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2023 Vol. 29, No. 6
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YANG Yongzhong, REN Junjie, LI Dongchen. 2023. Quantitative staging of alluvial fan geomorphic surfaces in arid areas based on SAR imagery: A case study of the Shule River alluvial fan in the western desert region of the Hexi Corridor. Journal of Geomechanics, 29(6): 842-855. doi: 10.12090/j.issn.1006-6616.2023080
Citation: YANG Yongzhong, REN Junjie, LI Dongchen. 2023. Quantitative staging of alluvial fan geomorphic surfaces in arid areas based on SAR imagery: A case study of the Shule River alluvial fan in the western desert region of the Hexi Corridor. Journal of Geomechanics, 29(6): 842-855. doi: 10.12090/j.issn.1006-6616.2023080
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Quantitative staging of alluvial fan geomorphic surfaces in arid areas based on SAR imagery: A case study of the Shule River alluvial fan in the western desert region of the Hexi Corridor

  • 1.

    School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • 2.

    National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China

    • Fund Project: This research is financially supported by the National Natural Science Foundation of China (Grants No. U2139201, 41941016, and U1839204), the Key Program of the Chinese Academy of Sciences (Grant No. KFZD-SW-422), and the Research Fund of the National Institute of Natural Hazards, Ministry of Emergency Management of China (Grant No. ZDJ2017-24).
More Information
Received Date:  21 May 2023
Revised Date:  26 September 2023
Accepted Date:  20 October 2023
Publish Date:  03 December 2023
    Abstract

  • The alluvial fans and river terraces formed by river processes effectively record past tectonic activities, climate changes, and geomorphic evolution processes. Accurately dividing the alluvial fan into stages is the basis for the subsequent research. Previous researchers used L-band SAR backscatter coefficient values as a substitute parameter for geomorphic roughness to achieve quantitative zoning of geomorphic surfaces. However, these studies did not consider the impact of different time data sources on the geomorphic surface results. This study selects the Shule River alluvial fan as the research object. It determines the optimal data source by analyzing the posterior statistical indicators of multi-temporal L-band SAR data and evaluating atmospheric conditions. The maximum likelihood classification method is used to complete the classification of backscatter intensity values and achieve quantitative staging of the geomorphic surface. The results indicate that the posterior statistical indicators of staging can be used as the standard for selecting the best temporal image data to obtain better staging results. L-band HH monopolarization data provides better staging results, demonstrating advantages in distinguishing landforms of different ages compared to C-band data. Moreover, L-band data is more accessible and holds potential for automated staging. SAR image quality and staging results are closely related to imaging atmospheric conditions but show minimal seasonal dependence. Therefore, the study recommends prioritizing images with low surface water content during imaging, such as in high-evaporation intensity summer seasons. The proposed method for analyzing remote sensing data quality and staging landforms can be applied to rapidly and quantitatively stage large-scale alluvial fans in arid regions, providing valuable information for studies on tectonics and climate.

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  • [1] AUBERT M, BAGHDADI N, ZRIBI M, et al. , 2011. Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust[J]. Remote Sensing of Environment, 115(8): 1801-1810. doi: 10.1016/j.rse.2011.02.021

    CrossRef Google Scholar

    [2] AYARI E, KASSOUK Z, LILI-CHABAANE Z, et al. , 2021. Cereal crops soil parameters retrieval using L-band ALOS-2 and C-band sentinel-1 sensors[J]. Remote Sensing, 13(7): 1393. doi: 10.3390/rs13071393

    CrossRef Google Scholar

    [3] BEAUDOIN A, LE TOAN T, GWYN Q H J, 1990. SAR observations and modeling of the C-band backscatter variability due to multiscale geometry and soil moisture[J]. IEEE Transactions on Geoscience and Remote Sensing, 28(5): 886-895. doi: 10.1109/36.58978

    CrossRef Google Scholar

    [4] BLAIR T C, MCPHERSON J G, 1994. Alluvial fan processes and forms[M]//ABRAHAMS A D, PARSONS A J. Geomorphology of desert environments. Dordrecht: Springer: 354-402.

    Google Scholar

    [5] BULL W B, 1977. The alluvial-fan environment[J]. Progress in Physical Geography: Earth and Environment, 1(2): 222-270. doi: 10.1177/030913337700100202

    CrossRef Google Scholar

    [6] BULL W B, 1991. Geomorphic responses to climatic change[M]. New York: Oxford University Press.

    Google Scholar

    [7] COPPO P, LUZI G, SCHIAVON G, 1995. Understanding microwave surface backscattering of bare soil by comparing models and experimental data collected during two different airborne campaigns[C]//1995 International geoscience and remote sensing symposium, IGARSS'95. Quantitative remote sensing for science and applications. Firenze: IEEE: 1346-1348.

    Google Scholar

    [8] D'ARCY M, MASON P J, RODA-BOLUDA D C, et al. , 2018. Alluvial fan surface ages recorded by Landsat-8 imagery in Owens Valley, California[J]. Remote Sensing of Environment, 216: 401-414. doi: 10.1016/j.rse.2018.07.013

    CrossRef Google Scholar

    [9] DE JEU R, OWE M, 2012. TMI/TRMM surface soil moisture (LPRM) L3 1 day 25 km x 25 km nighttime V001, Edited by Goddard Earth Sciences Data and Information Services Center (GES DISC) (Bill Teng), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC),doi: 10.5067/GWHRZEL8SA21.

    Google Scholar

    [10] ESCORIHUELA M J, KERR Y H, DE ROSNAY P, et al. , 2007. A simple model of the bare soil microwave emission at L-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 45(7): 1978-1987. doi: 10.1109/TGRS.2007.894935

    CrossRef Google Scholar

    [11] EVANS D L, FARR T G, VAN ZYL J J, 1992. Estimates of surface roughness derived from synthetic aperture radar (SAR) data[J]. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 382-389. doi: 10.1109/36.134087

    CrossRef Google Scholar

    [12] FAN L, ATKINSON P M, 2018. A new multi-resolution based method for estimating local surface roughness from point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 369-378. doi: 10.1016/j.isprsjprs.2018.08.003

    CrossRef Google Scholar

    [13] FRANKEL K L, DOLAN J F, 2007. Characterizing arid region alluvial fan surface roughness with airborne laser swath mapping digital topographic data[J]. Journal of Geophysical Research: Earth Surface, 112(F2): F02025.

    Google Scholar

    [14] FREEMAN A, 1992. SAR calibration: an overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 30(6): 1107-1121. doi: 10.1109/36.193786

    CrossRef Google Scholar

    [15] FUNG A K, LI Z, CHEN K S, 1992. Backscattering from a randomly rough dielectric surface[J]. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 356-369. doi: 10.1109/36.134085

    CrossRef Google Scholar

    [16] GREELEY R, LANCASTER N, SULLIVAN R J, et al. , 1988. A relationship between radar backscatter and aerodynamic roughness: preliminary results[J]. Geophysical Research Letters, 15(6): 565-568. doi: 10.1029/GL015i006p00565

    CrossRef Google Scholar

    [17] GUO N, 2003. Vegetation index and its advances[J]. Arid Meteorology, 21(4): 71-75. (in Chinese with English abstract)

    Google Scholar

    [18] GUO Q N, ZHOU Z F, WANG S, 2017. The source, flow rates, and hydrochemical evolution of groundwater in an alluvial fan of Qilian Mountain, northwest China[J]. Water, 9(12): 912. doi: 10.3390/w9120912

    CrossRef Google Scholar

    [19] GURALNIK B, MATMON A, AVNI Y, et al. , 2010. 10Be exposure ages of ancient desert pavements reveal Quaternary evolution of the Dead Sea drainage basin and rift margin tilting[J]. Earth and Planetary Science Letters, 290(1-2): 132-141. doi: 10.1016/j.jpgl.2009.12.012

    CrossRef Google Scholar

    [20] HAN L F, LIU J, YUAN Z D, et al. , 2019. Extracting features of alluvial fan and discussing landforms evolution based on high-resolution topography data: taking alluvial fan of Laohushan along Haiyuan fault zone as an instance[J]. Seismology and Geology, 41(2): 251-265. (in Chinese with English abstract)

    Google Scholar

    [21] HETZ G, MUSHKIN A, BLUMBERG D G, et al. , 2016. Estimating the age of desert alluvial surfaces with spaceborne radar data[J]. Remote Sensing of Environment, 184: 288-301. doi: 10.1016/j.rse.2016.07.006

    CrossRef Google Scholar

    [22] HOLECZ F, DWYER E, MONACO S, et al. , 2000. An operational rice field mapping tool using spaceborne SAR data[C]//ERS-ENVISAT symposium. mat.

    Google Scholar

    [23] HUANG F P, ZHANG H P, XIONG J G, et al. , 2021. Estimation of displacements along strike-slip fault on a million-year timescale: a case study of the AltynTagh fault system[J]. Journal of Geomechanics, 27(2): 208-217,doi: 10.12090/j.issn.1006-6616.2021.27.02.020. (in Chinese with English abstract)

    CrossRef Google Scholar

    [24] HUFFMAN G J, STOCKER E F, BOLVIN D T, et al. , 2019. GPM IMERG final precipitation L3 1 day 0.1 degree x 0.1 degree V06, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), doi: 10.5067/GPM/IMERGDF/DAY/06.

    Google Scholar

    [25] HUGGETT R J, 2016. Fundamentals of Geomorphology [M]. Routledge, London, New York.

    Google Scholar

    [26] LI S, 2007. Soil moisture inversion model research of multi-band and multi-polarization SAR based on IEM[D]. Beijing: Chinese Academy of Agricultural Sciences. (in Chinese with English abstract)

    Google Scholar

    [27] LIN G Q, GUO H D, ZHANG L, 2013. Study on roughness inversion of alluvial fan in arid areas based on SAR data[J]. Remote Sensing Technology and Application, 28(4): 659-665. (in Chinese with English abstract)

    Google Scholar

    [28] LUBETKIN L K C, CLARK M M, 1988. Late Quaternary activity along the Lone Pine fault, eastern California[J]. GSA Bulletin, 100(5): 755-766. doi: 10.1130/0016-7606(1988)100<0755:LQAATL>2.3.CO;2

    CrossRef Google Scholar

    [29] MAO H L, ZHAO H, LU Y C, et al. , 2007. Pollen assemblages and environment evolution in Shule River alluvial fan oasis of Gansu[J]. Acta Geoscientia Sinica, 28(6): 528-534. (in Chinese with English abstract)

    Google Scholar

    [30] MATHER P AND TSO B, 2016. Classification methods for remotely sensed data[M]. CRC press.

    Google Scholar

    [31] MATMON A, SCHWARTZ D P, FINKEL R, et al. , 2005. Dating offset fans along the Mojave section of the San Andreas fault using cosmogenic 26Al and 10Be[J]. GSA Bulletin, 117(5-6): 795-807.

    Google Scholar

    [32] MATTIA F, LE TOAN T, SOUYRIS J C, et al. , 1997. The effect of surface roughness on multifrequency polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 954-966. doi: 10.1109/36.602537

    CrossRef Google Scholar

    [33] REGMI NR, MCDONALD E V, BACON S N, 2014. Mapping Quaternary alluvial fans in the southwestern United States based on multiparameter surface roughness of lidar topographic data [J]. Journal of Geophysical Research: Earth Surface, 119(1): 12—27. doi: 10.1002/2012JF002711

    CrossRef Google Scholar

    [34] ROBINSON GU N P, ALLRED B W, JONES M O, et al. , 2017. A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States[J]. Remote sensing, 9(8): 863. doi: 10.3390/rs9080863

    CrossRef Google Scholar

    [35] SADEH Y, COHEN H, MAMAN S, et al. , 2018. Evaluation of Manning’s n roughness coefficient in arid environments by using SAR backscatter[J]. Remote Sensing, 10(10): 1505. doi: 10.3390/rs10101505

    CrossRef Google Scholar

    [36] SHAO Y, LV Y, DONG Q, et al. , 2002. Study on soil microwave dielectric characteristic as salinity and water content[J]. Journal of Remote Sensing, 6(6): 416-423. (in Chinese with English abstract)

    Google Scholar

    [37] SU Q, REN J J, LIANG O B, et al. , 2020. Quantitative mapping of the Moleqie River alluvial fan morphologic units in China based on ALOS PALSAR data[J]. Seismology and Geology, 42(1): 79-94. (in Chinese with English abstract)

    Google Scholar

    [38] SU Q, REN J J, WANG X Y, et al. , 2023. A power-law relation of surface roughness and ages of alluvial fans in a hyperarid environment: a case study in the Dead Sea area[J]. Progress in Physical Geography: Earth and Environment, 47(3): 348-368. doi: 10.1177/03091333221118641

    CrossRef Google Scholar

    [39] SUN P, 2017. Modeling the water infiltration and evaporation in unsaturated zone of the extremely arid area[D]. Lanzhou: Lanzhou University. (in Chinese with English abstract)

    Google Scholar

    [40] ULABY F T, BRADLEY G A, DOBSON M C, 1979. Microwave backscatter dependence on surface roughness, soil moisture, and soil texture(PartⅡ): Vegetation-covered soil [J]. IEEE Transactions on Geoscience Electronics, 17(2): 33—40. doi: 10.1109/TGE.1979.294626

    CrossRef Google Scholar

    [41] WANG P, 2003. Development of the Shulehe alluvial-fan and its response to tectonic activity in Gansu Province, China: characteristics of neotectonic activity of the East end of the Altyn Tagh fault[D]. Beijing: Institute of Geology, China Earthquake Administration. (in Chinese with English abstract)

    Google Scholar

    [42] WANG P, LU Y C, DING G Y, et al. , 2004. Response of the development of the shule river alluvial fan to tectonic activity[J]. Quaternary Sciences, 24(1): 74-81. (in Chinese with English abstract)

    Google Scholar

    [43] WU J, YANG Q K, LI Y R, 2018. Partitioning of terrain features based on roughness[J]. Remote Sensing, 10(12): 1985. doi: 10.3390/rs10121985

    CrossRef Google Scholar

    [44] XU X W, TAPPONNIER P, VAN DER WOERD J, et al. , 2003. Late Quaternary sinistral slip rate along the Altyn Tagh fault and its structural transformation model [J]. Science in China Series D 33(10): 967-974. (in Chinese with English abstract)

    Google Scholar

    [45] YANG X, 2008. An elementary introduction to supervised and unsupervised classification of remote sensing image[J]. Acta Geologica Sichuan, 28(3): 251-254. (in Chinese with English abstract)

    Google Scholar

    [46] YUN L, ZHANG J, WANG J, et al. , 2021. Discovery of active faults in the southern Beishan area, NW China: implications for regional tectonics[J]. Journal of Geomechanics, 27(2): 195-207,doi: 10.12090/j.issn.1006-6616.2021.27.02.019. (in Chinese with English abstract)

    CrossRef Google Scholar

    [47] ZELENIN E, BACHMANOV D, GARIPOVA S, et al. , 2022. The Active Faults of Eurasia Database (AFEAD): the ontology and design behind the continental-scale dataset[J]. Earth System Science Data, 14(10): 4489-4503. doi: 10.5194/essd-14-4489-2022

    CrossRef Google Scholar

    [48] ZHANG L, GUO H D, 2013. The temporal-spatial distribution of Shule river alluvial fan units in China based on SAR data and OSL dating[J]. Remote Sensing, 5(12): 6997-7016. doi: 10.3390/rs5126997

    CrossRef Google Scholar

    [49] ZHENG R Z, 2005. Tectonic uplift and deformation mechanism of the Altun structural system since the middle-late period of late Pleistocene time[D]. Beijing: Institute of Geology, China Earthquake Administration. (in Chinese with English abstract)

    Google Scholar

    [50] ZRIBI M, BAGHDADI N, HOLAH N, et al. , 2005. Evaluation of a rough soil surface description with ASAR-ENVISAT radar data[J]. Remote Sensing of Environment, 95(1): 67-76. doi: 10.1016/j.rse.2004.11.014

    CrossRef Google Scholar

    [51] ZHOU Z H, 2021. Machine learning[M]. Springer Nature.

    Google Scholar

    [52] 郭铌, 2003. 植被指数及其研究进展[J]. 干旱气象, 21(4): 71-75.

    Google Scholar

    [53] 韩龙飞, 刘静, 袁兆德, 等, 2019. 基于高分辨率地形数据的冲洪积扇特征提取与演化模式讨论: 以海原断裂带老虎山地区冲洪积扇为例[J]. 地震地质, 41(2): 251-265. doi: 10.3969/j.issn.0253-4967.2019.02.001

    CrossRef Google Scholar

    [54] 黄飞鹏, 张会平, 熊建国, 等, 2021. 走滑断裂百万年时间尺度位移量估计及其在阿尔金断裂系中的应用[J]. 地质力学学报, 27(2): 208-217,doi:10.12090/j.issn.1006-6616.2021.27.02.020.

    CrossRef Google Scholar

    [55] 李森, 2007. 基于IEM的多波段、多极化SAR土壤水分反演算法研究[D]. 北京: 中国农业科学院.

    Google Scholar

    [56] 林国青, 郭华东, 张露, 2013. 基于SAR数据的干旱区冲积扇地表粗糙度反演[J]. 遥感技术与应用, 28(4): 659-665. doi: 10.11873/j.issn.1004-0323.2013.4.659

    CrossRef Google Scholar

    [57] 毛洪亮, 赵华, 卢演俦, 等, 2007. 甘肃疏勒河冲积扇绿洲全新世孢粉组合和环境演化[J]. 地球学报, 28(6): 528-534. doi: 10.3321/j.issn:1006-3021.2007.06.003

    CrossRef Google Scholar

    [58] 邵芸, 吕远, 董庆, 等, 2002. 含水含盐土壤的微波介电特性分析研究[J]. 遥感学报, 6(6): 416-423. doi: 10.11834/jrs.20020604

    CrossRef Google Scholar

    [59] 苏强, 任俊杰, 梁欧博, 等, 2020. 基于ALOS PALSAR影像的莫勒切河洪积扇地貌面定量分期[J]. 地震地质, 42(1): 79-94. doi: 10.3969/j.issn.0253-4967.2020.01.006

    CrossRef Google Scholar

    [60] 孙朋, 2017. 极端干旱区沙漠包气带降水入渗与蒸发规律模拟研究[D]. 兰州: 兰州大学.

    Google Scholar

    [61] 王萍, 2003. 甘肃疏勒河冲积扇发育对构造活动的响应: 兼论阿尔金断裂东端新构造活动特征[D]. 北京: 中国地震局地质研究所.

    Google Scholar

    [62] 王萍, 卢演俦, 丁国瑜, 等, 2004. 甘肃疏勒河冲积扇发育特征及其对构造活动的响应[J]. 第四纪研究, 24(1): 74-81. doi: 10.3321/j.issn:1001-7410.2004.01.009

    CrossRef Google Scholar

    [63] 徐锡伟, TAPPONNIER P, VAN DER WOERD J, 等, 2003. 阿尔金断裂带晚第四纪左旋走滑速率及其构造运动转换模式讨论[J]. 中国科学(D辑), 33(10): 967-974. doi: 10.3321/j.issn:1006-9267.2003.10.007

    CrossRef Google Scholar

    [64] 杨鑫, 2008. 浅谈遥感图像监督分类与非监督分类[J]. 四川地质学报, 28(3): 251-254. doi: 10.3969/j.issn.1006-0995.2008.03.020

    CrossRef Google Scholar

    [65] 云龙, 张进, 王驹, 等, 2021. 甘肃北山南部活动断裂的发现及其区域构造意义[J]. 地质力学学报, 27(2): 195-207,doi:10.12090/j.issn.1006-6616.2021.27.02.019.

    CrossRef Google Scholar

    [66] 郑荣章, 2005. 阿尔金构造系晚更新世中晚期以来的构造隆升及其变形机制[D]. 北京: 中国地震局地质研究所.

    Google Scholar

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