Bathymetry Extraction From Spot 7 Satellite Imagery Using Random Forest Methods

Setiawan, Kuncoro Teguh; Suwargana, Nana; Ginting, Devica Natalia Br; Manessa, Masita Dwi Mandini; Anggraini, Nanin; Adawiah, Syifa Wismayati; Julzarika, Atriyon; Surahman, Surahman; Rosid, Syamsu; Supardjo, A. Harsono

doi:10.30536/j.ijreses.2019.v16.a3085

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…One of the most important spatial data is near-shore bathymetry data. Bathymetry is important information about water depth and underwater topography [2]. According to Manessa et al, bathymetry data is important for ship traffic, conservation, coastal zoning, and other environmental issues [3].…”

Section: Introductionmentioning

confidence: 99%

The Extraction of Near-Shore Bathymetry using Sentinel-2A Satellite Imagery: Algorithms and Their Modifications

Sukmono¹,

Aji²,

Amarrohman³

et al. 2022

TEM Journal

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Satellite-Derived Bathymetry (SDB) is one of the solution technologies for medium-scale bathymetry mapping in large areas. Various basic algorithms for bathymetry extraction with SDB have been developed. However, they require study and modification for different satellite imageries and different regional characteristics. In this study, the researchers explore three basic SDB algorithms which are often used, namely the Lyzenga algorithm, the Stumpf algorithm, and the Van Hengel & Spitzer (VHS) algorithm. These three algorithms are modified using the multilinear regression method with the ‘average if’ function to find out the in-situ depth using Sentinel-2A satellite imagery. These three algorithms can estimate the depth of shallow water bathymetry effectively up to a depth of 20 m. The accuracy test on the extraction results of the modification of the three basic algorithms proves to be able to increase the accuracy of the SDB depth estimation in the depth range of 0 – 20 m to an accuracy of 1.888 m for the Lyzenga algorithm, 2.093 m for the Stumpf algorithm, and 2.868 m for the VHS algorithm.

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Section: Introductionmentioning

confidence: 99%

The Extraction of Near-Shore Bathymetry using Sentinel-2A Satellite Imagery: Algorithms and Their Modifications

Sukmono¹,

Aji²,

Amarrohman³

et al. 2022

TEM Journal

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show abstract

“…This algorithm has a number of advantages, including, low-cost, simplicity, time-effectiveness, and its wide-coverage for shallow water. Thus, the random forest algorithm has been found to be applicable to construct regression models that rely on bathymetry data from satellite images [33][34][35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain)

Pérez

Bobadilla

Fernández

et al. 2021

JMSE

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One of the fundamental tasks in the maintenance of port operations is periodic dredging. These dredging operations facilitate the elimination of sediments that the coastal dynamics introduce. Dredging operations are increasingly restrictive and costly due to environmental requirements. Understanding the condition of the seabed before and after dredging is essential. In addition, determining how the seabed has behaved in recent years is important to consider when planning future dredging operations. In order to analyze the behavior of sediment transport and the changes to the seabed due to sedimentation, studies of littoral dynamics are conducted to model the deposition of sediments. Another methodology that could be used to analyze the real behavior of sediments would be to study and compare port bathymetries collected periodically. The problem with this methodology is that it requires numerous bathymetric surveys to produce a sufficiently significant analysis. This study provides an effective solution for obtaining a dense time series of bathymetry mapping using satellite data, and enables the past behavior of the seabed to be examined. The methodology proposed in this work uses Sentinel-2A (10 m resolution) satellite images to obtain historical bathymetric series by the development of a random forest algorithm. From these historical bathymetric series, it is possible to determine how the seabed has behaved and how the entry of sediments into the study area occurs. This methodology is applied in the Port of Luarca (Principality of Asturias), obtaining satellite images and extracting successive bathymetry mapping utilizing the random forest algorithm. This work reveals how once the dock was dredged, the sediments were redeposited and the seabed recovered its level prior to dredging in less than 2 months.

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Application of machine learning algorithms and Sentinel-2 satellite for improved bathymetry retrieval in Lake Victoria, Tanzania

Mabula

Kisanga

Pamba

2023

The Egyptian Journal of Remote Sensing and Space Sciences

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Bathymetry Extraction From Spot 7 Satellite Imagery Using Random Forest Methods

Cited by 4 publications

References 11 publications

The Extraction of Near-Shore Bathymetry using Sentinel-2A Satellite Imagery: Algorithms and Their Modifications

The Extraction of Near-Shore Bathymetry using Sentinel-2A Satellite Imagery: Algorithms and Their Modifications

Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain)

Application of machine learning algorithms and Sentinel-2 satellite for improved bathymetry retrieval in Lake Victoria, Tanzania

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