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Autonomous unmanned aerial vehicles (UAVs) often carry video cameras as part of their payload. Outdoor video captured by such cameras can be used to estimate the attitude and altitude of the UAV by detecting the location of the horizon in the video frames. This paper presents a video frame processing algorithm for estimating the pitch and roll of a UAV, as well as its altitude. The frames are obtained from a downward pointing video camera equipped with a fisheye lens. These open-loop estimates can serve as redundant data used to implement graceful-degradation in the event that the main closed-loop control sensors fail, or for fault-tolerance purposes to augment inertial sensors for increased accuracy. The estimated values had a mean error of AE0.7 angular degrees for roll and AE0.9 angular degrees for pitch, while the altitude estimation from the video had a mean error of AE0.9 meters. The results are presented and compared to actual attitude and altitude values obtained from a traditional inertial measurement unit and, in the case of altitude comparison, an absolute air pressure sensor. The algorithm was developed on a personal computer to work at 10 frames per second and uses only simple image processing functions that can be deployed using open source libraries on lightweight computing boards capable of image processing.

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This study investigates the best available methods for remote monitoring inland small-scale waterbodies, using remote sensing data from both Landsat-8 and Sentinel-2 satellites, utilizing a handheld hyperspectral device for ground truthing. Monitoring was conducted to evaluate water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and turbidity. Ground truthing was performed to select the most suitable atmospheric correction technique (ACT). Several ACT have been tested: dark spectrum fitting (DSF), dark object subtraction (DOS), atmospheric and topographic correction (ATCOR), and exponential extrapolation (EXP). Classical sampling was conducted first; then, the resulting concentrations were compared to those obtained using remote sensing analysis by the above-mentioned ACT. This research revealed that DOS and DSF achieved the best performance (an advantage ranging between 29% and 47%). Further, we demonstrated the appropriateness of the use of Sentinel-2 red and vegetation red edge reciprocal bands 1 / B 4 × B 6 for estimating Chl-a ( R 2 = 0.82 , RMSE = 14.52   mg / m 3 ). As for Landsat-8, red to near-infrared ratio ( B 4 / B 5 ) produced the best performing model ( R 2 = 0.71 , RMSE = 39.88   mg / m 3 ), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model ( R 2 = 0.85 , RMSE = 0.87 NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with R 2 = 0.64 , RMSE = 0.90 NTU) using Landsat-8 was performed by applying two bands ( B 1 / B 3 ). Mapping the water quality parameters using the best performance biooptical model showed the significant effect of the adjacent land on the boundary pixels compared to pixels of deeper water.