Optimal Band Ratio Analysis of Worldview-3 Imagery for Bathymetry of Shallow Rivers (Case Study: Sarca River, Italy)

Abstract

Commission VIII, WG VIII/4KEY WORDS: Bathymetry, River, Optimal Band Ratio Analysis, WorldView-3, GeoEye, Spectral Bands ABSTRACT:The Optimal Band Ratio Analysis (OBRA) could be considered as an efficient technique for bathymetry from optical imagery due to its robustness on substrate variability. This point receives more attention for very shallow rivers where different substrate types can contribute remarkably into total at-sensor radiance. The OBRA examines the total possible pairs of spectral bands in order to identify the optimal two-band ratio that its log transformation yields a strong linear relation with field measured water depths. This paper aims at investigating the effectiveness of additional spectral bands of newly launched WorldView-3 (WV-3) imagery in the visible and NIR spectrum through OBRA for retrieving water depths in shallow rivers. In this regard, the OBRA is performed on a WV-3 image as well as a GeoEye image of a small Alpine river in Italy. In-situ depths are gathered in two river reaches using a precise GPS device. In each testing scenario, 50% of the field data is used for calibration of the model and the remained as independent check points for accuracy assessment. In general, the effect of changes in water depth is highly pronounced in longer wavelengths (i.e. NIR) due to high and rapid absorption of light in this spectrum as long as it is not saturated. As the studied river is shallow, NIR portion of the spectrum has not been reduced so much not to reach the riverbed; making use of the observed radiance over this spectral range as denominator has shown a strong correlation through OBRA. More specifically, tightly focused channels of rededge, NIR-1 and NIR-2 provide a wealth of choices for OBRA rather than a single NIR band of conventional 4-band images (e.g. GeoEye). This advantage of WV-3 images is outstanding as well for choosing the optimal numerator of the ratio model. Coastal-blue and yellow bands of WV-3 are identified as proper numerators while only green band of the GeoEye image contributed to a reliable correlation of image derived values and field measured depths. According to the results, the additional and narrow spectral bands of WV-3 image lead to an average determination coefficient of 67% in two river segments, which is 10% higher than that of obtained from the 4-band GeoEye image. In addition, RMSEs of depth estimations are calculated as 4 cm and 6 cm respectively for WV-3 and GeoEye images, considering the optimal band ratio.

The Contribution of Multispectral Satellite Image to Shallow Water Bathymetry Mapping on the Coast of Misano Adriatico, Italy

“…Although the log-band ratio method uses a single band pair, the OBRA method explores all possible pair bands and chooses those that exhibit a strong correlation between in situ water depths and their relative SDB values. This was performed by estimating R 2 between the band ratios and in-situ bathymetry (Figures 10-12), whereby the band ratios less sensitive to the effect of substrate variability [45] presented a highly linear relationship with the in situ water depth information (Figure 12). Relative SDB values were obtained from the logarithmic expression of the blue and green bands (ln(blue)/ln(green)), whereas in situ water depth values were obtained from in situ bathymetry acquired during November 2007.…”

“…This method reduces the impact of atmospheric conditions, the water column, and the seafloor on the propagation of light via the use of electromagnetic bands affected simultaneously by these factors [44]. Furthermore, the OBRA method is a robust and optimized band ratio method for SDB estimations that explores all available pair bands to identify the pair that is less sensitive to substrate variability and exhibits the closest linear relationship with available in situ water depth information [45].…”

Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

“…The band ratio method can eliminate the interference of water surface roughness and background noise, and is a commonly used contrast enhancement operation in quantitative remote sensing inversion [37]. In this study, the exhaustive method was used to calculate the ratio between any two bands, and then the Pearson correlation coefficient was obtained with the Y_Nemerow_index [38][39][40]. The correlation is clearly improved, as shown in Figure 5.…”

Reconstruction of River Boundaries at Sub-Pixel Resolution: Estimation and Spatial Allocation of Water Fractions

“…The extraction of river areas is the primary task required for a wide range of remote sensing applications in fluvial systems spanning from hydrological, ecological, and morphological studies to mapping habitat suitability for different aquatic species [1][2][3][4][5][6]. Thresholding on spectral bands, such as the near-infrared (NIR) band, or on water indices, such as the normalized difference water index (NDWI), as well as image classification (supervised or unsupervised), are the main techniques for delineation of water bodies using optical imagery [7,8].…”