Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas
"> Figure 1
<p>Schematic diagram of laser footprints of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) on the ground in the forward orientation. The laser beam pattern is a 3 × 2 array in which one strong beam and one weak beam are grouped into a pair. When advanced topographic laser altimeter system (ATLAS) is oriented in the forward orientation, the weak beams are on the left side of the beam pair and are associated with ground tracks 1L, 2L, and 3L. When ATLAS is oriented in the backward orientation, the relative positions of weak and strong beams change, and the strong beams are on the left side of the ground track pairs and lead the weak beams [<a href="#B40-remotesensing-13-00863" class="html-bibr">40</a>]. The distance between each pair was approximately 3.3 km. In the same pair, the interval between the strong beam and the weak beam was 2.5 km. The cross-track distance between the two ground tracks in the same pair was 90 m.</p> "> Figure 2
<p>Geographical locations of four sites in this study.</p> "> Figure 3
<p>Theoretical signal–noise ratio (SNR) contours for the ICESat-2 strong and weak beams under different environmental conditions. (<b>a</b>) SNR corresponding to the strong laser beam, and (<b>b</b>) SNR corresponding to the weak laser beam. The solar spectral irradiance was set as 1.83 W/m<sup>2</sup>∙nm at the wavelength of 532 nm in calculation. The values of <span class="html-italic">σ</span><sub>h</sub> and Var(<span class="html-italic">ξ</span>) were both set as 0 because they had relatively weaker impacts on SNRs than the surface slopes and solar zenith angles.</p> "> Figure 4
<p>Circular and elliptical neighborhood of a given point <span class="html-italic">p</span> in the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. Red points and blue points correspond to signal and noise photons, respectively. (<b>a</b>) Circular neighborhood with a radius of <span class="html-italic">r</span>. (<b>b</b>) Elliptical neighborhood with a major axis of <span class="html-italic">a</span> and a minor axis of <span class="html-italic">b</span>. In addition, the rotation angle <span class="html-italic">θ</span> is defined as the angle between the major axis and the horizontal line in anticlockwise direction.</p> "> Figure 5
<p>Measured geolocated photons in an ICESat-2′s along-track segment in the first study area (i.e., ATL03_20181207065658_ 10640102_002_01, GT1R) and the SNR curves for the strong and weak beams. (<b>a</b>) Measured noisy geolocated photons (blue points) and the detected signal photons (red points) for the strong beam with the <span class="html-italic">Minpts</span> of 6. The <span class="html-italic">x</span>-axis represents relative ICESat-2 along-track distance and the <span class="html-italic">y</span>-axis represents the ellipsoid height in kilometers. (<b>b</b>) Measured noisy geolocated photons and the detected signal photons for the weak beam with the <span class="html-italic">Minpts</span> of 6. (<b>c</b>) Measured noisy geolocated photons and the detected signal photons for the weak beam with the <span class="html-italic">Minpts</span> of 4. (<b>d</b>) Theoretical SNRs of the strong beam (using blue solid curve) and the weak beam (using red dashed curve). In (<b>d</b>), two typical SNR values are marked by green points and were calculated by the measured signal and noise photons in the green boxes in (<b>a</b>,<b>b</b>) for the strong and weak beams, respectively.</p> "> Figure 6
<p>(<b>a</b>) Sampled along-track segment from the ICESat-2 strong beam in the first study area (i.e., ATL03_20181207065658_10640102_002_01, GT1R). The <span class="html-italic">x</span>-axis represents relative ICESat-2 along-track distance and the <span class="html-italic">y</span>-axis represents the ellipsoid height in kilometers. The measured geolocated photons marked with red are the detected signal photons. The geolocated photons marked with yellow are used to estimate the noise rate <span class="html-italic">f</span><sub>nc</sub> in every along-track segment. The total extent of the vertical window was approximately 800 m in the current data, and the elevation of the top of the vertical window was <span class="html-italic">H</span><sub>top</sub> and the elevation of the bottom of the vertical window was <span class="html-italic">H</span><sub>bottom</sub>. The yellow points correspond to noise photons within the vertical range of [<span class="html-italic">H</span><sub>top</sub>-300 m, <span class="html-italic">H</span><sub>top</sub>] and [<span class="html-italic">H</span><sub>bottom</sub>, <span class="html-italic">H</span><sub>bottom</sub> + 300 m]. (<b>b</b>) The along-track slope <span class="html-italic">σ</span><sub>L</sub> versus the background noise rates <span class="html-italic">f</span><sub>nc</sub> at different along-track distances. Measured noisy geolocated photons of the whole ground track in <a href="#remotesensing-13-00863-f006" class="html-fig">Figure 6</a>a were divided into a 20-m along-track segment with a step size of 5 m.</p> "> Figure 7
<p>Relationship between the along-track slope <span class="html-italic">σ</span><sub>L</sub> and the noise rate <span class="html-italic">f</span><sub>nc</sub>. The blue and red curves correspond to the measured results and fitted results, respectively. The mean and standard deviation of the measured along-track slopes with similar noise rates (within the scope of ±0.1 MHz) were calculated and shown as the error bars. (<b>a</b>) The adret slope. (<b>b</b>) The ubac slope.</p> "> Figure 8
<p>Flow chart of the modified DBSCAN algorithm in this study.</p> "> Figure 9
<p>(<b>a</b>) ICESat-2 ATLAS ground track (from south to north or an ascending track) and satellite imagery on the Tibetan Plateau to the east of Xigaze City. The Earth’s surface is composed of bare-land and sparse grasslands, and a river in the valley. (<b>b</b>) The signal photons extracted by different methods. The columns from left to right correspond to signal photons extracted by the confidence level >1 from the ATL03 dataset, the signal photons marked in the ATL08 product, the classical DBSCAN algorithm, and the modified DBSCAN method. The diameter and threshold of the classical DBSCAN were set as 5 m and 6, respectively. Two sampled segments within the yellow and green boxes are enlarged and illustrated in <a href="#remotesensing-13-00863-f010" class="html-fig">Figure 10</a>a,b, respectively.</p> "> Figure 10
<p>Detailed comparison of results from different signal photon extraction methods in the first study area. The rows from top to bottom correspond to signal photon extraction results from the ATL03 dataset, the ATL08 dataset, the classical DBSCAN algorithm, and the modified DBSCAN algorithm, respectively. The measured geolocated photons are drawn with blue points while the extracted signal photons are marked with red points. (<b>a</b>) Enlarged along-track segment corresponding to the yellow box in <a href="#remotesensing-13-00863-f009" class="html-fig">Figure 9</a>a at the along-track distance from 29 km to 32 km. (<b>b</b>) Enlarged along-track segment corresponding to the green box in <a href="#remotesensing-13-00863-f009" class="html-fig">Figure 9</a>a at the along-track distance from 46 km to 48 km.</p> "> Figure 11
<p>(<b>a</b>–<b>d</b>) Heat maps of background noise rates from the strong beam and weak beam in the same pair in four study areas. The abscissa and ordinate correspond to the noise rates from the weak beam and the strong beam data. The number of data at certain coordinates is marked with different colors and the color bar gives the data number.</p> "> Figure 12
<p>Normalized spatial density of the using data in <a href="#remotesensing-13-00863-f010" class="html-fig">Figure 10</a>a. The spatial density of each PE is calculated by the number of PEs within a circular neighborhood of a 10-m radius. The maximum spatial density in the whole along-track segment was set as 1. Several sub-segments marked with red circles demonstrate that the direction of the maximum spatial density was not consistent with the direction of the terrain profile due to the very low SNR.</p> "> Figure A1
<p>(<b>a</b>) ICESat-2 ATLAS ground track (from north to south or a descending track) and satellite imagery in the Altun Mountains. The Altun Mountains are located between the Taklimakan Desert and the Gurbantunggut Desert, which makes this area very dry. The surface is covered by bare-land and very sparse grasslands. (<b>b</b>) The signal photons extracted by different methods. The diameter and threshold of the classical DBSCAN were set as 4 m and 4, respectively. Two sampled segments within the yellow and green boxes were enlarged and illustrated in <a href="#remotesensing-13-00863-f0A2" class="html-fig">Figure A2</a>a,b, respectively.</p> "> Figure A2
<p>Detailed comparison of the results from different signal photon extraction methods in the second study area. (<b>a</b>) Enlarged along-track segment corresponding to the yellow box in <a href="#remotesensing-13-00863-f0A1" class="html-fig">Figure A1</a>a at the along-track distance from 0 km to 2 km. (<b>b</b>) Enlarged along-track segment corresponding to the green box in <a href="#remotesensing-13-00863-f0A1" class="html-fig">Figure A1</a>a at the along-track distance from 6 km to 8 km.</p> "> Figure A3
<p>(<b>a</b>) ICESat-2 ATLAS ground track (a descending track) and satellite imagery in the Tian Shan Mountains. The Tian Shan Mountains are located between the Tarim Basin and the Junggar Basin, which are the farthest mountains to the sea around the world. (<b>b</b>) The signal photons extracted by different methods. The diameter and threshold of the classical DBSCAN were set as 5.5 m and 5, respectively. Two sampled segments within the yellow and green boxes were enlarged and illustrated in <a href="#remotesensing-13-00863-f0A4" class="html-fig">Figure A4</a>a,b to show the details, respectively.</p> "> Figure A4
<p>Detailed comparison of the results from the different signal photon extraction methods in the third study area. (<b>a</b>) Enlarged along-track segment corresponding to the yellow box in <a href="#remotesensing-13-00863-f0A3" class="html-fig">Figure A3</a>a at the along-track distance from 2 km to 4 km. (<b>b</b>) Enlarged along-track segment corresponding to the green box in <a href="#remotesensing-13-00863-f0A3" class="html-fig">Figure A3</a>a at the along-track distance from 7 km to 9 km.</p> "> Figure A5
<p>(<b>a</b>) ICESat-2 ATLAS ground track (a descending track) and satellite imagery in the Tian Shan Mountains. (<b>b</b>) The signal photons extracted by different methods. The diameter and threshold of the classical DBSCAN were set as 6.5 m and 7, respectively. Two sampled segments within the yellow and green boxes were enlarged and illustrated in <a href="#remotesensing-13-00863-f0A6" class="html-fig">Figure A6</a>a,b to show the details, respectively.</p> "> Figure A6
<p>Detailed comparison of the results from the different signal photon extraction methods in the fourth study area. (<b>a</b>) Enlarged along-track segment corresponding to the yellow box in <a href="#remotesensing-13-00863-f0A5" class="html-fig">Figure A5</a>a at the along-track distance from 1 km to 2 km. (<b>b</b>) Enlarged along-track segment corresponding to the green box in <a href="#remotesensing-13-00863-f0A5" class="html-fig">Figure A5</a>a at the along-track distance from 4 km to 5 km.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) Photon-Counting LiDAR
2.2. Study Areas and Datasets
2.3. Signal–Noise Ratios (SNRs) of Different Laser Beams in Mountainous Areas
2.4. Current Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm and Its Modification
2.5. Parameters Determination for Modified DBSCAN
2.6. Modified DBSCAN Algorithm
- (1)
- Signal extraction and statistical parameter estimation from strong beam data
- (2)
- Noise–slope relationship fitting
- (3)
- Calculating parameters of the searching area and threshold in each cluster
- (4)
- Searching signal photons using the DBSCAN from weak beam data
- (5)
- Running a 3σ confidence filter to remove outliers
3. Results
4. Discussion
4.1. Precondition of Using the Algorithm
4.2. Why the Classical DBSCAN Failed
4.3. Potential Implications of the Algorithm
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
- Brenner, A.C.; DiMarzio, J.P.; Zwally, H.J. Precision and accuracy of satellite radar and laser altimeter data over the continental ice sheets. IEEE Trans. Geosci. Remote Sens. 2007, 45, 321–331. [Google Scholar] [CrossRef]
- Price, D.; Rack, W.; Haas, C.; Langhorne, P.J.; Marsh, O. Sea ice freeboard in McMurdo Sound, Antarctica, derived by surface-validated ICESat laser altimeter data. J. Geophys. Res. Ocean. 2013, 118, 3634–3650. [Google Scholar] [CrossRef]
- Neuenschwander, A.L.; Urban, T.J.; Gutierrez, R.; Schutz, B.E. Characterization of ICESat/GLAS waveforms over terrestrial ecosystems: Implications for vegetation mapping. J. Geophys. Res. 2008, 113, G02S03. [Google Scholar] [CrossRef] [Green Version]
- Hilbert, C.; Schmullius, C. Influence of surface topography on ICESat/GLAS forest height estimation and waveform shape. Remote Sens. 2012, 4, 2210–2235. [Google Scholar] [CrossRef] [Green Version]
- Hajj, M.E.; Baghdadi, N.; Fayad, I.; Vieilledent, G.; Bailly, J.-S.; Minh, D.H.T. Interest of integrating spaceborne LiDAR data to improve the estimation of biomass in high biomass forested areas. Remote Sens. 2017, 9, 213. [Google Scholar] [CrossRef] [Green Version]
- Urban, T.J.; Schutz, B.E. ICESat sea level comparisons. Geophys. Res. Lett. 2005, 32, L23S10. [Google Scholar] [CrossRef] [Green Version]
- Schutz, B.E.; Zwally, H.J.; Shuman, C.A.; Hancock, D.; DiMarzio, J.P. Overview of the ICESat Mission. Geophys. Res. Lett. 2005, 32, L21S01. [Google Scholar] [CrossRef] [Green Version]
- Abshire, J.B.; Sun, X.; Riris, H.; Sirota, J.M.; McGarry, J.F.; Palm, S.; Yi, D.; Liiva, P. Geoscience Laser Altimeter System (GLAS) on the ICESat mission: On-orbit measurement performance. Geophys. Res. Lett. 2005, 32, L21S02. [Google Scholar] [CrossRef] [Green Version]
- Neuenschwander, A.L.; Magruder, L.A. Canopy and terrain height retrievals with ICESat-2: A first look. Remote Sens. 2019, 11, 1721. [Google Scholar] [CrossRef] [Green Version]
- Martino, A.J.; Neumann, T.A.; Kurtz, N.T.; McLennan, D. ICESat-2 mission overview and early performance. In Sensors, Systems, and Next-Generation Satellites XXIII; Neeck, S.P., Kimura, T., Martimort, P., Eds.; SPIE: Bellingham, WA, USA, 2019; p. 11. [Google Scholar]
- Markus, T.; Neumann, T.; Martino, A.; Abdalati, W.; Brunt, K.; Csatho, B.; Farrell, S.; Fricker, H.; Gardner, A.; Harding, D.; et al. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation. Remote Sens. Environ. 2017, 190, 260–273. [Google Scholar] [CrossRef]
- Tang, H.; Swatantran, A.; Barrett, T.; DeCola, P.; Dubayah, R. Voxel-based spatial filtering method for canopy height retrieval from airborne single-photon lidar. Remote Sens. 2016, 8, 771. [Google Scholar] [CrossRef] [Green Version]
- Wang, X.; Glennie, C.; Pan, Z. An adaptive ellipsoid searching filter for airborne single-photon lidar. IEEE Geosci. Remote Sens. Lett. 2017, 14, 1258–1262. [Google Scholar] [CrossRef]
- Li, Q.; Degnan, J.J.; Barrett, T.; Shan, J. First evaluation on single photon-sensitive lidar data. Photogramm. Eng. Remote Sens 2016, 82, 455–463. [Google Scholar] [CrossRef]
- Hernandez-Marin, S.; Wallace, A.M.; Gibson, G.J. Bayesian analysis of lidar signals with multiple returns. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29, 2170–2180. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rapp, J.; Goyal, V.K. A Few photons among many: Unmixing signal and noise for photon-efficient active imaging. IEEE Trans. Comput. Imag. 2017, 3, 445–459. [Google Scholar] [CrossRef]
- Magruder, L.A.; Wharton, M.E.; Stout, K.D.; Neuenschwander, A.L. Noise filtering techniques for photon-counting Ladar data. In SPIE Defense, Security, and Sensing. International Society for Optics and Photonics; SPIE: Bellingham, WA, USA, 2012. [Google Scholar]
- Kwok, R.; Markus, T.; Morison, J.S.; Palm, P.; Neumann, T.A.; Brunt, K.M.; Cook, W.B.; Hancock, D.W.; Cunningham, G.F. Profiling sea ice with a Multiple Altimeter Beam Experimental Lidar (MABEL). J. Atmos. Ocean. Technol. 2014, 31, 1151–1168. [Google Scholar] [CrossRef] [Green Version]
- Herzfeld, U.C.; McDonald, B.W.; Wallin, B.F.; Neumann, T.A.; Markus, T.; Brenner, A.; Field, C. Algorithm for detection of ground and canopy cover in micropulse photon-counting lidar altimeter data in preparation for the ICESat-2 mission. IEEE Trans. Geosci. Remote Sens. 2014, 52, 2109–2125. [Google Scholar] [CrossRef]
- Moussavi, M.S.; Abdalati, W.; Scambos, T.; Neuenschwander, A. Applicability of an automatic surface detection approach to micro-pulse photon-counting lidar altimetry data: Implications for canopy height retrieval from future ICESat-2 data. Int. J. Remote Sens. 2014, 35, 5263–5279. [Google Scholar] [CrossRef]
- Gwenzi, D.; Lefsky, M.A.; Suchdeo, V.P.; Harding, D.J. Prospects of the ICESat-2 laser altimetry mission for Savanna ecosystem structural studies based on airborne simulation data. ISPRS J. Photogramm. Remote Sens. 2016, 118, 68–82. [Google Scholar] [CrossRef]
- Herzfeld, U.C.; Trantow, T.M.; Harding, D.; Dabney, P.W. Surface-height determination of crevassed glaciers—Mathematical principles of an autoadaptive density-dimension algorithm and validation using ICESat-2 simulator (SIMPL) data. IEEE Trans. Geosci. Remote Sens. 2017, 55, 1874–1896. [Google Scholar] [CrossRef]
- Popescu, S.C.; Zhou, T.; Nelson, R.; Neuenschwander, A.; Sheridan, R.; Narine, L.; Walsh, K.M. Photon counting LiDAR: An adaptive ground and canopy height retrieval algorithm for ICESat-2 data. Remote Sens. Environ. 2018, 208, 154–170. [Google Scholar] [CrossRef]
- Neuenschwander, A.; Pitt, K. The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sens. Environ. 2019, 221, 247–259. [Google Scholar] [CrossRef]
- Smith, B.; Fricker, H.A.; Holschuh, N.; Gardner, A.S.; Adusumilli, S.; Brunt, K.M.; Csatho, B.; Harbeck, K.; Huth, A.; Neumann, T.; et al. Land ice height-retrieval algorithm for NASA’s ICESat-2 photon-counting laser altimeter. Remote Sens. Environ. 2019, 233, 111352–111368. [Google Scholar] [CrossRef] [Green Version]
- Liu, M.; Popescu, S.; Malambo, L. Feasibility of burned area mapping based on ICESAT−2 photon counting data. Remote Sens. 2019, 12, 24. [Google Scholar] [CrossRef] [Green Version]
- Neuenschwander, A.L.; Pitts, K.L.; Jelley, B.P.; Robbins, J.; Klotz, B.; Popescu, S.C.; Nelson, R.F.; Harding, D.; Pederson, D.; Sheridan, R. ICE, CLOUD, and Land Elevation Satellite-2 (ICESat-2) Algorithm Theoretical Basis Document (ATBD) for Land-Vegetation Along-Track Products(ATL08); Land/Vegetation SDT Team Members and ICESat-2 Project Science Office: Greenbelt, MD, USA, 2020. [Google Scholar]
- Morison, J.; Hancock, D.; Dickinson, J.; Robbins, T.; Roberts, L.; Kwok, R.; Palm, S.; Jasinski, M.; Plant, B.; Urban, T. ICE, CLOUD, and Land Elevation Satellite-2 (ICESat-2) Project Algorithm Theoretical Basis Document (ATBD) for Ocean Surface Height (ATL12); Goddard Space Flight Center: Greenbelt, MD, USA, 2020. [Google Scholar]
- Jasinski, M.; Stoll, J.; Hancock, D.; Robbins, J.; Nattala, J.; Morison, J.; Jones, B.; Ondrusek, M.; Pavelsky, T.; Parrish, C.; et al. ICE, CLOUD, and Land Elevation Satellite-2 (ICESat-2) Project Algorithm Theoretical Basis Document (ATBD) for Inland Water Data Products (ATL13); Goddard Space Flight Center: Greenbelt, MD, USA, 2020. [Google Scholar]
- Malambo, L.; Popescu, S.C. PhotonLabeler: An inter-disciplinary platform for bisual interpretation and labeling of ICESat-2 geolocated photon data. Remote Sens. 2020, 12, 3168. [Google Scholar] [CrossRef]
- Wang, X.; Pan, Z.; Glennie, C. A novel noise filtering model for photon-counting laser altimeter data. IEEE Geosci. Remote Sens. Lett. 2016, 13, 947–951. [Google Scholar] [CrossRef]
- Zhang, J.; Kerekes, J.; Csatho, B.; Schenk, T.; Wheelwright, R. A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data. In Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 177–180. [Google Scholar]
- Ester, M.; Kriegel, H.-P.; Sander, J.; Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA, 2–4 August 1996; pp. 226–231. [Google Scholar]
- Ma, Y.; Zhang, W.; Sun, J.; Li, G.; Wang, X.; Li, S.; Xu, N. Photon-counting lidar: An adaptive signal detection method for different land cover types in coastal areas. Remote Sens. 2019, 11, 471. [Google Scholar] [CrossRef] [Green Version]
- Nie, S.; Wang, C.; Xi, X.; Luo, S.; Li, G.; Tian, J.; Wang, H. Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data. Opt. Express 2018, 26, A520–A540. [Google Scholar] [CrossRef]
- Zhu, X.; Nie, S.; Wang, C.; Xi, X.; Hu, Z. A ground elevation and vegetation height retrieval algorithm using micro-pulse photon-counting Lidar data. Remote Sens. 2018, 10, 1962. [Google Scholar] [CrossRef] [Green Version]
- Zhu, X.; Nie, S.; Wang, C.; Xi, X.; Wang, J.; Li, D.; Zhou, H. A noise removal algorithm based on OPTICS for photon-counting LiDAR data. IEEE Geosci. Remote Sens. Lett. 2020. [Google Scholar] [CrossRef]
- Huang, J.; Xing, Y.; You, H.; Qin, L.; Tian, J.; Ma, J. Particle swarm optimization-based noise filtering algorithm for photon cloud data in forest area. Remote Sens. 2019, 11, 980. [Google Scholar] [CrossRef] [Green Version]
- Brunt, K.M.; Neumann, T.A.; Amundson, J.M.; Kavanaugh, J.L.; Moussavi, M.S.; Walsh, K.M.; Cook, W.B.; Markus, T. MABEL photon-counting laser altimetry data for ICESat-2 simulations and development. Cryosphere 2016, 10, 1707–1719. [Google Scholar] [CrossRef] [Green Version]
- Neumann, T.A.; Martino, A.J.; Markus, T.; Bae, S.; Bock, M.R.; Brenner, A.C.; Brunt, K.M.; Cavanaugh, J.; Fernandes, S.T.; Hancock, D.W.; et al. The Ice, Cloud, and Land Elevation Satellite-2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System. Remote Sens. Environ. 2019, 233, 111325–111341. [Google Scholar] [CrossRef] [PubMed]
- Yang, G.; Martino, A.J.; Lu, W.; Cavanaugh, J.F.; Bock, M.R.; Krainak, M.A. IceSat-2 ATLAS photon-counting receiver: Initial on-orbit performance. In Advanced Photon Counting Techniques XIII; Itzler, M.A., McIntosh, K.A., Bienfang, J.C., Eds.; SPIE: Bellingham, WA, USA, 2019; p. 10. [Google Scholar]
- Neumann, T.A.; Brenner, A.; Hancock, D.; Robbins, J.; Saba, J.; Harbeck, K.; Gibbons, A.; Lee, J.; Luthcke, S.B.; Rebold, T.; et al. ATLAS/ICESat-2 L2A Global Geolocated Photon Data, Version 3; NASA National Snow and Ice Data Center Distributed Active Archive Center: Boulder, CO, USA, 2020. [CrossRef]
- Neumann, T.; Brenner, A.; Hancock, D.; Robbins, J.; Saba, J.; Harbeck, K.; Gibbons, A. ICE, CLOUD, and Land Elevation Satellite-2 (ICESat-2) Project Algorithm Theoretical Basis Document (ATBD) for Global Geolocated Photons ATL03; Goddard Space Flight Center: Greenbelt, MD, USA, 2019. [Google Scholar]
- McGill, M.; Markus, T.; Scott, V.S.; Neumann, T. The Multiple Altimeter Beam Experimental Lidar (MABEL): An Airborne Simulator for the ICESat-2 Mission. J. Atmos. Ocean. Technol. 2013, 30, 345–352. [Google Scholar] [CrossRef]
- Neuenschwander, A.L.; Pitts, K.L.; Jelley, B.P.; Robbins, J.; Klotz, B.; Popescu, S.C.; Nelson, R.F.; Harding, D.; Pederson, D.; Sheridan, R. ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 3; NASA National Snow and Ice Data Center Distributed Active Archive Center: Boulder, CO, USA, 2020. [CrossRef]
- Degnan, J.J. Photon-counting multikilohertz microlaser altimeters for airborne and spaceborne topographic measurements. J. Geodyn. 2002, 34, 503–549. [Google Scholar] [CrossRef] [Green Version]
- Li, S.; Zhang, Z.; Ma, Y.; Zeng, H.; Zhao, P.; Zhang, W. Ranging performance models based on negative-binomial (NB) distribution for photon-counting lidars. Opt. Express 2019, 27, A861–A877. [Google Scholar] [CrossRef]
- Gardner, C.S. Target signatures for laser altimeters: An analysis. Appl. Opt. 1982, 21, 448–453. [Google Scholar] [CrossRef]
- Greeley, A.P.; Neumann, T.A.; Kurtz, N.T.; Markus, T.; Martino, A.J. Characterizing the system impulse response function from photon-counting LiDAR data. IEEE Trans. Geosci. Remote Sens. 2019, 57, 6542–6551. [Google Scholar] [CrossRef]
- Gatt, P.; Johnson, S.; Nichols, T. Geiger-mode avalanche photodiode ladar receiver performance characteristics and detection statistics. Appl. Opt. 2009, 48, 3261–3276. [Google Scholar] [CrossRef]
- Ma, Y.; Li, S.; Zhang, W.; Zhang, Z.; Liu, R.; Wang, X.H. Theoretical ranging performance model and range walk error correction for photon-counting lidars with multiple detectors. Opt. Express 2018, 26, 15924–15934. [Google Scholar] [CrossRef]
- Zhang, Z.; Xu, N.; Ma, Y.; Liu, X.; Zhang, W.; Li, S. Land and snow-covered area classification method based on the background noise for satellite photon-counting laser altimeters. Opt. Express 2020, 28, 16030–16044. [Google Scholar] [CrossRef] [PubMed]
- Heris, M.K. DBSCAN Clustering in MATLAB; Yarpiz, 2015; Available online: https://yarpiz.com/255/ypml110-dbscan-clustering (accessed on 7 September 2015).
- Hripcsak, G. Agreement, the F-measure, and reliability in information retrieval. J. Am. Med. Inform. Assoc. 2005, 12, 296–298. [Google Scholar] [CrossRef] [PubMed]
- Neuenschwander, A.; Guenther, E.; White, J.C.; Duncanson, L.; Montesano, P. Validation of ICESat-2 terrain and canopy heights in boreal forests. Remote Sens. Environ. 2020, 251, 112110. [Google Scholar] [CrossRef]
- Tian, X.; Shan, J. Comprehensive evaluation of the ICESat-2 ATL08 terrain product. IEEE Trans. Geosci. Remote Sens. 2021. [Google Scholar] [CrossRef]
- Brunt, K.M.; Smith, B.; Suterley, T.; Kurtz, N.; Neumann, T. Comparisons of satellite and airborne altimetry with ground-based data from the interior of the Antarctic ice sheet. Geophys. Res. Lett. 2020, 48, e2020GL090572. [Google Scholar]
- Farrell, S.L.; Duncan, K.; Buckley, E.M.; Richter-Menge, J.; Li, R. Mapping Sea Ice Surface Topography in High Fidelity with ICESat-2. Geophys. Res. Lett. 2020, 47, e2020GL090708. [Google Scholar] [CrossRef]
- Li, G.; Tang, X.; Gao, X.; Wang, H.; Wang, Y. ZY-3 Block adjustment supported by GLAS laser altimetry data. Photogramm. Rec. 2016, 31, 88–107. [Google Scholar] [CrossRef]
Site Name | Geographical Location | ICESat-2 Data | Sentinel-2 Image | ||
---|---|---|---|---|---|
Acquisition Date and Season | Local Time | Acquisition Date | Environment | ||
Site 1: in the Tibetan Plateau I | 29°0′–29°30′ N, 89°25′–89°35′ E | 7 December 2018 in early winter | 12:56 a.m. | 6 December 2018 | Elevation above 3700 m, covered by bare-land, sparse grasslands, and rivers (in the valley) |
Site 2: in the Altun Mountains II | 38°41′–38°51′ N, 88°58′–89°01′ E | 29 September 2019 in autumn | 10:56 a.m. | 3 October 2019 | Elevation above 2500 m, covered by bare-land and very sparse grasslands |
Site 3: in the Tian Shan Mountains III | 42°32′–42°40′ N, 86°49′–86°51′ E | 13 February 2019 in late winter | 9:35 a.m. | 13 February 2019 | Elevation above 1800 m, covered by bare-land and sparse snow |
Site 4: in the Tian Shan Mountains IV | 42°36′–42°40′ N, 86°19′–86°21′ E | 12 June 2020 in summer | 10:39 a.m. | 22 June 2020 | Elevation above 1700 m, covered by bare-land and grasslands |
Parameter | Value | Parameter | Value |
---|---|---|---|
Filter bandpass, Δλ | 38 pm | Wavelength, λ | 532 nm |
Effective aperture area, Ar | 0.41 m2 | Flight height, z | 500 km |
Transmitting telescope efficiency, ηt | 40% | Receiving FOV, θr | 85 μrad |
Receiving telescope efficiency, ηr | 50.4% | Laser nadir angle, θp | 0° or 0.38° |
Detector quantum efficiency, ηQE | 15% | Detector dead-time, Td | 3.2 ns |
Half of the laser beam divergence, θT | 8.75 μrad at e−1/2 | Transmitted pulse width, σf | 1.5 ns (FWHM) |
Laser energy of strong beam, E0 | 95.7 μJ I | Laser energy of weak beam, E0 | 22.2 μJ II |
Location/Time | Ground Truth | ATL03 | ATL08 | Classical DBSCAN | Modified DBSCAN | ||||
---|---|---|---|---|---|---|---|---|---|
Signal Photon | Noise Photon | Signal Photon | Noise Photon | Signal Photon | Noise Photon | Signal Photon | Noise Photon | ||
Area 1 In the Tibetan Plateau In early winter | Signal photon | 56,914 (TP) | 3074 (FN) | 29,297 | 30,691 | 45,070 | 14,918 | 57,386 | 2602 |
Noise photon | 22,274 (FP) | 531,067 (TN) | 204 | 553,137 | 595 | 552,746 | 3318 | 550,023 | |
Precision, Ppre | 71.87% | 99.31% | 98.70% | 94.53% | |||||
Recall, Prec | 94.88% | 48.84% | 75.13% | 95.66% | |||||
F-score | 0.8178 | 0.6548 | 0.8532 | 0.9510 | |||||
Area 2 In the Altun Mountains In autumn | Signal photon | 12,360 | 11,814 | 4308 | 19,866 | 11,219 | 12,955 | 22,509 | 1665 |
Noise photon | 6148 | 330,307 | 38 | 336,417 | 505 | 335,950 | 270 | 336,185 | |
Precision, Ppre | 66.78% | 99.13% | 95.69% | 98.81% | |||||
Recall, Prec | 51.13% | 17.82% | 46.41% | 93.11% | |||||
F-score | 0.5792 | 0.3021 | 0.6250 | 0.9588 | |||||
Area 3 In the Tian Shan Mountains In late winter | Signal photon | 11,832 | 4365 | 11,765 | 4432 | 11,958 | 4239 | 14,666 | 1531 |
Noise photon | 2768 | 108,943 | 1333 | 110,378 | 2972 | 110,378 | 318 | 111,393 | |
Precision, Ppre | 81.04% | 89.82% | 80.09% | 97.88% | |||||
Recall, Prec | 73.05% | 72.64% | 73.83% | 90.55% | |||||
F-score | 0.7684 | 0.8032 | 0.7383 | 0.9407 | |||||
Area 4 In the Tian Shan Mountains In summer | Signal photon | 2638 | 2536 | 881 | 4293 | 2702 | 2472 | 4038 | 1136 |
Noise photon | 4884 | 147,877 | 119 | 152,642 | 4968 | 147,793 | 843 | 151,918 | |
Precision, Ppre | 35.07% | 88.10% | 35.23% | 82.73% | |||||
Recall, Prec | 50.99% | 17.03% | 52.22% | 78.04% | |||||
F-score | 0.4156 | 0.2854 | 0.4207 | 0.8032 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, Z.; Liu, X.; Ma, Y.; Xu, N.; Zhang, W.; Li, S. Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas. Remote Sens. 2021, 13, 863. https://doi.org/10.3390/rs13050863
Zhang Z, Liu X, Ma Y, Xu N, Zhang W, Li S. Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas. Remote Sensing. 2021; 13(5):863. https://doi.org/10.3390/rs13050863
Chicago/Turabian StyleZhang, Zhiyu, Xinyuan Liu, Yue Ma, Nan Xu, Wenhao Zhang, and Song Li. 2021. "Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas" Remote Sensing 13, no. 5: 863. https://doi.org/10.3390/rs13050863