A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic

Liu, Jun; Xie, Huan; Guo, Yalei; Tong, Xiaohua; Li, Peinan

doi:10.3390/rs14051130

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…TP R TP+FN = (10) 2 P R F P+R  = (11) where TP indicates that the true signal photons are correctly detected, FP indicates that the noise photons are incorrectly classified as signal photons, and FN indicates that the true signal photons are incorrectly detected. The overall accuracy (OA) of the indicators was used to evaluate the noise removal ability of the algorithm.…”

Section: E Performance Evaluation Methodsmentioning

confidence: 99%

A Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

Chang,

Jiang,

Lin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with an advanced topographic laser altimeter system (ATLAS) that obtains small spots and highdensity photon data. ATLAS has the potential for high-precision detection of global surfaces and helps calculate global carbon sinks. One important carbon sink includes forests and making it critical to precisely measure their carbon storage and obtain both ground and top-of-canopy (TOC) levels of vegetation. Here, we explored a precise and highly reliable ground and TOC detection method based on ICESat-2 data to accurately remove noise and classify photons. This method can be adapted to different scenes and topographies. The first step included removal of pronounced dispersion noise and pseudo-signal photon aggregation noise. We introduced two algorithms for coarse noise removal-an adaptive threshold ellipsoid filter algorithm and an outlier photon cluster removal algorithm based on the photon group distance. Then, the local outlier factor (LOF) algorithm was modified to remove the noise of near-signal photons to obtain the result of fine denoising. Finally, the modified local direction center algorithm was combined with the uniaxial inverse distance weighted statistics to distinguish the ground from the TOC. Across varied scenes and terrains, the proposed denoising method demonstrated an overall accuracy exceeding 0.94. Additionally, the root mean square error of the ground and TOC obtained via the classification method was under 1.4 m and 3.2 m, respectively. These findings highlight that this method has a high level of robustness in effectively detecting both the ground and TOC.

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Section: E Performance Evaluation Methodsmentioning

confidence: 99%

A Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

Chang,

Jiang,

Lin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, with the warming of the climate, the range of human activities in the Arctic region has been continuously expanding [1][2][3][4]. Therefore, there is increasing research on the Arctic ice region, such as the environmental characteristics of the Arctic Ocean [3], the thickness of the ice [5][6][7][8][9][10], sound propagation under the ice [11][12][13][14], the localization of underwater sound sources [15], and so on.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Variation in Bearing Estimation Error with Frequency Bands for a Source Beneath the Arctic Ice Using a Geophone

Chen,

Wen,

Yang

et al. 2023

Applied Sciences

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To obtain better frequency bands for source bearing estimation, experiments using an ice-mounted geophone were performed at three sites on the Arctic ice cap. By transforming to the frequency domain, errors of the bearing estimation in a number of frequency bands within 1000 Hz and the horizontal peak displacements received by the geophone in the corresponding frequency bands were obtained. After comparing the variation in errors to the peak displacements in the same frequency band, it was found that the errors are related to the peak displacements, and higher peak displacement values mean smaller errors. When the incident angle of the acoustic source and the ice thickness meet certain conditions, some modes of the Lamb waves of the ice plate are excited, the peak displacements in the same frequency band are increased, the errors of bearing estimation are reduced, and the effective frequency band for bearing estimation is broadened. These results provide some new ideas for the bearing estimation of acoustic sources beneath the Arctic ice.

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“…In addition, it has the advantage of long-term observation of the Earth. It can monitor changes in global natural properties such as ice cover [6,7], ocean depth [8,9], land topography, and vegetation [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Level Auto-Adaptive Noise-Filtering Algorithm for Land ICESat-2 Photon-Counting Data

Liu,

Xie

et al. 2023

Remote Sensing

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Due to atmospheric scattering, solar radiation, and other factors, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) product data suffer from a substantial amount of background noise. This poses a significant challenge when attempting to directly utilize the raw data. Consequently, data denoising becomes an indispensable preprocessing step for its subsequent applications, such as the extraction of forest structure parameters and ground elevation data. While the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is currently the most widely used method, it remains susceptible to complexities arising from terrain, low signal-to-noise ratio (SNR), and input parameter variations. This paper proposes an efficient Multi-Level Auto-Adaptive Noise Filter (MLANF) algorithm based on photon spatial density. Its purpose is to extract signal photons from ICESat-2 terrestrial data of different ground cover types. The algorithm follows a two-step process. Firstly, random noise photons are removed from the upper and lower regions of the signal photons through a coarse denoising process. Secondly, in the fine denoising step, the K-Nearest Neighbor (KNN) algorithm selects the K photons to calculate the slope along the track. The calculated slope is then used to rotate the direction of the searching neighborhood in the DBSCAN algorithm. The proposed algorithm was tested in eight datasets of four surface types: forest, grassland, desert, and urban, and the extraction results were compared with those from the ATL08 datasets and the DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the classification precision, recall, and F-score of our algorithm, as well as two other algorithms, were calculated. The MLANF could achieve a good balance between classification precision (97.48% averaged) and recall (97.96% averaged). Its F-score (97.69% averaged) was higher than that of the other two methods. This demonstrates that the MLANF algorithm successfully obtained a continuous surface profile from ICESat-2 datasets with different surface cover types, significant topographic relief, and low SNR.

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A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic

Cited by 5 publications

References 45 publications

A Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

A Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

Analysis of the Variation in Bearing Estimation Error with Frequency Bands for a Source Beneath the Arctic Ice Using a Geophone

A Multi-Level Auto-Adaptive Noise-Filtering Algorithm for Land ICESat-2 Photon-Counting Data

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