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.     

Variation in foliar nitrogen and albedo in response to nitrogen fertilization and elevated CO2

Foliar nitrogen has been shown to be positively correlated with midsummer canopy albedo and canopy near infrared (NIR) reflectance over a broad range of plant functional types (e.g., forests, grasslands, and agricultural lands). To date, the mechanism(s) driving the nitrogen–albedo relationship have not been established, and it is unknown whether factors affecting nitrogen availability will also influence albedo. To address these questions, we examined variation in foliar nitrogen in relation to leaf spectral properties, leaf mass per unit area, and leaf water content for three deciduous species subjected to either nitrogen (Harvard Forest, MA, and Oak Ridge, TN) or CO(2) fertilization (Oak Ridge, TN). At Oak Ridge, we also obtained canopy reflectance data from the airborne visible/infrared imaging spectrometer (AVIRIS) to examine whether canopy-level spectral responses were consistent with leaf-level results. At the leaf level, results showed no differences in reflectance or transmittance between CO(2) or nitrogen treatments, despite significant changes in foliar nitrogen. Contrary to our expectations, there was a significant, but negative, relationship between foliar nitrogen and leaf albedo, a relationship that held for both full spectrum leaf albedo as well as leaf albedo in the NIR region alone. In contrast, remote sensing data indicated an increase in canopy NIR reflectance with nitrogen fertilization. Collectively, these results suggest that altered nitrogen availability can affect canopy albedo, albeit by mechanisms that involve canopy-level processes rather than changes in leaf-level reflectance.     

基于光谱特征变量的湿地典型植物生态类型识别方法——以北京野鸭湖湿地为例

光谱特征变量的选择对于湿地植被识别的精度和效率有着直接的影响作用.以华北地区典型的淡水湿地——野鸭湖湿地为研究区,采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植物的冠层光谱.以野外高光谱数据为基础,首先利用一阶导数与包络线去除的方法,分析和对比不同植物生态类型的光谱特征,选定了用于识别植物生态类型的光谱特征变量,选定的8个光谱特征变量为红边位置WP_r、红边幅值Dr、绿峰位置WP_g、绿峰幅值Rg、510 nm附近的吸收深度DEP-510和吸收面积AREA-510、675 nm附近的吸收深度DEP-675和吸收面积AREA-675.其中,7种植物生态类型的一阶导数光谱特征差异较小,吸收特征差异性相对较大.除WP_r和WP _g外,沉水植物Rg和Dr平均值最低,湿生植物的Rg平均值最高,达到0.164,栽培植物的Dr平均值最高,达到0.012.7种植物生态类型在675 nm附近的DEP-675和AREA-675均高于510 nm附近的DEP-510与AREA-510,除去栽培植物,随着水分梯度的变化,其他6种植物生态类型的吸收深度和吸收面积都表现出先升高后降低的趋势.然后利用单因素方差分析(One-way ANOVA)验证了所选光谱特征变量的区分度,在P≤0.01的置信水平下,选取的8个光谱特征变量都能够较好的区分7种植物生态类型,区分度的最小值为13,最大值为18,并且吸收特征参数的区分度优于一阶导数参数.最后应用非线性的反向传播人工神经网络(BP-ANN)与线性判别分析(FLDA)的类型识别方法,利用选定的8个光谱特征变量进行湿地植物生态类型识别,取得了较好的识别精度,两种方法的总分类精度分别达到85.5％和87.98％.单因素方差分析(One-way ANOVA)和不同分类器的分类精度表明,所选的8个光谱特征变量具有一定的普适性和可靠性.     

无人机高光谱影像与冠层树种多样性监测

冠层树种多样性是自然森林生态系统功能和服务的重要基础。及时掌握冠层多样性的现状及变化趋势, 是探讨诸多重要生态学问题的前提, 更是制定合理生物多样性保护策略的基础。但受制于传统的多样性信息采集方法, 区域尺度的高精度冠层多样性监测发展较为缓慢; 许多在气候变化和人类干扰下的生物多样性分布信息得不到及时更新。近年来基于无人机的冠层高光谱影像收集与分析技术的发展, 使得冠层多样性监测迎来了新的发展契机。本文从森林冠层高光谱影像出发, 介绍了与多样性监测相关的无人机航拍和基于深度学习的图像处理技术, 并结合已有文献, 探讨了无人机高光谱应用于森林冠层树种多样性监测的研究现状、可行性、优势及缺陷等。我们认为冠层高光谱影像为多样性监测提供了不可或缺且丰富的原始信息; 而无人机与高光谱相机的结合, 使得区域化高频率(如每周)、高精度(如分米乃至厘米级)的冠层多样性信息自动化收集成为可能。然而高光谱影像数据量大、数据维度高与数据结构非线性的特点为影像处理带来了挑战, 而深度学习技术的飞跃, 使得从冠层高光谱影像中提取个体及物种信息达到了极高精度。恰当地使用这些技术将大大提升冠层树种多样性的自动化监测水平, 由此也将帮助我们在当前剧变环境下及时掌握森林冠层多样性的现状与变化, 为生物多样性研究与保护提供可靠的数据支撑。     

Using multitemporal digital elevation model data for detecting canopy gaps in tropical forests due to cyclone damage: An initial assessment

Abstract The detection and mapping of canopy disturbance caused by natural events such as cyclones in tropical rainforests is important for monitoring and understanding rainforest dynamics and recovery. In February 1999 tropical cyclone Rona crossed the coastline of Far North Queensland, Australia, causing significant damage to the rainforest canopy in some areas. This paper examines the application of multitemporal canopy digital elevation models (DEMs) generated by a NASA‐operated airborne radar mapping system called TOPSAR in 1996 and 2000, for detection of canopy disturbance caused by cyclone damage. Canopy damage was mapped here by identifying areas with a significant decrease in canopy height estimated from the difference of the 1996 and 2000 TOPSAR DEMs. Conventional aerial photographs, flown shortly after the cyclone (March 1999), were used to validate the resulting map of cyclone damaged rainforest canopy. The results showed that the DEM‐derived canopy damage map performed reasonably well when comparing the spatial distribution and size of damaged areas, while taking into account the time difference between cyclone damage and the second radar acquisition. Some errors were encountered on the steeper slopes which were related to terrain distortions inherent in radar images of steep terrain. The height accuracy of the DEM was close to the depth of some of the gaps being mapped, which also contributed to errors. Even so, the results demonstrate there may be potential for weather‐independent, regional‐scale mapping of forest canopy change from imaging radar that is not always possible from traditional optical means of measuring canopy elevation, for example, airborne laser data and stereo aerial photography. This may be possible in relatively flat areas, provided the second radar acquisition occurs within months of the damaging event. Further algorithm refinement is required to improve its robustness and the range of topographies where this approach can provide reliable estimates of the extent of canopy disturbance.     

Sources of variability in canopy reflectance and the convergent properties of plants

How plants interact with sunlight is central to the existence of life and provides a window to the functioning of ecosystems. Although the basic properties of leaf spectra have been known for decades, interpreting canopy-level spectra is more challenging because leaf-level effects are complicated by a host of stem- and canopy-level traits. Progress has been made through empirical analyses and models, although both methods have been hampered by a series of persistent challenges. Here, I review current understanding of plant spectral properties with respect to sources of uncertainty at leaf to canopy scales. I also discuss the role of evolutionary convergence in plant functioning and the difficulty of identifying individual properties among a suite of interrelated traits. A pattern that emerges suggests a synergy among the scattering effects of leaf-, stem- and canopy-level traits that becomes most apparent in the near-infrared (NIR) region. This explains the widespread and well-known importance of the NIR region in vegetation remote sensing, but presents an interesting paradox that has yet to be fully explored: that we can often gain more insight about the functioning of plants by examining wavelengths that are not used in photosynthesis than by examining those that are.     

Photosynthetic acclimation to simultaneous and interacting environmental stresses along natural light gradients: optimality and constraints

There is a strong natural light gradient from the top to the bottom in plant canopies and along gap-understorey continua. Leaf structure and photosynthetic capacities change close to proportionally along these gradients, leading to maximisation of whole canopy photosynthesis. However, other environmental factors also vary within the light gradients in a correlative manner. Specifically, the leaves exposed to higher irradiance suffer from more severe heat, water, and photoinhibition stresses. Research in tree canopies and across gap-understorey gradients demonstrates that plants have a large potential to acclimate to interacting environmental limitations. The optimum temperature for photosynthetic electron transport increases with increasing growth irradiance in the canopy, improving the resistance of photosynthetic apparatus to heat stress. Stomatal constraints on photosynthesis are also larger at higher irradiance because the leaves at greater evaporative demands regulate water use more efficiently. Furthermore, upper canopy leaves are more rigid and have lower leaf osmotic potentials to improve water extraction from drying soil. The current review highlights that such an array of complex interactions significantly modifies the potential and realized whole canopy photosynthetic productivity, but also that the interactive effects cannot be simply predicted as composites of additive partial environmental stresses. We hypothesize that plant photosynthetic capacities deviate from the theoretical optimum values because of the interacting stresses in plant canopies and evolutionary trade-offs between leaf- and canopy-level plastic adjustments in light capture and use.     

Label-Free Detection of Neuronal Differentiation in Cell Populations Using High-Throughput Live-Cell Imaging of PC12 Cells

Detection of neuronal cell differentiation is essential to study cell fate decisions under various stimuli and/or environmental conditions. Many tools exist that quantify differentiation by neurite length measurements of single cells. However, quantification of differentiation in whole cell populations remains elusive so far. Because such populations can consist of both proliferating and differentiating cells, the task to assess the overall differentiation status is not trivial and requires a high-throughput, fully automated approach to analyze sufficient data for a statistically significant discrimination to determine cell differentiation. We address the problem of detecting differentiation in a mixed population of proliferating and differentiating cells over time by supervised classification. Using nerve growth factor induced differentiation of PC12 cells, we monitor the changes in cell morphology over days by phase-contrast live-cell imaging. For general applicability, the classification procedure starts out with many features to identify those that maximize discrimination of differentiated and undifferentiated cells and to eliminate features sensitive to systematic measurement artifacts. The resulting image analysis determines the optimal post treatment day for training and achieves a near perfect classification of differentiation, which we confirmed in technically and biologically independent as well as differently designed experiments. Our approach allows to monitor neuronal cell populations repeatedly over days without any interference. It requires only an initial calibration and training step and is thereafter capable to discriminate further experiments. In conclusion, this enables long-term, large-scale studies of cell populations with minimized costs and efforts for detecting effects of external manipulation of neuronal cell differentiation.     

Diurnal, seasonal and interannual variability of carbon isotope discrimination at the canopy level in response to environmental factors in a boreal forest ecosystem

Accurate estimation of temporal and spatial variations in photosynthetic discrimination of 13C is critical to carbon cycle research. In this study, a combined ecosystem-boundary layer isotope model, which was satisfactorily validated against intensive campaign data, was used to explore the temporal variability of carbon discrimination in response to environmental driving factors in a boreal ecosystem in the vicinity of Fraserdale Tower, Ontario, Canada (49 degrees 52'30'N, 81 degrees 34'12'W). A 14 year (1990-1996 and 1998-2004) hourly CO2 concentration and meteorological record measured on this tower was used for this purpose. The 14 year mean yearly diurnal amplitude of canopy-level discrimination Delta(canopy) was computed to be 2.8 +/- 0.5 per thousand, and the overall diurnal cycle showed that the greatest Delta(canopy) values occurred at dawn and dusk, while the minima generally appeared in mid-afternoon. The average annual Delta(canopy) varied from 18.3 to 19.7 per thousand with the 14 year average of 19 +/- 0.4 per thousand. The overall seasonality of Delta(canopy) showed a gradually increasing trend from leaf emergence in May-September and with a slight decrease at the end of the growing season in October. Delta(canopy) was negatively correlated to vapour pressure deficit and air temperature across hourly to decadal timescales. A strong climatic control on stomatal regulation of ecosystem isotope discrimination was found in this study.     

High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail

While high resolution satellite remote sensing has been hailed as a very useful source of data for biodiversity assessment and monitoring, applications have been more developed in temperate areas. The biodiverse tropics offer a challenge of an altogether different magnitude for hyperspatial and hyperspectral remote sensing. This paper examines issues related to hyperspatial and hyperspectral remotely sensed imagery, which constitutes one of the most potentially powerful yet underutilized sources of for tropical research on biodiversity. Hyperspatial data with their increased pixel resolution are possibly best suited at facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity, particularly when spatial resolution becomes too fine and pixels are smaller than the size of the object (e.g., tree canopy) being identified. Hyperspectral data on the other hand, with their high spectral resolution, can be used to record information pertaining to a range of critical plant properties related to species identity, and can be very effective used for discriminating tree species in tropical forests, despite the greater complexity of such environments. There remains a glaring gap in the easy availability of hyperspectral and hyperspatial satellite data in the tropics due to reasons of cost, data coverage, and security restrictions. Stimulating discussion on the applications of this powerful, but underutilized tool by ecologists, is the first step in promoting a more extensive use of such data for ecological studies in tropical biodiversity rich areas.     

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.     

Optical trait indicators for remote sensing of plant species composition: Predictive power and seasonal variability

Most plant species feature similar biochemical compositions and thus similar spectral signals. Still, empirical evidence suggests that the spectral discrimination of species and plant assemblages is possible. Success depends on the presence or absence of faint but detectable differences in biochemical (e.g., pigments, leaf water and dry matter content) and structural properties (e.g., leaf area, angle, and leaf structure), i.e., optical traits. A systematic analysis of the contributions and spatio-temporal variability of optical traits for the remote sensing of organismic vegetation patterns has not yet been conducted. We thus use time series of optical trait values retrieved from the reflectance signal using physical models (optical trait indicators, OTIs) to answer the following questions: How are optical traits related among patterns of floristic composition and reflectance? How variable are these relations in space and time? Are OTIs suitable predictors of plant species composition?We conducted a case study of three temperate open study sites with semi-natural vegetation. The canopy reflectance of permanent vegetation plots was measured on multiple dates over the vegetation period using a field spectrometer. We recorded the cover fractions of all plant species found in the vegetation plots and extracted gradients of species composition from these data. The physical PROSAIL leaf and canopy optical properties model was inverted with random forest regression models to retrieve time series of OTIs for each plot from the reflectance spectra. We analyzed these data sets using correlation analyses. This approach allowed us to assess the distribution of optical traits across gradients of species composition. The predictive performance of OTIs was tested in relation to canopy reflectance using random forest models.OTIs showed pronounced relationships with floristic patterns in all three study sites. These relationships were subject to considerable temporal variability. Such variability was driven by short-term vegetation dynamics introduced by local resource stress. In 72% of all cases OTIs out-performed the original canopy reflectance spectra as indicators of plant species composition. OTIs are also easier to interpret in an ecological sense than spectral bands or features. We thus conclude that optical traits retrieved from reflectance data have a high indicative value for ecological research and applications.     

Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images

Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission.     

3D lidar imaging for detecting and understanding plant responses and canopy structure

Understanding and diagnosing plant responses to stress will benefit greatly from three-dimensional (3D) measurement and analysis of plant properties because plant responses are strongly related to their 3D structures. Light detection and ranging (lidar) has recently emerged as a powerful tool for direct 3D measurement of plant structure. Here the use of 3D lidar imaging to estimate plant properties such as canopy height, canopy structure, carbon stock, and species is demonstrated, and plant growth and shape responses are assessed by reviewing the development of lidar systems and their applications from the leaf level to canopy remote sensing. In addition, the recent creation of accurate 3D lidar images combined with natural colour, chlorophyll fluorescence, photochemical reflectance index, and leaf temperature images is demonstrated, thereby providing information on responses of pigments, photosynthesis, transpiration, stomatal opening, and shape to environmental stresses; these data can be integrated with 3D images of the plants using computer graphics techniques. Future lidar applications that provide more accurate dynamic estimation of various plant properties should improve our understanding of plant responses to stress and of interactions between plants and their environment. Moreover, combining 3D lidar with other passive and active imaging techniques will potentially improve the accuracy of airborne and satellite remote sensing, and make it possible to analyse 3D information on ecophysiological responses and levels of various substances in agricultural and ecological applications and in observations of the global biosphere.     

Multi-sensor plant imaging: Towards the development of a stress-catalogue

Agricultural production is limited by a wide range of abiotic (e.g. drought, waterlogging) and biotic (pests, diseases and weeds) stresses. The impact of these stresses can be minimized by appropriate management actions such as irrigation or chemical pesticide application. However, further optimization requires the ability to diagnose and quantify the different stresses at an early stage. Particularly valuable information of plant stress responses is provided by plant imaging, i.e. non-contact sensing with spatial resolving power: (i) thermal imaging, detecting changes in transpiration rate and (ii) fluorescence imaging monitoring alterations in photosynthesis and other physiological processes. These can be supplemented by conventional video imagery for study of growth. An efficient early warning system would need to discriminate between different stressors. Given the wide range of sensors, and the association of specific plant physiological responses with changes at particular wavelengths, this goal seems within reach. This is based on the organization of the individual sensor results in a matrix that identifies specific signatures for multiple stress types. In this report, we first review the diagnostic effectiveness of different individual imaging techniques and then extend this to the multi-sensor stress-identification approach.     

Mushroom diversity in sustainable shade tea forest and the effect of fire damage

A survey of the biodiversity of wild macrofungi, including edible species yields, was carried out from 1 May to 30 September 2007 at four different forest types (in mainly Miang tea forest). The plots 100 m², comprised a tea garden with a few planted canopy tree species (37.2% canopy cover), a cultivated sustainable tea forest (80.2% canopy cover), an abandoned sustainable tea forest (89.8% canopy cover), and an abandoned sustainable tea forest that had suffered fire damage (72.9% canopy cover). All visible mushrooms were collected during weekly forays. Macro-characters of the fungi were annotated and photographed by digital camera and the fungi were identified to genera and morphospecies (e.g. Agaricus sp.1, Agaricus sp.2). The fresh weight of wild edible mushrooms produced in the plots was also recorded during this period. The biodiversity of macrofungi in abandoned sustainable tea forest was highest with 115 species in 47 genera, followed by cultivated sustainable tea forest with 85 species in 42 genera and fire damaged abandoned sustainable tea forest with 48 species in 25 genera, while only 19 species belonging to 15 genera were found in the tea garden. Twenty-one species belonging to nine genera with a total of 60.9 kg of edible fungi were collected throughout this study. The fresh weight of edible mushrooms was recorded with the highest fresh weight (25.3 kg) collected from the abandoned sustainable tea forest, followed by 18.2 kg in the cultivated sustainable tea forest, while in the fire damaged sustainable tea forest 16.4 kg were collected and only 1 kg was collected in the tea garden. Abandoned sustainable tea forest contained a greater shade tree biodiversity and higher canopy cover than other plots. This area has a generally higher humidity, a greater ground litter cover, a greater number of microhabitats, a greater number of trees that can form mycorrhizal associations and this probably accounts for the higher diversity of macrofungi and production of edible mushrooms. The fire damaged sustainable tea forest had lower mushroom diversity and edible mushroom production possibly due to the loss of litter and lower tree diversity. The tea garden supported a poor diversity of mushrooms and almost no edible mushrooms. Shade tea forest (Miang tea forest) is a good method to produce tea in a sustainable way as it maintains diversity of mushrooms and other organisms and could be developed as an alternative to shade coffee.     

Hyperspectral remote sensing of plant pigments

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.     

Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest

Canopy structure and light interception were measured in an 18-m tall, closed canopy deciduous forest of sugar maple (Acer saccharum) in southwestern Wisconsin, USA, and related to leaf structural characteristics, N content, and leaf photosynthetic capacity. Light attenuation in the forest occurred primarily in the upper and middle portions of the canopy. Forest stand leaf area index (LAI) and its distribution with respect to canopy height were estimated from canopy transmittance values independently verified with a combined leaf litterfall and point-intersect method. Leaf mass, N and A _max per unit area (LMA, N/area and A _max/area, respectively) all decreased continuously by over two-fold from the upper to lower canopy, and these traits were strongly correlated with cumulative leaf area above the leaf position in the canopy. In contrast, neither N concentration nor A _max per unit mass varied significantly in relation to the vertical canopy gradient. Since leaf N concentration showed no consistent pattern with respect to canopy position, the observed vertical pattern in N/area is a direct consequence of vertical variation of LMA. N/area and LMA were strongly correlated with A _max/area among different canopy positions (r²=0.81 and r²=0.66, respectively), indicating that vertical variation in area-based photosynthetic capacity can also be attributed to variation in LMA. A model of whole-canopy photosynthesis was used to show that observed or hypothetical canopy mass distributions toward higher LMA (and hence higher N/area) in the upper portions of the canopy tended to increase integrated daily canopy photosynthesis over other LMA distribution patterns. Empirical relationships between leaf and canopy-level characteristics may help resolve problems associated with scaling gas exchange measurements made at the leaf level to the individual tree crown and forest canopy-level.     

Color Measurement of Tea Leaves at Different Drying Periods Using Hyperspectral Imaging Technique

This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380–1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (r_p) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.     

Nakagami imaging with related distributions for advanced thermogram pseudocolorization

Pseudocoloring algorithms embedded in the software of thermal cameras gradually colorize original intensity thermograms generated by detecting temperatures and contrast. Maximum and minimum based algorithms, however, executed by thresholding, applied to intensity thermograms for revealing and coloring the outliers instead. Although the common pseudocoloring protocols employed for general purposes may provide crucial information on the superficial contrast between radiation emitted by various sources; their common kernel is not sufficient for detecting and differentiating high radiated regions from surrounding areas, which is mandatory for recognition of abnormalities. Therefore, we propose novel imaging methodology based on Nakagami and related distributions, including gamma, Rayleigh, Weibull, chi-square and exponential, for enhancing thermal images and also for creating adequate discrimination. We initially define the boundaries of tumor and surrounding area in a synthetically generated breast thermogram already diagnosed as retroareolar tumor. Using Nakagami and transformations supported by mathematical foundations, we conducted several experiments to find the discrimination factor of the pseudocoloring techniques by calculating difference of average contrast between the tumor and the surrounding area. The performance is greatly encouraging that we achieved considerably better discrimination factor, designated for this study, up to 106.80 compared to the results of existing built-in pseudocolorization methods computed as 11.56.