Article
Environmental Sciences
Peng Zhang, Yan Chen, Youhua Ran, Yunping Chen
Summary: It is necessary to determine the approximate location and time of severe permafrost degradation before applying surface deformation monitoring techniques. This study analyzed the permafrost deformation mechanisms and identified early surface deformation signals using the case of an oil tank collapse in the Norilsk region. The results indicate that permafrost areas with large topographic slopes (>15 degrees) are more likely to experience severe surface deformation during the summer thaw period when the annual average temperature increases continuously at a rate of 2 degrees C/year for 2 to 3 years.
Article
Remote Sensing
Minju Kim, Chang-Uk Hyun
Summary: Oil spills can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. Satellite imagery can be used to detect oil spills by analyzing the reflectance of oil at specific wavelengths and developing oil spill indices. Google Earth Engine is a useful tool for efficiently analyzing large volumes of satellite imagery for oil spill detection. This study evaluated the applicability of four oil spill indices in different land cover areas and found that certain indices can effectively monitor oil spills in regions with complex land covers.
KOREAN JOURNAL OF REMOTE SENSING
(2023)
Article
Environmental Sciences
Niyazi Arslan, Meysam Majidi Nezhad, Azim Heydari, Davide Astiaso Garcia, Georgios Sylaios
Summary: Principal Component Analysis (PCA) is used to improve the accuracy of oil spill monitoring and assessment by distinguishing oil spills from clouds and seawater in remote sensing images.
Article
Engineering, Marine
Bo Li, Jin Xu, Xinxiang Pan, Rong Chen, Long Ma, Jianchuan Yin, Zhiqiang Liao, Lilin Chu, Zhiqiang Zhao, Jingjing Lian, Haixia Wang
Summary: Due to the rapid growth of ocean oil development and transportation, the risk of offshore oil spills has increased unevenly, posing a great threat to coastal cities. Therefore, an automatic oil spill detection method based on the YOLO deep learning network was proposed to minimize disaster losses. The detection model can effectively identify oil spill monitoring regions and extract oil slicks using an adaptive threshold. The proposed method provides real-time and effective data for routine patrols and emergency responses.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Review
Engineering, Marine
Huaxin Pan, Kangxu Tang, Jia Zhuo, Yuming Lu, Jialong Chen, Zhichao Lv
Summary: Acoustic monitoring is an efficient technique for real-time detection of underwater oil spills, providing valuable data for emergency response. This review summarizes and evaluates the existing research on acoustic technology for monitoring underwater oil spills, highlighting the need for further investigation into its feasibility, data accuracy, and comparison with other detection techniques. The study emphasizes the importance of examining the impact, advantages, and auxiliary mechanisms combined with acoustic technology in oil spill monitoring, as well as enriching and improving acoustic research methods and experimental techniques to unlock its future value.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Environmental Sciences
Bikram Koirala, Nicholus Mboga, Robrecht Moelans, Els Knaeps, Seppe Sels, Frederik Winters, Svetlana Samsonova, Steve Vanlanduit, Paul Scheunders
Summary: This study investigates the potential of using optical reflectance in the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spills. A physical model and an artificial neural network algorithm were developed to accurately estimate oil thickness and volume, while maintaining stability under changing conditions. Experimental validation was conducted on different types of oil, and the results demonstrate the effectiveness of the proposed method.
Article
Environmental Sciences
Fabian Low, Klaus Stieglitz, Olga Diemar
Summary: This study demonstrated the usability of freely available Sentinel satellite images and machine learning algorithms for mapping terrestrial oil spills with more than 90% classification accuracy. The addition of multi temporal information and spatial predictor variables, quantifying proximity to oil production infrastructure, significantly increased the classification accuracy to over 95%. Freely available Sentinel satellite images may present an accurate and efficient means for the regular monitoring of oil-impacted areas.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Environmental Sciences
Guannan Li, Ying Li, Yongchao Hou, Xiang Wang, Lin Wang
Summary: Marine oil spill detection is essential for emergency response and environmental recovery post-disaster. Pol-SAR technology can provide detailed information on oil slicks, look-alikes, and seawater scattering mechanisms. A novel polarimetric feature combination based on H and A efficiencies is proposed and proven to enhance oil slick distinguishability and suppress sea clutter.
Article
Environmental Sciences
Rubicel Trujillo-Acatitla, Jose Tuxpan-Vargas, Cesare Ovando-Vazquez
Summary: This study developed a model for oil-spill detection and concentration estimation based on spectral response data and machine learning techniques, showing potential applications in detecting and estimating oil spills.
MARINE POLLUTION BULLETIN
(2022)
Article
Green & Sustainable Science & Technology
Coskan Sevgili, Remzi Fiskin, Erkan Cakir
Summary: This study develops a model based on a data-driven Bayesian Network algorithm to predict the occurrence of oil spills following tankship accidents. The findings suggest that accident type, vessel age, vessel size, and waterway type are the most important variables affecting oil spill probability. These findings provide valuable insights for decision-making authorities.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Chemistry, Multidisciplinary
Pawel Tysiac, Tatiana Strelets, Weronika Tuszynska
Summary: In recent years, there has been an increasing use of satellite sensors for detecting and tracking oil spills. Satellite images are highly valuable for oil spill analysis, allowing for identification of the source of leakage and assessment of potential damage. However, the methodological approach to specific leakage cases is still unclear. This study focuses on remote sensing analysis of environmental changes through the development of oil spill detection processing methods, including long-term analysis of surface water changes.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Blake A. Schaeffer, Peter Whitman, Robyn Conmy, Wilson Salls, Megan Coffer, David Graybill, Marie C. Lebrasse
Summary: Extraction of petroleum oil resources may lead to oil spills in the aquatic environment. PlanetScope satellites can complement monitoring efforts by providing high-resolution coverage and filling temporal gaps in tracking oil slicks.
MARINE POLLUTION BULLETIN
(2022)
Article
Environmental Sciences
Junfang Yang, Jian Wang, Yabin Hu, Yi Ma, Zhongwei Li, Jie Zhang
Summary: This paper proposes a model based on the graph convolutional architecture and spatial-spectral information fusion for the oil spill detection of real oil spill incidents. The model is experimentally evaluated using both spaceborne and airborne hyperspectral oil spill images. Research findings demonstrate the superior oil spill detection accuracy of the developed model when compared to Graph Convolutional Network (GCN) and CNN-Enhanced Graph Convolutional Network (CEGCN), across two hyperspectral datasets collected from the Bohai Sea. Moreover, the performance of the developed model in oil spill detection remains optimal, even with only 1% of the training samples. Similar conclusions are drawn from the oil spill hyperspectral data collected from the Yellow Sea. These results validate the efficacy and robustness of the proposed model for marine oil spill detection.
Article
Environmental Sciences
Yunxia Du, Kaishan Song, Ge Liu
Summary: This study uses satellite remote sensing data to classify water bodies into three optical types based on unsupervised cluster analysis. The results show that each optical water type is associated with different bio-optical properties.
Article
Environmental Sciences
Sankaran Rajendran, Ponnumony Vethamony, Fadhil N. Sadooni, Hamad Al-Saad Al-Kuwari, Jassim A. Al-Khayat, Vashist O. Seegobin, Himanshu Govil, Sobhi Nasir
Summary: This study analyzed the Wakashio oil spill incident near Mauritius using satellite data to monitor the spreading of the spill and its impact on the coastal environment. Different image processing methods were used to detect and classify the oil spill, and the accuracy of the results was validated through field studies. The study demonstrated the potential of satellite sensors and image processing techniques in detecting, monitoring, and assessing the impact of oil spills on the environment.
ENVIRONMENTAL POLLUTION
(2021)
Article
Environmental Sciences
Subhanil Guha, Himanshu Govil
Summary: The present study examines the seasonal variation of land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI) in different land use/land cover (LULC) types in Raipur City, India. The findings show that LST is highest in bare lands and built-up areas, while it is lowest in green vegetation. The correlation between LST and NDVI varies across seasons and LULC types.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Subhanil Guha, Himanshu Govil
Summary: The study found significant correlations between land surface temperature and remote sensing indices in Raipur City, with the relationship between LST and NDBI being the most consistent.
GEOCARTO INTERNATIONAL
(2022)
Article
Engineering, Aerospace
Armugha Khan, Himanshu Govil, Haris Hasan Khan, Praveen Kumar Thakur, Ali P. Yunus, Padmini Pani
Summary: This paper presents a detailed analysis of flood inundation areas in Patna district of Bihar, India using Sentinel-1 SAR data. The study examines the spatial extent and temporal pattern of flooding, as well as the changes in river morphology and sandbar characteristics. The results show that the floods caused significant inundation and impacts on the river morphology and sandbars.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Subhanil Guha, Himanshu Govil
Summary: This study investigates the seasonal fluctuation of LST-NDVI relation in Raipur City, India, and finds a considerable rise in land surface temperature during the study period, showing a strong negative correlation with normalized difference vegetation index.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2022)
Editorial Material
Engineering, Aerospace
Shashi Kumar, Himanshu Govil
ADVANCES IN SPACE RESEARCH
(2022)
Article
Geochemistry & Geophysics
Subhanil Guha, Himanshu Govil, Ajay Kumar Taloor, Neetu Gill, Anindita Dey
Summary: This study evaluates the seasonal variability of the correlation coefficient between land surface temperature and spectral indices. The results show that land surface temperature has a positive correlation with NDBI and NDBaI, and a negative correlation with NDVI. The strongest correlation is observed during the post-monsoon period, while the weakest correlation is observed in winter.
GEODESY AND GEODYNAMICS
(2022)
Article
Engineering, Aerospace
Mahesh Kumar Tripathi, Himanshu Govil, Pralay Bhaumik
Summary: The synoptic views of satellite images have great utility in solving geological enigmas. By studying lineament structures, hydrothermal alteration zones, and litho-boundaries, it is found that there are significant correlations among them.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Remote Sensing
Yousef Salem, Habes Ghrefat, Rajendran Sankaran
Summary: Studying the grain size and mapping of aeolian dunes is crucial for understanding sand erosion, transport, dune movement, encroachment, and land degradation. This study analyzes the grain size and mineral composition of sand samples collected from crescentic dunes, and maps the dunes using satellite data. The results show that the sand deposits in the study area have diverse sources, mainly consisting of quartz, calcite, and haematite.
INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION
(2023)
Article
Geosciences, Multidisciplinary
Monika, H. Govil, Subhanil Guha
Summary: Surface deformation in the Korba Coal Mines of India was identified using Synthetic Aperture Radar (SAR) Technology. The DInSAR technique was used to generate deformation maps from images taken between 2015 and 2019. The study showed that the Rajgamar coal mine area experienced a slow rate of deformation.
JOURNAL OF APPLIED GEOPHYSICS
(2023)
Article
Environmental Sciences
Hrishikesh Kumar, Desikan Ramakrishnan, Ronak Jain, Himanshu Govil
Summary: In this study, AVIRIS-NG datasets were used to investigate the relationship between the spatial pattern of talc mineralization, associated alteration minerals, and iron-oxide enrichment in the talc deposits of Jahazpur, Rajasthan, India. It was found that the talc-bearing areas were characterized by the presence of clay minerals, such as a mixture of kaolinite and muscovite, illite, and dickite, as well as enhanced iron enrichment. The talc-bearing zones were located next to quartz-rich lithologies and were aligned along the Jahazpur thrust. Hydrothermal alteration of dolomites by silica and iron-rich fluid is proposed as the major factor controlling talc mineralization. This study has important implications for the identification of prospective zones of talc mineralization using imaging spectroscopy.
Article
Environmental Sciences
Neelam Agrawal, Himanshu Govil, Gaurav Mishra, Manika Gupta, Prashant K. Srivastava
Summary: This study evaluates the potential of the PRISMA dataset for mapping hydrothermally altered and weathered minerals using machine learning algorithms. The spectral angle mapper technique was used to generate distribution maps for minerals such as kaolinite, talc, and montmorillonite, which were verified through field validation surveys. The results demonstrate that the stochastic gradient descent and artificial neural network-based multilayer perceptron classifiers outperformed other algorithms in accuracy.
Article
Environmental Sciences
Divya Mohan, J. Aravinth, Sankaran Rajendran
Summary: This study focuses on image denoising, compression, and reconstruction of hyperspectral images using deep learning techniques. The proposed approach was evaluated using Python programming and the BGU-ICVL dataset, and achieved high-quality outputs at each stage. It demonstrates the effectiveness of the developed model in utilizing hyperspectral data.
Article
Environmental Sciences
Sankaran Rajendran, Hamad Al Saad Al Kuwari, Fadhil N. Sadooni, Sobhi Nasir, Himanshu Govil, Habes Ghrefat
Summary: The study maps aeolian deposits in part of the Arabian Desert using ASTER data and examines their impact on desertification, land encroachment, and degradation, as well as agricultural development in arid regions. The analysis of sand deposits' emissive spectra revealed triplet absorptions, which were then mapped using ASTER spectral bands. The study of the Abu Samra region in Qatar from 2000 to 2021 using ASTER quartz index (QI) images showed significant changes in desertification and land degradation.
ENVIRONMENTAL RESEARCH
(2023)
Article
Engineering, Geological
Subhanil Guha, Himanshu Govil
Summary: This study analyzes the seasonal variability of the relationship between land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India. The research finds that the correlation is strongest in the post-monsoon season, and water bodies and green vegetation show moderate to strong positive correlations in all seasons. The built-up area and bare land have moderate positive correlations in all seasons.
INTERNATIONAL JOURNAL OF ENGINEERING AND GEOSCIENCES
(2022)
