Spectral Indexing Research Articles

Molecular and biological activities of metal oxide-modified bioactive glass

Bioactive glass (BG) was prepared by sol–gel method following the composition 60-(x) SiO2.34CaO.6P2O5, where x = 10 (FeO, CuO, ZnO or GeO). Samples were then studied with FTIR. Biological activities of the studied samples were processed with antibacterial test. Model molecules for different glass compositions were built and calculated with density functional theory at B3LYP/6-31 g(d) level. Some important parameters such as total dipole moment (TDM), HOMO/LUMO band gap energy (ΔE), and molecular electrostatic potential beside infrared spectra were calculated. Modeling data indicated that P4O10 vibrational characteristics are enhanced by the addition of SiO2.CaO due to electron rush resonating along whole crystal. FTIR results confirmed that the addition of ZnO to P4O10.SiO2.CaO significantly impacted the vibrational characteristics, unlike the other alternatives CuO, FeO and GeO that caused a smaller change in spectral indexing. The obtained values of TDM and ΔE indicated that P4O10.SiO2.CaO doped with ZnO is the most reactive composition. All the prepared BG composites showed antibacterial activity against three different pathogenic bacterial strains, with ZnO-doped BG demonstrating the highest antibacterial activity, confirming the molecular modeling calculations.

Albedo change from snow algae blooms can contribute substantially to snow melt in the North Cascades, USA

Snow algae are ubiquitous in the Pacific Northwest cryosphere in the summer where snowmelt is an important contribution to regional watersheds. However, less attention has been given to biological impurities as drivers of snowmelt compared to inorganic light-absorbing particles. Here we map snow algae near Mt. Baker with a multispectral camera on an uncrewed aerial vehicle using (1) principal components and (2) spectral indexing. The two approaches are tested under differing bloom states and verified with coincident algal pigment and cell count data. During high bloom intensity we found an average instantaneous radiative forcing of 237 W m−2 with a maximum of 360 W m−2. This translated to 1,508 ± 536 m3 of melted snow water equivalent in the 0.1 km2 basin. These results demonstrate snow algae contribute to snowmelt at mid-latitudes and the potential for uncrewed autonomous vehicles to map snow algae over expansive areas of the cryosphere.

Effects of Mesoscale Stirring on Phytopigment Determinations in the Photic Water Layer from Multispectral Ocean Color Data (The Case of the Tasman Sea)

The inconstancy of phytopigment composition during intensive mesoscale mixing of the Tasman Sea photic layer was investigated using MODIS images of its surface. To do this, each pixel of such an image is assigned a WRM index equal to the sum of the wavelengths of the minima in the reflectance spectrum of the water surface within the boundaries of a pixel on the ground (Spectral Indexing of Pixels, or SIP approach). WRM is acceptable as an indicator of phytopigment composition variability in the water column, since the attenuation of light by water as a solvent and by its admixtures of other nature is inferior to light absorption by phytopigments in spectral selectivity, while the composition of phytopigments in the aquatic environment depends on the species composition of local phytoplankton. A co-analysis of WRM distributions and characteristics of Tasman Sea waters showed that with increased mesoscale variability in open ocean waters, phytopigment content in the near-surface layer reaches levels at which minimums of pigment origin at 400–550 nm, discernible by multispectral ocean color scanners, occur in the backscattered solar radiation spectrum. This phenomenon is ignored by common algorithms for chlorophyll determination based on the data of multispectral ocean color scanners (band-ratio algorithms) and, apparently, is one of the reasons for the known tendency of such algorithms to overestimate chlorophyll concentration relative to its real content in the water column. The conclusion is applicable to any ocean basins if they, like the Tasman Sea, are not affected by external sources of optically significant admixtures in water.

Optical classification of an urbanized estuary using hyperspectral remote sensing reflectance.

Optical water classification based on remote sensing reflectance (Rrs(λ)) data can provide insight into water components driving optical variability and inform the development and application of bio-optical algorithms in complex aquatic systems. In this study, we use an in situ dataset consisting of hyperspectral Rrs(λ) and other biogeochemical and optical parameters collected over nearly five years across a heavily urbanized estuary, the Long Island Sound (LIS), east of New York City, USA, to optically classify LIS waters based on Rrs(λ) spectral shape. We investigate the similarities and differences of discrete groupings (k-means clustering) and continuous spectral indexing using the Apparent Visible Wavelength (AVW) in relation to system biogeochemistry and water properties. Our Rrs(λ) dataset in LIS was best described by three spectral clusters, the first two accounting for the majority (89%) of Rrs(λ) observations and primarily driven by phytoplankton dynamics, with the third confined to measurements in river and river plume waters. We found AVW effective at tracking subtle changes in Rrs(λ) spectral shape and fine-scale water quality features along river-to-ocean gradients. The recently developed Quality Water Index Polynomial (QWIP) was applied to evaluate three different atmospheric correction approaches for satellite-derived Rrs(λ) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor in LIS, finding Polymer to be the preferred approach. Our results suggest that integrative, continuous indices such as AVW can be effective indicators to assess nearshore biogeochemical variability and evaluate the quality of both in situ and satellite bio-optical datasets, as needed for improved ecosystem and water resource management in LIS and similar regions.

Spectral Indexing of MODIS-Image Pixels to Reveal the Variability in the Phytopigment Composition in the Sea under the Influence of Mesoscale Water Dynamics

Spectral Indexing of MODIS-Image Pixels to Reveal the Variability in the Phytopigment Composition in the Sea under the Influence of Mesoscale Water Dynamics

Investigation on Spectral Indices and Soft Classifiers-Based Water Body Segmentation Approaches for Satellite Image Analysis

The emerging threat for eco-sustainability has led to a breakthrough in satellite image analyses and such instantaneous monitoring of hazards could replenish the rejuvenation of natural ecosystem. Generally, the satellite images are huge dimensional data with numerous bands of specific details about the observed region. To apply immediate precautionary measures for environmental hazards and natural devastations, deploying a cloud-based intelligent web service for handling real time satellite image processing is inevitable. Therefore, the cloud implementation could afford integrated huge storage and parallel data processing tasks, the outcome of instantaneous satellite image processing relies with the effective data processing methods of less complexity. In this regard to address a major hazard of today which is drought monitoring, this paper focuses on developing an effective water segmentation method for such geospatial cloud web services. The Landsat 8 images of Sambhar lake region has been chosen for exploiting the water segmentation results. Most prevalent approaches from Spectral indices and unsupervised clustering such as normalized difference water index (NDWI), modified normalized difference water index, fuzzy C means, K-Means, Adaptive Regularized kernel fuzzy C means (ARKFCM) and simple linear iterative clustering-based superpixel segmentation (SLIC-SUPER) are compared, respectively. On comparative assessment using standard image quality assessment metrics, NDWI and ARKFCM outstands the rest with more accurate water body delineation. However, based on reduced computational complexity and instant localization, NDWI of spectral indexing approach clearly portray the significance of spectral influence in water body segmentation from satellite images. And it can be adapted as a persistent choice for instantaneous water body segmentation in a cloud-centered geospatial module.

Studying phase change memory devices by coupling scanning precession electron diffraction and energy dispersive X-ray analysis

A new simultaneous acquisition software for Precession Electron Diffraction (PED) and Energy Dispersive X-ray Spectroscopy (EDX) mapping has been developed. This approach has been used to study the RESET operation in Ge-rich Ge-Sb-Te (Ge-rich GST) non-volatile Phase-Change Memory (PCM). Such an operation consists in the local amorphization of the device's active volume, which defines the memory state. Using PED patterns indexing, we were able to segment the amorphous area, and access its composition through EDX quantification. Moreover, both the spatial distribution and the chemical composition of the two crystalline phases overlapped in the chalcogenide layer were retrieved by using the spectral unmixing approach and PED patterns indexing. It evidences that Ge and Ge2Sb1Te2 (GST-212) crystals are separated in the studied devices. Despite this, we show that Ge-enrichment is preserved in the active volume of a functional device, which is crucial to guarantee the RESET state stability at high temperature. We also link the origin of the failure of the RESET operation observed in a dysfunctional device to the larger Ge-depleted area.

Beyond NDVI- Spectral indexing of biomass

The use of both terrestrial and satellite sensors often lacks the specific knowledge that makes them more effective. There are many reasons for this, one of them is agricultural digitalization to facilitate data availability and shortened automatic spectral indexing, but interpretation requires special remote sensing and agricultural knowledge. The absence of this complex knowledge, will result in misleading or incorrect solutions and on the other hand the existing technical possibilities cannot be exploited by the farmers. The application and calculation of the wide and narrow band channel VIs were presented on two agricultural test areas. The purpose of this publication is to briefly introduce the technical capabilities of vegetation indices based on a Hungarian test site and thereby reduce potential misinterpretation.

Extraction of Water-body Area from High-resolution Landsat Imagery

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey

In this study, spatiotemporal changes in Lake Burdur from 1987 to 2011 were evaluated using multi-temporal Landsat TM and ETM+ images. Support Vector Machine (SVM) classification and spectral water indexing, including the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI) and Automated Water Extraction Index (AWEI), were used for extraction of surface water from image data. The spectral and spatial performance of each classifier was compared using Pearson's r, the Structural Similarity Index Measure (SSIM) and the Root Mean Square Error (RMSE). The accuracies of the SVM and satellite-derived indexes were tested using the RMSE. Overall, SVM followed by the MNDWI, NDWI and AWEI yielded the best result among all the techniques in terms of their spectral and spatial quality.Spatiotemporal changes of the lake based on the applied method reveal an intense decreasing trend in surface area between 1987 and 2011, especially from 1987 to 2000, when the lake lost approximately one fifth of its surface area compared to that in 1987. The results show the effectiveness of SVM and MNDWI-based surface water change detection, particularly in identifying changes between specified time intervals.

Vehicle Engine Classification Using Spectral Tone-Pitch Vibration Indexing and Neural Network.

As a non-invasive and remote sensor, the Laser Doppler Vibrometer (LDV) has found a broad spectrum of applications in various areas such as civil engineering, biomedical engineering, and even security and restoration within art museums. LDV is an ideal sensor to detect threats earlier and provide better protection to society, which is of utmost importance to military and law enforcement institutions. However, the use of LDV in situational surveillance, in particular vehicle classification, is still in its infancy due to the lack of systematic investigations on its behavioral properties. In this work, as a result of the pilot project initiated by Air Force Research Laboratory, the innate features of LDV data from many vehicles are examined, beginning with an investigation of feature differences compared to human speech signals. A spectral tone-pitch vibration indexing scheme is developed to capture the engine's periodic vibrations and the associated fundamental frequencies over the vehicles' surface. A two-layer feed-forward neural network with 20 intermediate neurons is employed to classify vehicles' engines based on their spectral tone-pitch indices. The classification results using the proposed approach over the complete LDV dataset collected by the project are exceedingly encouraging; consistently higher than 96% accuracies are attained for all four types of engines collected from this project.

The effect of a paced auditory serial addition test (PASAT) intervention on the profile of volatile organic compounds in human breath: a pilot study

This study sought to identify if detectable changes in human breath profiles may be observed following a psychological intervention designed to induce stress, a paced auditory serial addition test (PASAT). Breath samples were collected from 22 participants (10 male and 12 female) following a double cross-over randomized design with two experimental interventions. One intervention required participants to listen to classical music chosen to be neutral. The other intervention required participants to undertake a PASAT that induced cardiovascular responses consistent with acute stress. Both interventions also involved two sequences of cognitive function tests. Blood-pressure and heart-rate were recorded throughout each intervention and distal breath samples were collected onto Tenax® TA/Carbograph 1 thermal desorption tubes, using an adaptive breath sampler. Samples were collected before and after the PASAT. Breath samples were analysed by thermal desorption gas chromatography-mass spectrometry. Data registration using retention indexing and peak deconvolution followed by partial least-squares discriminant analysis identified six stress sensitive compounds. A principal components analysis model based on these components generated a model that predicted post-PASAT versus post-neutral intervention samples with a sensitivity of 83.3% and a selectivity of 91.6% for females, compared to 100% sensitivity and 90% selectivity for males. Of the six compounds indole, 2-hydroxy-1-phenylethanone, benzaldehyde, and 2-ethylhexan-1-ol were identified on the basis of mass spectral, retention indexing and confirmation against pure standards. 2-methylpentadecane was tentatively identified from mass spectral and retention indexing, whilst one component has yet to be assigned, although the mass spectrum is indicative of a terpene. Indole and 2-methylpentadecane concentrations increased in response to the PASAT intervention, while the other compounds reduced in their abundance in human breath, possibly as a result of ventilation effects.

Tempest: GPU-CPU Computing for High-Throughput Database Spectral Matching

Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer.

Global distribution, composition, and abundance of olivine on the surface of Mars from thermal infrared data

We used spectral indexing and linear deconvolution to compare thermal infrared emission spectra of Fo91, Fo68, Fo53, Fo39, Fo18, and Fo1 olivine samples to Mars Global Surveyor Thermal Emission Spectrometer (TES) data over low‐albedo regions of Mars. The Fo91, Fo68, Fo53, and Fo39 spectral end‐members were confidently identified on Mars, a range of compositions wider than inferred from Martian meteorites. Small (less than hundreds of square kilometers) occurrences of the Fo91 spectral end‐member are present in the rims of the Argyre and Hellas impact basins and may represent Martian mantle materials. The Fo68 spectral end‐member is common throughout the highlands, chasmata, outflow channels, and Nili Fossae region. The Fo53 spectral end‐member occurs in eastern Syrtis Major, the Nili Fossae region, and smooth‐floored craters of the highlands. Although less abundant than Fo68 and Fo53, the distribution of the Fo39 spectral end‐member suggests that some olivine on Mars is more Fe‐rich than olivine in Martian meteorites. Global maps of olivine show that (1) materials containing 10–20% of olivine are common in the southern highlands of Mars, (2) olivine is most common near the topographic dichotomy boundary, and (3) olivine becomes uncommon near the poles suggesting that it may be influenced by topography and/or latitude (climate). Olivine is found in early Noachian to Amazonian terrains, some of which may be coeval with phyllosilicate and sulfate deposits detected by OMEGA implying that any early Noachian wet period of Mars' climate history may have been globally inhomogeneous or insufficient to weather the olivine that remains today.

A Novel Geometry-Based Feature-Selection Technique for Hyperspectral Imagery

In this letter, a geometry-based feature-selection method is proposed for efficient analysis of hyperspectral imagery. It searches the vertices that form the largest simplex iteratively in pixel space. These vertices are representative subsets of spectral bands. A distance measure is introduced in the simplex volume comparison for fast implementation of the proposed method. Fast principal component analysis and spectral band indexing are suggested for data preprocessing. This method can be implemented in supervised or unsupervised manner. It is automatic, fast, and distribution-free. Experimental results show the superiority of the proposed method in terms of quality and speed