70年代、80年代、90年代和21世纪初4个时期的遥感数据和同期的森林资源清查样地数据为基础, 应用遥感信息模型, 估算了黑龙江长白山地区的森林生物量, 分析了该地区森林生物量的时空动态变化, 以及森林生物量随高程、坡度和坡向的变化规律。结果表明: 该地区4个时期的森林平均生物量分别为81.56、44.27、48.27和54.82 t·hm^-2。4个时期总的森林生物量分别为5.37 × 10 ⁸、2.83 × 10 ⁸、3.06 × 10 ⁸和3.46 × 10 ⁸ t。20世纪70年代到21世纪初森林平均生物量和总的森林生物量都呈现出先降低后增加的趋势, 呈先下降趋势的主要原因是20世纪70-80年代以森林采伐为主, 后增加趋势的主要原因是实施天然林保护工程起到了很大的作用。该地区4个时期森林生物量随高程、坡度和坡向都表现出一致性的变化规律, 森林生物量随高程和坡度变化都呈先增加后减少的趋势, 导致这一现象的主要原因是, 高程、坡度和坡向变化引起了局地气候条件的变化, 从而直接影响森林生长环境, 造成森林分布的变化。森林生物量在200-400 m高程所占的比例最大, 约为35%, 在坡度5°-15°所占的比例接近50%。森林生物量在南坡和西南坡所占的比例最小, 为7%; 平坡所占的比例最大, 为28%; 南坡次之, 为19%。     

基于森林群落和光谱曲线特征分异的福建省森林生物量遥感反演

基于福建省Landsat8 OLI影像,利用混合像元分解模型筛选出"纯净"的植被像元,提取296个调查样地对应植被像元的红光和近红外波段的中心波长(分别CWR和CWNIR)及其对应的反射率(分别R和NIR),构建以(NIR-R)/(CWNIR-CWR)为特征指数的叶生物量回归模型。然后根据针叶林、阔叶林及针阔混交林叶生物量与干、枝、叶所组成的地上生物量的关系方程,结合福建省植被覆盖分类数据,估测了整个福建省针叶林、阔叶林、混交林的地上生物量,并绘制了福建省地上生物量分布图。结果表明:红光和近红外两个波段反射率和其中心波长所组成的斜率与叶生物量相关性显著,与针叶林、阔叶林、混交林叶生物量的精度分别达到70.55%、68.89%、51.75%,采用这种方法对福建省叶生物量和地上总生物量进行估算,并进行精度验证,其中,针叶林、阔叶林、混交林叶物量的模型误差(RMSE)分别达到29.2467 t/hm~2(R~2=66.64%)、14.0258 t/hm~2(R~2=61.13%)、10.1788 t/hm~2(R~2=55.43%),地上总生物量的模型精度分别达到49.8315 t/hm~2(R~2=54.65%)、45.1820 t/hm~2(R~2=49.01%)、41.5131 t/hm~2(R~2=38.79%),这说明,采用红光波段和近红外波段与其中心波长所组成的斜率估测森林叶生物量,进而估算其地上总生物量的方法是可行的。     

Effect of light intensity on algal biomass accumulation and biodiesel production for mixotrophic strains Chlorella kessleri and Chlorella protothecoide cultivated in highly concentrated municipal wastewater

In this research, the effect of light intensity on biomass accumulation, wastewater nutrient removal through algae cultivation, and biodiesel productivity was investigated with algae species Chlorella kessleri and Chlorella protothecoide. The light intensities studied were 0, 15, 30, 60, 120, and 200 μmol m(-2) s(-1). The results showed that light intensity had profound impact on tested responses for both strains, and the dependence of these responses on light intensity varied with different algae strains. For C. kessleri, the optimum light intensity was 120 μmol m(-2) S(-1) for all responses except for COD removal. For C. protothecoide, the optimum light intensity was 30 μmol m(-2) S(-1). The major components of the biodiesel produced from algae biomass were 16-C and 18-C FAME, and the highest biodiesel contents were 24.19% and 19.48% of dried biomass for C. kessleri and C. protothecoide, respectively. Both species were capable of wastewater nutrients removal under all lighting conditions with high removal efficiencies.     

Cell identification and sizing using digital image analysis for estimation of cell biomass in High Rate Algal Ponds

Current environmental concerns make estimation of microbial biomass apriority for monitoring purposes and to advance scientific understanding. Thispaper considers problems associated with algal cell imaging and measurement forcell biomass estimation in samples from high rate algal ponds. In a complexsystem, the only way of measuring microbial activity is to measure theindividual cells and estimate biovolumes. Accurate biomass determinationsdemanddirect microscopic counting and measurement of the sizes of individualmicrobialcells taken from known volumes of water. The system used for routinemeasurementat the laboratory where the images were generated, based on standard microscopeequipment, is only suitable for treatment of well dispersed specimens.Differential interference contrast (DIC) microscopy, on the other hand, offersthe best solution for optical enhancement of cell contrast, and produces animage with well defined edges, yet presents a great challenge to routine cellidentification by digital image analysis, owing to the bas-relief type imageproduced. The paper outlines several image analysis methods developedspecifically for this purpose, and presents illustrative results.     

Advanced phenotyping offers opportunities for improved breeding of forage and turf species

Background and Aims

Advanced phenotyping, i.e. the application of automated, high-throughput methods to characterize plant architecture and performance, has the potential to accelerate breeding progress but is far from being routinely used in current breeding approaches. In forage and turf improvement programmes, in particular, where breeding populations and cultivars are characterized by high genetic diversity and substantial genotype × environment interactions, precise and efficient phenotyping is essential to meet future challenges imposed by climate change, growing demand and declining resources.

Scope

This review highlights recent achievements in the establishment of phenotyping tools and platforms. Some of these tools have originally been established in remote sensing, some in precision agriculture, while others are laboratory-based imaging procedures. They quantify plant colour, spectral reflection, chlorophyll-fluorescence, temperature and other properties, from which traits such as biomass, architecture, photosynthetic efficiency, stomatal aperture or stress resistance can be derived. Applications of these methods in the context of forage and turf breeding are discussed.

Conclusions

Progress in cutting-edge molecular breeding tools is beginning to be matched by progress in automated non-destructive imaging methods. Joint application of precise phenotyping machinery and molecular tools in optimized breeding schemes will improve forage and turf breeding in the near future and will thereby contribute to amended performance of managed grassland agroecosystems.     

The ImageJ ecosystem: Open‐source software for image visualization,processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.     

Light/dark cyclic movement of algal culture (<Emphasis Type="Italic">Synechocystis aquatilis</Emphasis>) in outdoor inclined tubular photobioreactor equipped with static mixers for efficient production of biomass

Synechocystis aquatilis SI-2 was grown outdoors in a 12.5cm diam. tubular photobioreactor equipped with static mixers. The static mixers ensured that cells were efficiently circulated between the upper (illuminated) and lower (dark) sections of the tubes. The biomass productivity varied from 22 to 45g m^–2d^–1, with an average of 35g m^–2d^–1, etc which corresponded to average CO₂ fixation rate of about 57 g CO₂ m^–2 d^–1. The static mixers not only helped in improving the biomass productivities but also have a high potential to lower the photoinhibitory effect of light during the outdoor cultures of algae. Revisions requested 27 July 2004; Revisions received 12 November 2004