Deposited fine sediment is an essential component of freshwater ecosystems. Nonetheless, anthropogenic activities can modify natural fine sediment levels, impacting the physical, chemical and biological characteristics of these ecosystems. An ability to quantify deposited fine sediment is critical to understanding its impacts and successfully managing the anthropogenic activities that are responsible for modifying it. One widely used method, the visual estimate technique, relies on subjective estimates of particle size and percentage cover. In this paper, we present two novel alternative approaches, based on non-automated digital image analysis (DIA), which are designed to reduce the subjectivity of submerged and surficial fine sediment estimates, and provide a verifiable record of the conditions at the time of sampling. The DIA methods were tested across five systematically selected, contrasting temperate stream and river typologies, over three seasons of monitoring. The resultant sediment metrics were strongly, positively correlated with visual estimates (rs = 0.90, and rs = 0.82, p < 0.01), and similarly strongly, but negatively correlated with a sediment-specific biotic index, suggesting some degree of biological relevance. The DIA technique has the potential to be a valuable tool for application in numerous areas of river research, where a non-destructive, less subjective and verifiable method is desirable.
Bibliographical noteCopyright © 2016 The Authors River Research and Applications Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License,which permits use, distribution and reproduction in any medium, provided the original work is properly cited
- Deposited fine sediment
- visual estimates
- habitat assessment
- digital image analysis
- river substrate
Turley, M., Bilotta, G., Chadd, R., Extence, C., & Brazier, R. E. (2016). Quantifying Submerged Deposited Fine Sediments in Rivers and Streams Using Digital Image Analysis. River Research and Applications. https://doi.org/10.1002/rra.3073