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

Muzirafuti, Anselme; Barreca, Giovanni; Crupi, Antonio; Faina, Giancarlo; Paltrinieri, Diego; Lanza, Stefania; Randazzo, Giovanni

doi:10.3390/jmse8020126

Cited by 36 publications

(22 citation statements)

References 46 publications

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“…According to Muzirafuti et al (2020), the ability of satellite sensors to collect water information in multispectral bands allows for research on bathymetry. A methodological bathymetric approach using two spectral bands (blue and green) in conjunction with in situ bathymetry, taking into account the quality of water and the propagation of light through the water column has been proposed by Stumpf et al (2003).…”

Section: Methods 231 Bathymetric Estimationmentioning

confidence: 99%

Bathymetric Extraction Using Planetscope Imagery (Case Study: Kemujan Island, Central Java)

Sesama¹,

Setiawan²,

Julzarika³

2021

IJReSES

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Bathymetry refers to the depth of the seabed relative to the lowest water level. Depth information is essential for various studies of marine resource activities, for managing port facilities and facilities, supporting dredging operations, and predicting the flow of sediment from rivers into the sea. Bathymetric mapping using remote sensing offers a more flexible, efficient,and cost-effective method and covers a largearea. This study aims to determine the ability of Planet Scope imagery to estimate and map bathymetry and to as certain its accuracy using the Stumpf algorithm on the in-situ depth data. PlanetScope level 3B satellite imagery and tide-corrected survey dataare employed; satellite images are useful in high-precision bathymetry extraction.The bathymetric extraction method used the Stumpf algorithm. The research location was Kemujan Island, Karimunjawa Islands, Central Java. The selection of this region wasbased on its water characteristics, which have a reasonably high variation in depth. Based on the results of the data processing, it was found that the PlanetScope image data were able to estimate depths of up to 20 m. In the bathymetric results, the R2 accuracy value was 0.6952, the average RMSE value was 2.85 m,and the overall accuracy rate was 71.68%.

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Section: Methods 231 Bathymetric Estimationmentioning

confidence: 99%

Bathymetric Extraction Using Planetscope Imagery (Case Study: Kemujan Island, Central Java)

Sesama¹,

Setiawan²,

Julzarika³

2021

IJReSES

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show abstract

“…The approaches that are frequently used to derive bathymetry, water quality parameters, and benthic habitat diversity are based on empirical techniques or the inversion of radiative transfer models [36,37]. Empirical approaches for bathymetry retrieval such as OBRA [38], MODPA [39], and SMART-SDB [40] provide robust retrievals over optically variable conditions. The band ratio empirical techniques have two major drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

et al. 2021

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The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68.

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“…Syaiful et al (2019) research resulted in an RMSE of 4.00 and a correlation of 0.6976 using the blue/green band ratio. Another research was also conducted by Muzirafuti et al (2020) with the Empirical method. This study uses the Log-Band method on Quick Bird imagery.…”

Section: Introductionmentioning

confidence: 99%

Shallow Water Depth Mapping Using Single Band and Band Ratio on High-Resolution Imagery

Prayogo¹,

Basith²

2021

SNG

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One of the availabilities of remote sensing satellite imagery can be used as a provider of shallow sea depth information using the Satellite-Derived Bathymetry (SDB) technique. This technique's main problem is the variation in the bottom cover of waters such as coral reefs and seagrass, which distorts the spectral values. The use of band ratios can normalize variations in bottom water cover. This study compares the single band algorithm's accuracy with the band ratio depth data obtained by field survey around the port of Karimunjawa Islands, Central Java. The image used in this study is high-resolution imagery, Worldview 3. Preprocessing includes Sunglint correction to reduce the effect of sunglint in the waters and correction of depth data so that the data are free from tides' influence. The bands used are red, green, blue, and Near-Infrared, which results in 10 combinations. This study indicates that the band ratio method produces a smaller RMSE value than the single band. The blue/green ratio makes the best depth values with an RMSE of 1.669 meters at a depth of 0-5 meters. In comparison, single-band use shows that the best estimation result is with an RMSE of 2.373 meters in the green band. This study shows that the band ratio method produces better depth estimates than the single band method.

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

Cited by 36 publications

References 46 publications

Bathymetric Extraction Using Planetscope Imagery (Case Study: Kemujan Island, Central Java)

Bathymetric Extraction Using Planetscope Imagery (Case Study: Kemujan Island, Central Java)

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

Shallow Water Depth Mapping Using Single Band and Band Ratio on High-Resolution Imagery

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