1,624 research outputs found

    Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

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    Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).</p

    Space object identification and classification from hyperspectral material analysis

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    This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects

    Surface-Enhanced Coherent Raman scattering (SE-CRS) with Noble Metal Nanoparticles

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    Early cancer detection remains challenging due to numerous complex tempo-spatial metabolic changes in cell physiology. Based on their ability to recognise molecular structures and pathological changes at molecular levels, spectroscopic have recently emerged as promising non-invasive, non-ionising, and cost-efficient tools to help detect cancer, and other human pathologies. Raman spectroscopy is a valuable technique that provides information regarding the chemical properties of materials. Nevertheless, it has limitations due to the limited amount of Raman light scattered. Strategies for cancer diagnostics and therapies are based on the hypothesis that nanoparticles (NPs) can be precisely tailored to target cancer cells. However, the tools required to image NPs at cellular levels remain scarce in the literature. The work outlined in this thesis, for the first time, utilises noble metal NPs and Raman reporters, with the mechanisms of surface enhanced Raman scattering (SERS) and coherent anti-Stokes Raman scattering (CARS), in cancer cells and tumour spheroids to address the demerits of low spatial resolution, signal-to-noise ratio, and chemical specificity. SERS and CARS have broadly been explored in this regard. To increase the effectiveness of Raman scattering, a variety of techniques have been devised to boost its intensity. Primarily, I studied four techniques to increase Raman scattering intensity with the ultimate objective of improving sensitivity and assessing limits of various Raman methods: SERS, surface-enhanced coherent anti-Stokes Raman scattering (SE-CARS), surface-enhanced stimulated Raman scattering (SE-SRS), and broadband coherent anti-Stokes Raman scattering (BCARS). Coherent Raman scattering (CRS) is utilised to enhance weak Raman bands. The signal is enhanced by nonlinear interaction of the excitation lasers within the sample. Despite the advantages offered over Raman, CRS has been relatively unexploited for image Raman tagged NPs. This challenge has recently been addressed using surface plasmon enhancement, which gives significantly enhanced inelastic scattering signals as well as reduced signal-to-noise ratio. Surface-enhanced coherent Raman scattering (SE-CRS) has been characterised by using a variety of techniques such as SERS, CARS, and SE-CARS. This work provides a step forward to develop plasmon enhanced SRS and CARS in addressing critical biological questions using nonlinear bio-photonics. In the first part of this thesis, I developed a reproducible substrate that mimics gold nanoparticles (AuNPs) and allows forward detection which is critical for CRS. I investigated the effects of annealing on gold films deposited on glass substrates with thicknesses from 3 nm to 15 nm as described in depth in chapter 5. In addition to this, it provides an explanation of the work that was performed to explore the interaction between Raman tags BPT (biphenyl-4-thiol), BPE trans-1,2-bis(4-pyridyl) ethylene, and IR 820 (new indocyanine green) on gold films substrates using 785 nm laser excitation. In the second part of this thesis, I investigated the interactions between Raman tags of BPT on gold films substrates using CRS and broadband CARS techniques. These experiments also offer the SE-CRS enhancement signal. The research done to examine gold thin film substrates and to offer SE-SRS and SE-CARS enhancement signals in the fingerprint region as described in chapter 6. Using CRS microscopy, the investigations in this chapter study these interactions. In the third part of this thesis, I developed a novel imaging methodology for the visualisation of AuNPs inside cellular structures and spheroids, with the intention of acquiring distinct spectroscopic fingerprints. Consequently, I undertook the task of devising protocols for visualising AuNPs and Raman reporter molecules within cancer cell models, spheroids, and animal tissues as described in chapter 7. The aim was to attain distinctive spectroscopic profiles by employing the SE-CRS technique, achieved by illuminating AuNPs along with Raman reporter molecules (BPT, BPE, IR 820) using low intensity infrared light, with both the pump and Stokes beams operating at intensities below 0.2 mW. In summary, this thesis sheds light on the development of surface plasmon resonance phenomena based on metallic nanostructures for use in nonlinear inelastic scattering systems, including surface-enhanced Raman scattering (SERS), coherent Raman scattering (CRS), and surface-enhanced coherent Raman scattering (SE- CRS). The primary focus is to use this system for disease diagnostics, rooted in SERS, reflects a commitment to advancing cancer diagnostics, based on SERS thereby enhancing the precision and discrimination of molecular signals, making a significant stride towards more effective and nuanced cancer diagnostics

    High-throughput multimodal wide-field Fourier-transform Raman microscope

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    Raman microscopy is a powerful analytical technique for materials and life sciences that enables mapping the spatial distribution of the chemical composition of a sample. State-of-the-art Raman microscopes, based on point-scanning frequency-domain detection, have long (∼1 s) pixel dwell times, making it challenging to acquire images of a significant area (e.g., 100×100 μm). Here we present a compact wide-field Raman microscope based on a time-domain Fourier-transform approach, which enables parallel acquisition of the Raman spectra on all pixels of a 2D detector. A common-path birefringent interferometer with exceptional delay stability and reproducibility can rapidly acquire Raman maps (∼30 min for a 250 000 pixel image) with high spatial (&lt;1 μm) and spectral (∼23 cm-1) resolutions. Time-domain detection allows us to disentangle fluorescence and Raman signals, which can both be measured separately. We validate the system by Raman imaging plastic microbeads and demonstrate its multimodal operation by capturing fluorescence and Raman maps of a multilayer-WSe2 sample, providing complementary information on the strain and number of layers of the material

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Remotely sensing ecological genomics

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    Solar radiation is the prime energy source on Earth. It reaches any object in the form of electromagnetic radiation that may be absorbed, transmitted or reflected. The magnitude of these optical processes depends on the optical properties of each object, which in the case of plants relate to their biochemical and structural traits. These plant phenotypic traits result from gene expression underpinned by an individual’s genotype constrained by phylogeny, the environment the individual is exposed to, and the interaction between genotype and the environment. Remote observations of plant phenotypes across space and time may thus hold information about the composition and structure of genetic variation, if a link between spectral and genetic information can be established. This dissertation encompasses studies linking information derived from imaging spectrometer acquisitions under natural conditions with in situ collected information about genetic variation within a tree species, the European beech Fagus sylvatica. It presents the correlation between spectral and genetic information by sequentially expanding temporal, spatial and genetic aspects, and simultaneously accounting for environmental contexts that impact gene expression. By evaluating spectral-genetic similarities across decadal airborne imaging spectrometer acquisitions and accounting for spectral phenotypes and whole-genome sequences of tree individuals from across the species range, the studies provide a proof that observed reflectance spectra hold information about genetic variation within the species. Further, by accounting on uncertainties of spectral measurements and deriving genetic structure of the most abundant tree species in Europe, the dissertation advances the current remote sensing approaches and the knowledge on intraspecific genetic variation. The studies focus particularly on the genetic relatedness between the trees of the test species, whereas the acquired data may allow to establish direct associations between genes and spectral features. The methods used may be expanded to other tree species or applied to spectral data acquired by upcoming spaceborne imaging spectrometers, which overcome current spatiotemporal limitations of data collection, and demonstrate further paths towards the association of genetic variation with variation in spectral phenotypes. The thesis presents the potential of spectral derivation of intraspecific genetic variation within tree species and discusses associated limitations induced by spectral, temporal, spatial and genetic scopes of analysis. This sets a stage towards establishing a means of remote observations of spectral signatures to contribute to monitoring biological variation at the fundamental genetic level, which correlates with ecosystem performance and is an insurance mechanism for populations to adapt to global change
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