Browsing by Subject "Bathymetry"

Sub-arctic river ecosystems are recognized as biodiversity hotspots in the region. However, gaps exist in understanding the potential impacts of the current climate change on these sensitive environments due to a lack of local data and knowledge of suitable monitoring methods. Remote sensing offers a promising approach to map river bathymetry, a critical factor in monitoring river environments. In this study, I investigated the efficacy of three remote sensing methods – multibeam echo sounding, green-wavelength airborne laser scanning, and multispectral satellite imagery – for mapping bathymetry along the Tana River, located at the border of northern Finland and Norway. Multibeam echo sounding, and laser scanning offer high-resolution and good penetration capabilities for mapping shallow river bathymetry. Satellite imagery can cover large spatial areas, often more cost-effectively than other remote sensing methods, albeit with lower spatial resolution. The analysis involved processing field survey data from two study sites with varying topography, slope, and flow velocity. I conducted the bathymetric model generation by using Inverse Distance Weighting interpolation and Lyzenga algorithm methods. The validation was carried out through error assessment. Performance evaluation of the bathymetric models and their differences were conducted by calculating linear regression and Pearson’s correlation coefficient, vertical difference, and bottom roughness. The results suggest that all three remote sensing methods successfully captured the main characteristics of the river bottom shape, aligning with the geomorphological characteristics of the study sites. The multibeam echo sounding provided densest and most coherent bathymetric models (mean R2=0.57 and RMSE=0.80). The airborne laser scanning produced bathymetric models with highest uncertainty in elevation due to noise and gaps in the data and showed better accuracy above the water surface than below (mean R2=0.27 and RMSE=1.18, compared to R2=0.98 and RMSE=0.18). The comparability of the bathymetry derived from satellite imagery against other methods was not optimal due to notably lower spatial resolution, but the satellite-based bathymetric models were able to capture the general variations in river bottom elevation (mean R2=0.48 and RMSE=0.96). The study area that was shallower, had a slow flow rate, and had a sandy bottom yielded more accurate bathymetric models. This was evidenced by strong positive correlation coefficients (mean r=0.83, compared to r=0.23 in the other site) and fewer river bottom profile differences between the models. The findings highlight the importance of comprehensive validation data and proper data pre-processing. Addressing these challenges is important for advancing the understanding of how to map and monitor sub-arctic river bathymetry.