Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic

Retrieval of Remotely Sensed Sediment Grain Size Evolution Characteristics along the Southwest Coast of Laizhou Bay Based on Support Vector Machine Learning

Grain size is the basic property of intertidal zone sediment. Grain size acts as an indicator of sedimentary processes and geomorphological evolution under human and nature interactions. The remote sensing technique provides an alternative for sediment grain-size parameter monitoring with the advantages of wide coverage and real-time surveying. This paper attempted to map the distributions of three sediment grain size contents and the mean grain size with multitemporal Landsat images along the southwestern coast of Laizhou Bay, China, from 1989 to 2015. Considering the low correlations between the measured reflectance and grain-size parameters, we used a support vector machine (SVM) to develop a nonlinear calibration model by taking several band indices as input variables. Then, the performance of the back propagation neural network (BPNN) was determined and discussed with that of the SVM. The SVM performed better than the BPNN in calibrating the four grain-size parameters based on a comparison of R2 and the root-mean-square error (RMSE). Moreover, an atmospheric correction algorithm originally proposed for case II water enabled the TM\ETM+ images to be precisely atmospherically corrected in this study. The SVM-mapped spatial-temporal grain-size variation showed a coarsening trend, which agreed with that obtained during in situ measurements in a former study. The changes in Yellow River discharge and precipitation associated with the coarsening trend were further analyzed. The yielded results showed that the coarsening trend and reduction in tidal flat area might be aggravated with overutilization. More reasonable planning would be necessary in this case.

Characterisation of Coastal Sediment Properties from Spectral Reflectance Data

Remote sensing of coastal sediments for the purpose of automated mapping of their physical properties (grain size, mineralogy and carbonate content) across space has not been widely applied globally or in South Africa. This paper describes a baseline study towards achieving this aim by examining the spectral reflectance signatures of field sediment samples from a beach–dune system at Oyster Bay, Eastern Cape, South Africa. Laboratory measurements of grain size and carbonate content of field samples (n = 134) were compared to laboratory measurements of the spectral signature of these samples using an analytical spectral device (ASD), and the results interrogated using different statistical methods. These results show that the proportion of fine sand, CaCO3 content and the distributional range of sediment grain sizes within a sample (here termed span) are the parameters with greatest statistical significance—and thus greatest potential interpretive value—with respect to their spectral signatures measured by the ASD. These parameters are also statistically associated with specific wavebands in the visible and near infrared, and the shortwave infrared parts of the spectrum. These results show the potential of spectral reflectance data for discriminating elements of grain size properties of coastal sediments, and thus can provide the baseline towards achieving automated spatial mapping of sediment properties across coastal beach–dune environments using hyperspectral remote sensing techniques.

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red–Green–Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue–Saturation–Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden’s j-index ranging from 0.6 to 0.97 depending on flight date and site.