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Articles 1 - 30 of 65
Full-Text Articles in Remote Sensing
Monitoring Koyna Dam Displacements Using Persistent Scatterer Interferometry, Sara Zouriq, Gehan Hamdy, Amr Fawzy, Rejoice Thomas, Hesham El-Askary, Eehab Khalil, Mohamed Elsayad, Tarik El-Salawaky
Monitoring Koyna Dam Displacements Using Persistent Scatterer Interferometry, Sara Zouriq, Gehan Hamdy, Amr Fawzy, Rejoice Thomas, Hesham El-Askary, Eehab Khalil, Mohamed Elsayad, Tarik El-Salawaky
Mathematics, Physics, and Computer Science Faculty Articles and Research
Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr−1, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven …
Changes In Land, Ocean, Atmospheric Parameters Associated With The 2025 Myanmar (Mw 7.7) Earthquake, Feng Jing, Akshansa Chauhan, Ashwani Raju, Ramesh P. Singh
Changes In Land, Ocean, Atmospheric Parameters Associated With The 2025 Myanmar (Mw 7.7) Earthquake, Feng Jing, Akshansa Chauhan, Ashwani Raju, Ramesh P. Singh
Mathematics, Physics, and Computer Science Faculty Articles and Research
Multiple parameters associated with the land, atmosphere, and ocean were analyzed to study short-term and immediate pre-earthquake changes associated with the 28 March 2025 Myanmar earthquake (Mw 7.7). Anomalous clear-sky outgoing longwave radiation (ClrOLR) and trace gases (CH₄, CO, and O₃) were detected within two months prior to the mainshock. Vertical changes at different pressure levels suggest a possible underground source. High-temporal-resolution observations of the infrared brightness temperature and surface air pressure revealed short-lived fluctuations shortly before the earthquake, which may reflect localized stress adjustments and surface latent heat flux release during the final stage of earthquake preparation. In the …
Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh
Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh
Mathematics, Physics, and Computer Science Faculty Articles and Research
Punjab, India's primary rice and wheat production hub, has witnessed rapid expansion of paddy cultivation over the past two decades, driven by minimum support price incentives, changes in government policies, alignment of sowing with the monsoon season and the adoption of high-yielding varieties. This transition has intensified groundwater extraction and shortened the fallow period between rabi and kharif crop seasons, reducing the window between rice harvesting and wheat sowing, leading to widespread open-field burning of rice residue and recurrent post-monsoon air-quality deterioration across the Indo-Gangetic Plain. Despite numerous short-term or single-pollutant assessments, a spatially resolved, multi-pollutant and multi-decadal evaluation linking …
Super-Resolution Remote Sensing Datasets For Application To Caral–Supe Archeological Sites Employing Sar And Dems, Jungrack Kim, Ramesh P. Singh
Super-Resolution Remote Sensing Datasets For Application To Caral–Supe Archeological Sites Employing Sar And Dems, Jungrack Kim, Ramesh P. Singh
Mathematics, Physics, and Computer Science Faculty Articles and Research
Publicly accessible spaceborne remote sensing datasets often lack the spatial resolution required to reliably distinguish archeological features from their surrounding geomorphological contexts. In this study, we assess the potential of super-resolution (SR) products derived from multiple public-domain remote sensing datasets for a systematic archeological survey in the Caral–Supe region. We focus on Synthetic Aperture Radar (SAR) and topographic datasets—including Sentinel-1, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), and Digital Elevation Models (DEMs)—because of their capacity to detect subtle surface expressions and shallow subsurface structures obscured by vegetation or sediment cover. Using state-of-the-art deep learning algorithms, …
A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary
A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify …
The World's Largest Saddle Dam At Risk: Multisensor Geohazard Analysis And Downstream Impacts, Hesham El-Askary, Hesham Morgan, Surendra Maharjan, Ali Elgendy, Wenzhao Li, Rejoice Thomas, Austin Madson, Cyril Rakovski
The World's Largest Saddle Dam At Risk: Multisensor Geohazard Analysis And Downstream Impacts, Hesham El-Askary, Hesham Morgan, Surendra Maharjan, Ali Elgendy, Wenzhao Li, Rejoice Thomas, Austin Madson, Cyril Rakovski
Mathematics, Physics, and Computer Science Faculty Articles and Research
The Grand Ethiopian Renaissance Dam (GERD) Saddle Dam, which holds approximately 89% of the main reservoir's live storage, is one of the largest and most critical auxiliary dams globally; its construction on Ethiopia's Blue Nile has consequently raised significant regional and international concerns regarding potential environmental impacts and geohazard risks. This study presents a comprehensive risk assessment of the GERD Saddle Dam by integrating high-resolution satellite data (GRACE, Sentinel-1, Sentinel-2, WorldView-3), hydrological modeling (SWAT), Persistent Scatterer Interferometry (PSI), geospatial analysis, and advanced statistical techniques. The results highlight critical structural vulnerabilities, including groundwater infiltration estimated at approximately 41 ± 6.2 billion …
Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary
Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …
Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary
Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km2 in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R2 = 0.82), rice (R2 = 0.71), and walnut (R2 = 0.68), …
Spectral–Spatial Transformer With Multiscale Convolutional Attention For Hyperspectral Image Classification, Junde Chen, Wenzhao Li, Hesham El-Askary
Spectral–Spatial Transformer With Multiscale Convolutional Attention For Hyperspectral Image Classification, Junde Chen, Wenzhao Li, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Hyperspectral image (HSI) classification plays a vital role in remote sensing by leveraging rich spectral and spatial information for accurate material recognition. However, existing methods, particularly Transformer-based approaches, still face challenges in effectively modeling multiscale spatial–spectral features, preserving local details, and maintaining robustness to noise. To mitigate these limitations, we propose TMCANet, a spectral–spatial Transformer with multiscale convolutional attention, designed to effectively leverage both local and global contextual dependencies for HSI classification. Our design is guided by three core strategies: first, a convolutional feature extraction module, consisting of four convolutional layers, to learn hierarchical spectral multiscale representations and enhance local …
Assessing Coastal Vulnerability And Climate-Driven Migration Risk In West Africa, Wenzhao Li, Surendra Maharjan, Hesham El-Askary
Assessing Coastal Vulnerability And Climate-Driven Migration Risk In West Africa, Wenzhao Li, Surendra Maharjan, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
This study presents a GIS-based multi-criteria decision-making framework to assess climate-induced migration risk along the West African coast. We developed a comprehensive risk index that integrates environmental hazards such as flood frequency and socio-economic vulnerability indicators, including poverty levels, population density, and adaptive capacity. By utilizing datasets such as the Geocoded Disasters (GDIS) dataset, Social Vulnerability Index (SVI), Poverty and Adaptive Capacity Index (PACI), and the Population Exposure Index (PEI), the study identifies regions most susceptible to displacement. Results reveal that areas like Benin’s Abomey-Calavi, Cotonou, and Akpo-Misserete are especially vulnerable due to high disaster frequency, substantial population exposure, and …
Insights From Swot Data On Transboundary Upstream-Downstream Impacts In The Nile Basin, Hesham Morgan, Wenzhao Li, Ali Elgendy, Surendra Maharjan, Rejoice Thomas, Hesham El-Askary
Insights From Swot Data On Transboundary Upstream-Downstream Impacts In The Nile Basin, Hesham Morgan, Wenzhao Li, Ali Elgendy, Surendra Maharjan, Rejoice Thomas, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
This study utilizes high-resolution data from NASA's Surface Water and Ocean Topography (SWOT) mission to investigate water dynamics and upstream-downstream impacts across key reservoirs in the Nile Basin. Focusing on the Grand Ethiopian Renaissance Dam (GERD), Rosaries Dam, Merowe Dam, and the Aswan High Dam, the analysis spans 15 months (August 2023 to October 2024). By systematically selecting 30 points across each reservoir, monthly boxplots of surface water elevation were generated, revealing significant temporal and spatial variability. The results show that GERD’s filling phase led to a steady increase in water levels (peaking at ~615 meters from June to October …
Tackling Data Quality Challenges In Remote Sensing: Solutions For Reliable Urban Heat Island Analysis, Wei Xia, Aqil Tariq, Hesham El-Askary, Rana Waqar Aslam, Elgar Barboza, Dmitry E. Kucher, Youssef M. Youssef, Habib Kraiem
Tackling Data Quality Challenges In Remote Sensing: Solutions For Reliable Urban Heat Island Analysis, Wei Xia, Aqil Tariq, Hesham El-Askary, Rana Waqar Aslam, Elgar Barboza, Dmitry E. Kucher, Youssef M. Youssef, Habib Kraiem
Mathematics, Physics, and Computer Science Faculty Articles and Research
Urban heat islands (UHIs) pose critical challenges to public health, energy demand, and environmental sustainability, particularly in rapidly expanding urban regions. This study examines the complex relationship between building configurations and integrated green spaces, as well as their combined impact on thermal regulation. It focuses on addressing data quality issues commonly encountered in remote sensing applications. Using high-resolution multispectral and thermal imagery, we developed an integrated modeling approach that captures the collective influence of built form and green infrastructure on urban microclimates. A key finding is the significant linear inverse relationship between green space coverage and land surface temperature, underscoring …
High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le
Mathematics, Physics, and Computer Science Faculty Articles and Research
Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3–5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.
Hyperspectral Band Selection Via Heterogeneous Graph Convolutional Self-Representation Network, Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary
Hyperspectral Band Selection Via Heterogeneous Graph Convolutional Self-Representation Network, Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain, often overlooking the structural characteristics of pixels and spectral bands, such as spatial continuity and spectral dependencies. In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI …
Geospatial Intelligence And Multi-Criteria Analysis For Mapping Groundwater Potential Zones And Sustainable Resource Management In Wadi Qena Basin, Eastern Desert, Egypt, El-Taher M. M. Shams, Rashad Sawires, Sahar N. E. Tawfiq, Hanaa R. Youssef, Wenzhao Li, Hesham El-Askary
Geospatial Intelligence And Multi-Criteria Analysis For Mapping Groundwater Potential Zones And Sustainable Resource Management In Wadi Qena Basin, Eastern Desert, Egypt, El-Taher M. M. Shams, Rashad Sawires, Sahar N. E. Tawfiq, Hanaa R. Youssef, Wenzhao Li, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Groundwater is a rare and valuable resource in arid and hyperarid areas. Over the past few decades, population growth, urbanization, and agricultural activities—particularly in developing countries like Egypt—have greatly increased the demand for water supplies. The purpose of this study is to apply a multi-criteria analytical hierarchy process (AHP) in conjunction with remote sensing and geographic information systems methodologies to identify potential zones for groundwater recharge in Wadi Qena, Eastern Desert of Egypt. This valley is considered as one of the most potential valleys for government-led land reclamation and development initiatives. Using several data sources (e.g., Landsat-8 Enhanced Thematic Mapper …
Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary
Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …
Multi-Temporal Analysis Of Urbanization-Driven Slope And Ecological Impact Using Machine-Learning And Remote Sensing Techniques, Zhang Hao, Muhammad Haseeb, Zheng Xiangtian, Zainab Tahir, Syed Amer Mahmood, Aqil Tariq, Rana Waqar Aslam, M. Abdullah-Al-Wadud, Hesham El-Askary
Multi-Temporal Analysis Of Urbanization-Driven Slope And Ecological Impact Using Machine-Learning And Remote Sensing Techniques, Zhang Hao, Muhammad Haseeb, Zheng Xiangtian, Zainab Tahir, Syed Amer Mahmood, Aqil Tariq, Rana Waqar Aslam, M. Abdullah-Al-Wadud, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Rapid urbanization in Lahore, Pakistan, has led to significant ecological and thermal challenges, particularly the intensification of Urban Heat Island (UHI) effects and increased thermal stress as measured by the Urban Thermal Field Variance Index (UTFVI). This study employs a multi-temporal evaluation of Landsat satellite imagery and GIS-based analysis to investigate the Spatio-temporal trends in land-use and land-cover (LULC) changes from 1994 to 2024. We detected substantial changes in urban growth, vegetation cover, and barren areas using supervised classification (Random Forest) methods and remote sensing indices such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), NDBI (Normalized …
Climatological Trends And Effects Of Aerosols And Clouds On Large Solar Parks: Application Examples In Benban (Egypt) And Al Dhafrah (Uae), Harshal Dhake, Panagiotis Kosmopoulos, Antonis Mantakas, Yashwant Kashyap, Hesham El-Askary, Omar Elbadawy
Climatological Trends And Effects Of Aerosols And Clouds On Large Solar Parks: Application Examples In Benban (Egypt) And Al Dhafrah (Uae), Harshal Dhake, Panagiotis Kosmopoulos, Antonis Mantakas, Yashwant Kashyap, Hesham El-Askary, Omar Elbadawy
Mathematics, Physics, and Computer Science Faculty Articles and Research
Solar energy production is vastly affected by climatological factors. This study examines the impact of two primary climatological factors, aerosols and clouds, on solar energy production at two of the world’s largest solar parks, Benban and Al Dhafrah Solar Parks, by using Earth observation data. Cloud microphysics were obtained from EUMETSAT, and aerosol data were obtained from the CAMS and assimilated with MODIS data for higher accuracy. The impact of both factors was analysed by computing their trends over the past 20 years. These climatological trends indicated the variations in the change in each of the factors and their resulting …
Seasonal Dynamics In Land Surface Temperature In Response To Land Use Land Cover Changes Using Google Earth Engine, Lei Feng, Sajjad Hussain, Narcisa G. Pricope, Sana Arshad, Aqil Tariq, Li Feng, Muhammad Mubeen, Rana Waqar Aslam, Mohammed S. Fnais, Wenzhao Li, Hesham El-Askary
Seasonal Dynamics In Land Surface Temperature In Response To Land Use Land Cover Changes Using Google Earth Engine, Lei Feng, Sajjad Hussain, Narcisa G. Pricope, Sana Arshad, Aqil Tariq, Li Feng, Muhammad Mubeen, Rana Waqar Aslam, Mohammed S. Fnais, Wenzhao Li, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Changes in land use and land cover (LULC) are critical for evaluating global spatiotemporal trends, especially regarding climate change and urbanization. This study investigates the dynamics of Landsat surface temperature (LST) in response to LULC changes and their effects on the seasonal microclimate in Kasur District, Pakistan. Using the Google Earth Engine platform, we employed a random forest algorithm to detect LULC changes (cropland, forest, built-up, fallow, barren, and water) and analyze seasonal spectral indices from Landsat imagery for 1988, 2002, and 2022. Significant LULC changes were observed, including a 9.8% increase in built-up areas, a 4.2% decrease in cropland, …
Study The Global Earthquake Patterns That Follow The St. Patrick’S Day Geomagnetic Storms Of 2013 And 2015, Dimitar Ouzounov, Galina Khachikyan
Study The Global Earthquake Patterns That Follow The St. Patrick’S Day Geomagnetic Storms Of 2013 And 2015, Dimitar Ouzounov, Galina Khachikyan
Mathematics, Physics, and Computer Science Faculty Articles and Research
A response of global seismic activity to the geomagnetic storms of St. Patrick’s Day (March 17) in 2013 and 2015 is investigated. These two storms occurred during nearly identical storm sudden commencement times and similar solar flux levels. We have revealed a rather similar pattern of the most substantial earthquakes that have occurred since these storms. Two major crust continental earthquakes, in Iran (M = 7.7), 16 April 2013, and in Nepal (M = 7.8), 25 April 2015, have occurred with a time delay of ~30 and ~39 days after geomagnetic storm onsets in 2013 and 2015, respectively. After that, …
Ground Electric Field, Atmospheric Weather And Electric Grid Variations In Northeast Greece Influenced By The March 2012 Solar Activity And The Moderate To Intense Geomagnetic Storms, Georgios Anagnostopoulos, Anastasios Karkanis, Athanasios Kampatagis, Panagiotis Marhavilas, Sofia-Anna Menesidou, Dimitrios Efthymiadis, Stefanos Keskinis, Dimitar Ouzounov, Nick Hatzigeorgiu, Michael Danakis
Ground Electric Field, Atmospheric Weather And Electric Grid Variations In Northeast Greece Influenced By The March 2012 Solar Activity And The Moderate To Intense Geomagnetic Storms, Georgios Anagnostopoulos, Anastasios Karkanis, Athanasios Kampatagis, Panagiotis Marhavilas, Sofia-Anna Menesidou, Dimitrios Efthymiadis, Stefanos Keskinis, Dimitar Ouzounov, Nick Hatzigeorgiu, Michael Danakis
Mathematics, Physics, and Computer Science Faculty Articles and Research
In a recent paper, we extended a previous study on the solar solar influence to the generation of the March 2012 heatwave in the northeastern USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), Interplanetary space (ACE satellite), the Earth’s magnetosphere (Earth-based measurements, NOAA-19 satellite), the top of the clouds (Terra and Aqua satellites), and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather …
Spatial Analyses On Pre-Earthquake Ionospheric Anomalies And Magnetic Storms Observed By China Seismo-Electromagnetic Satellite In August 2018, Jann-Yeng Tiger Liu, Xuhui Shen, Fu-Yuan Chang, Yuh-Ing Chen, Yang-Yi Sun, Chieh‑Hung Chen, Sergey Pulinets, Katsumi Hattori, Dimitar Ouzounov, Valerio Tramutoli, Michel Parrot, Wei-Sheng Chen, Cheng-Yan Liu, Fei Zhang, Dapeng Liu, Xue-Min Zhang, Rui Yan, Qiao Wang
Spatial Analyses On Pre-Earthquake Ionospheric Anomalies And Magnetic Storms Observed By China Seismo-Electromagnetic Satellite In August 2018, Jann-Yeng Tiger Liu, Xuhui Shen, Fu-Yuan Chang, Yuh-Ing Chen, Yang-Yi Sun, Chieh‑Hung Chen, Sergey Pulinets, Katsumi Hattori, Dimitar Ouzounov, Valerio Tramutoli, Michel Parrot, Wei-Sheng Chen, Cheng-Yan Liu, Fei Zhang, Dapeng Liu, Xue-Min Zhang, Rui Yan, Qiao Wang
Mathematics, Physics, and Computer Science Faculty Articles and Research
The China Seismo-Electromagnetic Satellite (CSES), with a sun-synchronous orbit at 507 km altitude, was launched on 2 February 2018 to investigate pre-earthquake ionospheric anomalies (PEIAs) and ionospheric space weather. The CSES probes manifest longitudinal features of four-peak plasma density and three plasma depletions in the equatorial/low-latitudes as well as mid-latitude troughs. CSES plasma and the total electron content (TEC) of the global ionosphere map (GIM) are used to study PEIAs associated with a destructive M7.0 earthquake and its followed M6.5 and M6.3/M6.9 earthquakes in Lombok, Indonesia, on 5, 17, and 19 August 2018, respectively, as well as to examine ionospheric …
Application Of Model-Based Time Series Prediction Of Infrared Long-Wave Radiation Data For Exploring The Precursory Patterns Associated With The 2021 Madoi Earthquake, Jingye Zhang, Ke Sun, Junqing Zhu, Ning Mao, Dimitar Ouzounov
Application Of Model-Based Time Series Prediction Of Infrared Long-Wave Radiation Data For Exploring The Precursory Patterns Associated With The 2021 Madoi Earthquake, Jingye Zhang, Ke Sun, Junqing Zhu, Ning Mao, Dimitar Ouzounov
Mathematics, Physics, and Computer Science Faculty Articles and Research
Taking the Madoi MS 7.4 earthquake of 21 May 2021 as an example, this paper proposes using time series prediction models to predict the outgoing long-wave radiation (OLR) anomalies and study short-term pre-earthquake signals. Five time series prediction models, including autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM), were trained with the OLR time series data of the aseismic moments in the 5° × 5° spatial range around the epicenter. The model with the highest prediction accuracy was selected to retrospectively predict the OLR values during the aseismic period and before the earthquake in the area. It …
Monitoring Dam Stability Using Psi And Sbas Analysis, Rejoice Thomas, Wenzhao Li, Shahryar Fazli, Nikolay Grisel Todorov, Hesham El-Askary
Monitoring Dam Stability Using Psi And Sbas Analysis, Rejoice Thomas, Wenzhao Li, Shahryar Fazli, Nikolay Grisel Todorov, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Water preservation and maximization of its efficient use is key in areas facing water scarcity like California. One of the most important resources available to us are dams, which are useful to address a variety of needs like water supply, flood control, and maintaining environmental flows. However, if not managed properly, dams can be disastrous to humans and wildlife alike, different water species, habitats, and even impact water quality for a region. In this context, we have used newer Synthetic Aperture Radar Interferometry techniques like Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) to estimate the displacement rates at …
Mapping California Rice Using Optical And Sar Data Fusion With Phenological Features In Google Earth Engine, Li Wenzhao, Hesham El-Askary, Daniele C. Struppa
Mapping California Rice Using Optical And Sar Data Fusion With Phenological Features In Google Earth Engine, Li Wenzhao, Hesham El-Askary, Daniele C. Struppa
Mathematics, Physics, and Computer Science Faculty Articles and Research
California, known for its diverse agriculture, is also a major producer of rice, especially in its northern regions in Sacramento River Valley. Traditional methods, predominantly reliant on optical-based satellite imagery, encounter limitations due to atmospheric interference and sensor resolution. The ability of Synthetic Aperture Radar (SAR) to penetrate atmospheric distortions and exhibit high sensitivity to vegetation structure presents a distinct advantage over optical-based methods. Utilizing Optical and SAR data fusion, this study advances the enhanced pixel-based phenological feature composite (Eppf) method using SVM classification algorithm, which can track phenological changes and patterns, providing valuable insights for agricultural planning and management. …
Titaniferous-Vanadiferous, Magnetite-Ilmenite Mineralization In A Mafic Suite Within The Chhotanagpur Gneissic Complex, Bihar, India, Ashmeer Mohammad, Anup K. Prasad, Kehe-U Wetsah, Mohammad Azad, Vivek Aryan, Hesham El-Askary
Titaniferous-Vanadiferous, Magnetite-Ilmenite Mineralization In A Mafic Suite Within The Chhotanagpur Gneissic Complex, Bihar, India, Ashmeer Mohammad, Anup K. Prasad, Kehe-U Wetsah, Mohammad Azad, Vivek Aryan, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Titanium or vanadium metals or their alloys are important industrial metals/alloys. Because these resources are in short supply, the investigation of potential titaniferous-vanadiferous deposits needs special attention to bridge the supply-demand gap. The study integrates geological, geochemical, remote sensing, and geophysical data for assessing the potentiality of titaniferous-vanadiferous, magnetite-ilmenite mineralization in and around the Sudamakund and Paharpur areas, Gaya and Jehanabad districts, Bihar, India, and delineation of specific targets for detailed exploration. Field visits for large scale mapping on (1:12,500 scale) were used to conduct a reconnaissance survey for magnetite-ilmenite mineralization in parts of toposheet number 72G/04 in the Gaya …
Accuracy Assessment, Comparative Performance, And Enhancement Of Public Domain Digital Elevation Models (Aster 30 M, Srtm 30 M, Cartosat 30 M, Srtm 90 M, Merit 90 M, And Tandem-X 90 M) Using Dgps, Kumari Preety, Anup K. Prasad, Atul K. Varma, Hesham El-Askary
Accuracy Assessment, Comparative Performance, And Enhancement Of Public Domain Digital Elevation Models (Aster 30 M, Srtm 30 M, Cartosat 30 M, Srtm 90 M, Merit 90 M, And Tandem-X 90 M) Using Dgps, Kumari Preety, Anup K. Prasad, Atul K. Varma, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) …
Snow Cover Variability And Trend Over The Hindu Kush Himalayan Region Using Modis And Srtm Data, Nirasindhu Desinayak, Anup K. Prasad, Hesham El-Askary, Menas Kafatos, Ghassem R. Asrar
Snow Cover Variability And Trend Over The Hindu Kush Himalayan Region Using Modis And Srtm Data, Nirasindhu Desinayak, Anup K. Prasad, Hesham El-Askary, Menas Kafatos, Ghassem R. Asrar
Mathematics, Physics, and Computer Science Faculty Articles and Research
Snow cover changes have a direct bearing on the regional and global energy and water cycles and the change in the Earth's climate conditions. We studied the relatively long-term (2000–2017) altitudinal spatiotemporal changes in the coverage of snow and glaciers in one of the world's largest mountainous regions, the Hindu Kush Himalayan (HKH) region, including Tibet, using remote sensing data (5 km grid resolution) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. This dataset provided a unique opportunity to study zonal and hypsographic changes in the intra-annual (accumulating season and melting season) and interannual variations in …
Editorial: Geospace Observation Of Natural Hazards, Dimitar Ouzounov, Jann-Yenq Liu, Patrick T. Taylor, Katsumi Hattori
Editorial: Geospace Observation Of Natural Hazards, Dimitar Ouzounov, Jann-Yenq Liu, Patrick T. Taylor, Katsumi Hattori
Mathematics, Physics, and Computer Science Faculty Articles and Research
"This collection of technical papers aims to bring recent data from many sources into the study of natural hazards. They represent a multi-instrumental approach using both ground observations: Global Navigation Satellite System (GNSS); and Low Earth Orbiting Electromagnetic (LEO EM) satellites missions together with Earth Observations (EO), which could reveal new information. Results from latest satellite missions, [(NPP/NASA/NOAA(US), CENTINEL, Swarm/ESA (EU), HIMAWARI (JMA, Japan), FORMOSAT-5 (Taiwan, August 2017), CSES1 (China/Italy, Feb 2018), and FORMOSAT-7/COSMIC-2 (Taiwan/United States, May 2019)], are represented in this volume."
Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen
Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen
Mathematics, Physics, and Computer Science Faculty Articles and Research
During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky …