Accuracy assessment of near-shore bathymetry information retrieved from Landsat-8 imagery

The improvement in the capabilities of Landsat-8 imagery to retrieve bathymetric information in shallow coastal waters was examined. Landsat-8 images have an additional band named coastal/aerosol, Band 1: 435–451 nm in comparison with former generation of Landsat imagery. The selected Landsat-8 operational land image (OLI) was of Chabahar Bay, located in the southern part of Iran (acquired on February 22, 2014 in calm weather and relatively low turbidity). Accurate and high resolution bathymetric data from the study area, produced by field surveys using a single beam echo-sounder, were selected for calibrating the models and validating the results. Three methods, including traditional linear and ratio transform techniques, as well as a novel proposed integrated method, were used to determine depth values. All possible combinations of the three bands [coastal/aerosol (CB), blue (B), and green (G)] have been considered (11 options) using the traditional linear and ratio transform techniques, together with five model options for the integrated method. The accuracy of each model was assessed by comparing the determined bathymetric information with field measured values. The standard error of the estimates, correlation coefficients (R ² ) for both calibration and validation points, and root mean square errors (RMSE) were calculated for all cases. When compared with the ratio transform method, the method employing linear transformation with a combination of CB, B, and G bands yielded more accurate results (standard error = 1.712 m, R ² _calibration = 0.594, R ² _validation = 0.551, and RMSE =1.80 m). Adding the CB band to the ratio transform methodology also dramatically increased the accuracy of the estimated depths, whereas this increment was not statistically significant when using the linear transform methodology. The integrated transform method in form of Depth = b ₀  + b ₁ X _CB  + b ₂ X _B  + b ₅ ln(R _CB )/ln(R _G ) + b ₆ ln(R _B )/ln(R _G ) yielded the highest accuracy (standard error = 1.634 m, R ² _calibration = 0.634, R ² _validation = 0.595, and RMSE = 1.71 m), where R _i (i = CB, B, or G) refers to atmospherically corrected reflectance values in the i ^th band [X _i  = ln(R _i -R _{deep water})].     

Datation par la méthode U/Th d'un travertin quaternaire du Sud-Est marocain : implications paléoclimatiques pendant le Pléistocène moyen et supérieur

A large travertine outcrop south of Errachidia, southern Morocco, was studied and U/Th dated. The carbonate fraction was provided by groundwaters then, as now, from the eastern High Atlas percolating through the regional Infra-Cenomanian aquifer. There were two main periods of accumulation at ca 262 kyr BP and 20–11.5 kyr BP separated by a long discontinuity with some limited weathering and erosion and correlated in part with a period of erosion at 30–20 kyr BP further to the west. The two travertine-deposition periods suggest increased rainfall and/or cooler thermal conditions in the eastern High Atlas source regions. Massive travertine accumulation ceased at the end of the Upper Pleistocene. To cite this article: L. Boudad et al., C. R. Geoscience 335 (2003).To cite this article: L. Boudad et al., C. R. Geoscience 335 (2003).     

Flow structure over a wavy bed with vegetation cover

On the basis of experiments carried out in flume with a wavy bed with vegetation cover, flow velocity, turbulence intensities and Reynolds stress distributions are investigated. The wavy bed was similar to dune in this study. The fixed artificial dunes were constructed over the bed and artificial vegetation put over them in a laboratory flume. An Acoustic Doppler Velocimeter and spatially-averaged method were applied to determine turbulent flow components and shear velocity. Results were compared with a gravel bedform. It was observed that vegetation cover influences considerably the flow structure and displays clearly the flow separation and reattachment point. The law of the wall was not valid within the vegetation cover, but it was fitted well to the zone above the vegetation cover within the inner layer. For a wavy bed having the same dimensions, shear velocity and friction factor over vegetation cover are 1.7 and 2.6 times of those for the gravel bedform, respectively. The results of laboratory study were compared with those of river study.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.