High-resolution benthic habitat mapping from machine learning on PlanetScope imagery and ICESat-2 data

An, Nguyen Van; Quang, Nguyen Hao; Son, Tong Phuoc Hoang; An, Tran Thi

doi:10.1080/10106049.2023.2184875

Cited by 8 publications

(3 citation statements)

References 71 publications

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“…Multiple studies confirmed the ability to retrieve bathymetry to ∼1 Secchi depth, or >40 m in very clear waters, with typical accuracies on the order of 0.5 m (Albright & Glennie, 2021; Chen et al., 2021; Le Quilleuc et al., 2022; Parrish et al., 2019; Ranndal et al., 2021; Watkins et al., 2023; Zhang et al., 2022). These bathymetric capabilities were soon shown to be of value for a wide range of science uses, from the study of coral reefs and other sensitive marine habitats to assessment of seafloor morphological change (Herrmann et al., 2022; Le Quilleuc et al., 2022; Selamat et al., 2021; Van An et al., 2023). One growing application is the fusion of ICESat‐2 bathymetric data with optical satellite imagery to create satellite derived bathymetry (SDB) maps in shallow coastal environments (Babbel et al., 2021; Cao et al., 2021; Ma et al., 2020; Thomas et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Analysis of ICESat‐2 Data Acquisition Algorithm Parameter Enhancements to Improve Worldwide Bathymetric Coverage

Dietrich,

Rackley Reese,

Gibbons

et al. 2024

Earth and Space Science

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A major advance in global bathymetric observation occurred in 2018 with the launch of NASA’s ICESat‐2 satellite, carrying a green‐wavelength, photon‐counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre‐ and post‐launch studies revealed ATLAS’s notable bathymetric mapping capability. ICESat‐2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat‐2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat‐2’s receiver algorithm parameters with the goal of increasing the area for which ICESat‐2 can provide bathymetry. This paper details the parameter changes and presents the results of a two‐phased study designed to investigate ICESat‐2’s bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat‐2. The site‐specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat‐2 bathymetry and offshore sonar data.

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Section: Introductionmentioning

confidence: 99%

Analysis of ICESat‐2 Data Acquisition Algorithm Parameter Enhancements to Improve Worldwide Bathymetric Coverage

Dietrich,

Rackley Reese,

Gibbons

et al. 2024

Earth and Space Science

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“…More importantly, remote sensing-based mapping requires only a limited number of field data points to train and validate the retrieval models, which confers great advantages, such as long-term and wide geographical observation, very low cost, reliability, and flexibility in retrieval computation. Hence, the use of remotely based approaches is becoming more popular for bathymetry mapping with a special focus on air-borne (e.g., UAV image [10], air-borne LiDAR [11]) and space-borne datasets, such as LiDAR data (e.g., IceSat-2 [12] and satellite images (e.g., Landsat, Sentinel, WorldView [13][14][15][16], Pléiades [17], SPOT [18], and Planet [19]). Of the satellite sensors in operation, Landsat is a common remotely sensed dataset used for bathymetry mapping with different levels of success and certainty.…”

Section: Introductionmentioning

confidence: 99%

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Tran,

Nguyen,

Pham

et al. 2024

Geosciences

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Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

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“…The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is the first Earth observation satellite equipped with a photon-counting laser altimeter system, the Advanced Topographic Laser Altimeter System (ATLAS), which adopts a sampling strategy with unprecedented spatial details [2]. It is highly sensitive, capable of micro-pulse and high-repetition-rate detection, and can continuously detect the surface profile in detail, rendering it highly applicable in polar ice sheet monitoring [3][4][5][6], forest biomass estimation [7][8][9][10], lake level monitoring [11][12][13][14], and ocean observation [15][16][17][18]. However, the low energy of the laser pulses emitted by the system renders it susceptible to factors such as surface reflectance, atmospheric scattering, solar radiation, and detector dark counts, resulting in noisy data [19], which, to a certain extent, limits the scientific application of ICESat-2.…”

Section: Introductionmentioning

confidence: 99%

Signal Photon Extraction Method for ICESat-2 Data Using Slope and Elevation Information Provided by Stereo Images

Gu,

Fan,

et al. 2023

Sensors

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Laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) contain a lot of noise, which necessitates the requirement for a signal photon extraction method. In this study, we propose a density clustering method, which combines slope and elevation information from optical stereo images and adaptively adjusts the neighborhood search direction in the along-track direction. The local classification density threshold was calculated adaptively according to the uneven spatial distribution of noise and signal density, and reliable surface signal points were extracted. The performance of the algorithm was validated for strong and weak beam laser altimetry data using optical stereo images with different resolutions and positioning accuracies. The results were compared qualitatively and quantitatively with those obtained using the ATL08 algorithm. The signal extraction quality was better than that of the ATL08 algorithm for steep slope and low signal-to-noise ratio (SNR) regions. The proposed method can better balance the relationship between recall and precision, and its F1-score was higher than that of the ATL08 algorithm. The method can accurately extract continuous and reliable surface signals for both strong and weak beams among different terrains and land cover types.

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High-resolution benthic habitat mapping from machine learning on PlanetScope imagery and ICESat-2 data

Cited by 8 publications

References 71 publications

Analysis of ICESat‐2 Data Acquisition Algorithm Parameter Enhancements to Improve Worldwide Bathymetric Coverage

Analysis of ICESat‐2 Data Acquisition Algorithm Parameter Enhancements to Improve Worldwide Bathymetric Coverage

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Signal Photon Extraction Method for ICESat-2 Data Using Slope and Elevation Information Provided by Stereo Images

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