2023 CSDMS meeting-115
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PING-Mapper: An open-source framework to transform recreation-grade sonar systems into scientific mapping instruments
Cameron Bodine,
(He/Him),Northern Arizona University Flagstaff Arizona, United States. csb67@nau.edu
Daniel Buscombe, Marda Science LLC / USGS Santa Cruz Santa Cruz California, United States. dbuscombe@gmail.com
Predictive understanding of the variation and distribution of substrates at large spatial extents in aquatic systems is severely lacking. This hampers efforts to numerically predict the occurrence and distribution of specific benthic habitats, which must be observed in the field. Existing survey methods are limited in scale, require heavy and technically sophisticated survey equipment, or are prohibitively expensive for surveying and mapping. Recreation-grade side scan sonar (SSS) instruments, or fishfinders, have demonstrated their unparalleled value in a lightweight and easily-to-deploy system to image benthic habitats efficiently at the landscape-level. Existing methods for generating geospatial datasets from these sonar systems require a high-level of interaction from the user and are primarily closed-source, limiting opportunities for community-driven enhancements. We introduce PING-Mapper, an open-source and freely available Python-based software for generating geospatial benthic datasets from recreation-grade SSS systems. PING-Mapper is an end-to-end framework for surveying and mapping aquatic systems at large spatial extents reproducibly, with minimal intervention from the user. Version 1.0 of the software (Summer 2022) decodes sonar recordings from any existing Humminbird® side imaging system, export plots of sonar intensities and sensor-derived bedpicks and generates georeferenced mosaics of geometrically corrected sonar imagery. Version 2.0 of the software, to be released Summer 2023, extends PING-Mapper functionality by incorporating deep neural network models that automatically locate and mask sonar shadows, calculate independent bedpicks from both side scan channels, and classify substrates at the pixel level. The widespread availability of substrate information in aquatic systems will facilitate development of the next generation of in-stream models for routing flows of sediment and water, as well as more sophisticated simulations of specific habitats.
