ISIS: a swath imaging sonar for shelf edge survey

Author(s):  
M.L. Somers
Keyword(s):  
Diversity ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 57 ◽  
Author(s):  
Sean N. Porter ◽  
Michael H. Schleyer

Coral communities display spatial patterns. These patterns can manifest along a coastline as well as across the continental shelf due to ecological interactions and environmental gradients. Several abiotic surrogates for environmental variables are hypothesised to structure high-latitude coral communities in South Africa along and across its narrow shelf and were investigated using a correlative approach that considered spatial autocorrelation. Surveys of sessile communities were conducted on 17 reefs and related to depth, distance to high tide, distance to the continental shelf edge and to submarine canyons. All four environmental variables were found to correlate significantly with community composition, even after the effects of space were removed. The environmental variables accounted for 13% of the variation in communities; 77% of this variation was spatially structured. Spatially structured environmental variation unrelated to the environmental variables accounted for 39% of the community variation. The Northern Reef Complex appears to be less affected by oceanic factors and may undergo less temperature variability than the Central and Southern Complexes; the first is mentioned because it had the lowest canyon effect and was furthest from the continental shelf, whilst the latter complexes had the highest canyon effects and were closest to the shelf edge. These characteristics may be responsible for the spatial differences in the coral communities.


2021 ◽  
Vol 13 (14) ◽  
pp. 2671
Author(s):  
Xiaoqin Zang ◽  
Tianzhixi Yin ◽  
Zhangshuan Hou ◽  
Robert P. Mueller ◽  
Zhiqun Daniel Deng ◽  
...  

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.


Sedimentology ◽  
2021 ◽  
Author(s):  
Gianni Mallarino ◽  
Jason M. Francis ◽  
Stephan Jorry ◽  
James J. Daniell ◽  
André W. Droxler ◽  
...  

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