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

Sesama, Asih Sekar; Setiawan, Kuncoro Teguh; Julzarika, Atriyon

doi:10.30536/j.ijreses.2020.v17.a3445

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Rows in the confusion matrix Table 1 show the pixel class of in-situ values, and the column section shows the results of the Sentinel-2 satellite image pixel class estimation. The diagonal part of the matrix provides accurate classification of pixels [14]. The spatial accuracy obtained was 75.21%, which can be said to be in a good category because the overall accuracy was still above 60% [10].…”

Section: Resultsmentioning

confidence: 99%

Bathymetry mapping with empirical bathymetry method based on sentinel application platform case study: Bawean Island, Central Java

Ananda Putra Suardana,

Thayyib,

Setiawan

et al. 2024

BIO Web Conf.

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Bathymetry information is an important requirement for the marine and fisheries sector in Indonesia. However, the acquisition of information is done in the traditional way, namely surveys using single/multibeam echosounder devices carried by ships. This method was inefficient, especially to cover a wider area. The development of remote sensing technology can be a solution and has become a source of data for satellite-derived bathymetry (SDB). One of the areas of concern in this research is the eastern waters of Bawean Island located in the Java Sea. This research uses the SNAP method to determine the results and accuracy of bathymetry mapping in shallow sea waters east of Bawean Island. This method is generated from regression modeling between spectral values of satellite image data and in-situ depth data. The satellite data used is the Sentinel-2A image recorded on December 9, 2022. The results showed that the Sentinel-2A satellite can produce depth information up to 27.9 meters. The coefficient of determination, R2, is 86%, and the confusion matrix accuracy value is 75.21%.

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Section: Resultsmentioning

confidence: 99%

Bathymetry mapping with empirical bathymetry method based on sentinel application platform case study: Bawean Island, Central Java

Ananda Putra Suardana,

Thayyib,

Setiawan

et al. 2024

BIO Web Conf.

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“…Hasil pemodelan kedalaman absolut dibagi menjadi empat kelas mengacu pada skema klasifikasi kedalaman perairan oleh (Sesama et al, 2020). Klasifikasi kedalaman perairan juga dihitung akurasinya menggunakan confusion matrix yang membandingkan ketepatan antara kelas kedalaman hasil pemodelan dengan kelas kedalaman hasil in-situ.…”

Section: Y = a + Bxunclassified

Deteksi Batimetri Perairan Dangkal di Pulau Menjangan, Provinsi Bali Menggunakan Citra Landsat

Bayuaji

Rijal²,

Setiawan³

et al. 2022

Jambura Geosci. Rev.

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Remote sensing-based research in Indonesia using satellite imagery frequently faces the challenge of cloud coverage due to the tropical country. One spatial data that can be extracted from satellite imagery is bathymetry. However, cloud-covered water bathymetric extraction still needs to be examined. This study aims to understand the ability of Landsat 7 ETM+ acquired on 29 July 2013, and Landsat 8, acquired on 24 July 2020, as the representative of non-cloudy image compared to Landsat 8, acquired on 9 August 2020, as the cloudy image. Stumpf algorithm was applied, including a statistical approach of linear regression analysis with in-situ data measurement from Single Beam Echo-Sounder (SBES) to derive the absolute bathymetric map with several classes of depth ranging from 0 – 2 m up to 10 m. To assess the accuracy, RMSE and confusion matrix was used. The result shows that Landsat 7 ETM+ yields the highest R2 with 0,52, while the lowest total RMSE (8,167 m) and highest overall accuracy of about 69% from the confusion matrix was achieved by the cloudy image of Landsat 8. Nevertheless, the highest absolute depth value yield by Landsat 8 non-cloudy image with 16,1 m. This research confirms that the highest R2 value does not always produce the best model, but it is still promised to be used. Furthermore, the quality of the imagery based on its percentage of cloud coverage is affecting the resulted model.

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“…Several recent investigations have shown good results in the accuracy of water depth estimation using machine learning (ML) approaches [23] with RMSE below 1 meter [8] and [24]. Previous research using an empirical approach on PlanetScope imagery still shows fluctuating accuracy with a range of below 1 meter [25], and between 2 -3 meters [26]. On the Sentinel-2A image side, using an empirical approach, the accuracy is shown to be around 1 meter [27].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Empirical Near-Shore Bathymetry Model Using New Emerged PlanetScope Instrument and Sentinel-2 Data in Coastal Shallow Waters

2024

IJG

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Shallow water bathymetric information is important for human life because it has a strong influence on phenomena and dynamics in coastal areas. Conventional bathymetric mapping methods are capable of obtaining high-precision accuracy, but require expensive and complex resources. Specifically, in shallow waters, survey instruments have difficulty obtaining adequate depth data due to the many obstacles that must be overcome. Remote sensing-based shallow water bathymetry called as Satellite-Derived Bathymetry (SDB) is a reasonable and efficient choice. Technological developments enable SDB data processing to be much more efficient in terms of time and storage by utilizing cloud-based platforms such as Google Earth Engine (GEE). Spatial and temporal resolution is still a challenge in SDB, so in this condition PlanetScope with daily temporal resolution capabilities is an optimistic choice. However, this image falls into a relatively new image category. In this study we tested the performance of Sentinel-2A imagery and new PlanetScope imagery bands. The existence of the new sensor owned by PlanetScope allows an increased choice of SDB data sources with high spatial and temporal resolution that is better than the general datasets currently available. There are four additional channels are Coastal Blue, Green I, Yellow, and Red Edge. Assessment is needed to test the capabilities of each new channel using empirical methods via the Stumpf algorithm. Based on accuracy assessment observations, the Sentinel-2A channel combination is still better in terms of accuracy and determination, because it is able to represent the depth in the study area up to 70%. The potential use of PlanetScope's new channels for SDB applications can still be seen in the combination of the Coastal Blue and Yellow channels. This channel combination is still able to represent 47% of the depth variations in the study area.

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

Cited by 4 publications

References 7 publications

Bathymetry mapping with empirical bathymetry method based on sentinel application platform case study: Bawean Island, Central Java

Bathymetry mapping with empirical bathymetry method based on sentinel application platform case study: Bawean Island, Central Java

Deteksi Batimetri Perairan Dangkal di Pulau Menjangan, Provinsi Bali Menggunakan Citra Landsat

Assessment of Empirical Near-Shore Bathymetry Model Using New Emerged PlanetScope Instrument and Sentinel-2 Data in Coastal Shallow Waters

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