Sufficient Spectral Information Research Articles

Spectral theory and self-similar blowup in wave equations

This is an expository article that describes the spectral-theoretic aspects in the study of the stability of self-similar blowup for nonlinear wave equations. The linearization near a self-similar solution leads to a genuinely nonself-adjoint operator which is difficult to analyze. The main goal of this article is to provide an accessible account of the only known method that is capable of providing sufficient spectral information to complete the stability analysis. The exposition is based on a mini course given at the Summer School on Geometric Dispersive PDEs in Obergurgl, Austria, in September 2022.

Saliency-Guided Sparse Low-Rank Tensor Approximation for Unsupervised Anomaly Detection of Hyperspectral Remote Sensing Images

Hyperspectral anomaly detection can separate sparse anomalies from the low-rank background component under an unsupervised behavior due to sufficient spectral information. Therefore, hyperspectral image anomaly detection technology has great application potential and value in public security and national defense. Currently, most existing models attempt to detect anomalous targets with a sparsity prior, without further considering the visual saliency of the targets themselves. To tackle this issue, this paper proposes a saliency-guided sparse low-rank tensor approximation model, called SSLR, to detect anomalous targets from hyperspectral remote sensing images in an unsupervised manner. Specifically, we first explore the saliency information of each pixel for regularizing the sparse anomaly matrix. We then suggest a three-directional tensor nuclear norm to obtain a low-rank background to characterize the background component. We solve the SSLR optimization problem by an efficient alternating direction method of multipliers framework. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed SSLR outperforms some state-of-the-art anomaly detection methods.

Assessing the impact of spatial resolution of UAS-based remote sensing and spectral resolution of proximal sensing on crop nitrogen retrieval accuracy

ABSTRACT Foliar nitrogen (N) plays a central role in photosynthetic machinery of plants, regulating their growth rates. However, field-based methods for monitoring plant N concentration are costly and limited in their ability to cover large spatial extents. In this study, we had two objectives: (1) assess the capability of unoccupied aerial system (UAS) and non-imaging spectroscopic data in estimating sorghum and corn N concentration and (2) determine the impact of spatial and spectral resolution of reflectance data on estimating sorghum and corn N concentration. We used a UAS and an ASD spectroradiometer to collect canopy- and leaf-level spectral data from sorghum and corn at experimental plots located in Stillwater, Oklahoma, U.S. We also collected foliage samples in the field and measured foliar N concentration in the lab for model validation. To assess the impact of spectral scale on estimating N concentration, we resampled our leaf-level ASD data to generate datasets with coarser spectral resolutions. To determine the impact of spatial scale on estimating N concentration, we resampled our UAS data to simulate five datasets with varying spatial resolutions ranging from 5 cm to 1 m. Finally, we used a suite of vegetation indices (VIs) and machine learning algorithms (MLAs) to estimate N concentration. Results from leaf-level ASD spectral data showed that the resampled data matching the spectral resolution of our UAS-based data at five spectral bands ranging from 360 to 900 nm provided sufficient spectral information to estimate plot-level sorghum and corn N concentration. Regarding spatial resolution, canopy-level UAS data resampled at multiple pixel sizes, ranging from 1 cm to 1 m were consistently capable of estimating N concentration. Overall, our findings indicate the possibility of developing monitoring instruments with optimal spectral and spatial resolution for estimating N concentration in crops.

Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index

Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R2) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages.

A multispectral method for retrieving overlapping cloud top heights from passive radiometers

Cloud top height (CTH) is an important factor influencing its radiative effects, weather and climate change, and spaceborne passive radiometers widely used to retrieve CTH mostly use an oversimplified single-layer cloud assumption. Nevertheless, natural clouds exhibit complex vertical inhomogeneity, such as overlapping clouds. This study investigates the potential of multispectral observations for characterizing overlapping cloud properties and introduces a new CTH retrieval algorithm for overlapping CTHs. Synthetic radiative transfer simulations and an information content analysis are applied to demonstrate the theoretical basis of using passive radiometers for overlapping cloud property retrievals. Specifically, this method leverages the merits of four shortwave infrared (0.87, 1.6, 2.13 and 2.25 μm) channels in distinguishing cloud optical and microphysical properties in different phases and the capabilities of longwave infrared (8.6, 11 and 12 μm) channels for the corresponding CTHs. The method performs effectively for overlapping clouds with an optically thin but detectable ice layer (optical thickness less than ∼7) above a liquid water layer. We consider collocated Advanced Himawari Imager (AHI)/Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements to provide sufficient spectral information. Compared with conventional single-layer-assumed CTH retrievals, our CTHs for both upper-layer ice and lower-layer water clouds show closer agreement with those indicated by active instruments. Statistically, the average bias of the upper ice CTH results is constrained from −2.89 km to −1.36 km and we obtain lower water CTHs with an average bias of −1.21 km. The cloud properties based on our retrievals also make the simulated multichannel radiances more consistent with the observed ones. The new method will greatly improve our understanding of the vertical structures of overlapping clouds and their parameterization in general circulation models; thus, we suggest that including increased numbers of cloud-sensitive channels is necessary to better determine cloud properties.

Centimeter-scale mapping of phototrophic biofilms in glacial forefields using visible band ratios and UAV imagery

ABSTRACT Microbial biofilms have received great attention in the last few decades from both aquatic ecologists and biogeomorphologists. Most recently, this has focused on mapping biofilms to understand their spatial distributions and ecosystem services. Such studies often involve the use of satellite imagery, which typically provides large temporal and spatial scales and wide-range spectral information. Although satellites have the advantage of multi- and hyper-spectral sensors, images often have low spatial resolution that limits their use in river studies, where both rivers are narrower and stream processes occur at resolutions smaller than the footprint of satellite sensors. Spatial resolution is sensor quality dependent but also controlled by sensor elevation above the ground. Hence, high resolutions can be achieved either by using a very expensive sensor or by decreasing the distance between the target area and the sensor itself. To date, sensor technology has advanced to a point where multi- or even hyper-spectral cameras can be easily carried out by an Uncrewed Aerial Vehicle (UAV) at unprecedented spatial resolutions. Where such sensors have high spectral resolution, they are often prohibitively expensive, especially as their use in extreme environments such as glacial forefields risks UAV damage. In this paper, we test the performance of visible band ratios in mapping biofilms in an Alpine glacier forefield characterized by a well-developed and heterogeneous stream ecosystem but using a low-cost UAV. The paper shows that low-cost and consumer grade UAVs can be easily deployed in such extreme environments, delivering both quality RGB images for photogrammetric (SfM-MVS) processing and sufficient spectral information for benthic biofilm mapping at high temporal and spatial resolution.

Analysing spectral changes over time to identify articulatory impairments in dysarthria.

Identifying characteristics of articulatory impairment in speech motor disorders is complicated due to the time-consuming nature of kinematic measures. The goal is to explore whether analysing the acoustic signal in terms of total squared changes of Mel-Frequency Cepstral Coefficients (TSC_MFCC) and its pattern over time provides sufficient spectral information to distinguish mild and moderate dysarthric French speakers with Amyotrophic Lateral Sclerosis (ALS) and Parkinson's Disease (PD) from each other and from healthy speakers. Participants produced the vowel-glide sequences /ajajaj/, /ujujuj/, and /wiwiwi/. From the time course of TSC_MFCCs, event-related and global measures were extracted to capture the degree of acoustic change and its variability. In addition, durational measures were obtained. For both mild and moderately impaired PD and ALS speakers, the degree of acoustic change and its variability, averaged over the complete contour, separated PD and ALS speakers from each other and from healthy speakers, especially when producing the sequences /ujujuj/ and /wiwiwi/. Durational measures separated the moderate ALS from healthy and moderate PD speakers. Using the approach on repetitive sequences targeting the lingual and labial articulators to characterize articulatory impairment in speech motor disorders is promising. Findings are discussed against prior findings of articulatory impairment in the populations studied.

Multispectral Image Reconstruction From Color Images Using Enhanced Variational Autoencoder and Generative Adversarial Network

Since multispectral images (MSIs) have much more sufficient spectral information than RGB images (RGBs), reconstructing MS images from RGB images is a severely underconstrained problem. We have to generate colossally different information between the two scopes. Almost all previous approaches are based on static and dependent neural networks, which fail to explain how to supplement the massive lost information. This paper presents a low-cost and high-efficiency approach, “VAE-GAN”, based on stochastic neural networks to directly reconstruct high-quality MSIs from RGBs. Our approach combines the advantages of the Generative Adversarial Network (GAN) and the Variational Autoencoder (VAE). The VAE undertakes the generation of the lost variational MS distributions by reparameterizing the latent space vector with sampling from Gaussian distribution. The GAN is responsible for regulating the generator to produce MSI-like images. In this way, our approach can create huge missed information and make the outputs look real, which also solves the previous problem. Moreover, we use several qualitative and quantitative methods to evaluate our approach and obtain excellent results. In particular, with much less training data than the previous approaches, we obtained comparable results on the CAVE dataset and surpassed state-of-the-art results on the ICVL dataset.

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.

Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data

Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving vegetation parameters, is constrained by spectral saturation problems and cloud cover. On the other hand, LiDAR data, which have been extensively used to estimate forest structure attributes, cannot provide sufficient spectral information of vegetation canopies. Thus, this study aimed to develop a novel synergistic approach to estimating biomass by integrating LiDAR data with Landsat 8 imagery through a deep learning-based workflow. First the relationships between biomass and spectral vegetation indices (SVIs) and LiDAR metrics were separately investigated. Next, two groups of combined optical and LiDAR indices (i.e., COLI1 and COLI2) were designed and explored to identify their performances in biomass estimation. Finally, five prediction models, including K-nearest Neighbor, Random Forest, Support Vector Regression, the deep learning model, i.e., Stacked Sparse Autoencoder network (SSAE), and multiple stepwise linear regressions, were individually used to estimate biomass with input variables of different scenarios, i.e., (i) all the COLI1 (ACOLI1), (ii) all the COLI2 (ACOLI2), (iii) ACOLI1 and all the optical (AO) and LiDAR variables (AL), and (iv) ACOLI2, AO and AL. Results showed that univariate models with the combined optical and LiDAR indices as explanatory variables presented better modeling performance than those with either optical or LiDAR data alone, regardless of the combination mode. The SSAE model obtained the best performance compared to the other tested prediction algorithms for the forest biomass estimation. The best predictive accuracy was achieved by the SSAE model with inputs of combined optical and LiDAR variables (i.e., ACOLI1, AO and AL) that yielded an R2 of 0.935, root mean squared error (RMSE) of 15.67 Mg/ha, and relative root mean squared error (RMSEr) of 11.407%. It was concluded that the presented combined indices were simple and effective by integrating LiDAR-derived structure information with Landsat 8 spectral data for estimating forest biomass. Overall, the SSAE model with inputs of Landsat 8 and LiDAR integrated information resulted in accurate estimation of forest biomass. The presented modeling workflow will greatly facilitate future forest biomass estimation and carbon stock assessments.

Underwater Image Color Correction using Exposure-Bracketing Imaging

Absorption and scattering of light in an underwater scene saliently attenuate red spectrum components. They cause heavy color distortions in the captured underwater images. In this letter, we propose a method for color-correcting underwater images, utilizing a framework of gray information estimation for color constancy. The key novelty of our method is to utilize exposure-bracketing imaging: a technique to capture multiple images with different exposure times for color correction. The long-exposure image is useful for sufficiently acquiring red spectrum information of underwater scenes. In contrast, pixel values in the green and blue channels in the short-exposure image are suitable because they are unlikely to attenuate more than the red ones. By selecting appropriate images (i.e., least over- and under-exposed images) for each color channel from those taken with exposure-bracketing imaging, we fuse an image that includes sufficient spectral information of underwater scenes. The fused image allows us to extract reliable gray information of scenes; thus, effective color corrections can be achieved. We perform color correction by linear regression of gray information estimated from the fused image. Experiments using real underwater images demonstrate the effectiveness of our method.

심층 컨볼루션 신경망을 사용한 초분광 영상의 공간 분광학적 분류 기법

초분광 영상 데이터는 픽셀마다 수백 개의 스펙트럼 밴드에 대한 정보가 주어지는 고차원 데이터로, 농업, 식품처리, 광물학, 물리학, 환경학, 지리학 등 광범위한 분야에 활용되고 있다. 그 중 하나는 토지 피복의 분류 문제인데, 이는 자연 재해 예방, 자연 자원 감시, 환경에 대한 정보 수집에 있어서 중요한 문제이다. 하지만 차원의 저주, 시공간적 변동성, 레이블된 데이터의 부족 때문에 토지 피복의 정확한 분류에는 어려움이 따른다. 이 논문에서는 이러한 문제를 해결하기 위해 컨볼루션 신경망에 기반한 새로운 심층 학습 구조를 제안한다. 제안된 구조는 원하는 지점 주변 픽셀의 정보를 컨볼루션 신경망을 통해 처리하고, 그 지점의 스펙트럼 정보를 강조하기 위해 컨볼루션 층의 출력과 스펙트럼 정보를 함께 소프트맥스 분류기의 입력으로 사용한다. 이 구조는 추가적인 특징 추출 과정을 필요로 하지 않고, 그래픽 처리 장치 등을 이용한 병렬화가 간편하다는 점에서 기존 방법들보다 유리하다. 실험 결과, 제안된 구조는 기존에 가장 좋은 성능을 보인 분류기와 비슷하거나 더 좋은 분류 정확도를 보여 좋은 일반화 성능을 보이는 것을 확인할 수 있었다. Land cover classification is an important tool for preventing natural disasters, collecting environmental information, and monitoring natural resources. Hyperspectral imaging is widely used for this task thanks to sufficient spectral information. However, the curse of dimensionality, spatiotemporal variability, and lack of labeled data make it difficult to classify the land cover correctly. We propose a novel classification framework for land cover classification of hyperspectral data based on convolutional neural networks. The proposed framework naturally incorporates full spectral features with the information from neighboring pixels and has advantages over existing methods that require additional feature extraction or pre-processing steps. Empirical evaluation results show that the proposed framework provides good generalization power with classification accuracies better than (or comparable to) the most advanced existing classifiers.

Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier.

Neural Representation of Concurrent Vowels in Macaque Primary Auditory Cortex.

Successful speech perception in real-world environments requires that the auditory system segregate competing voices that overlap in frequency and time into separate streams. Vowels are major constituents of speech and are comprised of frequencies (harmonics) that are integer multiples of a common fundamental frequency (F0). The pitch and identity of a vowel are determined by its F0 and spectral envelope (formant structure), respectively. When two spectrally overlapping vowels differing in F0 are presented concurrently, they can be readily perceived as two separate “auditory objects” with pitches at their respective F0s. A difference in pitch between two simultaneous vowels provides a powerful cue for their segregation, which in turn, facilitates their individual identification. The neural mechanisms underlying the segregation of concurrent vowels based on pitch differences are poorly understood. Here, we examine neural population responses in macaque primary auditory cortex (A1) to single and double concurrent vowels (/a/ and /i/) that differ in F0 such that they are heard as two separate auditory objects with distinct pitches. We find that neural population responses in A1 can resolve, via a rate-place code, lower harmonics of both single and double concurrent vowels. Furthermore, we show that the formant structures, and hence the identities, of single vowels can be reliably recovered from the neural representation of double concurrent vowels. We conclude that A1 contains sufficient spectral information to enable concurrent vowel segregation and identification by downstream cortical areas.

Fast experiments for structure elucidation of small molecules: Hadamard NMR with multiple receivers.

We propose several significant improvements to the PANSY (Parallel NMR SpectroscopY) experiments-PANSY COSY and PANSY-TOCSY. The improved versions of these experiments provide sufficient spectral information for structure elucidation of small organic molecules from just two 2D experiments. The PANSY-TOCSY-Q experiment has been modified to allow for simultaneous acquisition of three different types of NMR spectra-1D C-13 of non-protonated carbon sites, 2D TOCSY and multiplicity edited 2D HETCOR. In addition the J-filtered 2D PANSY-gCOSY experiment records a 2D HH gCOSY spectrum in parallel with a (1) J-filtered HC long-range HETCOR spectrum as well as offers a simplified data processing. In addition to parallel acquisition, further time savings are feasible because of significantly smaller F1 spectral windows as compared to the indirect detection experiments. Use of cryoprobes and multiple receivers can significantly alleviate the sensitivity issues that are usually associated with the so called direct detection experiments. In cases where experiments are sampling limited rather than sensitivity limited further reduction of experiment time is achieved by using Hadamard encoding. In favorable cases the total recording time for the two PANSY experiments can be reduced to just 40 s. The proposed PANSY experiments provide sufficient information to allow the CMCse software package (Bruker) to solve structures of small organic molecules.

Measurements of multiple gas parameters in a pulsed-detonation combustor using time-division-multiplexed Fourier-domain mode-locked lasers

Hyperspectral absorption spectroscopy is being used to monitor gas temperature, velocity, pressure, and H(2)O mole fraction in a research-grade pulsed-detonation combustor (PDC) at the Air Force Research Laboratory. The hyperspectral source employed is termed the TDM 3-FDML because it consists of three time-division-multiplexed (TDM) Fourier-domain mode-locked (FDML) lasers. This optical-fiber-based source monitors sufficient spectral information in the H(2)O absorption spectrum near 1350 nm to permit measurements over the wide range of conditions encountered throughout the PDC cycle. Doppler velocimetry based on absorption features is accomplished using a counterpropagating beam approach that is designed to minimize common-mode flow noise. The PDC in this study is operated in two configurations: one in which the combustion tube exhausts directly to the ambient environment and another in which it feeds an automotive-style turbocharger to assess the performance of a detonation-driven turbine. Because the enthalpy flow [kilojoule/second] is important in assessing the performance of the PDC in various configurations, it is calculated from the measured gas properties.

Determination by Fourier Transform Raman Spectroscopy of Conjugated Linoleic Acid in I2-Photoisomerized Soybean Oil

The potential of Fourier transform (FT)-Raman spectroscopy to quantify the total conjugated linoleic acid (CLA) content was evaluated to find a technique for the routine control of CLA synthesis by chemical procedures. The calibration and validation samples were obtained by photoisomerization of linoleic acid contained in soybean oil. The catalyst was iodine (I(2)), and the light source was the green line (514.5 nm) of an argon ion laser. The criteria to select the best partial least-squares (PLS) calibration model were a low standard error of prediction (SEP), a high correlation coefficient (R), and the selection of relevant variables of the Raman spectrum to reduce spectral interferences. The total CLA content of the 22 samples ranged from 0.05 to 3.28% of total lipids. The best PLS calibration model was obtained with three optimal factors, a SEP of 0.22, and a R of 0.97. This calibration model was obtained after baseline correction of the CC stretching region (1642-1680 cm(-1)), which contained sufficient spectral information for reliable CLA quantification.

The use and characterization of a backilluminated charge-coupled device in investigations of pulsed x-ray and radiation sources

Examinations of bremsstrahlung and energetic electron beams from a novel laser plasma source motivate and assist characterization of a backthinned, backilluminated direct detection x-ray charge-coupled device (CCD), a topology that is uncommon in hard x-ray work. Behavior toward pseudomonochromatic ((55)Fe) and multichromatic ((241)Am) sources is briefly reviewed under optimized noise conditions. Results collectively establish the previously unknown functional depth structure. Several modes of usage are illustrated in approximately 4-20 keV x-ray laser plasma source investigations, where the significance of the characterization is briefly discussed. The spectral redistribution associated with this CCD topology is unfavorable, yet appropriate analysis ensures that sufficient spectral information remains for quantitative determination of broadband x-ray flux and spectra in essentially single laser shot measurements. The energy dependence of nascent electron cloud radii in silicon is determined using broadband x-rays from the laser plasma source, turning the narrow depletion depth to advantage. Finally, the characterization is used to quantify recent x-ray spectral explorations of the water jet laser plasma source operating under aspirator vacuum. These results will have key value for establishment of laboratory based ultrafast extended x-ray absorption fine structure experiments using microbolometric detectors.

Tropical mangrove species discrimination using hyperspectral data: A laboratory study

The aim of this study is to test whether spectra of crown canopy leaves of various tropical mangrove species measured under laboratory conditions contain sufficient spectral information for discriminating mangroves at the species level. This laboratory-level study is one of the most important prerequisites to the future use of airborne and satellite hyperspectral sensors for mangrove studies. First, spectral responses of 16 Thai tropical mangrove species (2151 spectral bands between 350 nm and 2500 nm) were recorded from the leaves, using a spectrometer under laboratory conditions. Next, the mangrove spectra were statistically tested using one-way ANOVA to see whether they significantly differ at every spectral location. Finally, the spectral separability between each pair of mangrove species was quantified using the Jeffries–Matusita (J–M) distance measure. It turned out that the 16 mangrove species under study were statistically different at most spectral locations, with a 95% confidence level ( p < 0.05). The total number of spectral bands that had p-values less than 0.05 was 1941, of which 477 bands had a 99% confidence level ( p < 0.01). Moreover, the J–M distance indices calculated for all pairs of the mangrove species illustrated that the mangroves were spectrally separable except the pairs that comprised the members of Rhizophoraceae. Although the difficulties of discriminating the members of Rhizophoraceae are expected, the overall result encourages further investigations into the use of on-board hyperspectral sensors to see whether mangrove species can be separated when the difficulties of the field conditions are taken into account.

Near-Infrared Spectroscopy for Measuring Urea in Hemodialysis Fluids

Near-infrared spectroscopy is proposed as a method for providing real-time urea concentrations during hemodialysis treatments. The feasibility of such noninvasive urea measurements is evaluated in undiluted dialysate fluid. Near-infrared spectra were collected from calibration solutions of urea prepared in dialysate fluid. Spectra were collected over three distinct spectral regions, and partial least-squares calibration models were optimized and compared for each. Selectivity for urea was demonstrated with two-component samples composed of urea and glucose in the dialysate matrix. The clinical significance of this approach was assessed by measuring urea in real hemodialysate samples. Urea absorptions within the combination and short-wavelength, near-infrared spectral regions provided sufficient spectral information for sound calibration models in the dialysate matrix. The combination spectral region had SEs of calibration (SEC) and prediction (SEP) of 0.38 mmol/L and 0.26 mmol/L, respectively, over the 4720-4600 cm(-1) spectral range with 5 partial least-square factors. A second calibration model was established over the combination region from a series of solutions prepared with independently variable concentrations of urea and glucose. The best calibration model for urea in the presence of variable glucose concentrations had a SEC of 0.6 mmol/L and a SEP of 0.4 mmol/L for a 5-factor model over the 4600-4350 cm(-1) spectral range. There was no significant decrease in SEP when the 4720-4600 cm(-1) calibration model was used to measure urea in real samples collected during actual hemodialysis. Urea can be determined with sufficient sensitivity and selectivity for clinical measurements within the matrix of the hemodialysis fluid.