Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface.
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http://dx.doi.org/10.3390/s17081772 | DOI Listing |
Sci Rep
January 2025
School of Electronics and Information, Xijing University, Xi'an, 710123, China.
To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery.
View Article and Find Full Text PDFNat Commun
January 2025
Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Monitoring methane (CH) emissions from terrestrial ecosystems is essential for assessing the relative contributions of natural and anthropogenic factors leading to climate change and shaping global climate goals. Fires are a significant source of atmospheric CH, with the increasing frequency of megafires amplifying their impact. Global fire emissions exhibit large spatiotemporal variations, making the magnitude and dynamics difficult to characterize accurately.
View Article and Find Full Text PDFSci Data
January 2025
Division: Geosciences | Permafrost Research, Alfred Wegener Institute - Helmholtz Center for Polar and Marine Research, Telegrafenberg A45, 14473, Potsdam, Germany.
This study presents a new dataset of remote sensing-derived Transient Snowline Altitude (TSLA) measurements for glaciers in High Mountain Asia. We use the Google Earth Engine to obtain TSLA data for approx. 2.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, 16802, USA.
Despite rapid developments of wearable self-powered sensors, it is still elusive to decouple the simultaneously applied multiple input signals. Herein, we report the design and demonstration of stretchable thermoelectric porous graphene foam-based materials via facile laser scribing for self-powered decoupled strain and temperature sensing. The resulting sensor can accurately detect temperature with a resolution of 0.
View Article and Find Full Text PDFBiol Rev Camb Philos Soc
January 2025
School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, Victoria, 3800, Australia.
Techniques for non-invasive sampling of ecophysiological data in wild animals have been developed in response to challenges associated with studying captive animals or using invasive methods. Of these, drones, also known as Unoccupied Aerial Vehicles (UAVs), and their associated sensors, have emerged as a promising tool in the ecophysiology toolkit. In this review, we synthesise research in a scoping review on the use of drones for studying wildlife ecophysiology using the PRISMA-SCr checklist and identify where efforts have been focused and where knowledge gaps remain.
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