Search results for: spectral reflectance

Authors: V. Sivakumar, R. Neelakantan

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Mineral mapping on the Moon surface provides the clue to understand the origin, evolution, stratigraphy and geological history of the Moon. Recently, reflectance imaging spectroscopy plays a significant role in identifying minerals on the planetary surface in the Visible to NIR region of the electromagnetic spectrum. The Moon Mineralogy Mapper (M3) onboard Chandrayaan-1 provides unprecedented spectral data of lunar surface to study about the Moon surface. Here we used the M3 sensor data (hyperspectral imaging spectroscopy) for analysing mineralogy of Orientale basin region on the Moon surface. Reflectance spectrums were sampled from different locations of the basin and continuum was removed using ENvironment for Visualizing Images (ENVI) software. Reflectance spectra of unknown mineral composition were compared with known Reflectance Experiment Laboratory (RELAB) spectra for discriminating mineralogy. Minerals like olivine, Low-Ca Pyroxene (LCP), High-Ca Pyroxene (HCP) and plagioclase were identified. In addition to these minerals, an unusual type of spectral signature was identified, which indicates the probable Fe-Mg-spinel lithology in the basin region.

Keywords: chandryaan-1, moon mineralogy mapper, mineral, mare orientale, moon

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Authors: Iwan Cony Setiadi, Aulia M. T. Nasution

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The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of in-vivo skin chromophores by analyzing spectral data at each reflected image pixel. For ergonomic purpose, our multispectral imaging system is decomposed in two parts: a light source compartment based on LED with 11 different wavelenghts and a monochromatic 8-Bit CCD camera with C-Mount Objective Lens. The software based on GUI MATLAB to control the system was also developed. Our system provides 11 monoband images and is coupled with a software reconstructing hyperspectral cubes from these multispectral images. In this paper, we proposed a new method to build a hyperspectral reflectance cube based on artificial neural network algorithm. After preliminary corrections, a neural network is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure involves acquisition, by a spectrophotometer. This neural network is then used to retrieve a megapixel multispectral cube between 380 and 880 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. As hyperspectral cubes contain spectra for each pixel; comparison should be done between the theoretical values from the spectrophotometer and the reconstructed spectrum. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). To validate reconstruction, the set of 8 colour patches reconstructed by our MSI system and the one recorded by the spectrophotometer were compared. The average GFC was 0.9990 (standard deviation = 0.0010) and the average RMSE is 0.2167 (standard deviation = 0.064).

Keywords: multispectral imaging, reflectance cube, spectral reconstruction, artificial neural network

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Authors: Golayeh Yousefi, Mehdi Homaee, Ali Akbar Norouzi

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Ongoing monitoring of soil contamination by heavy metals is critical for ecosystem stability and environmental protection, and food security. The conventional methods of determining these soil contaminants are time-consuming and costly. Spectroscopy in the visible near-infrared (VNIR) - short wave infrared (SWIR) region is a rapid, non-destructive, noninvasive, and cost-effective method for assessment of soil heavy metals concentration by studying the spectral properties of soil constituents. The aim of this study is to derive spectral bands and important ranges that are sensitive to heavy metals and can be used to estimate the concentration of these soil contaminants. In other words, the change in the spectral properties of spectrally active constituents of soil can lead to the accurate identification and estimation of the concentration of these compounds in soil. For this purpose, 325 soil samples were collected, and their spectral reflectance curves were evaluated at a range of 350-2500 nm. After spectral preprocessing operations, the partial least-squares regression (PLSR) model was fitted on spectral data to predict the concentration of Cu and Ni. Based on the results, the spectral range of Cu- sensitive spectra were 480, 580-610, 1370, 1425, 1850, 1920, 2145, and 2200 nm, and Ni-sensitive ranges were 543, 655, 761, 1003, 1271, 1415, 1903, 2199 nm. Finally, the results of this study indicated that the spectral data contains a lot of information that can be applied to identify the soil properties, such as the concentration of heavy metals, with more detail.

Keywords: heavy metals, spectroscopy, spectral bands, PLS regression

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Authors: Hung Chih Hsieh, Cheng Hao Chang, Yun Hsiang Chang, Yu Lin Chang

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In the applications of the spectrometer, the stray light that comes from the environment affects the measurement results a lot. Hence, environment and instrument quality control for the stray reduction is critical for the spectral reflectance measurement. In this paper, a simple and practical method has been developed to correct a spectrometer's response for measurement errors arising from the environment's and instrument's stray light. A sinusoidal modulated light intensity signal was incident on a tested sample, and then the reflected light was collected by the spectrometer. Since a sinusoidal signal modulated the incident light, the reflected light also had a modulated frequency which was the same as the incident signal. Using the three-parameter sine curve fitting algorithm, we can extract the primary reflectance signal from the total measured signal, which contained the primary reflectance signal and the stray light from the environment. The spectra similarity between the extracted spectra by this proposed method with extreme environment stray light is 99.98% similar to the spectra without the environment's stray light. This result shows that we can measure the reflectance spectra without the affection of the environment's stray light.

Keywords: spectrometer, stray light, three-parameter sine curve fitting, spectra extraction

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Authors: Abdelhamid Zerroug, Mlle Ismahene Sehili

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Spectral methods are usually applied to solve uni-dimensional boundary value problems. With the advantage of the creation of multidimensional basis, we propose a new spectral method for bi-dimensional problems. In this article, we start by creating bi-spectral basis by different ways, we developed also a new relations to determine the expressions of spectral coefficients in different partial derivatives expansions. Finally, we propose the principle of a new bi-spectral method for the bi-dimensional problems.

Keywords: boundary value problems, bi-spectral methods, bi-dimensional Legendre basis, spectral method

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Authors: Karima Kimouche

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In this paper, we consider the asymptotic problems in spectral analysis of stationary causal random fields. We impose conditions only involving (conditional) moments, which are easily verifiable for a variety of nonlinear random fields. Limiting distributions of periodograms and smoothed periodogram spectral density estimates are obtained and applications to the spectral domain bootstrap are given.

Keywords: spatial nonlinear processes, spectral estimators, GMC condition, bootstrap method

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Authors: Mouhamadou Dosso

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This paper presents methods of spectral dichotomy of a matrix which compute spectral projectors on the subspace associated with the eigenvalues external to the parabolas described by a general equation. These methods are modifications of the one proposed in [A. N. Malyshev and M. Sadkane, SIAM J. MATRIX ANAL. APPL. 18 (2), 265-278, 1997] which uses the spectral dichotomy method of a matrix with respect to the imaginary axis. Theoretical and algorithmic aspects of the methods are developed. Numerical results obtained by applying methods presented on matrices are reported.

Keywords: spectral dichotomy method, spectral projector, eigensubspaces, eigenvalue

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Authors: Zahraa Al Ali, Ammar Abulibdeh, Talal Al-Awadhi, Midhun Mohan, Mohammed Al-Barwani, Mohammed Al-Barwani, Sara Al Nabbi, Meshal Abdullah

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This study aims to evaluate the performance and effectiveness of six spectral water indices - derived from Multispectral sentinel-2 data - to detect soil moisture and inundated area in arid regions to be used as an indicator of mudflow phenomena to predict high-risk areas. Herein, the validation of the performance of spectral indices was conducted using threshold method, spectral curve performance, and soil-line method. These indirect validation techniques play a key role in saving time, effort, and cost, particularly for large-scale and inaccessible areas. It was observed that the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (mNDWI), and RSWIR indices have the potential to detect soil moisture and inundated areas in arid regions. According to the temporal spectral curve performance, the spectral characteristics of water and soil moisture were distinct in the Near infrared (NIR), Short-wave Infrared (SWIR1,2) bands. However, the rate and degree differed between these bands, depending on the amount of water in the soil. Furthermore, the soil line method supported the appropriate selection of threshold values to detect soil moisture. However, the threshold values varied with location, time, season, and between indices. We concluded that considering the factors influencing the behavior of water and soil reflectivity could support decision-makers in identifying high-risk mudflow locations.

Keywords: spectral reflectance curve, soil-line method, spectral indices, Shaheen cyclone

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Authors: Rubab Ammad, Abdelgadir Abuelgasim

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UAE typically lies in one of the aridest regions of the world and is thus home to geologic features common to such climatic conditions including vast open deserts, sand dunes, saline soils, inland Sabkha and coastal Sabkha. Sabkha are characteristic salt flats formed in arid environment due to deposition and precipitation of salt and silt over sand surface because of low laying water table and rates of evaporation exceeding rates of precipitation. The study area, which comprises of western UAE, is heavily concentrated with inland Sabkha. Remote sensing is conventionally used to study the soil salinity of agriculturally degraded lands but not so broadly for Sabkha. The focus of this study was to identify these highly saline Sabkha areas on remotely sensed data, using salinity indices. The existing salinity indices in the literature have been designed for agricultural soils and they have not frequently used the spectral response of short-wave infra-red (SWIR1 and SWIR2) parts of electromagnetic spectrum. Using Landsat 8 OLI data and field ground truthing, this study formulated indices utilizing NIR-SWIR parts of spectrum and compared the results with existing salinity indices. Most indices depict reasonably good relationship between salinity and spectral index up until a certain value of salinity after which the reflectance reaches a saturation point. This saturation point varies with index. However, the study findings suggest a role of incorporating near infra-red and short-wave infra-red in salinity index with a potential of showing a positive relationship between salinity and reflectance up to a higher salinity value, compared to rest.

Keywords: Sabkha, salinity index, saline soils, Landsat 8, SWIR1, SWIR2, UAE desert

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Authors: Yessica Nataliani, Miin-Shen Yang

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Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices.

Keywords: group technology, cell formation, spectral clustering, grouping efficiency

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Authors: Dalila Hocine, Mohammed Said Belkaid, Abderahmane Moussi

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In this study, pure rutile TiO2 thin films were directly synthesized on silicon substrates by Atmospheric Pressure Chemical Vapor Deposition technique (APCVD) using TiCl4 as precursor. We studied the physicochemical properties and the optical properties of the produced coatings by means of standard characterization techniques of Fourier Transform Infrared Spectroscopy (FTIR) combined with UV-Vis Reflectance Spectrophotometry. The absorption peaks at 423 cm-1 and 610 cm-1 were observed for the rutile TiO2 thin films, by FTIR measurements. The absorption peak at 739 cm-1 due to the vibration of the Ti-O bonds, was also detected. UV-Vis Reflectance Spectrophotometry is employed for measuring the optical band gap from the measurements of the TiO2 films reflectance. The optical band gap was then extracted from the reflectance data for the TiO2 sample. It was estimated to be 3.05 eV which agrees with the band gap of commercial rutile TiO2 sample.

Keywords: titanium dioxide, physicochemical properties, APCVD, FTIR, band gap

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Authors: Nazmi Mat Nawi, Muhamad Najib Mohamad Nor, Che Dini Maryani Ishkandar

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This study was conducted to evaluate the capability of spectroscopic methods to detect the presence of pesticide residues on leaf mustard. A total of 105 leaf mustard used were divided into five batches, four batches were treated with four different types of pesticides whereas one batch with no pesticide applied. Spectral data were obtained using visible shortwave near infrared spectrometer (VSWNIRS) which is Ocean Optics HR4000 High-resolution Miniature Fiber Optic Spectrometer. Reflectance value was collected to determine the difference between one pesticide to the other. The obtained spectral data were pre-processed for optimum performance. The effective wavelength of approximate 880 nm, 675-710 nm also 550 and 700 nm indicates the overtones -CH stretching vibration, tannin, also chlorophyll content present in the leaf mustard respectively. This study has successfully demonstrated that the spectroscopic method was able to differentiate between leaf mustard sample with and without pesticide residue.

Keywords: detect, leaf mustard, non-destructive, pesticide residue

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Authors: Sami Iqbal, Azam Hussain, Weiping Wu, Guo Xinli, Tong Zhang

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A novel octagonal upright micro-pyramid structure was generated by wet chemical anisotropic etching on a monocrystalline silicon wafer (100). The primary objectives are to reduce front surface reflectance of silicon wafers, improve wettability, enhance surface morphology, and maximize the area coverage by generated octagonal pyramids. Under rigorous control and observation, the etching process' response time was maintained precisely. The experimental outcomes show a significant decrease in the optical surface reflectance of silicon wafers, with the lowest reflectance of 8.98%, as well as enhanced surface structure, periodicity, and surface area coverage of more than 85%. The octagonal silicon pyramid was formed with a high etch rate of 0.41 um/min and a much shorter reaction time with the addition of hydrofluoric acid coupled with magnetic stirring (mechanical agitation) at 300 rpm.

Keywords: octagonal pyramids, rapid etching, solar cells, surface engineering, surface reflectance

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Authors: İsmail Kavdır, M. Burak Büyükcan, Ferhat Kurtulmuş

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Table olive is a valuable product especially in Mediterranean countries. It is usually consumed after some fermentation process. Defects happened naturally or as a result of an impact while olives are still fresh may become more distinct after processing period. Defected olives are not desired both in table olive and olive oil industries as it will affect the final product quality and reduce market prices considerably. Therefore it is critical to sort table olives before processing or even after processing according to their quality and surface defects. However, doing manual sorting has many drawbacks such as high expenses, subjectivity, tediousness and inconsistency. Quality criterions for green olives were accepted as color and free of mechanical defects, wrinkling, surface blemishes and rotting. In this study, it was aimed to classify fresh table olives using different classifiers and NIR spectroscopy readings and also to compare the classifiers. For this purpose, green (Ayvalik variety) olives were classified based on their surface feature properties such as defect-free, with bruised defect and with fly defect using FT-NIR spectroscopy and classification algorithms such as artificial neural networks, ident and cluster. Bruker multi-purpose analyzer (MPA) FT-NIR spectrometer (Bruker Optik, GmbH, Ettlingen Germany) was used for spectral measurements. The spectrometer was equipped with InGaAs detectors (TE-InGaAs internal for reflectance and RT-InGaAs external for transmittance) and a 20-watt high intensity tungsten–halogen NIR light source. Reflectance measurements were performed with a fiber optic probe (type IN 261) which covered the wavelengths between 780–2500 nm, while transmittance measurements were performed between 800 and 1725 nm. Thirty-two scans were acquired for each reflectance spectrum in about 15.32 s while 128 scans were obtained for transmittance in about 62 s. Resolution was 8 cm⁻¹ for both spectral measurement modes. Instrument control was done using OPUS software (Bruker Optik, GmbH, Ettlingen Germany). Classification applications were performed using three classifiers; Backpropagation Neural Networks, ident and cluster classification algorithms. For these classification applications, Neural Network tool box in Matlab, ident and cluster modules in OPUS software were used. Classifications were performed considering different scenarios; two quality conditions at once (good vs bruised, good vs fly defect) and three quality conditions at once (good, bruised and fly defect). Two spectrometer readings were used in classification applications; reflectance and transmittance. Classification results obtained using artificial neural networks algorithm in discriminating good olives from bruised olives, from olives with fly defect and from the olive group including both bruised and fly defected olives with success rates respectively changing between 97 and 99%, 61 and 94% and between 58.67 and 92%. On the other hand, classification results obtained for discriminating good olives from bruised ones and also for discriminating good olives from fly defected olives using the ident method ranged between 75-97.5% and 32.5-57.5%, respectfully; results obtained for the same classification applications using the cluster method ranged between 52.5-97.5% and between 22.5-57.5%.

Keywords: artificial neural networks, statistical classifiers, NIR spectroscopy, reflectance, transmittance

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Dehni Abdellatif, Lounis Mourad

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The new approach of applied spatial geostatistics in materials sciences, agriculture accuracy, agricultural statistics, permitted an apprehension of managing and monitoring the water and groundwater qualities in a relationship with salt-affected soil. The anterior experiences concerning data acquisition, spatial-preparation studies on optical and multispectral data has facilitated the integration of correction models of electrical conductivity related with soils temperature (horizons of soils). For tomography apprehension, this physical parameter has been extracted from calibration of the thermal band (LANDSAT ETM+6) with a radiometric correction. Our study area is Oran region (Northern West of Algeria). Different spectral indices are determined such as salinity and sodicity index, the Combined Spectral Reflectance Index (CSRI), Normalized Difference Vegetation Index (NDVI), emissivity, Albedo, and Sodium Adsorption Ratio (SAR). The approach of geostatistical modeling of electrical conductivity (salinity), appears to be a useful decision support system for estimating corrected electrical resistivity related to the temperature of surface soils, according to the conversion models by substitution, the reference temperature at 25°C (where hydrochemical data are collected with this constraint). The Brightness temperatures extracted from satellite reflectance (LANDSAT ETM+) are used in consistency models to estimate electrical resistivity. The confusions that arise from the effects of salt stress and water stress removed followed by seasonal application of the geostatistical analysis in Geographic Information System (GIS) techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt-affected soil.

Keywords: geostatistical modelling, landsat, brightness temperature, conductivity

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Authors: K. K. Saju, A. R. Reghuraj

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High reflectance of surgical instruments under bright light hinders the visual clarity during laparoscopic surgical procedures leading to loss of precision and device control and creates strain and undesired difficulties to surgeons. Majority of the surgical instruments are made of surgical grade steel. Instruments with a non reflective surface can enhance the visual clarity during precision surgeries. A conversion coating of black oxide has been successfully developed 410 grade surgical stainless steel .The characteristics of the developed coating suggests the application of this technique for developing 410 grade surgical instruments with minimal reflectance.

Keywords: conversion coatings, 410 stainless steel, black oxide, reflectance

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Authors: Chor Nejmeddine, Ines Ben Omrane, Mohamed Abdelwahed

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In this paper, we present a posteriori analysis of the discretization of the heat equation by spectral element method. We apply Euler's implicit scheme in time and spectral method in space. We propose two families of error indicators, both of which are built from the residual of the equation and we prove that they satisfy some optimal estimates. We present some numerical results which are coherent with the theoretical ones.

Keywords: heat equation, spectral elements discretization, error indicators, Euler

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Authors: Sunghun Jung, Wonkook Kim

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Due to the difficulty of the utilization of satellite and an aircraft, conventional ocean color remote sensing has a disadvantage in that it is difficult to obtain images of desired places at desired times. These disadvantages make it difficult to capture the anomalies such as the occurrence of the red tide which requires immediate observation. It is also difficult to understand the phenomena such as the resuspension-precipitation process of suspended solids and the spread of low-salinity water originating in the coastal areas. For the remote sensing reflectance of seawater, above-water radiometers (AWR) have been used either by carrying portable AWRs on a ship or installing those at fixed observation points on the Ieodo ocean research station, Socheongcho base, and etc. In particular, however, it requires the high cost to measure the remote reflectance in various seawater environments at various times and it is even not possible to measure it at the desired frequency in the desired sea area at the desired time. Also, in case of the stationary observation, it is advantageous that observation data is continuously obtained, but there is the disadvantage that data of various sea areas cannot be obtained. It is possible to instantly capture various marine phenomena occurring on the coast using the unmanned aerial system (UAS) including vertical takeoff and landing (VTOL) type unmanned aerial vehicles (UAV) since it could move and hover at the one location and acquire data of the desired form at a high resolution. To remotely estimate seawater constituents, it is necessary to install an ultra-spectral sensor. Also, to calculate reflected light from the surface of the sea in consideration of the sun’s incident light, a total of three sensors need to be installed on the UAV. The remote sensing reflectance of seawater is the most basic optical property for remotely estimating color components in seawater and we could remotely estimate the chlorophyll concentration, the suspended solids concentration, and the dissolved organic amount. Estimating seawater physics from the remote sensing reflectance requires the algorithm development using the accumulation data of seawater reflectivity under various seawater and atmospheric conditions. The UAS with three AWRs is developed for the remote reflection sensing on the surface of the sea. Throughout the paper, we explain the details of each UAS component, system operation scenarios, and simulation and experiment results. The UAS consists of a UAV, a solar tracker, a transmitter, a ground control station (GCS), three AWRs, and two gimbals.

Keywords: above-water radiometers (AWR), ground control station (GCS), unmanned aerial system (UAS), unmanned aerial vehicle (UAV)

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Authors: Tuba Zahra, Aakash Parekh

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Hyperspectral remote sensing refers to imaging of objects or materials in narrow conspicuous spectral bands. Hyperspectral images (HSI) enable the extraction of spectral signatures for objects or materials observed. These images contain information about the reflectance of each pixel across the electromagnetic spectrum. It enables the acquisition of data simultaneously in hundreds of spectral bands with narrow bandwidths and can provide detailed contiguous spectral curves that traditional multispectral sensors cannot offer. The contiguous, narrow bandwidth of hyperspectral data facilitates the detailed surveying of Earth's surface features. This would otherwise not be possible with the relatively coarse bandwidths acquired by other types of imaging sensors. Hyperspectral imaging provides significantly higher spectral and spatial resolution. There are several use cases that represent the commercial applications of hyperspectral remote sensing. Each use case represents just one of the ways that hyperspectral satellite imagery can support operational efficiency in the respective vertical. There are some use cases that are specific to VNIR bands, while others are specific to SWIR bands. This paper discusses the different commercially viable use cases that are significant for HSI application areas, such as agriculture, mining, oil and gas, defense, environment, and climate, to name a few. Theoretically, there is n number of use cases for each of the application areas, but an attempt has been made to streamline the use cases depending upon economic feasibility and commercial viability and present a review of literature from this perspective. Some of the specific use cases with respect to agriculture are crop species (sub variety) detection, soil health mapping, pre-symptomatic crop disease detection, invasive species detection, crop condition optimization, yield estimation, and supply chain monitoring at scale. Similarly, each of the industry verticals has a specific commercially viable use case that is discussed in the paper in detail.

Keywords: agriculture, mining, oil and gas, defense, environment and climate, hyperspectral, VNIR, SWIR

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Authors: Serifenur Cebesoy, Yelda Aygar, Elgiz Bairamov

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In this study, we examine some spectral properties of non-selfadjoint matrix-valued difference equations consisting of a polynomial type Jost solution. The aim of this study is to investigate the eigenvalues and spectral singularities of the difference operator L which is expressed by the above-mentioned difference equation. Firstly, thanks to the representation of polynomial type Jost solution of this equation, we obtain asymptotics and some analytical properties. Then, using the uniqueness theorems of analytic functions, we guarantee that the operator L has a finite number of eigenvalues and spectral singularities.

Keywords: asymptotics, continuous spectrum, difference equations, eigenvalues, jost functions, spectral singularities

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Authors: Bagher Bayat, Wouter Verhoef, Behnaz Arabi, Christiaan Van der Tol

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The aim of this study was to follow the canopy reflectance patterns in response to soil water deficit and to detect trends of changes in biophysical and biochemical parameters of grass (Poa pratensis species). We used visual interpretation, imaging spectroscopy and radiative transfer model inversion to monitor the gradual manifestation of water stress effects in a laboratory setting. Plots of 21 cm x 14.5 cm surface area with Poa pratensis plants that formed a closed canopy were subjected to water stress for 50 days. In a regular weekly schedule, canopy reflectance was measured. In addition, Leaf Area Index (LAI), Chlorophyll (a+b) content (Cab) and Leaf Water Content (Cw) were measured at regular time intervals. The 1-D bidirectional canopy reflectance model SAIL, coupled with the leaf optical properties model PROSPECT, was inverted using hyperspectral measurements by means of an iterative optimization method to retrieve vegetation biophysical and biochemical parameters. The relationships between retrieved LAI, Cab, Cw, and Cs (Senescent material) with soil moisture content were established in two separated groups; stress and non-stressed. To differentiate the water stress condition from the non-stressed condition, a threshold was defined that was based on the laboratory produced Soil Water Characteristic (SWC) curve. All parameters retrieved by model inversion using canopy spectral data showed good correlation with soil water content in the water stress condition. These parameters co-varied with soil moisture content under the stress condition (Chl: R2= 0.91, Cw: R2= 0.97, Cs: R2= 0.88 and LAI: R2=0.48) at the canopy level. To validate the results, the relationship between vegetation parameters that were measured in the laboratory and soil moisture content was established. The results were totally in agreement with the modeling outputs and confirmed the results produced by radiative transfer model inversion and spectroscopy. Since water stress changes all parts of the spectrum, we concluded that analysis of the reflectance spectrum in the VIS-NIR-MIR region is a promising tool for monitoring water stress impacts on vegetation.

Keywords: hyperspectral remote sensing, model inversion, vegetation responses, water stress

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of World-View 2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows effectively and automatically.

Keywords: spectral index, shadow detection, remote sensing images, World-View 2

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Authors: Chao Xu

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Selecting ground motion intensity measures reasonably is one of the very important issues to affect the input ground motions selecting and the reliability of vulnerability analysis results. In this paper, inelastic spectral displacement is used as an alternative intensity measure to characterize the ground motion damage potential. The inelastic spectral displacement is calculated based modal pushover analysis and inelastic spectral displacement based incremental dynamic analysis is developed. Probability seismic demand analysis of a six story and an eleven story RC frame are carried out through cloud analysis and advanced incremental dynamic analysis. The sufficiency and efficiency of inelastic spectral displacement are investigated by means of regression and residual analysis, and compared with elastic spectral displacement. Vulnerability curves are developed based on inelastic spectral displacement. The study shows that inelastic spectral displacement reflects the impact of different frequency components with periods larger than fundamental period on inelastic structural response. The damage potential of ground motion on structures with fundamental period prolonging caused by structural soften can be caught by inelastic spectral displacement. To be compared with elastic spectral displacement, inelastic spectral displacement is a more sufficient and efficient intensity measure, which reduces the uncertainty of vulnerability analysis and the impact of input ground motion selection on vulnerability analysis result.

Keywords: vulnerability, probability seismic demand analysis, ground motion intensity measure, sufficiency, efficiency, inelastic time history analysis

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Authors: Levent Basayigit, Sinan Demir, Yusuf Ucar, Burhan Kara

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Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.

Keywords: Gaussian mixture discriminant analysis, spectral mixture model, Worldview-2, land parcels

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Authors: Mohammad Ahmad, Dayalan Kasilingam

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In this paper, a spectral domain implementation of the fast multipole method is presented. It is shown that the aggregation, translation, and disaggregation stages of the fast multipole method (FMM) can be performed using the spectral domain (SD) analysis. The spectral domain fast multipole method (SD-FMM) has the advantage of eliminating the near field/far field classification used in conventional FMM formulation. The study focuses on the application of SD-FMM to one-dimensional (1D) and two-dimensional (2D) electric field integral equation (EFIE). The case of perfectly conducting strip, circular and square cylinders are numerically analyzed and compared with the results from the standard method of moments (MoM).

Keywords: electric field integral equation, fast multipole method, method of moments, wave scattering, spectral domain

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Authors: Khaled Missaoui, Amina Beldjazia, Rachid Gharzouli, Yamna Djellouli

Abstract:

Optical remote sensing makes use of visible, near infrared and short-wave infrared sensors to form images of the earth's surface by detecting the solar radiation reflected from targets on the ground. Different materials reflect and absorb differently at different wavelengths. Thus, the targets can be differentiated by their spectral reflectance signatures in the remotely sensed images. In this work, we are interested to study the distribution of vegetation in the massif forest of Boutaleb (North East of Algeria) which suffered between 1998 and 1999 very large fires. In this case, we use remote sensing with Landsat images from two dates (1984 and 2000) to see the results of these fires. Vegetation has a unique spectral signature which enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. Normalized Difference Vegetation Index (NDVI) is calculated with ENVI 4.7 from Band 3 and 4. The results showed a very important floristic diversity in this forest. The comparison of NDVI from the two dates confirms that there is a decrease of the density of vegetation in this area due to repeated fires.

Keywords: remote sensing, boutaleb, diversity, forest

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Authors: A. Manallah, C. Meier

Abstract:

Confocal spectral interferometry (CSI) is an innovative optical method for determining microtopography of surfaces and thickness of transparent layers, based on the combination of two optical principles: confocal imaging, and spectral interferometry. Confocal optical system images at each instant a single point of the sample. The whole surface is reconstructed by plan scanning. The interference signal generated by mixing two white-light beams is analyzed using a spectrometer. In this work, five ‘rugotests’ of known standard roughnesses are investigated. The topography is then measured and illustrated, and the equivalent roughness is determined and compared with the standard values.

Keywords: confocal spectral interferometry, nondestructive testing, optical metrology, surface topography, roughness

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Authors: Ankita Gaur, Mouli Karmakar, Shyam

Abstract:

In this study, a silicon solar cell has been modeled and analyzed to enhance its electrical performance by improving the optical properties using an antireflecting coating (ARC). The dynamic optical reflectance, transmittance along with the net transmissivity absorptivity product of each layer are assessed as per the diurnal variation of the angle of incidence using MATLAB 2019. The model is tested with various Anti-Reflective coatings and the performance has also been compared with uncoated cells. ARC improves the optical transmittance of the photon. Higher transmittance of ⁓96.57% with lowest reflectance of ⁓ 1.74% at 12.00 hours was obtained with MgF₂ coated silicon cells. The electrical efficiency of the configured solar cell was evaluated for a composite climate of New Delhi, India, for all weather conditions. The annual electricity generation for Anti-reflective coated and uncoated crystalline silicon PV Module was observed to be 103.14 KWh and 99.51 KWh, respectively.

Keywords: antireflecting coating, electrical efficiency, reflectance, solar cell, transmittance

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Authors: Marianna Bolla

Abstract:

The aim of this paper is to compare spectral, discrepancy, and degree properties of expanding graph sequences. As we can prove equivalences and implications between them and the definition of the generalized (multiclass) quasirandomness of Lovasz–Sos (2008), they can be regarded as generalized quasirandom properties akin to the equivalent quasirandom properties of the seminal Chung-Graham-Wilson paper (1989) in the one-class scenario. Since these properties are valid for deterministic graph sequences, irrespective of stochastic models, the partial implications also justify for low-dimensional embedding of large-scale graphs and for discrepancy minimizing spectral clustering.

Keywords: generalized random graphs, multiway discrepancy, normalized modularity spectra, spectral clustering

Procedia PDF Downloads 162