Determine the Stumpf 2003 Model Parameters for Multispectral Remote Sensing Shallow Water Bathymetry

Zhu, Jinshan; Hu, Peng; Zhao, Lulu; Gao, Lei; Qi, J.; Ya, Zhang; Wang, Ruifu

doi:10.2112/si102-007.1

Cited by 8 publications

(9 citation statements)

References 9 publications

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“…When the initial value is unsuitable, the model parameters cannot be obtained. Therefore, according to the literature [4], [6], [10]- [14], the study of the ranges of initial values and how to give the ranges is very important for ensuring that the algorithm can return reliable parameters.…”

Section: B the Results Of The Initial Range For The Initial Valuementioning

confidence: 99%

“…Multispectral optical remote sensing imagery provides another way to quantitatively invert shallow water bathymetry [2], [3]. Compared with conventional acoustic and laser sounding, optical remote sensing has the advantages of a large coverage area, high efficiency and low cost and is an effective supplement to traditional sounding methods in areas that have complex underwater topography or are affected by maritime disputes [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

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Determination of the Initial Value Ranges of Nonlinear Solutions for a Log Ratio Bathymetric Inversion Model and Bathymetry Retrieval

Qi¹,

Zhang²,

Ren³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The log-ratio model (LRM) with three model parameters (𝒏, 𝒎 𝟏 and 𝒎 𝟎 ) has been widely used in shallow water bathymetry of multispectral images. At present, obtaining LRM model parameters by optimization algorithms depends on the setting of initial values ( 𝒏(𝟎) , 𝒎 𝟏 (𝟎) and 𝒎 𝟎 (𝟎)) or initial value ranges. However, how to set the initial value can obtain model parameters more effectively and credibly, and the significance of each initial value in the optimization process is very important for LRM bathymetric retrieval method. In this study, remote sensing images and bathymetric data were used to determine the initial value ranges of credible model parameters, and then the sensitivity of each parameter was analysed in the algorithm optimization process from the partial differential perspective, and the bathymetry was retrieved in the sea area near Ganquan Island. The results show that, according to the partial differential analysis of model parameters, 𝒏 with the smallest change rate in three LRM parameters and the rate of 𝒎 𝟏 and 𝒎 𝟎 is at least 5 times of it. So 𝒏(𝟎) needs more iterations to achieve the optimal. Therefore, the initial value 𝒏(𝟎) determines whether the optimization method can obtain credible LRM model parameters and the algorithm convergence speed. And when 𝒏(𝟎) ∈ (𝟏/𝑹 𝒎𝒊𝒏 , 𝟏𝟎𝟎𝟎], (𝑹 𝒎𝒊𝒏 is the minimum reflectance in blue and green bands.) there must be a credible model parameter solution. The bathymetric retrieval results of the sea area with coral and sand substrates show the applicability of our initial value ranges in different substrate area.

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Section: B the Results Of The Initial Range For The Initial Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of the Initial Value Ranges of Nonlinear Solutions for a Log Ratio Bathymetric Inversion Model and Bathymetry Retrieval

Qi¹,

Zhang²,

Ren³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Water depth was manually measured by using simple tools in the past years; in the following years echosounding technique have been widely used in bathymetry surveys. In addition to the use of traditional methods for many years, RS technologies (Stumpf et al 2003a, b;Gao 2009;Jawak et al 2015;Zhu et al 2020) such as LIDAR (Quadros et al 2008;Mandlburger et al 2015;Pratomo et al 2019) and satellite-derived technology (Akgül et al 2018;Dewi et al 2019;Sagawa et al 2019) for bathymetry mapping have been used for not only depth estimation but also for getting environmental information with its analysis tools. Sediment-related problems have been mentioned in numerous studies (Zhou and Wu 2008;Wang and Hu 2009;Jain and Jain 2011).…”

Section: Introductionmentioning

confidence: 99%

Investigation of sediment accumulation in Berdan Dam Reservoir using bathymetric measurements and Sentinel-2 Data

Güvel¹,

Akgül²,

Yurtal

2021

Arab J Geosci

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Water depth data of a reservoir is used in calculation of water volume storage and sedimentation volume in reservoirs. The goal of this study is to examine the recent developments in sediment accumulation in Berdan Dam Reservoir as of the year 2019, to examine the risks caused by sedimentation in terms of sustainability within the framework of dam safety principles and to assess the possibilities of using remote sensing data to determine bathymetric water depths. In this study, the water depth of Berdan Dam Reservoir is investigated to determine bathymetric elevations of the reservoir by using the Log Ratio Transformation (LRT) method which is developed by the US National Oceanic and Atmospheric Admisinistration (NOAA). 25 control points were used for calibration of water depth. The depths of these points were selected from the July 2019 bathymetric map of Berdan Dam Reservoir with a depth difference of 1 m between the points. As remote sensing data, archive scanning was performed by considering the date of the bathymetric map and cloudlessness, the image of Sentinel-2 dated 28 July 2019 was selected and analyzed. As a result, high correlation was found between the predicted water depths and surveyed water depths by using logaritmic nonlinear regression analysis for chosen 25 control points. The regression value between the average bathymetric elevations was found to be 0.944 and the root mean square error (RMSE) was calculated as 1.70 m.

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“…This technique provides low-cost, high-efficiency bathymetric surveys in shallow waters, meeting the requirements for short cycles and timeliness. [1][2][3][5][6][7][8][9] Water depth is estimated by SDB technology based on correlations between the remotely sensed reflectance values of satellite imagery observed with optical multispectral sensors and the water depth during image acquisition. While SDB can be generally applied to depths up to 20 m, it may only be applicable to depths of up to 10 m, depending on the characteristics of the marine region.…”

mentioning

confidence: 99%

“…Meanwhile, the Stumpf logarithmic band ratio model estimates water depth using the reflectance ratio of the seabed to the water based on various experimental parameters. 3,8,9,[12][13][14] However, these models adopt empirical methodologies; consequently, the input values for depth estimation are variable between marine regions, impeding the construction of a universally applicable model. 3 Accordingly, recent research has actively focused on developing depth estimation models using various machine learning algorithms and independent satellite imagery to ensure universality.…”

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confidence: 99%

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

Kwon,

Shin,

Kim

et al. 2024

J. Appl. Rem. Sens.

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Bathymetry, the measurement of sea depth, has conventionally been conducted using echo-sounders on vessels. However, various factors limit conventional shipborne surveys in coastal regions, including data continuity, geographic obstacles, diplomatic concerns, and marine infrastructures. Remote sensing technology can address these limitations, particularly with the advancement of satellite imaging technology. Indeed, many studies are underway to develop machine learning-based water depth estimation technologies. However, previous studies have focused on clear waters with low turbidity or uniform seabed sediment. Therefore, in this study, we developed a satellite-derived bathymetry (SDB) model using the random forest machine learning algorithm, which was applied to three coastal areas around the Korean Peninsula with distinct characteristics: clear waters (Samcheok), high turbidity (Cheonsuman), and varied seabed sediments (Hallim). We then compared the accuracy of the bathymetric mapping data derived in these three areas. The estimated depth values exhibited the highest accuracy in Samcheok, followed by Hallim and Cheonsuman. Based on Worldview-3 images and on-site surveys, we confirmed the presence of basalt on the seabed. However, the remote reflectance was attenuated due to the effect of the black rock, leading to an overestimation of the depth. In the future, additional satellite images will be applied as training data for the machine learning model to advance the SDB technology using turbidity and seabed sediment distribution data for each area. Ultimately, the SDB results will be applied as depth monitoring data to facilitate safe ship passage in coastal areas, including ports that require periodic and consistent coastal bathymetry. In addition, they can be applied as input data for numerical ocean models, contributing to various fields.

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Determine the Stumpf 2003 Model Parameters for Multispectral Remote Sensing Shallow Water Bathymetry

Cited by 8 publications

References 9 publications

Determination of the Initial Value Ranges of Nonlinear Solutions for a Log Ratio Bathymetric Inversion Model and Bathymetry Retrieval

Determination of the Initial Value Ranges of Nonlinear Solutions for a Log Ratio Bathymetric Inversion Model and Bathymetry Retrieval

Investigation of sediment accumulation in Berdan Dam Reservoir using bathymetric measurements and Sentinel-2 Data

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

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