Extraction of small river cross-section in mountainous based on ICESat-2 data

LI Dan; QIN Chao; XUE Yuan; XU Mengzhen; LIU Yingjie

doi:10.16511/j.cnki.qhdxxb.2024.21.021

Journal of Tsinghua University(Science and Technology) >

2024 , Vol. 64 >Issue 12: 2122 - 2131

DOI: https://doi.org/10.16511/j.cnki.qhdxxb.2024.21.021

HYDRAULIC ENGINEERING

Extraction of small river cross-section in mountainous based on ICESat-2 data

LI Dan ,
QIN Chao ,
XUE Yuan ,
XU Mengzhen ,
LIU Yingjie

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1. Emergency Science Research Academy, China Coal Research Institute, Beijing 100013, China;
2. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;
3. Key Laboratory of Hydrosphere Science of the Ministry of Water Resources, Tsinghua University, Beijing 100084, China;
4. Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

Received date: 2024-03-25

Online published: 2024-11-22

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Abstract

[Objective] Rivers carry water and material transport within certain boundary forms. Due to the difficulties of in-site measurement, limited hydrological observation stations, and the precision constraints of digital elevation model (DEM), there is a significant scarcity of information on small river cross-sections distributed in river source areas, mountainous regions, and remote areas, which hinders research on river hydrology and hydraulic processes. Since the beginning of this century, the International Association of Hydrological Sciences (IAHS) has been advocating for solutions to the challenges of hydrological prediction in ungauged basins (PUB). Despite large-scale remote sensing technology is increasingly applied to the extraction of river hydraulic parameters, the spatial resolution of satellite altimetry data is too low for small rivers (river width less that 150 m), which account for a high proportion of river networks. The National Aeronautics and Space Administration (NASA) launched the ICESat-2 (Ice, Cloud and land Elevation Satellite-2) satellite in 2018. This satellite was equipped with the Advanced Topographic Laser Altimeter System (ATLAS), a photon-counting LiDAR system for the first time. The light spot (footprint) it projects onto the Earth's surface has a diameter of about 17 m, with a center-to-center distance between spots of only 0.7 m. This allows for the acquisition of photon point cloud data with smaller spots and higher density along the track. The high-density photon point cloud provided by ICESat-2 offers the possibility of extracting hydrological parameters of narrow rivers with high precision. This study focuses on the small rivers with a river width of less than 10 m in the Huangfu River basin, a first-order tributary of the middle reaches of the Yellow River, which is a data-scarce region. [Methods] A method for extracting the cross-sectional morphology of small rivers using ICESat-2 ATL03 data has been proposed. First, photons with medium to high confidence levels are selected to eliminate most of the noise. Then, a smoothing filter is applied for precise de-noising. Finally, the point cloud that has been precisely de-noised is manually edited to generate a DEM. The cross-sectional morphology of three different locations of small rivers were extracted based on DEM and compared with unmanned aerial vehicle (UAV) in-situ measurement results. [Results] The results show that: (1) the method proposed in this study, which combines the selection of medium to high confidence point clouds with filtering denoising, can effectively remove the noise from photon point clouds, with a denoising rate above 63%; (2) the completeness and richness of ground points extracted based on ATL03 data are superior to those obtained by reclassifying ATL03 using ATL08 product; (3) the results of river cross-sections extracted based on ATL03 data are basically consistent with the UAV in-situ measurement results (R²>0.96, RMSE=0.69 m). [Conclusions] The research results preliminarily demonstrate the feasibility of using ICESat-2 altimetry data for extracting cross-sections of small rivers in data-scarce areas, partially supplying the three-dimensional spatiotemporal of small rivers in data-deficient areas, providing technical support for construction of three-dimensional river network across entire basins and the simulation of hydrological and hydraulic processes. This also indicates that ICESat-2 altimetry data have research prospects in obtaining hydrological parameters of rivers in data-scarce areas.

Key words： ICESat-2; digital elevation model; river cross-section morphology; smooth filtering; unmanned aerial vehicle

Cite this article

LI Dan , QIN Chao , XUE Yuan , XU Mengzhen , LIU Yingjie . Extraction of small river cross-section in mountainous based on ICESat-2 data[J]. Journal of Tsinghua University(Science and Technology), 2024 , 64(12) : 2122 -2131 . DOI: 10.16511/j.cnki.qhdxxb.2024.21.021

References

[1] 胡春宏, 郑春苗, 王光谦, 等. “西南河流源区径流变化和适应性利用”重大研究计划进展综述[J]. 水科学进展, 2022, 33(3): 337-359. HU C H, ZHENG C M, WANG G Q, et al. Reviews of the major research plan “runoff change and its adaptive management in the source region of major rivers in Southwestern China” [J]. Advances in Water Science, 2022, 33(3): 337-359. (in Chinese)
[2] POWERS C, HANLON R, SCHMALE D G. Tracking of a fluorescent dye in a freshwater lake with an unmanned surface vehicle and an unmanned aircraft system [J]. Remote Sensing, 2018, 10(1): 81.
[3] GU P F, LIAO A M, LIU H W, et al. River cross-section measurement using unreviewed aerial vehicle with an improved bathymetry instrument [J]. Journal of Hydrology, 2023, 624: 129737.
[4] QIN C, WU B S, WANG G Q, et al. Spatial distributions of at-many-stations hydraulic geometry for mountain rivers originated from the Qinghai-Tibet Plateau [J]. Water Resources Research, 2021, 57(6): e2020WR029090.
[5] MA H B, NITTROUER J A, FU X D, et al. Amplification of downstream flood stage due to damming of fine-grained rivers [J]. Nature Communications, 2022, 13(1): 3054.
[6] VERMA R K, ASHWINI K, SINGH A. Channel morphology and prediction of mid-line channel migration in the reach of Ganga River using GIS and ARIMA modeling during 1975—2020[J]. H2Open Journal, 2021, 4(1): 321-335.
[7] 薛源, 覃超, 徐梦珍, 等. 基于多源遥感数据的河流断面提取进展与展望[J/OL]. 遥感学报, (2024-03-08) [2024-03-12]. https://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20243315/. XUE Y, QIN C, XU M Z, et al. Progress and prospects in river cross section extraction based on multi-source remote sensing [J/OL]. National Remote Sensing Bulletin, (2024-03-08) [2024-03-12]. https://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20243315/. (in Chinese)
[8] 姚檀栋, 王伟财, 安宝晟, 等. 1949—2017年青藏高原科学考察研究历程[J]. 地理学报, 2022, 77(7): 1586-1602. YAO T D, WANG W C, AN B S, et al. The scientific expedition and research activities on the Tibetan Plateau in 1949—2017[J]. Acta Geographica Sinica, 2022, 77(7): 1586-1602. (in Chinese)
[9] ABBASPOUR K C, ROUHOLAHNEJAD E, VAGHEFI S, et al. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model [J]. Journal of Hydrology, 2015, 524: 733-752.
[10] CHEN X, LONG D, HONG Y, et al. Improved modeling of snow and glacier melting by a progressive two-stage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin? [J] Water Resources Research, 2017, 53(3): 2431-2466.
[11] FRIAS M C, LIU S X, MO X G, et al. River hydraulic modeling with ICESat-2 land and water surface elevation [J]. Hydrology and Earth System Sciences, 2023, 27(5): 1011-1032.
[12] 龙笛, 李雪莹, 李兴东, 等. 遥感反演2000—2020年青藏高原水储量变化及其驱动机制[J]. 水科学进展, 2022, 33(3): 375-389. LONG D, LI X Y, LI X D, et al. Remote sensing retrieval of water storage changes and underlying climatic mechanisms over the Tibetan Plateau during 2000—2020[J]. Advances in Water Science, 2022, 33(3): 375-389. (in Chinese)
[13] SCHERER D, SCHWATKE C, DETTMERING D, et al. ICESat-2 based river surface slope and its impact on water level time series from satellite altimetry [J]. Water Resources Research, 2022, 58(11): e2022WR032842.
[14] XUE Y, QIN C, WU B S, et al. Simulation of runoff process based on the 3-D river network [J]. Journal of Hydrology, 2023, 626: 130192.
[15] ZHOU H W, LIU S X, MO X G, et al. Calibrating a hydrodynamic model using water surface elevation determined from ICESat-2 derived cross-section and Sentinel-2 retrieved sub-pixel river width [J]. Remote Sensing of Environment, 2023, 298: 113796.
[16] PETIKAS I, KERAMARIS E, KANAKOUDIS V. A novel method for the automatic extraction of quality non-planar river cross-sections from digital elevation models [J]. Water, 2020, 12(12): 3553.
[17] TIAN X X, SHAN J. Comprehensive evaluation of the ICESat-2 ATL08 terrain product [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10): 8195-8209.
[18] 周智标, 周辉, 马跃, 等. ICESat-2激光雷达海面信号提取和海浪要素计算[J]. 红外与激光工程, 2023, 52(2): 204-212. ZHOU Z B, ZHOU H, MA Y, et al. ICESat-2 lidar sea surface signal extraction and ocean wave element calculation [J]. Infrared and Laser Engineering, 2023, 52(2): 204-212. (in Chinese)
[19] 朱笑笑, 王成, 习晓环, 等. ICESat-2星载光子计数激光雷达数据处理与应用研究进展[J]. 红外与激光工程, 2020, 49(11): 20200259. ZHU X X, WANG C, XI X H, et al. Research progress of ICESat-2/ATLAS data processing and applications [J]. Infrared and Laser Engineering, 2020, 49(11): 20200259. (in Chinese)
[20] LIU C, HU R H, WANG Y F, et al. Monitoring water level and volume changes of lakes and reservoirs in the Yellow River Basin using ICESat-2 laser altimetry and Google Earth Engine [J]. Journal of Hydro-environment Research, 2022, 44: 53-64.
[21] SONG L J, SONG C Q, LUO S X, et al. Integrating ICESat-2 altimetry and machine learning to estimate the seasonal water level and storage variations of national-scale lakes in China [J]. Remote Sensing of Environment, 2023, 294: 113657.
[22] XIAO W X, HUI F M, CHENG X, et al. An automated algorithm to retrieve the location and depth of supraglacial lakes from ICESat-2 ATL03 data [J]. Remote Sensing of Environment, 2023, 298: 113730.
[23] ZHANG Z J, BO Y C, JIN S G, et al. Dynamic water level changes in Qinghai Lake from integrating refined ICESat-2 and GEDI altimetry data (2018—2021) [J]. Journal of Hydrology, 2023, 617: 129007.
[24] 郭孝祖, 金双根. 利用ICESat-2激光测高监测长江三峡水位变化[J]. 测绘科学, 2022, 47(7): 21-26. GUO X Z, JIN S G. Water level changes in the Three Gorges of the Yangtze River from ICESat-2 laser altimeter data [J]. Science of Surveying and Mapping, 2022, 47(7): 21-26. (in Chinese)
[25] 薛源, 覃超, 吴保生, 等. 基于多源国产高分辨率遥感影像的山区河流信息自动提取[J]. 清华大学学报(自然科学版), 2023, 63(1): 134-145. XUE Y, QIN C, WU B S, et al. Automatic extraction of mountain river information from multiple Chinese high-resolution remote sensing satellite images [J]. Journal of Tsinghua University (Science and Technology), 2023, 63(1): 134-145. (in Chinese)
[26] 毕彩霞. 黄河中游皇甫川流域产沙性降雨及其对径流输沙的影响[D]. 北京: 中国科学院研究生院, 2013. BI C X. Study on watershed sediment yield rainfall and contributions of the watershed sediment yield rainfall to runoff and sediment in Huangfuchuan River Basin [D]. Beijing: Graduate University of Chinese Academy of Sciences, 2013. (in Chinese)
[27] WU T, LI J Y, LI T J, et al. High-efficient extraction of drainage networks from digital elevation models constrained by enhanced flow enforcement from known river maps [J]. Geomorphology, 2019, 340: 184-201.
[28] 何明琴, 金双根, 张志杰, 等. 利用ICESat-2激光测高监测和评估鄱阳湖水位变化特征[J/OL]. 南京信息工程大学学报(自然科学版), (2022-06-17) [2024-03-12]. https://kns.cnki.net/kcms/detail/32.1801.n.20220616.0946.002.html. HE M Q, JIN S G, ZHANG Z J, et al. Monitoring and assessment of water level variation characteristics in Poyang Lake by ICESat-2 altimeter [J/OL]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), (2022-06-17) [2024-03-12]. https://kns.cnki.net/kcms/detail/32.1801.n.20220616.0946.002.html. (in Chinese)
[29] NEUMANN T A, BRENNER A, HANCOCK D, et al. Ice, Cloud, and Land Elevation Satellite (ICESat-2) Project Algorithm Theoretical Basis Document (ATBD) for Global Geolocated Photons ATL03, Version 6. ICESat-2 Project [Z/OL]. (2022-10-04)[2024-03-12]. https://nsidc.org/sites/default/files/documents/technical-reference/icesat2_atl03_Atbd_v006.pdf.
[30] LI D, WANG G, QIN C, et al. River extraction under bankfull discharge conditions based on Sentinel-2 imagery and DEM data [J]. Remote Sensing, 2021, 13: 2650.
[31] LI D, XUE Y, QIN C, et al. A bankfull geometry dataset for major exorheic rivers on the qinghai-tibet plateau [J]. Scientific Data, 2022, 9(1): 498.
[32] 张少华, 施银迪, 谭莲红, 等. 基于改进DBSCAN的单光子激光点云去噪算法[J]. 矿山测量, 2022, 50(1): 32-37. ZHANG S H, SHI Y D, TAN L H, et al. Single-photon laser point cloud noise reduction algorithm based on improved DBSCAN [J]. Mine Surveying, 2022, 50(1): 32-37. (in Chinese)

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