The combination of thermal satellite remote sensing and geochemical tracing has been demonstrated as a robust and cost effective technique to identify potential groundwater discharge sites in coastal areas on a regional scale. Here, the approach is evaluated in its applicability to lakes, demonstrated through a case study in the west of Ireland. Surface water temperature patterns generated from Landsat 7 ETM+ Thermal Infrared (TIR) images are used to detect groundwater inputs captured as anomalous cold plumes visibly emanating from shallow lake margins during summer months. Qualitative assessments of groundwater inputs are completed using natural tracers (radon (222Rn, t½ = 3.8 days) and conductivity) to verify the presence of groundwater and to identify localized seepage sites or groundwater “hotspots”. Despite the difficulties in acquiring cost- and cloud free satellite imagery and the inevitable mismatch between satellite image acquisition and in-situ lake survey dates, the results are extremely promising. Temperature values generated from the thermal images reveal a strong negative correlation with measured radon activity which implies that decreases in surface water temperatures are associated with increases in radon activity and hence groundwater inputs to the lake. The study demonstrates the suitability of the approach as a comprehensive and cost-effective preliminary assessment tool for identification and localization of groundwater discharge entry points for use potentially in any region where discernible temperature differences exist. Understanding where groundwater discharge occurs is the first step towards more in-depth geochemical surveys that seek to clarify the role played by groundwater in lacustrine biogeochemical budgets.
Underwater survey videos of the seafloor are usually plagued with heavy vignetting (radial falloff) outside of
the light source beam footprint on the seabed. In this paper we propose a novel multi-frame approach for
removing this vignetting phenomenon which involves estimating the light source footprint on the seafloor, and
the parameters for our proposed vignetting model. This estimation is accomplished in a bayesian framework with
an iterative SVD-based optimization. Within the footprint, we leave the image contents as is, whereas outside
this region, we perform vignetting correction. Our approach does not require images with different exposure
values or recovery of the camera response function, and is entirely based on the attenuation experienced by
point correspondences accross multiple frames. We verify our algorithm with both synthetic and real data, and
then compare it with an existing technique. Results obtained show significant improvement in the fidelity of the
restored images.
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