663 research outputs found
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment
publishedVersio
Evaluation of mixed microalgae species biorefinery of Desmodesmus sp. And Scenedesmus sp. For bioproducts synthesis
Microalgae is known to produce numerous bioactive compounds for instance proteins, fatty acid, polysaccharides, enzymes, sterols, and antioxidants. Due to their valuable biochemical composition, microalgae are regarded as a very intriguing source to produce novel food products and can be utilised to improve the nutritional content of traditional foods. Additionally, microalgae are used as animal feed and additives in the cosmetics, pharmaceutical as well as nutraceutical industries. As compared to other terrestrial plants and other microorganisms, microalgae possess few advantages: (1) rapid growth rate; (2) able to grow in non-arable land and harsh cultivation conditions; (3) low nutritional requirements; (4) high productivity; and (5) reduce emission of carbon dioxide. Despite the large number of microalgae species found in nature, only a few species are identified and commercialized such as Chlorella sp., Spirulina sp. Haematococcus pluvialis, Nannochloropsis sp. and Chlamydomonas reinhardtii, which is one of the major obstacles preventing the full utilisation of microalgae-based technology.
This thesis provides information on the overall composition of mixed microalgae species, Desmodesmus sp. and Scenedesmus sp., for instance protein, carbohydrate, lipid, antioxidants, and pigment. This thesis firstly introduces the application of triphasic partitioning (TPP) in the extraction and partitioning of the biomolecules from the microalgae. The latest advancement of technology has evolved from a liquid biphasic flotation (LBF) to TPP. T-butanol and ammonium sulphate are used in TPP to precipitate desired biomolecules from the aqueous solutions with the formation of three layer. TPP is a simple, time- and cost- efficient, as well as scalable process that does not require toxic organic solvents. Lipase is abundantly produced by microbes, bacteria, fungi, yeast, mammals, and plants. Lipase is widely used in the oleochemical, detergent, dairy, leather, cosmetics, paper, cosmetics, and nutraceutical industries. Therefore, this thesis also discusses the possibility of identifying and extracting enzyme lipase from the microalgae using LBF. Several parameters (volume and concentration of solvents, weight of biomass, flotation kinetics and solvent types, etc.) have been investigated to optimize the lipase extraction from LBF.
Chlorophyll is the main pigment present in the microalgae. Thus, this work proposes the digital imaging approach to determine the chlorophyll concentration in the microalgae rapidly because the chlorophyll content has a significant impact on microalgae physiological health status as well as identifies the chlorophyll concentration in the production of by-products. Lastly, microalgae oil can be used as the feedstock for biodiesel as well as nutraceutical, pharmaceutical, and health-care products. The challenge in the lipid extraction is the co-extraction of chlorophyll into the oil, which can have serious consequences for downstream processing. Therefore, the removal of the chlorophyll from the microalgae using activated clay or sodium chlorite in the pre-treatment procedure are examined. The research achievements in these works and future opportunities are highlighted in the last chapter of the thesis
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Remotely sensing ecological genomics
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
Estimating Solar Energy Production in Urban Areas for Electric Vehicles
Cities have a high potential for solar energy from PVs installed on buildings\u27 rooftops. There is an increased demand for solar energy in cities to reduce the negative effect of climate change. The thesis investigates solar energy potential in urban areas. It tries to determine how to detect and identify available rooftop areas, how to calculate suitable ones after excluding the effects of the shade, and the estimated energy generated from PVs. Geographic Information Sciences (GIS) and Remote Sensing (RS) are used in solar city planning. The goal of this research is to assess available and suitable rooftops areas using different GIS and RS techniques for installing PVs and estimating solar energy production for a sample of six compounds in New Cairo, and explore how to map urban areas on the city scale.
In this research, the study area is the new Cairo city which has a high potential for harvesting solar energy, buildings in each compound have the same height, which does not cast shade on other buildings affecting PV efficiency. When applying GIS and RS techniques in New Cairo city, it is found that environmental factors - such as bare soil - affect the accuracy of the result, which reached 67% on the city scale. Researching more minor scales, such as compounds, required Very High Resolution (VHR) satellite images with a spatial resolution of up to 0.5 meter. The RS techniques applied in this research included supervised classification, and feature extraction, on Pleiades-1b VHR. On the compound scale, the accuracy assessment for the samples ranged between 74.6% and 96.875%.
Estimating the PV energy production requires solar data; which was collected using a weather station and a pyrometer at the American University in Cairo, which is typical of the neighboring compounds in the new Cairo region. It took three years to collect the solar incidence data. The Hay- Devis, Klucher, and Reindl (HDKR) model is then employed to extrapolate the solar radiation measured on horizontal surfaces β =0°, to that on tilted surfaces with inclination angles β =10°, 20°, 30° and 45°. The calculated (with help of GIS and Solar radiation models) net rooftop area available for capturing solar radiation was determined for sample New Cairo compounds . The available rooftop areas were subject to the restriction that all the PVs would be coplanar, none of the PVs would protrude outside the rooftop boundaries, and no shading of PVs would occur at any time of the year; moreover typical other rooftop occupied areas, and actual dimensions of typical roof top PVs were taken into consideration. From those calculations, both the realistic total annual Electrical energy produced by the PVs and their daily monthly energy produced are deduced. The former is relevant if the PVs are tied to a grid, whereas the other is more relevant if it is not; optimization is different for both. Results were extended to estimate the total number of cars that may be driven off PV converted solar radiation per home, for different scenarios
Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area
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