Analysis Of Spectral Signatures Research Articles

Three Years of Google Earth Engine-Based Archaeological Surveys in Iraqi Kurdistan: Results from the Ground

This paper presents the results of a three-year survey (2021–2023), conducted in an area of approximately 356 km2 in Iraqi Kurdistan with the aim of identifying previously undetected archaeological sites. Thanks to the development of a multi-temporal approach based on open multispectral satellite data, greater effectiveness was achieved for the recognition of archaeological sites when compared to the use of single archival or freely accessible satellite images, which are typically employed in archaeological research. In particular, the Google Earth Engine services allowed for the efficient utilization of cloud computing resources to handle hundreds of remote sensing images. Using different datasets, namely Landsat 5, Landsat 7 and Sentinel-2, several products were obtained by processing entire stacks of images acquired at different epochs, thus minimizing the adverse effects on site visibility caused by vegetation, crops and cloud coverage and permitting an effective visual inspection and site recognition. Furthermore, spectral signature analysis of every potential site complemented the method. The developed approach was tested on areas that belong to the Land of Nineveh Archaeological Project (LoNAP) and the Upper Greater Zab Archaeological Reconnaissance (UGZAR) project, which had been intensively surveyed in the recent past. This represented an additional challenge to the method, as the most visible and extensive sites (tells) had already been detected. Three years of direct ground-truthing in the field enabled assessment of the outcomes of the remote sensing-based analysis, discovering more than 60 previously undetected sites and confirming the utility of the method for archaeological research in the area of Northern Mesopotamia.

Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

Intrinsic tryptophan fluorescence quenching by iodine in non-canonical amino acid reveals alteration of the hydrogen bond network in the photoactive orange carotenoid protein

The Orange Carotenoid Protein (OCP) regulates cyanobacterial photosynthetic activity through photoactivation in intense light. A hydrogen bonding network involving the keto-carotenoid oxygen and Y201 and W288 residues prevents the spontaneous activation of dark-adapted OCP. To investigate the role of the hydrogen bonds in OCP photocycling, we introduced non-canonical amino acids near the keto-carotenoid, particularly iodine at the meta-position of Y201. This modification significantly increased the yield of red OCP photoproducts, albeit with a shorter lifetime. Changes in tryptophan fluorescence during photocycling influenced by the presence of iodine near W288 revealed interactions between Y201 and W288 in the absence of the carotenoid in the C-domain. We propose that upon the relaxation of red states, a ternary complex with the carotenoid is formed. Analysis of spectral signatures and interaction energies indicates that the specific iodo-tyrosine configuration enhances interactions between the carotenoid and W288.

Connecting Obsidian Artifacts with Their Sources Using Multivariate Statistical Analysis of LIBS Spectral Signatures

With the recent introduction of handheld instruments for field use, laser-induced breakdown spectroscopy (LIBS) is emerging as a practical technology for real-time in situ geochemical analysis in the field. LIBS is a form of optical emission spectroscopy that is simultaneously sensitive to all elements with a single laser shot so that a broadband LIBS spectrum can be considered a diagnostic geochemical fingerprint. Sets of LIBS spectra were collected for seven obsidian centers across north-central California, with data processed using multivariate statistical analysis and pattern recognition techniques. Although all obsidians exhibit similar bulk compositions, different regional obsidian sources were effectively discriminated via partial least squares discriminant analysis. Obsidian artifacts from seven archaeological sites were matched to their putative sources with a high degree of confidence.

In Situ Compositional Analysis of Tomato Plants and Cell Wall Using Fiber Optic Fourier-Transform Near-Infrared Spectroscopy

This research was intended to define and interpret cell wall attributes and other chemical composition of eight different varieties of tomato plants by utilizing fiber optic Fourier-transform near-infrared spectroscopy (FT-NIR) to acquire in situ chemical signatures of leaf, flower, fruit, and stem of tomato plant and cell wall at different developmental stages. Chemical spectral signatures of the tomato’s leaf, flower, fruit, and stem were only acquired during its session and in live mode such as green, yellow, and red in cell wall color. The spectral signature analysis of each tomato plant was performed to see substantial differences in chemical compositions using chemometric data modeling of FT-NIR spectra. In addition, principal component analysis (PCA) was performed to discriminate leaf, flower, fruit, and stem from the same variety. PCA was also performed to differentiate eight different varieties of tomato plants. The study showed how in situ FT-NIR could distinguish eight types of tomato leaf, flower, fruit, and stem chemical composition at different developmental stages related to cell wall and other attributes. This study has also demonstrated how in situ FT-NIR can discriminate between rusty vs. healthy leaf and intact fruit vs. off-the-plant fruit. The main objective of this study is to present the chemical signature differences in the live and developing tomato plants to improve crucial factors of tomatoes that would benefit plant breeding, tomato cell wall study, and ultimately human health.

Diagnosis in cucurbits plants based on Wavelet and Fourier coefficients using support vector machines

Spectral signature analysis is one of the most widely used techniques for diagnosing diseased plants. For this, it is necessary to consider different techniques for the feature extraction that allow the identification of different damage levels of a specific pathology, such as the case of fungal disease in cucurbits plants. In this study, reflectance measurements of healthy and diseased leaves are used to identify three main stages of powdery mildew levels: leaves in the germination stage of the fungus leaves with first symptoms and diseased leaves. Then, a proposal to use frequency analysis of the spectral signatures using the Wavelet transforms and the Fourier transforms to the feature extraction from the obtained coefficients and determines the damage levels using multi-classification in support vector machine blocks. Classification accuracy of 94.6% and 98.3%, respectively, was demonstrated. Therefore, this methodology is important for the diagnosis of damage levels on cucurbits leaves and other similar plants.

Mobile application devices for MATAG coconut variety detection based on spectral signature analysis: A review

A smartphone is a high-speed technology with many new features available. Users can install games, education, health, financial, safety, and even agriculture apps. Some smartphones can detect plant species based on the processed images from RGB and its algorithm. MATAG hybrid process including fermentation is being described. This review also shows the current trend of MATAG variety identification based on spectral signature differences between MATAG hybrid. It can be linked via mobile applications to detect the species. This review then elaborates the image segmentation in the smartphone for the segregation of digital images into several segments and locates the objects and the bounding lines of the pictures. Applications of computer vision techniques such as machine learning and support vector machine to identify MATAG variety based on spectral analysis is being reviewed. As a user-friendly approach, the mobile application is essential for farmers to capture the MATAG information from smartphones’ cameras with all the processing links to the server and finally will help farmers identify the species using the mobile apps.

Non-Invasive Three-Dimensional Cell Analysis in Bioinks by Raman Imaging.

3D bioprinting is an emerging biofabrication strategy using bioinks, comprising cells and biocompatible materials, to produce functional tissue models. Despite progress in building increasingly complex objects, biological analyses in printed constructs remain challenging. Especially, methods that allow non-invasive and non-destructive evaluation of embedded cells are largely missing. Here, we implemented Raman imaging for molecular-sensitive investigations on bioprinted objects. Different aspects such as culture formats (2D, 3D-cast, and 3D-printed), cell types (endothelial cells and fibroblasts), and the selection of the biopolymer (alginate, alginate/nanofibrillated cellulose, alginate/gelatin) were considered and evaluated. Raman imaging allowed for marker-independent identification and localization of subcellular components against the surrounding biomaterial background. Furthermore, single-cell analysis of spectral signatures, performed by multivariate analysis, demonstrated discrimination between endothelial cells and fibroblasts and identified cellular features influenced by the bioprinting process. In summary, Raman imaging was successfully established to analyze cells in 3D culture in situ and evaluate them with regard to the localization of different cell types and their molecular phenotype as a valuable tool for quality control of bioprinted objects.

Enhanced lithological mapping of Durba-Araba basement blocks, along the eastern margin of the Central Gulf of Suez Rift, Egypt, using Landsat-8 Data

The eastern margin of the central Gulf of Suez rift exposes unique rotated basement blocks of Gabal Durba-Araba. According to geological maps, these blocks are composed of Dokhan volcanic, older, and younger granites cut by basaltic dikes and overlayed by a tilted sedimentary succession of Paleozoic to quaternary. Herein, we propose a workflow of stepwise preprocessing and classification procedures for Landsat-8 (OLI) data verified by control points from previous maps to update and improve lithological discrimination in converging areas that require further field mapping. The workflow applies digital image-processing techniques, including spectral signature analysis, band-ratio transformation, maximum likelihood, and Mahalanobis supervised classifications. The accuracy assessment of the lithological mapping reaches 86.6% and 75.7% for maximum likelihood and Mahalanobis classifications, respectively. By running the Tactical Hyperspectral Operations Resource (THOR) algorithm on the classified images, we could accurately map and modify the lithological boundaries for the following rock units: (1) gneiss-amphibolite-schist, tonalite, and granodiorite at Gabal Araba; (2) granitic gneiss, granodiorite, biotite-muscovite-leucogranite, and riebeckite-syenite-albitite at Gabal Abu Haswa; (3) Dokhan volcanic, granite biotite-muscovite-leucogranite, granites riebeckite-syenite-albitite, Pyroclastic breccia and lava flow, and NW tertiary dike at Gabal Abu Durba.

Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review

The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests and diseases with a hyperspectral sensor more efficiently than the conventional method, which takes more time to monitor the entire crop field. This review aims to show the current and future trends of mobile computing based on spectral signature analysis for pest and disease management. In this review, the use of mobile applications for pest and disease monitoring is evaluated based on image processing, the systems developed for pest and disease extraction, and the structure of steps outlined in developing a mobile application. Moreover, a comprehensive literature review on the utilisation of spectral signature analysis for pest and disease management is discussed. The spectral reflectance used in monitoring plant health and image processing for pest and disease diagnosis is mentioned. The review also elaborates on the integration of a spectral signature library within mobile application devices to obtain information about pests and disease in crop fields by extracting information from hyperspectral datasets. This review demonstrates the necessary scientific knowledge for visualising the spectral signature of pests and diseases using a mobile application, allowing this technology to be used in real-world agricultural settings.

Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability

The complex nature of hyperspectral images makes the analysis of spectral signatures a challenging task in remote sensing. For quantitative analysis, spectral unmixing is a well-established and effective tool to analyze the spectra and spatial distribution of substances in the scene. The classical unmixing algorithms usually fail to tackle spectral variability caused by variations in environmental conditions. Many variants based on the linear mixing process have been proposed to tackle this problem; however, the spectral variability modeling capacity of these algorithms is usually insufficient. In this article, we present a probabilistic generative model to address endmember variability and provide more accurate abundance and endmember estimates. The proposed model simultaneously extracts the endmembers and estimates abundances in an unsupervised manner. In particular, it allows fitting arbitrary endmember distributions through the nonlinear modeling capability of neural networks compared to other methods that use parametric endmember variability models. The performance of the proposed approach is evaluated on both synthetic and real datasets. Experimental results show its superiority in comparison with other state-of-the-art methods. The code of this work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shuaikaishi/PGMSU</uri> for the sake of reproducibility.

Detection of cancer markers or marker surrogates by infrared spectral histopathology (SHP): PD-L1 and Her2/neu

In this contribution, data will be presented that suggest that certain cancer markers or their surrogates can be detected by infrared spectral imaging methodology. In particular, the co-localization of certain regions in spectral images with PD-L1-postive immunohistochemical response suggests that either the PD-L1 protein or a surrogate associated with its presence are detectable. In the case of Her2/neu, an analysis of mean spectral signatures of over forty thousand individual spectra revealed small, but statistically significant spectral differences between cancer marker positive and negative tissues in spectral regions associated with protein phosphorylation.

Spectral signature analysis to determine mangrove species delineation structured by anthropogenic effects

In this study, we used the spectral information divergence (SID) and spectral angle mapper (SAM) algorithm classification to identify ~ 40,000 individual trees from 19 mangrove species; these data were collected from 7 sampling points spread across 40,288 ha of the Matang Mangrove Forest Reserve (MMFR) in the state of Perak, Malaysia. This information was examined and analysed to answer two questions: (1) can medium resolution of satellite image identify the individual mangrove species (2) how are the distributions of individual species within the mangrove zonation associated with the anthropogenic matrix? Species identification with spectral library derived from the in-situ measurements using the SID algorithm was compared with that derived from the Landsat 8 using the SAM algorithm; it was found that the two methods offered different but complementary information with different rates of accuracy. The observed levels of classification accuracy are at 84.95% and 85.21%, respectively for SID and SAM algorithm classification. The mangrove species distribution has a correlation with the anthropogenic activities, but the distribution occurred randomly without specific zones. All the 19 selected mangrove species are closely related with different correlation by the anthropogenic activities. The use of the SID and SAM algorithms may provide the most promising way of classification for improving the mangrove species identification, with medium resolution of satellite image. Recognising the characteristics of mangrove zonation may give a better understanding of mangrove species appearances and conservation. However, mangrove zonation will remain a daunting mapping task and is a challenge for the ecologists in the future.

Identification of spectral signature for in situ real-time monitoring of smoltification.

We describe the use of an optical hyperspectral sensing technique to identify the smoltification status of Atlantic salmon (Salmo salar) based on spectral signatures, thus potentially providing smolt producers with an additional tool to verify the osmoregulatory state of salmon. By identifying whether a juvenile salmon is in the biological freshwater stage (parr) or has adapted to the seawater stage (smolt) before transfer to sea, negative welfare impacts and subsequent mortality associated with failed or incorrect identification may be reduced. A hyperspectral imager has been used to collect data in two water flow-through and one recirculating production site in parallel with the standard smoltification evaluations applied at these sites. The results from the latter have been used as baseline for a machine-learning algorithm trained to identify whether a fish was parr or smolt based on its spectral signature. The developed method correctly classified fish in 86% to 100% of the cases for individual sites, and had an overall average classification accuracy of 90%, thus indicating that analysis of spectral signatures may constitute a useful tool for smoltification monitoring.

Wind turbine blade defect detection using hyperspectral imaging

Regardless of the evolution in the wind turbine industry, the operation of wind farms faces critical challenges when it comes to maintaining the lowest possible cost of energy. It is essential to early detect or predict wind turbine breakdowns due to different factors such as material degradation, electrical or mechanical failures, faults, or environmental damage. Wind turbine blades are the most expensive and most exposed parts of a wind turbine and suffer from many shortcomings, mainly cracks and erosion, which reduces their performance. Hence, there is an essential requirement for using non-destructive diagnostic of wind turbine blades. This paper lists some of the current non-destructive techniques for wind turbine blades analysis, their applicability, advantages, and drawbacks. Nevertheless, these methods face drawbacks that can be overcome by remote sensing. Hyperspectral imaging is a spectral imaging technique that integrates imaging and spectroscopy. It also enables the analysis and identification of distinctive spectral signatures and assigns them to the examined sample elements. Thus, this paper describes hyperspectral imaging implementation in image acquisition, handling, and flaw recognition as well as the detection of cracks and erosion. This technique's field output results show that blade defect detection's accuracy and precision are significantly enhanced.

Spectral library creation and analysis of urban built-up surfaces and materials using field spectrometry

Hyperspectral remote sensing is useful for the study of urban environment due to its ability to examine the comprehensive spectral characteristics of urban built-up surfaces and materials. This study deals with the creation of a spectral library and statistical analysis of spectral signatures of urban built-up surfaces and materials. Field measurement has been carried out using a spectroradiometer over the wavelength range of 350 to 2500 nm. Further, this study investigates the unique spectral characteristics and complexity of heterogeneous urban environments using mean spectral signatures of major built-up surfaces and materials. The study area belongs to the Udaipur, Rajasthan, India, which is an amalgamation of urban built-up and non-built-up surfaces. The results of the analysis suggest that various built-up surfaces and materials can be identified by investigating different absorption features in different spectral regions of a particular urban built-up class.

Spectral signature analysis of false positive burned area detection from agricultural harvests using Sentinel-2 data

Accurate mapping of burned area is of key importance for fire emissions modeling and post-fire rehabilitation planning. In this research, Sentinel-2 data were used to analyze the difference in spectral signature between burned area and false positives from agricultural harvests. 26 fires that were mapped in the field using Global Navigation Satellite System during 2017 and 2018 were analyzed over California and Utah, USA. Individual Sentinel-2 bands and a wide range of commonly used spectral indices for burned area were tested using a spectral separability index. The separability index assessed discrimination between the classes burned area and 1) unburned area and 2) area in agricultural land that were flagged as false positive from agricultural harvest. Separability values higher than one indicate good separation and the higher the values, the better the separation. For each class, we first determined the multitemporal difference, i.e. the absolute value of the pre-minus-post-change value. Second, we compared with other classes by using the spectral separability index. We found that for the burned-to-unburned comparison the near and shortwave infrared spectral regions and spectral indices that make use of these spectral regions, were the best discriminators (separability (M) values of approximately 2), corroborating findings from earlier works. For the burned-to-agricultural false positive comparison Sentinel-2 bands 4 and 5, corresponding with the Red and Red-edge spectral regions, were the best discriminator (M−values greater than 2). Consequently, spectral indices containing the Red band show a similar strong separability of agricultural false positives. The results from our research reveal an additional layer of information that could be exploited to minimize false positives in agricultural lands in space-borne burned area products.

Uncertainty quantification for a global imaging spectroscopy surface composition investigation

Airborne and orbital imaging spectroscopy can facilitate the quantification of chemical and physical attributes of surface materials through analysis of spectral signatures. Prior to analysis, estimates of surface reflectance must be inferred from radiance measurements in a process known as atmospheric correction, which compensates for the distortion of the electromagnetic signal by the atmosphere. Inaccuracies in the correction process can alter characteristic spectral signatures, leading to subsequent mischaracterization of surface properties. Global observations pose new challenges for mapping surface composition, as varied atmospheric conditions and surface biomes challenge traditional atmospheric correction methods. Recent work adopted an optimal estimation (OE) approach for retrieving surface reflectance from observed radiance measurements, providing the reflectance estimates with a posterior probability. This work incorporates these input probabilities to improve the accuracy of surface feature measurements. We demonstrate this using a generic feature-fitting method that is applicable to a wide range of Earth surface studies including geology, ecosystem studies, hydrology and urban studies. Specifically, we use a probabilistic framework based on generalized Tikhonov-regularized least squares, a rigorous formulation for appropriate weighting of features by their observation uncertainty and leveraging of prior knowledge of material abundance for improving estimation accuracy. We demonstrate the validity of this procedure and quantify the increase in model performance by simulating expected accuracies in the reflectance estimation. To evaluate global uncertainties in mineral estimation, we simulate observations representative of the expected global range of atmospheric water vapor and aerosol levels, and characterize the sensitivity of our procedure to those quantities.

WATER SALINITY MAPPING OF KARUN BASIN LOCATED IN IRAN USING THE SVR METHOD

Abstract. Water salinity is a complex issue in coastal and estuarine areas. Currently, remote sensing techniques have been widely used to monitor water quality changes, ranging from river to oceans. The salinity of Karun River has been increasing due to some critical factors, therefore, This study aimed at building regression models to ascertain the water salinity through the relationship between the reflectance of the Landsat-8 OLI and In situ measurements. A total of 102 observed samples were divided into 70% training and 30% test from June 2013 to July 2018 along the Karun River. Spectral signature analysis showed that band 1 - Coastal/Aerosol (0.433–0.453 μm), band 2 - Blue (0.450–0.515 μm) and band 3 - Green (0.525–0.600 μm) are sensitive to salinity . Furthermore, to have a comprehensive investigation, the Support Vector Regression (SVR) method was applied. The outcomes related to the quality of the SVR depend on several factors e.g. proper setting of the SVR meta-parameters, therefore, to deal with this issue Genetic Algorithm (GA) was applied. The SVR model resulted in values of R2 and RMSE for test data which are respectively obtained to be 0.7 and 390 μs cm−1. Eventually, Karun water salinity maps were prepared by SVR method to demonstrate the Karun water salinity on 1 February 2015 and 5 September 2018.

Soil Spectral Signature Analysis for Influence of Fertilizers on Two Different Crops in Raver Tahshil

Soils as a significant ingredient of terrestrial ecosystems are extremely important in agriculture field. Soil having different physical, biological, chemical properties. Physicochemical soil properties are the basic indicator of soil efficiency, it is strongly related to agronomic output. Soil parameters are very important in soil fertility that helps for plant growth as well as production. The soil quality is uniformly important as crop invention. The primary goal of this research is to use remote sensing techniques to evaluate soil properties. This goal is satisfied by emerging soil analysis on the basis of spectral data collected by FieldSpec4 spectroradiometer. The spectral analysis technique includes soil samples preparation, acquisition of spectral signatures and selection suitable statistical method. In this regard, soil properties and their consequent soil spectral signature measure. Statistical mean and series of processes are performed using View Spec pro Software. In the collected soil samples, surface soil parameters are more reflected than subsurface soil parameters. The spectral reflectance data can be alternative to the traditional methods for determining soil properties.