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

Abstract

Remote sensing has been used since the 1980s to study parameters in relation with coastal zones. It was not until the beginning of the twenty-first century that it started to acquire imagery with good temporal and spectral resolution. This has encouraged the development of reliable imagery acquisition systems that consider remote sensing as a water management tool. Nevertheless, the spatial resolution that it provides is not adapted to carry out coastal studies. This article introduces a new methodology for estimating the most fundamental physical property of intertidal sediment, the grain size, in coastal zones. The study combines hyperspectral information (CASI-2 flight), robust statistic, and simultaneous field work (chemical and radiometric sampling), performed over Santander Bay, Spain. Field data acquisition was used to build a spectral library in order to study different atmospheric correction algorithms for CASI-2 data and to develop algorithms to estimate grain size in an estuary. Two robust estimation techniques (MVE and MCD multivariate M-estimators of location and scale) were applied to CASI-2 imagery, and the results showed that robust adjustments give acceptable and meaningful algorithms. These adjustments have given the following R(2) estimated results: 0.93 in the case of sandy loam contribution, 0.94 for the silty loam, and 0.67 for clay loam. The robust statistic is a powerful tool for large dataset.

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

“…Recombining the subpixel end member abundance through multivariate regression analysis significantly improved the image calibration for both sediment clay and sand content (R 2 > 0.8) in the ATM images [8]. Two robust adjustment techniques (MVE and MCD multivariate M-estimators of location and scale) provided acceptable algorithms for grain-size mapping with CASI-2 data in Santander Bay, Spain [9]. Furthermore, calibration algorithms provided an estimated R 2 of 0.91 for mud flat facies and 1 for sand flat facies with IKONOS data for the Hwangdo tidal flat, Cheonsu Bay, Korea.…”

“…Intertidal sediment types have been classified by multispectral satellite remote sensing [7], multispectral airborne remote sensing [8], and hyperspectral airborne remote sensing [9,10]. For example, sediment types were classified into different types by supervised classification methods [11].…”

Study of the spatial and temporal distributions of tidal flat surface sediment based on multitemporal remote sensing

“…Insufficient field data may lead to the oversimplification of the particle size distribution and hinder the prediction and modelling of tidal flat erosion and deposition trends (Buscombe et al, 2014). In view of the above problems, airborne and spaceborne remote sensing are used to map the clay content and intertidal grain size distribution (Rainey et al, 2003), the inverse contents of sand, silt and clay (Castillo et al, 2011) and different sediment types (Adam et al, 2008) to facilitate data acquisition. Additionally, various types of hyperspectral, multispectral and microwave satellite data are used to classify sediment types in intertidal zones (Yates et al, 1993), and inverse particle size parameters can be determined.…”

Characterisation of Coastal Sediment Properties from Spectral Reflectance Data

“…In addition, studies also use different sampling strategies and methods of data analysis, which means that results from these individual studies may not be comparable. By contrast, remote sensing methods using a variety of platforms have potential to consistently map and quantify spatial patterns of sediments and landforms across beach-dune systems, and this has been undertaken in several studies e.g., [16][17][18][19][20][21]. There Appl.…”