Surface-Height Determination of Crevassed Glaciers—Mathematical Principles of an Autoadaptive Density-Dimension Algorithm and Validation Using ICESat-2 Simulator (SIMPL) Data

Herzfeld, U. C.; Trantow, T.; Harding, David J.; Dabney, Philip W.

doi:10.1109/tgrs.2016.2617323

Cited by 34 publications

(47 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) Feature calculation: The third surface detection method is a key step in surface detection. There are four main features that have been calculated in the existing studies, namely, the data distribution feature [28,39], the photon density [25,[30][31][32][40][41][42][43][44][45][46][47][48][49], the k-nearest neighbor distance [24,50], and the local outlier factor [51,52]. Among the different features, the photon density is the most commonly adopted feature.…”

Section: B Surface Detectionmentioning

confidence: 99%

“…Photons in the filter kernel are given a linear weight in time and are Gaussian in height, and the maximum density is considered as the final density. In 2017, Herzfeld et al [42] calculated the density weight using the radial basis function for an ice surface. Since the ice surface extends more in the horizontal direction than the vertical direction, the anisotropic norm is combined to make the horizontal direction weight greater than the vertical direction weight.…”

Section: Step 1: Feature Value Calculation Of Each Photonmentioning

confidence: 99%

“…If the difference is greater than 3T, it is considered to be a background photon; otherwise, it is considered to be a surface photon. In 2017, Herzfeld et al [42] calculated two adaptive density thresholds. The first threshold was the maximum density of the background photon area plus the offset value, and the second threshold was the percentile that met the first threshold.…”

Section: Step 2: Surface Detection By Separating Featuresmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

Xie

et al. 2021

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

View full text Add to dashboard Cite

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) was launched on September 15, 2018. It is the first photon-counting laser altimeter satellite, which is of great significance for the research into laser altimetry. ICESat-2 is, however, highly sensitive and susceptible to environmental influences. In addition to surface returns, a lot of non-surface 1 photons are found in the data. It is therefore necessary to study an effective method to separate the surface signal from background information. In this paper, we review the existing surface detection methods for photon point cloud data and select seven methods for comparison. Four sources of photon-counting data were considered in the experiments: The Multiple Altimeter Beam Experimental Lidar(MABEL), the Chinese Multi-Beam LiDAR(MBL), The Advanced Topographic Laser Altimeter System(ICESat-2/ATLAS), and MATLAS(using MABEL data to simulate the expected ATLAS photon point cloud). Four scenarios of land, land ice, sea ice, and ocean were also considered. Each surface detection method was tested in 12 experiments, and the different methods were finally compared by qualitative and quantitative measures. We were thus able to establish the advantages and disadvantages of each method, which will be of great significance for scholars studying surface detection methods.

show abstract

Section: B Surface Detectionmentioning

confidence: 99%

Section: Step 1: Feature Value Calculation Of Each Photonmentioning

confidence: 99%

Section: Step 2: Surface Detection By Separating Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

Xie

et al. 2021

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

View full text Add to dashboard Cite

show abstract

“…Centering on each point, the points within the ellipse are given Gaussian weights [8] . The sum of the weights of all points in the ellipse is the density of the center point.…”

Section: The Elliptical Gaussian Density Calculationmentioning

confidence: 99%

An Adaptive Filter Based on Local Slope by Adjusting Elliptic Filter Kernel for ICESat-2 Photon Counting Laser Altimetric Data

Xie

Hai

et al. 2020

EPJ Web Conf.

View full text Add to dashboard Cite

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018, which continues to perform measurement tasks as the successor to ICESat. Unlike full waveform technology of ICESat, ICESat-2 employs micropulse photon counting technology, which provides higher accuracy, but produces plenty of noise. This paper proposes an adaptive filter based on local slope by adjusting elliptic filter kernel. The general approach is 1) data preprocessing, 2) Gaussian density calculation, 3) OTSU adaptive threshold calculation. This method is seen to be robust in identifying signal points from high background noise points and suitable for low density data caused by slope.

show abstract

“…Previous studies focusing on the development of surface extraction algorithms from ICESat-2 data used single photon-counting altimetry measurements from NASA's Multiple Altimeter Beam Experimental Lidar (MABEL) and Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) systems ((Herzfeld et al, 2017, Brunt et al, 2016. However, inclusion of single-photon laser altimetry data into a change detection algorithm requires ICESat-2 measurements extending existing altimetry time series.…”

Section: Applicationsmentioning

confidence: 99%

Ice Sheet Elevation Mapping and Change Detection With the Ice, Cloud and Land Elevation Satellite-2

Csathó

Schenk

Neumann

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

On September 15, 2018, ICESat-2 (Ice, Cloud, and land Elevation satellite) was successfully launched to measure ice sheet and glacier elevation change, sea ice freeboard, and vegetation. This paper describes the computation of surface elevation change rates obtained with SERAC (Surface Elevation Reconstruction And Change detection) from ICESat-2 observations. After summarizing some relevant aspects of ICESat-2 and its sole instrument ATLAS (Advanced Topographic Laser Altimetry System) the paper focuses on how we calculate time series of elevation change rates from ICESat-2's data product ATL03. Since real ICESat-2 data suitable for generating time series of several time epochs are not yet available, we used simulated data for this study. We will start generating time series from real ICESat-2 data after the conclusion of the ongoing calibration and validation phase and we expect to present real-world examples at the WG III/9 meeting in June, 2019 in Enschede, The Netherlands.

show abstract

Surface-Height Determination of Crevassed Glaciers—Mathematical Principles of an Autoadaptive Density-Dimension Algorithm and Validation Using ICESat-2 Simulator (SIMPL) Data

Cited by 34 publications

References 47 publications

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

An Adaptive Filter Based on Local Slope by Adjusting Elliptic Filter Kernel for ICESat-2 Photon Counting Laser Altimetric Data

Ice Sheet Elevation Mapping and Change Detection With the Ice, Cloud and Land Elevation Satellite-2

Contact Info

Product

Resources

About