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Error elimination and time sequence inversion of Landsat 5 TM dune migration field： Taking sand dunes in northwestern of Mu Us Sandy as example

  • role： First author第一作者
  • Affiliation：

    Radar Remote Sensing Laboratory, School of Earth Sciences and Information Physics, Central South University, Changsha 410083, China

  • Email：1552128516@qq.com
  • Introduction：杨定江，研究方向为光学影像匹配与沙丘迁移监测。E-mail：1552128516@qq.com
YANG Dingjiang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Radar Remote Sensing Laboratory, School of Earth Sciences and Information Physics, Central South University, Changsha 410083, China

  • Email：fredgps@csu.edu.cn
  • Introduction：冯光财，研究方向为影像大地测量与地球物理参数反演。E-mail：fredgps@csu.edu.cn
FENG Guangcai1*,  
  • Affiliation：

    Radar Remote Sensing Laboratory, School of Earth Sciences and Information Physics, Central South University, Changsha 410083, China

FENG Zhixiong1,  
  • Affiliation：

    Key Laboratory of Environmental Change and Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences & National Qinghai-Tibet Plateau Science Data Center, Beijing 100101 ,China

    State Key Laboratory of Tibetan Plateau Earth System Science (LATPES), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

LI Guoshuai23,  
  • Affiliation：

    Basic Surveying and Mapping Institute of Qinghai Province, Xining 810001, China

ZHANG Jie4

Resümee

Processing Landsat 5 TM through COSI-Corr software can better quantify the migration mode and speed of sand dunes. This way can also provide specific guidance and suggestions for the implementation of sand prevention and control projects. At present, few studies on the error source analysis and timing inversion of Landsat 5 TM migration field are available. This work takes the northwestern part of the Mu Us Sand Land as the study area to obtain the dune migration time series and annual average velocity field from 1991 to 2000. Results show that the main errors of the Landsat 5 TM migration field are the orbit error, the attitude angle error, and the miscorrelated noise. After the error is eliminated, the accuracy has been increased by 23%—34%, 4%—20%, and 2%—5% successively, and it can be increased by 13%—14% after inversion by the least square method. The monitoring results show that the sand dunes in the northwest of the Mu Us Sandy Land continued to advance to the southeast from 1991 to 2000, and the maximum speed could exceed 6 m/yr. However, under the action of the northwest-southeast wind, periodic back-and-forth movement occurred along the northwest-southeast direction. The study area has a velocity of 0—1 m/year accounting for 63.9%, and the velocity of more than 3 m/a is only 4.2%. The error elimination and time sequence inversion of Landsat 5 TM can effectively improve the accuracy of the migration field, which results in a more accurate and reliable time sequence. Different from previous studies, the error elimination and time sequence inversion of the dune migration field can reflect the actual wind energy environment on the dune surface in more detail.

Schlüsselwort

Landsat 5 TM;COSI-Corr;error analysis;dune migration;Mu Us sandy

References

  1. 1.
    Ali E, Xu W B and Ding X L. 2020. Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 106-124
  2. 2.
    Ayoub F, Avouac J P, Newman C E, Richardson M I, Lucas A, Leprince S and Bridges N T. 2014. Threshold for sand mobility on mars calibrated from seasonal variations of sand flux. Nature Communications, 5(1): 5096
  3. 3.
    Ayoub F, Leprince S and Keene L. 2009. User’s Guide to COSI-CORR Co-Registration of Optically Sensed Images and Correlation. California Institute of Technology,Pasadena,CA,USA,1-38.
  4. 4.
    Baird T, Bristow C S and Vermeesch P. 2019. Measuring sand dune migration rates with COSI-Corr and landsat: opportunities and challenges. Remote Sensing, 11(20): 2423
  5. 5.
    Bridges N T, Ayoub F, Avouac J P, Leprince S, Lucas A and Mattson S. 2012. Earth-like sand fluxes on mars. Nature, 485(7398): 339-342
  6. 6.
    Bridges N T, Sullivan R, Newman C E, Navarro S, Van Beek J, Ewing R C, Ayoub F, Silvestro S, Gasnault O, Le Mouélic S, Lapotre M G A and Rapin W. 2017. Martian aeolian activity at the Bagnold Dunes, Gale Crater: the view from the surface and orbit. Journal of Geophysical Research: Planets, 122(10): 2077-2110
  7. 7.
    Brown D G and Arbogast A F. 1999. Digital photogrammetric change analysis as applied to active coastal dunes in Michigan. Photogrammetric Engineering and Remote Sensing, 65(4): 467-474
  8. 8.
    Bruno L, Horvat M and Raffaele L. 2018. Windblown sand along railway infrastructures: a review of challenges and mitigation measures. Journal of Wind Engineering and Industrial Aerodynamics, 177: 340-365
  9. 9.
    Dabboor M D, Braun A and Kneen M A. 2013. Tracking sand dune migration in the Rub Al-Khali with ICESat laser altimetry. International Journal of Remote Sensing, 34(11): 3832-3847
  10. 10.
    Ding C, Feng G C, Li Z W, Shan X J, Du Y N and Wang H Q. 2016. Spatio-temporal error sources analysis and accuracy improvement in landsat 8 image ground displacement measurements. Remote Sensing, 8(11): 937
  11. 11.
    Ding C, Feng G C, Liao M S, Tao P J, Zhang L and Xu Q. 2021. Displacement history and potential triggering factors of Baige landslides, China revealed by optical imagery time series. Remote Sensing of Environment, 254: 112253
  12. 12.
    Ding C, Feng G C, Liao M S and Zhang L. 2020a. Change detection, risk assessment and mass balance of mobile dune fields near Dunhuang Oasis with optical imagery and global terrain datasets. International Journal of Digital Earth, 13(12): 1604-1623
  13. 13.
    Ding C, Zhang L, Liao M S, Feng G C, Dong J, Ao M and Yu Y H. 2020b. Quantifying the spatio-temporal patterns of dune migration near Minqin Oasis in northwestern China with time series of Landsat-8 and Sentinel-2 observations. Remote Sensing of Environment, 236: 111498
  14. 14.
    Dong G R, Li B S, Gao S Y, Wu Z and Shao Y J. 1983. Discovery of quaternary Paleoeolian sand on the Ordos Plateau and its significance. Chinese Science Bulletin, 28(16): 998-1001
  15. 15.
    Dong Z B, Wang X M and Chen G T. 2000. Monitoring sand dune advance in the Taklimakan Desert. Geomorphology, 35(3/4): 219-231
  16. 16.
    Feng G C, Xu B, Shan X J, Li Z W and Zhang G H. 2015. Coseismic deformation and source parameters of the 24 September 2013 Awaran, Pakistan MW7.7 Earthquake derived from optical Landsat 8 satellite images. Chinese Journal of Geophysics, 58(5): 1634-1644
  17. 17.
    Fryberger S G. 1979. Dune forms and wind regimes//McKee E D, ed. A Study of Global Sand Seas. Reston: United States Geological Survey: 137-140
  18. 18.
    Guo H D. 1986. Space shuttle radar response from sand dunes and subsurface rocks of alashan Plateau. Remote Sensing of Environment, 1(1): 34-43
  19. 19.
    Ha S, Zhuang Y M, Wang L and Wang Z. 2006. Grain-size variation on a transverse dune and response to wind direction changes on southern edge of Mu us desert. Progress in Geography, 25(6): 42-51
  20. 20.
    He L J, Feng G C, Feng Z X and Gao H. 2019. Coseismic deformations of 2016 MW7.8 Kaikoura, New Zealand earthquake, using sentinel-2 optical images. Acta Geodaetica et Cartographica Sinica, 48(3): 339-351
  21. 21.
    年MW7.8新西兰凯库拉地震为例. 测绘学报, 48(3): 339-351
  22. 22.
    Hugenholtz C H and Barchyn T E. 2010. Spatial analysis of sand dunes with a new global topographic dataset: new approaches and opportunities. Earth Surface Processes and Landforms, 35(8): 986-992
  23. 23.
    Jin Y Q. 1997. Analysis of SSM/I data over the desert areas of China. Journal of Remote Sensing, 1(3): 192-197
  24. 24.
    Leprince S, Ayoub F, Klinger Y and Avouac J P. 2007a. Co-registration of optically sensed images and correlation (COSI-Corr): an operational methodology for ground deformation measurements//2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona: IEEE: 1943-1946
  25. 25.
    Leprince S, Barbot S, Ayoub F and Avouac J P. 2007b. Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Transactions on Geoscience and Remote Sensing, 45(6): 1529-1558
  26. 26.
    Li Z P, Yue L P, Xue X X, Wang M, Yang L R, Nie H G and Chen Chao. 2006. Grain size distribution characteristics of different geo-genetic types of sandy desertification and their geological significance in southeast Mo Us desert. Acta Sedimentologica Sinica, 24(2): 267-275
  27. 27.
    Liu Q Q and Yang X P. 2018. Geochemical composition and provenance of aeolian sands in the Ordos Deserts, northern China. Geomorphology, 318: 354-374
  28. 28.
    Necsoiu M, Leprince S, Hooper D M, Dinwiddie C L, McGinnis R N and Walter G R. 2009. Monitoring migration rates of an active subarctic dune field using optical imagery. Remote Sensing of Environment, 113(11): 2441-2447
  29. 29.
    Ould Ahmedou D, Ould Mahfoudh A, Dupont P, Ould El Moctar A, Valance A and Rasmussen K R. 2007. Barchan dune mobility in Mauritania related to dune and interdune sand fluxes. Journal of Geophysical Research: Earth Surface, 112(F2): F02016
  30. 30.
    Pang Y J, Wu B, Jia X H, Shi L, Gaodabuxilatu and Li S Z. 2019. Characteristics of wind regime and drift potential in Mu Us sandy land. Journal of Desert Research, 39(1): 62-67
  31. 31.
    Scherler D, Leprince S and Strecker M R. 2008. Glacier-surface velocities in alpine terrain from optical satellite imagery—Accuracy improvement and quality assessment. Remote Sensing of Environment, 112(10): 3806-3819
  32. 32.
    Shao M, Luo W Y and Che X H. 2020. Preliminary application and accuracy assessment of COSI-Corr in the study of Aeolian geomorphology. Journal of Desert Research, 40(3): 151-158
  33. 33.
    Silvestro S, Vaz D A, Yizhaq H and Esposito F. 2016. Dune-like dynamic of Martian Aeolian large ripples. Geophysical Research Letters, 43(16): 8384-8389
  34. 34.
    Tsoar H and Blumberg D G. 2002. Formation of parabolic dunes from barchan and transverse dunes along Israel’s Mediterranean coast. Earth Surface Processes and Landforms, 27(11): 1147-1161
  35. 35.
    Vermeesch P and Drake N. 2008. Remotely sensed dune celerity and sand flux measurements of the world’s fastest barchans (Bodélé, Chad). Geophysical Research Letters, 35(24): L24404
  36. 36.
    Wang J P, Liu L Y and Shen L L. 2013. Research of the Barchan dunes movement in the Mu Us sandy land on google earth software. Remote Sensing Technology and Application, 28(6): 1094-1100
  37. 37.
    Wang X M, Chen F H, Hasi E and Li J C. 2008. Desertification in China: an assessment. Earth-Science Reviews, 88(3/4): 188-206
  38. 38.
    Xu M J, Lü P, Xiao N, Yang J H, Liu Z Y, Feng M Y and Liang Z. 2020. Effect of vegetation cover on dune migration in northwest Mu Us sandy land. Journal of Desert Research, 40(4): 71-80
  39. 39.
    Yao Z Y, Wang T, Han Z W, Zhang W M and Zhao A G. 2007. Migration of sand dunes on the northern Alxa Plateau, Inner Mongolia, China. Journal of Arid Environments, 70(1): 80-93
  40. 40.
    Zhu Z D, Chen Z P, Wu Z, Li J Z, Li B Y and Wu G C. 1981. Aeolian Landform Research of Taklimakan Desert. Beijing: Science Press

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