NASA ICESat-2: Space-Borne LiDAR for Geological Education and Field Mapping of Aeolian Sand Dune Environments

Rehman, Khushbakht; Fareed, Nadeem; Chu, Hsin

doi:10.3390/rs15112882

Cited by 6 publications

(4 citation statements)

References 71 publications

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“…In addition, a successive bottom profile is of great importance for the water depth correction and extraction. The DBSCAN [29], HDBSCAN [25], Gaussian Mixture Model (GMM) [23], quadtree classification (QC) [24], and ATL08 algorithms [30] were used as a comparison experiment for the proposed method. The signal photon extraction accuracy F values of the six methods are shown in Table 2, and the signal extraction performances of different methods are shown in Figure 13.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, an efficient method is needed to extract the signal photons from the geolocated photons, which contain a large number of noise photons. Up to now, based on the spatial distribution characteristics of lidar points, many methods have been proposed to extract the signal photons or points [22], e.g., the spatial nonlinear clustering algorithms including the Gaussian Mixture Model (GMM), quadtrees, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [23][24][25], etc. The three algorithmic models have different clustering methods: the GMM algorithm determines the point cloud category via probability density model estimation, the quadtree achieves the identification of point clouds in different spatial regions via spatial segmentation, and DBSCAN classifies point clouds based on the density of the domain in which the point cloud is located, in which DBSCAN was successfully applied to ICESat-2 geolocated photons [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

Song,

Ma,

Zhou

et al. 2024

Remote Sensing

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The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. The lack of classification information limits large-scale bathymetric applications of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). In this study, we propose a photon extraction–classification method to process geolocated photons in coastal areas from the ICESat-2 ATL03 product. The basic idea is to extract the signal photons using an adaptive photon clustering algorithm, and the extracted signal photons are classified based on the accumulated histogram and triangular grid. We also generate the bottom profile using the weighted interpolation. In four typical coastal areas (artificial coast, natural coast, island, and reefs), the extraction accuracy of a signal photons exceeds 0.90, and the Kappa coefficients of four surface types exceed 0.75. This method independently extracts and classifies signal photons without relying on auxiliary data, which can greatly improve the efficiency of obtaining bathymetric points in all kinds of coastal areas and provide technical support for other coastal studies using ICESat-2 data.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

Song,

Ma,

Zhou

et al. 2024

Remote Sensing

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“…On 15 September 2018, the National Aeronautics and Space Administration (NASA) launched ICESat-2 to continually support the quantification of ice-sheet contributions to sea-level rise, estimating sea-ice thickness, and monitoring glacier-melting outlets [28]. This new generation spaceborne lidar satellite was developed by the United States following the failure of its predecessor, the ICESat-1.…”

Section: Icesat-2 Datamentioning

confidence: 99%

Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method

2023

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The TanDEM-X Digital Elevation Model (DEM) is limited by the radar side-view imaging mode, which still has gaps and anomalies that directly affect the application potential of the data. Many methods have been used to improve the accuracy of TanDEM-X DEM, but these algorithms primarily focus on eliminating systematic errors trending over a large area in the DEM, rather than random errors. Therefore, this paper presents the least-squares collocation-based error correction algorithm (LSC-TXC) for TanDEM-X DEM, which effectively eliminates both systematic and random errors, to enhance the accuracy of TanDEM-X DEM. The experimental results demonstrate that TanDEM-X DEM corrected by the LSC-TXC algorithm reduces the root mean square error (RMSE) from 6.141 m to 3.851 m, resulting in a significant improvement in accuracy (by 37.3%). Compared to three conventional algorithms, namely Random Forest, Height Difference Fitting Neural Network and Back Propagation in Neural Network, the presented algorithm demonstrates a reduction in the RMSEs of the corrected TanDEM-X DEMs by 6.5%, 7.6%, and 18.1%, respectively. This algorithm provides an efficient tool for correcting DEMs such as TanDEM-X for a wide range of areas.

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“…The RGI 6.0 (Randolph Glacier Inventory 6.0) database, released in July 2017, was used in this paper for glacier profile confirmation and assisted estimation of glacier area in the study area [59].…”

Section: Datasetsmentioning

confidence: 99%

The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

Yang,

Du,

et al. 2023

Water

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Quantifying the effects of alpine GMB (Glacier Mass Balance) on river runoff is an important content of climate change. Uncertainty exists in GMB monitoring when applying remote-sensing technology. There are several reasons for these uncertainties, such as terrain deviation co-registration among different topographic data, the mismatch between GSE (Glacier Surface Elevation) from satellite monitoring and the GMB that comprises the physical glacier properties, the driving factors of GMB, and the response patterns of the runoff within the basin. This paper proposed a method based on the ridge line co-registration of DEMs (Digital Elevation Models), and the Tailan River basin, which is a typical glacier melt runoff recharge basin located in the southern Tianshan Mountains, was selected. Abnormal values in GSE changes were removed using ice thickness data, and the GSE results were optimized based on the regularity of the GSE change with altitude to estimate the GMB. The driving factors of the GMB and the response characteristics of the runoff in the basin were also explored. The results showed that the accuracy of the optimized GSE results across different periods has improved by more than 25%. The mean annual thinning value of GSE in the basin from 2000 to 2022 was −0.25 ± 0.02 m·a−1, corresponding to a GMB value of −0.30 ± 0.02 m w.e.a−1, indicating a consistent GMB loss state. Combined with climate data, the glaciers in the basin were impacted by rising temperatures, and the smallest increase in annual precipitation in the basin was insufficient to compensate for the GMB loss. Moreover, in the past 22 years, glacier meltwater accounts for 46.15% of the total runoff in the Tailan River basin.

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NASA ICESat-2: Space-Borne LiDAR for Geological Education and Field Mapping of Aeolian Sand Dune Environments

Cited by 6 publications

References 71 publications

Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method

The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin

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