Linear Unmixing Approach Research Articles

Marine plastic detection using PRISMA hyperspectral satellite imagery in a controlled environment

ABSTRACT The amount of plastics on seawater is causing ecosystem damage to both land areas and water bodies. It is proven that once plastic particles reach the sea, they will be degraded. As their detection is not easy with in situ sampling, remote sensing techniques could help detect and evaluate their impact on ecosystems. To understand the main limitations of detecting floating plastic material through remote images, an a pilot experiment was carried out on an artificial floating plastic target deployed at the Aegean Sea (Greece) within the Plastic Litter Project 2021. Hyperspectral PRISMA, multispectral PlanetScope and visible Unmanned Aerial Systems (UAS) acquisitions were analysed. A nonlinear unmixing technique was applied to derive the spectral signature of the plastic target; finally, the linear unmixing approach allowed for determining the plastic percentage occupation at the pixel level. Different band combinations of the PRISMA data were selected to evaluate which provided the best result for the detection; one of these band combinations was retrieved via the principal component analysis. Only slight differences were achieved using the different PRISMA band-sets. As expected, since the artificial plastic target had a diameter comparable with the PRISMA spatial resolution (~30 m), its detection was a challenging task caused by the water influence in the pixel (mixed pixels). The detection was realized by benefitting from the high amount of available spectral bands, as confirmed by the comparative test with a PlanetScope image used at its original spatial resolution (3 m) and after degrading it at the PRISMA level. Results demonstrated that an optimal target detection was possible with few spectral bands, only taking advantage of the high spatial resolution (compared with the target dimension). Indeed, unreliable plastic fractions were derived at the PRISMA spatial resolution with a limited number of spectral bands.

Modelling Spectral Unmixing of Geological Mixtures: An Experimental Study Using Rock Samples

Spectral unmixing of geological mixtures, such as rocks, is a challenging inversion problem because of nonlinear interactions of light with the intimately mixed minerals at a microscopic scale. The fine-scale mixing of minerals in rocks limits the sensor’s ability to identify pure mineral endmembers and spectrally resolve these constituents within a given spatial resolution. In this study, we attempt to model the spectral unmixing of two rocks, namely, serpentinite and granite, by acquiring their hyperspectral images in a controlled environment, having uniform illumination, using a laboratory-based imaging spectroradiometer. The endmember spectra of each rock were identified by comparing a limited set of pure hyperspectral image pixels with the constituent minerals of the rocks based on their diagnostic spectral features. A series of spectral unmixing paradigms for explaining geological mixtures, including those ranging from simple physics-based light interaction models (linear, bilinear, and polynomial models) to classification-based models (support vector machines (SVMs) and half Siamese network (HSN)), were tested to estimate the fractional abundances of the endmembers at each pixel position of the image. The analysis of the results of the spectral unmixing algorithms using the ground truth abundance maps and actual mineralogical composition of the rock samples (estimated using X-ray diffraction (XRD) analysis) indicate a better performance of the pure pixel-guided HSN model in comparison to the linear, bilinear, polynomial, and SVM-based unmixing approaches. The HSN-based approach yielded reduced errors of abundance estimation, image reconstruction, and mineralogical composition for serpentinite and granite. With its ability to train using limited pure pixels, the half-Siamese network model has a scope for spectrally unmixing rock samples of varying mineralogical composition and grain sizes. Hence, HSN-based approaches effectively address the modelling of nonlinear mixing in geological mixtures.

Learned spectral decoloring enables photoacoustic oximetry

The ability of photoacoustic imaging to measure functional tissue properties, such as blood oxygenation sO_2, enables a wide variety of possible applications. sO_2 can be computed from the ratio of oxyhemoglobin HbO_2 and deoxyhemoglobin Hb, which can be distuinguished by multispectral photoacoustic imaging due to their distinct wavelength-dependent absorption. However, current methods for estimating sO_2 yield inaccurate results in realistic settings, due to the unknown and wavelength-dependent influence of the light fluence on the signal. In this work, we propose learned spectral decoloring to enable blood oxygenation measurements to be inferred from multispectral photoacoustic imaging. The method computes sO_2 pixel-wise, directly from initial pressure spectra S_{text {p}_0}(lambda , mathbf {x}), which represent initial pressure values at a fixed spatial location mathbf {x} over all recorded wavelengths lambda. The method is compared to linear unmixing approaches, as well as pO_2 and blood gas analysis reference measurements. Experimental results suggest that the proposed method is able to obtain sO_2 estimates from multispectral photoacoustic measurements in silico, in vitro, and in vivo.

Hyperspectral Measurements Allow Separation of FRET Signals from Non-Uniform Background Fluorescence

In recent years Forster resonance energy transfer (FRET) has become a standard imaging approach to gain insight into localized biochemical processes within cells. These processes include changes in protein colocalization, second messenger concentration, and activation of effector proteins such as protein kinase A (PKA). However, there are several limitations in FRET measurements that are not often considered. In recent years our group has used hyperspectral imaging approaches to address a subset of these limitations, including the inherently low signal-to-noise ratio of standard two and three filter set FRET measurements. Here we present data demonstrating that hyperspectral imaging approaches can be used to correct for non-uniform background fluorescence in low intensity FRET measurements. We utilized the FRET-based cAMP probe H188. The H188 probe contains a cAMP binding domain between donor (Turquoise) and acceptor (Venus) fluorescent proteins. The probe was transfected into pulmonary microvascular endothelial cells plated on glass coverslips. Hyperspectral image stacks were acquired using a Nikon A1R inverted confocal microscope. Changes in cAMP were triggered by addition of 0.1 µM isoproterenol (a beta adrenergic receptor agonist) 0.1 µM PGE1 (a prostanoid receptor agonist) or 50 µM forskolin (an adenylyl cyclase activator). Data were analyzed using Nikon Elements software and custom MATLAB scripts. Results demonstrate that non-uniform background fluorescence associated with glass coverslips can contaminate FRET measurements from weak fluorescence signals. Linear unmixing approaches were able to separate the abundances of background, donor and acceptor fluorescence signals. Thus, results from this study demonstrate that hyperspectral unmixing approaches can readily separate non-uniform background fluorescence from other fluorescence signatures, allowing for more accurate quantification of FRET efficiency. This work was supported by NIH P01HL066299, NIH S10RR027535, NIH T32HL076125, AHA 16PRE27130004, USA SURF Program, and the Abraham Mitchell Cancer Research Fund.

Comparison of linear and nonlinear spectral unmixing approaches: a case study with multispectral TM imagery

ABSTRACTSpectral unmixing has been widely used by researchers in quantitative remote sensing due to the prevalence of mixed pixels in low- or middle-resolution images. In this article, six linear and nonlinear unmixing approaches – fully constrained least squares (FCLS), bilinear-Fan model (BFM), polynomial post-nonlinear model (PPNM), supervised fuzzy c-means (SFCM), Support Vector Machine (SVM), and artificial neural network (ANN) – are applied with multispectral Landsat Thematic Mapper (TM) data in order to systematically compare their performance under different scenarios. In addition, a strategy of band selection was proposed for solving the endmember variability issue. The unmixing results were analysed in terms of the overall performance, pure and mixed data set, sub-scenes with different mixture proportions by calculating the accuracy indices: root mean square error (RMSE) and the Pearson correlation coefficient (r). Nonlinear approaches can generate a closer abundance fraction map to reference, and have a higher overall accuracy than the linear approach. Nevertheless, the performance of nonlinear approaches differed dramatically with the increased proportion of mixed pixels in different study areas. SVM, SFCM, BFM, and PPNM depicted a scenario better when the proportion of mixed pixels was high, whereas ANN worked more effectively when processing large amounts of relatively pure pixels (or mixed pixels with large/extreme proportions). The linear approach, in contrast, performed more consistently for various areas. Overall, our study indicates that nonlinear approaches are more effective than the linear one, especially for a study area consisting of different small parcels. The performance of nonlinear approaches is more sensitive to the proportion change of mixed pixels in a study area. The linear approach, however, is more appropriate for a rough estimation, particularly with little prior knowledge of the study area.

Geochemical constraints on the presence of clay minerals in the Burns formation, Meridiani Planum, Mars

Burns formation sandstones, deposited by aeolian processes and preserved at Meridiani Planum, Mars, contain abundant sulfate minerals. These sedimentary rocks are thought to be representative of a sulfate-rich geological epoch during late Noachian – early Hesperian time that followed an earlier clay-rich epoch. Twenty Burns formation targets, abraded by the Rock Abrasion Tool (RAT) and for which alpha-particle X-ray spectrometry (APXS) and Mössbauer spectroscopy data are available, were selected for geochemical modeling. A linear unmixing modeling approach was employed. Mineralogical constituents quantitatively constrained by Mössbauer and Mini-TES spectroscopy and interpreted to be chemically precipitated from aqueous fluids during deposition and/or early diagenesis were subtracted from the bulk chemistry. Resulting residual chemical compositions, interpreted to be dominated by detrital siliciclastic components and representing ∼21–35% of the rocks, were then geochemically evaluated to constrain the potential for the presence of clay minerals or their poorly-crystalline or non-crystalline precursors/chemical equivalents. Calculations incorporated a robust estimate of the uncertainties in mineral abundances. On Al2O3 – (CaO+Na2O) – K2O (A-CN-K) and Al2O3 – (CaO+Na2O+K2O) – (FeOtotal+MgO) (A-CNK-FM) molar ternary diagrams, removal of chemical constituents resulted in a shift from igneous–like compositions to compositions consistent with secondary mineral assemblages containing significant aluminous clay mineral components. All of the residual compositions are corundum-normative, further supportive of the presence of highly aluminous phases. On the A-CNK-FM diagram, clay minerals plotting closest to the residual field are natural montmorillonites but could also represent mixtures of various Mg/Fe-rich phyllosilicates, such as nontronite or saponite, and other more Al-rich minerals such as Al-montmorillonite, kaolinite or illite. Depending on the age of clay mineral formation, occurrence of clay minerals or their poorly crystalline precursors/chemical equivalents in the Burns formation could suggest that any global transition from clay-rich to sulfate-rich environments on early Mars was more complex than previously recognized. Results are also consistent with models for the Burns formation aqueous history in which acidic conditions were more restricted in time and/or space than previously thought and thus may also be consistent with growing evidence that changing redox conditions, rather than global pH variations, was an important factor in the environmental evolution of early Mars.

Use of Earth observation for monitoring soil sealing trends in Flanders and Brussels between 1976 and 2013

The on-going growth of urban area in Flanders and in the Brussels Capital Region over the past decades has resulted in a highly sprawled urban tissue, consisting of large and smaller urban agglomerations, connected by a well-developed transportation network. The conversion of open land to urban area is accompanied by an increase in soil sealing, affecting the hydrological cycle and the urban climate. Despite a growing interest in monitoring the process of soil sealing in urban areas, to date no detailed information on the presence and evolution of sealed surfaces is available for Flanders. In this paper a linear regression unmixing approach is proposed to map and monitor changes of sealed surface cover at the regional scale, using medium as well as high resolution remote sensing data. Applied to Flanders and the Brussels Capital Region, a total sealed area of 2687 km² for 2013 is found, corresponding to an increase of 82% since 1976. Residential areas account for nearly half of the sealed area and show the largest increase in sealed surface cover over the past 37 years.

Bartonella Henselae Initial Infection of Mature Human Erythrocytes Observed in Real Time Using Bacterial Endogenous Fluorescence

Bartonella henselae is a causative agent of anemia, cat scratch disease, bacillary angiomatosis, recurrent fever, hepatitis, endocarditis, chronic lymphadenopathy, joint and neurological disorders. B. henselae are intra-erythrocytic bacteria. The goal of this study was to visualize the B. henselae invasion into enucleated human red blood cells in real time using bacterium endogenous fluorescence. We took advantage of the unique fluorescence emission spectral profile of the bacteria. We used a linear unmixing approach to separate the fluorescence emission spectra of human erythrocytes from native B. henselae when excited at 488nm. Human blood samples were inoculated with B. henselae and incubated for 60 hours. 3-D live images were captured at select intervals using multi-photon laser scanning microscopy. Uninfected blood samples were also analyzed. This study revealed bacteria entering mature erythrocytes over a 60 hour time period.

基于Hyperion的锡林郭勒草原光合植被、非光合植被覆盖度估算

PDF HTML阅读 XML下载 导出引用 引用提醒 基于Hyperion的锡林郭勒草原光合植被、非光合植被覆盖度估算 DOI: 10.5846/stxb201308142075 作者: 作者单位: 中国科学院遥感与数字地球研究所,中国科学院遥感与数字地球研究所,中国科学院遥感与数字地球研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技支撑计划(2011BAH23B04);国家科技重大专项(21-Y30B05-9001-13/15) Estimating fractional cover of photosynthetic vegetation and non-photosynthetic vegetation in the Xilingol steppe region with EO-1 hyperion data Author: Affiliation: Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:掌握草原生态系统光合植被覆盖度(fPV)与非光合植被覆盖度(fNPV)时空动态对了解干旱半干旱草原生态系统特征(覆盖状况、火灾负载、载畜量、干扰及恢复等)及进行科学、有效地草地资源管理具有重要的意义。选取锡林郭勒典型草原为试验区,以Hypeiron高光谱数据为数据源,利用NDVI-CAI三元线性混合模型对试验区fPV和fNPV的时空动态分布进行了估算,并对不同端元选择方法(最小包含端元特征法、纯净象元指数法和实测法)对估算结果的影响进行了比较分析。研究结果表明,NDVI-CAI三元线性混合模型是同时估测锡林郭勒草原fPV和fNPV的有效方法,且估算的fPV和fNPV的季节变化与牧草的物候发育特征相吻合。不同端元选择方法对估算精度具有一定的影响,其中基于最小包含端元特征法提取端元进行估算的精度最高,fPV估算的均方根误差RMSE=4.57,估算精度EA=91.2%;fNPV估算的RMSE=5.90,EA=67.91%(样本数N=52)。 Abstract:Quantitative estimation of the spatial and temporal dynamics of the fractional cover of photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) in semi-arid grasslands is critical for understanding grassland conditions such as vegetation abundance, drought severity, fire fuel load, stocking rate, and disturbance events and recovery. It is also important for scientific grassland resource management. Over the past several decades, remote sensing has become an important tool for estimating the fractional cover of vegetation, which is a key descriptor of ecosystem function. However, most efforts have been devoted to the estimation of fPV rather than fNPV, although the latter is equally important, especially in arid and semi-arid ecosystems. This study describes a linear unmixing approach for estimating fPV and fNPV in the Xilingol steppe region with hyperspectral and field investigation data. Five Hyperion images acquired on April 4, May 20, July 27, August 30, and November 15 in 2012 and a field-measured spectral library were utilized to explore the spectral feature space of fPV and fNPV in order to validate the feasibility of a linear unmixing model. This model is based on two complementary spectral indices of vegetation that have been used in remote sensing analyses to discriminate green and dry vegetation from soils: the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Different end-member extraction methods, including the Minimum-Volume Enclosing (MVE) method, the Pixel Purity Index (PPI) method, and a field measurement method, were adopted to retrieve the end-member values of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil, respectively, from NDVI and CAI. Then, the influence of end-member extraction on the accuracy of the fPV and fNPV estimation was evaluated through comparison with field-measured fPV and fNPV values acquired from classifications performed on fisheye photos (N=52). Subsequently, the optimum unmixing strategy was utilized to retrieve the temporal dynamics of fPV and fNPV in a fenced area, where the grassland was not influenced by human activities, so that the usefulness of these fractional coverage indices could be validated by checking their consistency with the phenology of natural grassland. The result shows that the linear unmixing model based on NDVI and CAI was effective for estimating fPV and fNPV in the Xilingol steppe region. The NDVI-CAI feature space follows a triangular distribution, where the three vertexes represent photosynthetic vegetation, non-photosynthetic vegetation, and bare soil, meeting the essential requirements of the linear unmixing model. The estimation accuracy was different for the different end-member extraction methods. The MVE-based estimation had the highest accuracy, with estimated accuracy of 91.2% and 67.91% for fPV and fNPV, respectively, followed by the PPI-based estimation (91.0% and 59.5% for fPV and fNPV, respectively) and the field-measurement-based estimation (86.2% and 56.7% for fPV and fNPV, respectively). In general, the estimation accuracy was higher for fPV than fNPV, and the field-measured end-member performed worse than the image end-member, which was probably due to the inconsistency between the field-measured spec and the Hyperion spec. Additionally, the temporal dynamics of fPV and fNPV were confirmed to be consistent with the phenological seasonal change in natural grasslands. Therefore, the method proposed here can be used to monitor the temporal and spatial variations of fPV and fNPV in semi-arid grasslands. 参考文献 相似文献 引证文献

Effects of forest age on albedo in boreal forests estimated from MODIS and Landsat albedo retrievals

The relationship between boreal forest age and albedo in the visible, near-infrared and shortwave broadband was studied using MODIS and Landsat albedo retrievals and forest resource maps. The dependency of forest albedo on stand age was estimated from MODIS albedo retrievals using a linear unmixing approach combined with non-linear regression. Exponential function was appropriate to describe the decline of coniferous forest albedo with stand age in all seasons and the decline of deciduous broadleaved albedo in winter. The effect of stand age on albedo was stronger for spruce than for pine, and the variation in albedo as a function of stand age was larger in winter than in summer for all species and wavelength regions. When applied to the test data, the albedo values predicted using the estimated age dependent albedo functions produced somewhat better results than when using only the species specific mean albedos as predictors. The tendency of the albedo values predicted using the age dependent functions towards the mean of the observed values suggested that the models did not cover all variation in the species specific forest albedos. A larger variation in the predicted albedos was obtained when age dependent albedo functions were estimated using Landsat albedo estimates.

Time-resolved fluorescence spectroscopy investigation of the effect of 4-hydroxynonenal on endogenous NAD(P)H in living cardiac myocytes

Lipid peroxidation is a major biochemical consequence of the oxidative deterioration of polyunsaturated lipids in cell membranes and causes damage to membrane integrity and loss of protein function. 4-hydroxy-2-nonenal (HNE), one of the most reactive products of n-6 polyunsaturated fatty acid peroxidation of membrane phospholipids, has been shown to be capable of affecting both nicotinamide adenine dinucleotide (phosphate) reduced [NAD(P)H] as well as NADH production. However, the understanding of its effects in living cardiac cells is still lacking. Our goal was to therefore investigate HNE effects on NAD(P)H noninvasively in living cardiomyocytes. Spectrally resolved lifetime detection of endogenous fluorescence, an innovative noninvasive technique, was employed. Individual fluorescence components were resolved by spectral linear unmixing approach. Gathered results revealed that HNE reduced the amplitude of both resolved NAD(P)H components in a concentration-dependent manner. In addition, HNE increased flavoprotein fluorescence and responsiveness of the NAD(P)H component ratio to glutathione reductase (GR) inhibitor. HNE also increased the percentage of oxidized nucleotides and decreased maximal NADH production. Presented data indicate that HNE provoked an important cell oxidation by acting on NAD(P)H regulating systems in cardiomyocytes. Understanding the precise role of oxidative processes and their products in living cells is crucial for finding new noninvasive tools for biomedical diagnostics of pathophysiological states.

Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery

Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hyperspectral image is considered as a linear combinations of the spectra of the vegetation and the bare soil. Recently developed linear unmixing approaches are evaluated in this paper, which automatically extracts the spectra of the vegetation and bare soil from the images. The vegetation abundances are then computed based on the extracted spectra. In order to reduce the influences of this uncertainty and obtain a robust estimation results, the vegetation abundances extracted on two different dates on the same fields are then combined. The experiments are carried on the multidate hyperspectral images taken from two grain sorghum fields. The results show that the correlation coefficients between the vegetation abundances obtained by unsupervised linear unmixing approaches are as good as the results obtained by supervised methods, where the spectra of the vegetation and bare soil are measured in the laboratory. In addition, the combination of vegetation abundances extracted on different dates can improve the correlations (from 0.6 to 0.7).

Linear unmixing of MODIS albedo composites to infer subpixel land cover type albedos

We use a linear unmixing approach to test how land use and forestry maps, in combination with the MODIS BRDF/albedo product, can be used to estimate land cover type albedos in boreal regions. Operational land use maps from three test areas in Finland and Canada were used to test the method. The resulting endmember albedo estimates had low standard errors of the mean and were realistic for the main land cover types. The estimated albedos were fairly consistent with albedo measurements conducted with a telescope mast and pure pixel albedos. Problems with the method are the possible errors in the land cover maps, lack of good quality winter MODIS albedo composites and the mismatch between the MODIS pixels and the true observation area. The results emphasize the role of tree species as determinant of forest albedo. Comprehensive spatial and temporal measurements of land cover albedo are usually not possible with in situ mast measurements, and the spatial resolution of MODIS albedo product is often too low to allow direct comparison of pixel albedos and land cover types in areas with heterogeneous vegetation. Hence, and since local forestry maps exist for most temperate and boreal regions, we believe that the proposed method will be useful in estimating average regional land cover type albedos as well as in tracking changes in them.

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVI's response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a region's FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.

Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study

As the volume of hyperspectral data for planetary exploration increases, efficient yet accurate algorithms are decisive for their analysis. In this paper, the capability of spectral unmixing for analyzing hyperspectral images from Mars is investigated. For that purpose, we consider the Russell megadune observed by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and the High-Resolution Imaging Science Experiment (HiRISE) instruments. In late winter, this area of Mars is appropriate for testing linear unmixing techniques because of the geographical coexistence of seasonal CO2 ice and defrosting dusty features that is not resolved by CRISM. Linear unmixing is carried out on a selected CRISM image by seven state-of-the-art approaches based on different principles. Three physically coherent sources with an increasing fingerprint of dust are recognized by the majority of the methods. Processing of HiRISE imagery allows the construction of a ground truth in the form of a reference abundance map related to the defrosting features. Validation of abundances estimated by spectral unmixing is carried out in an independent and quantitative manner by comparison with the ground truth. The quality of the results is estimated through the correlation coefficient and average error between the reconstructed and reference abundance maps. Intercomparison of the selected linear unmixing approaches is performed. Global and local comparisons show that misregistration inaccuracies between the HiRISE and CRISM images represent the major source of error. We also conclude that abundance maps provided by three methods out of seven are generally accurate, i.e., sufficient for a planetary interpretation.

Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Quantitative estimation of fractional cover of photosynthetic vegetation ( f PV), non-photosynthetic vegetation ( f NPV) and bare soil ( f BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f PV, f NPV and f BS across the ~ 2 million km 2 Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f PV, f NPV and f BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000–2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f PV, f NPV and f BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world.

Semiautomated Multiplexed Quantum Dot-Based in Situ Hybridization and Spectral Deconvolution

Gene expression profiling has identified several potentially useful gene signatures for predicting outcome or for selecting targeted therapy. However, these signatures have been developed in fresh or frozen tissue, and there is a need to apply them to routinely processed samples. Here, we demonstrate the feasibility of a potentially high-throughput methodology combining automated in situ hybridization with quantum dot-labeled oligonucleotide probes followed by spectral imaging for the detection and subsequent deconvolution of multiple signals. This method is semiautomated and quantitative and can be applied to formalin-fixed, paraffin-embedded tissues. We have combined dual in situ hybridization with immunohistochemistry, enabling simultaneous measurement of gene expression and cell lineage determination. The technique achieves levels of sensitivity and specificity sufficient for the potential application of known expression signatures to biopsy specimens in a semiquantitative way, and the semiautomated nature of the method enables application to high-throughput studies.

Spectral Unmixing of Flavin Autofluorescence Components in Cardiac Myocytes

We applied linear unmixing approach to reveal individual components of intrinsic flavin fluorescence signal recorded in living cardiac cells by spectrally resolved confocal microscopy. Responses of whole-cell autofluorescence to modulators of cell metabolism and respiration were used as a tool of separation of its components; their spectral profiles, estimated by principal component analysis, correspond to free FAD and FAD bound to different enzymes of electron transport chain.

Cropland distributions from temporal unmixing of MODIS data

Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R 2 ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km 2. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.

Detecting land cover changes from NOAA-AVHRR data by using spectral mixture analysis

To date, NOAA-AVHRR images have been utilized very little for land cover analysis purposes. The method suggested here is a linear unmixing approach, which transforms satellite images into the proportions of spectrally predefined land cover types. Although some of the bands utilized by NOAA do not follow the underlying assumption of linearity, their inclusion helps to distinguish some of the defined cover types. The resulting fraction images show the abundance of different cover types and have great potential for change detection purposes.