[1] Inlets provide a critical ecological link between restricted bays and estuaries to the coastal ocean. The net fluxes of water and suspended sediment are presented in this study. These fluxes are obtained based on data from a multidisciplinary, full tidal cycle survey across Barataria Pass in southern Louisiana on 31 July to 1 August 2008. The velocity profiles were obtained with an acoustic Doppler current profiler mounted on a small boat continuously crossing the inlet, which contains swift and turbulent tidal currents. Water samples were collected six times in a 24 h period at three discrete depths and three locations across the inlet. The observations delineated a clear eddy on the western side of the inlet which causes a low R 2 value of the tidal harmonic analysis on the edges of the inlet. The net flux of total suspended sediment out of the bay was determined to be 8800 t of which 20% was organic matter, demonstrating a significant source of organic matter to the base of the coastal ocean detrital food chain. The time evolution and net fluxes of water, and suspended sediments showed that the net flow resembles conventional estuarine circulation patterns with net outward flow on the surface and shallow ends of the inlet and with net inward flow in the center and at the bottom of the center of the inlet. The west side has a much larger outward flow than the east side while the east side is fresher. These differences suggest that the Louisiana Coastal Current from around the Bird's Foot Delta derived from the mixing of shelf water with the Mississippi River freshwater may have entered the bay. This must have been mostly from the east side during the survey, which resulted in a smaller outward flow on the eastern side. A numerical experiment further confirmed this assumption and the model was verified by field observations on 5 May 2010.

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Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.