Biskra region currently shows signs of stress and a high risk of groundwater contamination by various chemicals and pesticides. For this purpose, a modified integrated susceptibility index (SI) is coupled with remote sensing (RS) and WetSpass model to assess the sensitivity of the groundwater and the risk of pollution in the most exploited aquifer (Quaternary aquifer) in the study area. The results of the modified SI model show that a major part of the aquifer is at risk of contamination if the farmers do not implement good agricultural practices. Four sensitivity levels are considered, reflecting a vulnerability rating that ranges from low to very high. The very high category is observed in the agricultural areas with an estimated pollution index ranging from 84 to 90.57, while a large part of the aquifer shows a high vulnerability to contamination (64 < SI ≤ 84). This category is found in areas characterized by the dominance of bare soil. In urban areas, the vulnerability level decreases to low category (37 < SI ≤ 45). However, the area of forests is classified as moderate to vulnerability (45 < SI ≤ 64). The different statistical and GIS methods confirm the reliability of the obtained SI map. The combination of the SI method with WetSpass model and RS can give a reliable map to help and assist the authorities and decision-makers in groundwater resources planning and the implementation of monitoring programs and networks to control the quality of groundwater in arid environments.
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http://dx.doi.org/10.1007/s10661-022-10189-3 | 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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