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1.
Sensors (Basel) ; 19(5)2019 Feb 26.
Article in English | MEDLINE | ID: mdl-30813630

ABSTRACT

Together with direct Global Navigation Satellite System (GNSS) signals, the signals reflected at the water surface can be received by an unmanned aerial vehicle (UAV). From the range difference between two GNSS signal paths, the height of the UAV above the water level can be geometrically estimated using the weighted least squares method, called GNSS reflectometry (GNSS-R) altimetry. Experimental low-cost GNSS-R altimetry flights with a UAV were conducted at the coast of Lake Biwa, Japan. Although the height estimated by the GNSS-R altimeter included large short-term noises up to 8 m amplitude, it agreed well with the UAV altitude measured by the post-processed kinematic positioning. By selecting better weight functions in the least square method and using sufficient temporal averaging, the GNSS-R altimetry achieved accuracy in the order of 0.01 m if a sufficient number of GNSS satellites with high elevation angles were available. The dependency of the results on the weight functions is also discussed.

2.
Mar Pollut Bull ; 132: 33-43, 2018 Jul.
Article in English | MEDLINE | ID: mdl-29773443

ABSTRACT

The amount of marine debris washed ashore on a beach in Newport, Oregon, USA was observed automatically and sequentially using a webcam system. To investigate potential causes of the temporal variability of marine debris abundance, its time series was compared with those of satellite-derived wind speeds and sea surface height off the Oregon coast. Shoreward flow induced by downwelling-favorable southerly winds increases marine debris washed ashore on the beach in winter. We also found that local sea-level rise caused by westerly winds, especially at spring tide, moved the high-tide line toward the land, so that marine debris littered on the beach was likely to re-drift into the ocean. Seasonal and sub-monthly fluctuations of debris abundance were well reproduced using a simple numerical model driven by satellite-derived wind data, with significant correlation at 95% confidence level.


Subject(s)
Environmental Monitoring/methods , Water Pollution/analysis , Oregon , Pacific Ocean , Seasons , Video Recording , Wind
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