Monitoring benthic habitats using Lyzenga model features from Landsat multi-temporal images in Google Earth Engine

Ahmed, Ahmed Fuad; Mutua, Felix Nzive; Kenduiywo, Benson Kipkemboi

doi:10.1007/s40808-020-00960-1

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…In this case, Depth Invariant Indices (DII) using the Lyzenga model and Principal Components Analysis (PCA) have been suggested to extract new data from spectral bands in satellite images, which can then be added for image classification algorithms increasing the separability of benthic features. 22,23,24 While DII is a commonly used water column correction derivative, it has been found to be inefficient in terms of increasing separability of features. DII may be influenced by differences in benthic features, requiring the creation of specific DII for different features, resulting in costly data processing.…”

Section: Related Studiesmentioning

confidence: 99%

Comparative analysis of random forest and support vector machine for benthic habitat segmentation

Narciso,

Tamondong,

Blanco

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Escalating climate impacts prompt governments to act as seen in the fifth Conference of the Parties (COP), demanding eco-friendly practices to limit warming to 1.5°C. Carbon accounting is vital for global sustainability, requiring robust national monitoring of stocks and emissions. Remote sensing technology and satellite data enable modeling terrestrial carbon reserves, though challenges remain for coastal areas due to water attenuation. Ongoing studies aim to prove the technology's viability, despite accuracy issues in capturing shallow coastal environments. With this being gap, this study developed a methodology to map a coastal environment using satellite data and machine learning. Sentinel-2 MSI, an open-source multispectral image, was utilized in this study. Geospatial derivatives such as ratios of the visible bands, bathymetry model using the Stumpf's ratio and principal components which contained at least 90% of uncorrelated data were also integrated in the modeling process to improve benthic feature separability. Different combinations of the datasets were also explored in this study. Benthic habitat models were produced using Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms for each variable combination. The generated models generated overall accuracies ranging from 0.69 to 0.74 and 0.22 to 0.68 respectively. This translated to a maximum percent difference of 77% for the case of RGB model only and a minimum of 8% using all the variables. In terms of using different variable combinations, RF exhibited robust performance showing relatively consistent results compared to SVM which produced a wide range of accuracy values across the different models.

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Section: Related Studiesmentioning

confidence: 99%

Comparative analysis of random forest and support vector machine for benthic habitat segmentation

Narciso,

Tamondong,

Blanco

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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show abstract

“…For shallow water benthic mapping, a RF classifier can still outperform a SVM classifier in benthic habitat mapping [185], or at least have an identical overall accuracy but with a better spatial distribution. Globally, RF classifiers can map benthic habitat with an overall accuracy ranging from 60% to 85% [85,[201][202][203][204][205], depending on the study site, the satellite imagery involved, and the preprocessing steps applied to the images.…”

Section: Random Forestmentioning

confidence: 99%

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

et al. 2021

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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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Modeling of hydro-oceanographic parameters and its possible impact on coral reef cover in Derawan Island waters, East Kalimantan, Indonesia

Adalya

Mutaqin

2022

Model. Earth Syst. Environ.

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Monitoring benthic habitats using Lyzenga model features from Landsat multi-temporal images in Google Earth Engine

Cited by 10 publications

References 12 publications

Comparative analysis of random forest and support vector machine for benthic habitat segmentation

Comparative analysis of random forest and support vector machine for benthic habitat segmentation

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

Modeling of hydro-oceanographic parameters and its possible impact on coral reef cover in Derawan Island waters, East Kalimantan, Indonesia

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