Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Nguyen, Teo; Liquet, Benoît; Mengersen, Kerrie; Sous, Damien

doi:10.3390/rs13214470

Cited by 9 publications

(7 citation statements)

References 248 publications

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“…Apart from DSF, several ACs processor tools have their mechanisms for adjusting for inland water bodies, but a comparative analysis indicated that six such tools showed high levels of uncertainties in the water-leaving reflectance estimation (Warren et al, 2019). Similar uncertainty has been found for atmospheric correctness computed for oceans when mapping coral reefs (Nguyen et al, 2021).…”

Section: Atmospheric Correctionmentioning

confidence: 81%

“…Change detection using MSI is particularly important for applications related to environmental monitoring, for example, surface water and flood detection (Albertini et al, 2022), shoreline indicators (McAllister et al, 2022), coral reefs mapping (Nguyen et al, 2021), and biodiversity monitoring (Kuenzer et al, 2014; Schulte to Bühne & Pettorelli, 2018). The latter pertains to specific regions, such as forests (Kacic & Kuenzer, 2022), grasslands (Jackson et al, 2022).…”

Section: Multimodal Data Miningmentioning

confidence: 99%

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Multispectral data mining: A focus on remote sensing satellite images

Lim,

Sreevalsan‐Nair,

Daya Sagar

2023

WIREs Data Min & Knowl

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This article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state‐of‐the‐art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

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Section: Atmospheric Correctionmentioning

confidence: 81%

Section: Multimodal Data Miningmentioning

confidence: 99%

Multispectral data mining: A focus on remote sensing satellite images

Lim,

Sreevalsan‐Nair,

Daya Sagar

2023

WIREs Data Min & Knowl

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“…The MLC method has been frequently used and regarded as a standard approach in remote sensing image processing [39,40]. This approach assumes that the probability density function follows the normal distribution [41].…”

Section: Classification Maximum Likelihood (Mlc)mentioning

confidence: 99%

Coral Reefs Detecting with Artificial Neural Network Classification and PlanetScope Imagery in Cu Lao Xanh Island, Binh Dinh province

Dung

Anh

Nga³

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Coral reefs are a vital component of coastal and marine ecosystems. They are now under strong environmental challenges and are being harmed by human activities and rising sea surface temperatures, which are reducing the living coral cover. The purpose of this research was to evaluate the mapping accuracy of coral covers using PlanetScope satellite pictures with the Artificial Neural Network (ANN) method surrounding Cu Lao Xanh Island in Binh Dinh province. To adjust for the sunglint effect, the bands were corrected using the Hedley technique. After that, the Depth-Invariant Index technique was utilized to reduce the influence of the water column, and the ANN algorithm was employed for mapping. Hard coral, soft coral, seagrass, deep water, and bare bottom were identified as the five kinds of benthic habitat. The accuracy of the classification results was assessed using field data collected on May 10 and 11, 2022. The results indicated that the artificial neural network (ANN) technique had a higher accuracy, with a total classification accuracy of 89.55% and a kappa value of 0.87. Cu Lao Xanh’s coral area is around 68 hectares, with soft corals mostly found west and southwest of the island and hard corals in the east. This finding demonstrates that Planetscope satellite imagery is effective at monitoring shallow coral reefs on small islands, providing a scientific foundation and reliable data for the development of a more comprehensive coral reef ecological monitoring and management.

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“…In particular, any in situ measurements of coral reefs are challenging for the remote Spratly Islands [11,18], which contain the elements of the present political situation. Currently, in situ measurements for acquiring bathymetric maps in remote coral reefs can be replaced using satellite-derived bathymetry (SDB) [19][20][21][22].…”

Section: Of 19mentioning

confidence: 99%

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Cheng,

Chu

et al. 2023

IJGI

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Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R2) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies.

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Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Cited by 9 publications

References 248 publications

Multispectral data mining: A focus on remote sensing satellite images

Multispectral data mining: A focus on remote sensing satellite images

Coral Reefs Detecting with Artificial Neural Network Classification and PlanetScope Imagery in Cu Lao Xanh Island, Binh Dinh province

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

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