Satellite-derived bathymetry to improve bathymetric map of Indonesia

Dewi, Ratna Sari; Hartanto, Prayudha; Oktaviani, Nadya; Pujawati, Intan; Nursugi, Nursugi; Aditya, Sandi

doi:10.1117/12.2540779

Cited by 2 publications

(2 citation statements)

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“…Similar approaches for Indonesian water have also been conducted, but with other regression methods (Dewi et al, 2019;Manessa et al, 2016;Wicaksono, 2015). However, we argue that study on another approach is still necessary to find the optimum algorithm and model with a good data fit between in-situ data and the related SDB model.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

Geographically Weighted Regression Approach for Shallow Water Depth Estimation Using Multispectral Satellite Imageries

Lumban-Gaol

Dewi

Oktaviani

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Shallow water depth is essential for coastal planning, monitoring, and research. Bathymetry data is mostly produced from hydrographic survey using echosounder. The generic result from those measurements is discrete values while the desired output is a continuous depth model. To fill the gaps in the sounding data, we use Satellite Derived Bathymetry (SDB) approach with Geographically Weighted Regression (GWR). This study aims to investigate the feasibility of GWR to model bathymetry of shallow water in the eastern part of Indonesia. We explore the correlation between the number of training data and the predicted result. Two different satellites images are used, namely: Sentinel-2A and Landsat 8 OLI/TIRS with 10 and 30 m resolutions respectively. For the experiment, in-situ data are set into training and validation in three different ratios. The model is developed using adaptive GWR approach in which the parameter of regression would adapt the local data set within different kernel sizes. Finally, we compute RMSE (Root Mean Square Error), R2, and TVU (Total Vertical Uncertainty) to assess the quality of our model. In general, Sentinel-2A produces more detailed information due to higher resolution than Landsat 8 OLI/TIRS. Sentinel-2A also obtains more accurate results based on RMSE values. The percentage number of the estimated depth that fulfils TVU requirements is up to 83%. These assessment quality results give us an insight that the SDB approach using GWR is promising. Thus, the GWR method may be able to provide an estimate of bathymetry for many coastal areas in Indonesia.

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Section: Related Work and Motivationsmentioning

confidence: 99%

Geographically Weighted Regression Approach for Shallow Water Depth Estimation Using Multispectral Satellite Imageries

Lumban-Gaol

Dewi

Oktaviani

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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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Satellite-derived bathymetry to improve bathymetric map of Indonesia

Cited by 2 publications

References 0 publications

Geographically Weighted Regression Approach for Shallow Water Depth Estimation Using Multispectral Satellite Imageries

Geographically Weighted Regression Approach for Shallow Water Depth Estimation Using Multispectral Satellite Imageries

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

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