Advertisement

New automated method for extracting river information using optimized spectral threshold water index

  • Chaojun Li
  • Shijie Wang
  • Xiaoyong BaiEmail author
  • Qiu Tan
  • Yujie Yang
  • Qin Li
  • Luhua Wu
  • Jianyong Xiao
  • Qinghuan Qian
  • Fei Chen
  • Huiwen Li
  • Yue Cao
  • Mingming Wang
  • Jinfeng Wang
  • Shiqi Tian
  • Qian Lu
Original Paper
  • 86 Downloads

Abstract

The accurate extraction of mountain river information is highly significant in water resource investigation and ecological environment protection. However, there are some problems in the existing methods of river information extraction, which are mainly the interference from shadows and buildings. Such interference leads to erroneous and redundant extraction of river information, which leads to inaccuracy or incompleteness. In this study, a precise extraction method of mountain river information is established using Landsat8 image and digital elevation model. The main steps of the river extraction method are as follows: (1) we propose the optimized spectral threshold water index to extract river information; (2) based on digital elevation model data, we simulate the mountain shadows of the study area to remove interference from them; (3) we establish the buffer zone of the river network using digital elevation model data to solve the problem of redundant extraction of river information; (4) we separately calculate and then standardize land surface temperature, albedo, and normalized different building index. The effects of buildings near the river are removed. Results show a relative accuracy of 97.52%. The new method decreases the interference of mountain shadows and buildings.

Keywords

Landsat8 Digital elevation model Mountain shadow River buffer zone Building removal 

Notes

Acknowledgments

This research work was supported jointly by the National Key Research Program of China (Nos. 2016YFC0502300 and 2016YFC0502102), Chinese Academy of Science and Technology Services Network Program (No. KFJ-STS-ZDTP-036) and International Cooperation Agency International Partnership Program (Nos. 132852KYSB20170029 and 2014-3), Guizhou High-Level Innovative Talent Training Program “Ten” Level Talents Program (No. 2016-5648), United Fund of Karst Science Research Center (No. U1612441), International Cooperation Research Projects of the National Natural Science Fund Committee (Nos. 41571130074 and 41571130042), and the Science and Technology Plan of Guizhou Province of China (No. 2017-2966).

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

References

  1. Acharya TD, Dong HL, Yang IT, Kang LJ (2016) Identification of water bodies in a Landsat 8 OLI image using a J48 decision tree. Sensors 16(7):1075.  https://doi.org/10.3390/s16071075 CrossRefGoogle Scholar
  2. Aiazzi B, Baronti S, Selva M, Alparone L (2006) Enhanced Gram-Schmidt spectral sharpening based on multivariate regression of MS and Pan data. In: IEEE International Conference on Geoscience and Remote Sensing Symposium. IEEE, Denver, pp 3806–3809.  https://doi.org/10.1109/igarss.2006.975
  3. Akay H, Baduna Kocyigit M, Yanmaz AM (2018) Effect of using multiple stream gauging stations on hydrologic parameters and estimation of hydrograph of ungauged neighboring basin. Arab J Geosci 11:282.  https://doi.org/10.1007/s12517-018-3642-z CrossRefGoogle Scholar
  4. Baduna Kocyigit M, Akay H, Yanmaz AM (2017) Effect of watershed partitioning on hydrologic parameters and estimation of hydrograph of an ungauged basin: a case study in Gokirmak and Kocanaz, Turkey. Arab J Geosci 10:331.  https://doi.org/10.1007/s12517-017-3132-8 CrossRefGoogle Scholar
  5. Bao PY, ZhangY J, Gong L, Jian P (2007) Study on consistency of land surface albedo obtained from ETM+ and MODIS. J Hohai Univ 35(1):67–71Google Scholar
  6. Bisson M, Sulpizio R, Zanchetta G, Demi F, Santacroce R (2010) Rapid terrain-based mapping of some volcaniclastic flow hazard using Gis-based automated methods: a case study from southern Campania, Italy. Nat Hazards 55(2):371–387.  https://doi.org/10.1007/s11069-010-9533-6 CrossRefGoogle Scholar
  7. Cai LN, Tang DL, Li CY (2015) An investigation of spatial variation of suspended sediment concentration induced by a bay bridge based on Landsat TM and OLI data. Adv Space Res 56(2):293–303.  https://doi.org/10.1016/j.asr.2015.04.015 CrossRefGoogle Scholar
  8. Deng YH, Wang S, Bai X, Tian Y, Wu L, Xiao J, Chen F, Qian Q (2018) Relationship among land surface temperature and LUCC, NDVI in typical karst area. Sci Rep 8:641.  https://doi.org/10.1038/s41598-017-19088-x CrossRefGoogle Scholar
  9. Feyisa G L, Meilby H, Fensholt R, et al. (2014). Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ.140(1):23–35. doi: https://doi.org/10.1016/j.rse.2013.08.029
  10. Frazier PS, Page KJ (2000) Water body detection and delineation with Landsat TM data. Photogramm Eng Remote Sens 66(12):1461–1467Google Scholar
  11. Jain SK, Singh RD, Jain MK, Lohani AK (2005) Delineation of flood-prone areas using remote sensing techniques. Water Resour Manag 19(4):333–347.  https://doi.org/10.1007/s11269-005-3281-5 CrossRefGoogle Scholar
  12. Jenson K, Domingue O (1988) Extracting topographic structure from digital elevation data for geographic system analysis. Sensing 54:1593–1600Google Scholar
  13. Ji L, Zhang L, Wylie B (2009) Analysis of dynamic thresholds for the normalized difference water index. Photogramm Eng Remote Sens 75(11):1307–1317.  https://doi.org/10.14358/pers.75.11.1307 CrossRefGoogle Scholar
  14. Jiang H, Feng M, Zhu Y, Lu N, Huang J, Xiao T (2014) An automated method for extracting rivers and lakes from Landsat imagery. Remote Sens 6(6):5067–5089.  https://doi.org/10.3390/rs6065067 CrossRefGoogle Scholar
  15. Jimenezmunoz JC et al (2009) Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Trans Geosci Remote Sens 47(1):339–349.  https://doi.org/10.1109/tgrs.2008.2007125 CrossRefGoogle Scholar
  16. Li Z, Liu C, Zhao C, Cheng Y (2009) An image thresholding method based on human visual perception. J Math Anal Appl 423(1):701–719.  https://doi.org/10.1109/cisp.2009.5302884 CrossRefGoogle Scholar
  17. Li SC et al (2011) The normalization process of the multi field information from a coal mine water inrush model test. J China Coal Soc 36(3):11911–11937Google Scholar
  18. Liang S et al (2001) Narrowband to broadband conversions of land surface albedo I: algorithms. Remote Sens Environ 76(2):213–238.  https://doi.org/10.1016/s0034-4257(00)00205-4 CrossRefGoogle Scholar
  19. Liu X, Deng R, Xu J, Zhang F (2017) Coupling the modified linear spectral mixture analysis and pixel-swapping methods for improving subpixel water mapping: application to the Pearl River Delta, China. Water 9(9):658.  https://doi.org/10.3390/w9090658 CrossRefGoogle Scholar
  20. Lloyd CD, Mcdonnell RA, Burrough PA (1986) Principles of geographical information systems. Landsc Urban Plan 15(3):357–358.  https://doi.org/10.1016/0169-2046(88)90059-x CrossRefGoogle Scholar
  21. Lu JJ, Li SH (1992) Improvement of the techniques for distinguishing water bodies from TM data. J Remote Sens (1):17–23Google Scholar
  22. Lu S, Wu B, Yan N, Wang H (2011) Water body mapping method with HJ-1A/B satellite imagery. Int J Appl Earth Obs Geoinf 13(3):428–434.  https://doi.org/10.1016/j.jag.2010.09.006 CrossRefGoogle Scholar
  23. Luo GJ et al (2016) Delineating small karst watersheds based on digital elevation model and eco-hydrogeological principles. Solid Earth Discussions 7:1–28.  https://doi.org/10.5194/se-2016-20 CrossRefGoogle Scholar
  24. Mcfeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432.  https://doi.org/10.1080/01431169608948714 CrossRefGoogle Scholar
  25. Narumalani S, Zhou Y, Jensen JR (1997) Application of remote sensing and geographic information systems to the delineation and analysis of riparian buffer zones. Aquat Bot 58(3–4):393–409.  https://doi.org/10.1016/S0304-3770(97)00048-X CrossRefGoogle Scholar
  26. O'Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Comp Vision Graph Image Process 28(3):323–344.  https://doi.org/10.1016/S0734-189X(84)80011-0 CrossRefGoogle Scholar
  27. Otukei JR, Blaschke T, Woldai T, Annegarn H (2010) Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int J Appl Earth Obs Geoinf 12(1):S27–S31.  https://doi.org/10.1016/j.jag.2009.11.002 CrossRefGoogle Scholar
  28. Qin ZH, Zhang MH, Karnieli A, Berliner P (2001) Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data. Acta Geograph Sin 56:456–466.  https://doi.org/10.11821/xb200104009 CrossRefGoogle Scholar
  29. Qin LY, Bai X, Wang S, Zhou D, Li Y, Peng T, Tian Y, Luo G (2015) Major problems and solutions on surface water resource utilisation in karst mountainous areas. Agric Water Manag 159:55–65.  https://doi.org/10.1016/j.agwat.2015.05.024 CrossRefGoogle Scholar
  30. Rozenstein O, Qin Z, Derimian Y, Karnieli A (2014) Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors 14(4):5768–5780.  https://doi.org/10.3390/s140405768 CrossRefGoogle Scholar
  31. Seyler F, Muller F, Cochonneau G, Guimarães L, Guyot JL (2010) Watershed delineation for the Amazon sub-basin system using GTOPO30 DEM and a drainage network extracted from JERS SAR images. Hydrol Process 23(22):3173–3185.  https://doi.org/10.1002/hyp.7397 CrossRefGoogle Scholar
  32. Silva AMD et al (2007) Soil loss risk and habitat quality in streams of a meso-scale river basin. Sci Agric 64(4):336–343.  https://doi.org/10.1590/s0103-90162007000400004 CrossRefGoogle Scholar
  33. Song X, Zhang J, Zhan C, Liu JF (2013) Advances in digital watershed features extracting based on DEM. Prog Geogr 32(1):31–40.  https://doi.org/10.11820/dlkxjz.2013.01.003 CrossRefGoogle Scholar
  34. Stroeve J, Box JE, Gao F, Liang S, Nolin A, Schaaf C (2005) Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements. Remote Sens Environ 94(1):46–60.  https://doi.org/10.1016/j.rse.2004.09.001 CrossRefGoogle Scholar
  35. Wan JP, Guan YL, Ye SA, Ma QR (2015) Water extraction based on comprehensive weight water index—a case study in region of Poyang Lake. J East China Inst Technol (Natural Science) 38:206–221.  https://doi.org/10.3969/j.issn.1674-3504.2015.02.011 CrossRefGoogle Scholar
  36. Wang G, Chen J, Wang F, Chen S (2010) Typical disaster damage target extraction method based on objectoriented classification. Seventeenth China Symposium on Remote Sensing. Int Soc Optics Photonics 2011:361–372.  https://doi.org/10.1117/12.910423
  37. Xu HQ (2005) A study on information extraction of water body with the modified normalized difference water index (MNDWI). J Remote Sensing.  https://doi.org/10.3321/j.issn:1007-4619.2005.05.012
  38. Yan P, Zhang YJ, Zhang Y (2007) A study on information extraction of water system in semi-arid regions with the enhanced water index (EWI) and GIS based noise remove techniques. Remote Sensing Inf.  https://doi.org/10.3969/j.issn.1000-3177.2007.06.015
  39. Yang X, Zhao S, Qin X, Liang L (2017) Mapping of urban surface water bodies from Sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sens 9(6):596.  https://doi.org/10.3390/rs9060596 CrossRefGoogle Scholar
  40. Zhang MH (2008) Extracting water-body information with improved model of spectral relationship in a higher mountain area. Geography and Geo-Information ScienceGoogle Scholar
  41. Zhang YS, Odeh IOA, Han CF (2009) Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. Int J Appl Earth Obs Geoinf 11(4):256–264.  https://doi.org/10.1016/j.jag.2009.03.00 CrossRefGoogle Scholar
  42. Zhang et al (2014) The research on water information extraction based on multisource remote sensing data. Geomatics & Spatial Information Technology (5):47–50.  https://doi.org/10.3969/j.issn.1672-5867.2014.05.015
  43. Zhou CH (2001) Geoscience understanding and analysis of remote sensing images. Science PressGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • Chaojun Li
    • 1
    • 2
    • 3
  • Shijie Wang
    • 1
    • 3
  • Xiaoyong Bai
    • 1
    • 3
    Email author
  • Qiu Tan
    • 2
  • Yujie Yang
    • 1
    • 2
    • 3
  • Qin Li
    • 1
    • 3
  • Luhua Wu
    • 1
    • 3
  • Jianyong Xiao
    • 1
    • 2
    • 3
  • Qinghuan Qian
    • 1
    • 2
    • 3
  • Fei Chen
    • 1
    • 2
    • 3
  • Huiwen Li
    • 1
    • 3
  • Yue Cao
    • 1
    • 3
  • Mingming Wang
    • 1
    • 3
  • Jinfeng Wang
    • 1
    • 3
  • Shiqi Tian
    • 1
    • 2
    • 3
  • Qian Lu
    • 1
    • 3
  1. 1.State Key Laboratory of Environmental Geochemistry, Institute of GeochemistryChinese Academy of SciencesGuiyangPeople’s Republic of China
  2. 2.School of Geography and Environmental SciencesGuizhou Normal UniversityGuiyangChina
  3. 3.Puding Karst Ecosystem Observation and Research StationChinese Academy of SciencesPudingPeople’s Republic of China

Personalised recommendations