Debris-Flow Watersheds and Fans: Morphology, Sedimentology and Dynamics

de Haas, Tjalling; Lau, Carie-Ann; Ventra, Dario

doi:10.1007/978-3-031-48691-3_2

Geoenvironmental Disaster Reduction

2024

DOI: 10.1007/978-3-031-48691-3_2

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Debris-Flow Watersheds and Fans: Morphology, Sedimentology and Dynamics

Tjalling de Haas,

Carie-Ann Lau,

Dario Ventra

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Cited by 2 publications

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Imbrication fabric as a diagnostic feature for the genetic classification of gravels deposited by fluid‐gravity versus sediment‐gravity subaerial flows

Brenna,

Martini,

Menapace

et al. 2024

Earth Surf Processes Landf

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Gravel transport in subaerial environments occurs through different flows that are conveniently classified as debris flows, debris floods and water flows based on their distinct morpho‐sedimentary dynamics and different implications for geomorphic hazard. Because distinctive features allowing gravelly sedimentary bodies to be ascribed to related genetic process are still a matter of discussion, this study aims to establish whether imbrication fabric represents a sedimentological fingerprint potentially applicable towards a more robust genetic classification of gravels. We analysed the fabric of 1007 imbricated clasts from modern and ancient deposits. Our results highlight statistically significant differences between imbrication fabrics in gravels deposited by different flows. Particles imbricated by water flows are typified by low imbrication angles (median of 35°) and elongated clasts oriented perpendicular to the flow. In contrast, debris‐flow gravels exhibit high imbrication angles (median of 65°) and elongated clasts oriented parallel to the flow. Debris‐flood deposits display elongated clasts both parallel and transverse to the main flow and intermediate values of imbrication angle (median of 47°). We propose that imbrication angles result from the combination of stability‐driven selection—a process acting under tractional transport and promoting the remobilization of high‐angle imbrication fabrics—and shear‐stress‐driven overriding—a mechanism leading to the formation of the higher imbrication angles—with the first dominating in water flows and the latter being effective in mass transport processes. The progressive change in imbrication fabrics from fluid‐gravity to sediment‐gravity flow deposits offers easily quantifiable sedimentological evidence to help in distinguishing genetic processes that contribute to the accumulation of gravels in alluvial and colluvial settings. Analysis of imbrication fabric can add valuable information, particularly as regards the classification of (1) coarse deposits in stratigraphic records and (2) modern debris flood deposits.

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Imbrication fabric as a diagnostic feature for the genetic classification of gravels deposited by fluid‐gravity versus sediment‐gravity subaerial flows

Brenna,

Martini,

Menapace

et al. 2024

Earth Surf Processes Landf

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LandslideSegNet: an effective deep learning network for landslide segmentation using remote sensing imagery

Şener,

Ergen

2024

Earth Sci Inform

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In recent years, remote sensing technologies have played a crucial role in the detection and management of natural disasters. In this context, deep learning models are of great importance for the early detection of natural disasters such as landslides. Landslide segmentation is a fundamental tool for the development of geographic information systems, natural disaster management and risk mitigation strategies. In this study, we propose a new semantic segmentation model called LandslideSegNet to improve early intervention capabilities for potential landslide scenarios. LandslideSegNet incorporates an encoder-decoder architecture that integrates local and contextual information, advanced encoder-decoder residual blocks and Efficient Hybrid Attentional Atrous Convolution. Thanks to this structure, the model is able to extract high-resolution feature maps from remote sensing imagery, accurately delineate the landslide areas and minimize the loss of contextual information. The developed LandslideSegNet model has shown significantly higher accuracy rates with fewer parameters compared to existing image segmentation models. The model was trained and tested using the Landslide4Sense dataset specially prepared for landslide detection. LandslideSegNet achieved an accuracy of 97.60% and 73.65% mean Intersection over Union of 73.65 on this dataset, demonstrating its efficiency. These results indicate the potential usability of the model in landslide detection and related disaster management applications.

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Debris-Flow Watersheds and Fans: Morphology, Sedimentology and Dynamics

Cited by 2 publications

References 364 publications

Imbrication fabric as a diagnostic feature for the genetic classification of gravels deposited by fluid‐gravity versus sediment‐gravity subaerial flows

Imbrication fabric as a diagnostic feature for the genetic classification of gravels deposited by fluid‐gravity versus sediment‐gravity subaerial flows

LandslideSegNet: an effective deep learning network for landslide segmentation using remote sensing imagery

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