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Monitoring River Flow Status Using Low-Cost Wildlife Camera and Image Segmentation Artificial Intelligence
  • +6
  • Jie Bao,
  • Yunxiang Chen,
  • Lupita Renteria,
  • Morgan Barnes,
  • Brieanne Forbes,
  • Sophia Mckever,
  • Amy Goldman,
  • Timothy Scheibe,
  • James Stegen
Jie Bao
Pacific Northwest National Laboratory

Corresponding Author:[email protected]

Author Profile
Yunxiang Chen
Pacific Northwest National Laboratory
Lupita Renteria
Pacific Northwest National Laboratory
Morgan Barnes
Pacific Northwest National Laboratory
Brieanne Forbes
Pacific Northwest National Laboratory
Sophia Mckever
Pacific Northwest National Laboratory
Amy Goldman
Pacific Northwest National Laboratory
Timothy Scheibe
Pacific Northwest National Laboratory
James Stegen
Pacific Northwest National Laboratory


Continuous measurement and monitoring of river or creek surface water coverage is crucial for studying the exchange fluxes between the surface and subsurface water. These fluxes directly impact carbon and nitrogen exchange and cycles, which are related to organic matter transport and reactions. While satellite and related techniques have been widely used for large-scale monitoring, they may not be accurate, sensitive, or cost-efficient for monitoring and tracking of surface water at fine-scale spatial (i.e., sub-meter) and temporal (i.e., daily) variations. This is especially true for small creeks with large plant canopy coverage. On-site in-situ sensors monitoring methods primarily yield point data, often insufficient in capturing the entire spatial distribution. Wildlife cameras have proven a cost-efficient way to continuously monitor surface water coverage of rivers and creeks. To efficiently analyze the images and/or videos from the wildlife cameras, in this study, two machine learning approaches, YOLOv8 and Mask2Former, have been applied. Both models were trained by images obtained from the public dataset ADE20k along with a small dataset from wildlife camera photos collected at the current study area. Once surface water coverage is segmented, the width of the surface water in real world can be approximated according to the wildlife camera, lens, and positioning parameters. In this study, surface water was detected and monitored by applying the proposed approaches for the six wildlife cameras in the Yakima River Basin in 2023 to 2024 in United States of America. Though Mask2Former model provides slightly better transferability, both models can accurately capture the surface water from the wildlife cameras, which are installed in significantly different environments, such as the different brightness, contrast, and varying front scene object blockages. The proposed approach enables long-term continuous monitoring and quantification of river intermittency and water availability with high accuracy and low-cost, which will benefit river ecosystem research and management.
17 Mar 2024Submitted to ESS Open Archive
18 Mar 2024Published in ESS Open Archive