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Detection of Vegetation Patch Growth by Absorption Feature Analysis on Tasseled Cap Brightness of Transects from Landsat 7 ETM+ Images

  • Qingsheng LiuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Vegetation patches are worldwide distributed in arid and semi-arid ecosystems. Mapping vegetation patch dynamics provides valuable information for regional vegetation recovery and re-establishment. The high spatial resolution images may be powerful for the decametric-scale vegetation patch detection. However, the dense and long time series of fine spatial resolution (better than 2.5 m) imagery were not available for large regions until recently due to design considerations on satellite and sensors, satellite data transmission, satellite life and revisited period, and further effects like atmospheric absorption and cloud. For multispectral images (less than 10 m), it was often a challenge for detecting these vegetation patches through visual interpretation and the common image classifications. In this paper, our proposed method based on analysis of absorption features of tasseled cap brightness of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images along transect provided the acceptable results for vegetation patch recovery detection, which represented a efficient, economical and straightforward procedures for local vegetation management in the Yellow River Delta, China and other similar landscapes.

Keywords

Vegetation patch Absorption feature Landsat 7 ETM+ Tasseled cap transformation Transect 

Notes

Acknowledgment

This research work was jointly financially supported by the National Natural Science Foundation of China (Project No.41671422, 41661144030, 41561144012), the National Mountain Flood Disaster Investigation Project (SHZH-IWHR-57), the Innovation Project of LREIS (Project No.088RA20CYA, 08R8A010YA).

References

  1. 1.
    Liu, Q.S., Liu, G.H., Huang, C., Xie, C.J.: Vegetation patch structure and dynamics at gudong oil field of the yellow river delta, China. In: Bian, F., et al. (eds.) The 2013 International Conference on Geo-Informatics in Resource Management & Sustainable Ecosystem (GRMSE2013), Part I, CCIS, vol. 398, pp. 177–187. Springer, Heidelberg (2013)Google Scholar
  2. 2.
    Liu, Q.S., Liu, G.H., Huang, C., Xie, C.J.: Using SPOT5 fusion-ready imagery to detect Chinese Tamarisk (Saltcedar) with mathematical morphological method. Int. J. Digital Earth 7(3), 217–228 (2014)CrossRefGoogle Scholar
  3. 3.
    Bi, X.L., Wang, B., Lu, Q.S.: Fragmentation effects of oil wells and roads on the yellow river delta. North China. Ocean Coast Manage. 54, 256–264 (2011)CrossRefGoogle Scholar
  4. 4.
    Kadmon, R., Harari-Kremer, R.: Studying long-term vegetation dynamics using digital processing of historical aerial photographs. Remote Sens. Environ. 6(8), 164–176 (1999)CrossRefGoogle Scholar
  5. 5.
    Laliberte, A.S., Rango, A., Havstad, K.M., Paris, J.E., Beck, R.F., McNeely, R., Gonzalez, A.L.: Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sens. Environ. 93, 198–210 (2004)CrossRefGoogle Scholar
  6. 6.
    Barbier, N., Couteron, P., Lejoly, J., Deblauwe, V., Lejeune, O.: Self-organized vegetation patterning as a fingerprint of climate and human impact on semi-arid ecosystems. J. Ecol. 94, 537–547 (2006)CrossRefGoogle Scholar
  7. 7.
    Liu, Q.S., Liu, G.H., Chu, X.L.: Comparison of different spatial resolution bands of SPOT 5 to vegetation community patch detection. In: Proceedings of the 5th International Congress on Image and Signal Processing (CISP 2012), pp. 1190–1194 (2012)Google Scholar
  8. 8.
    Liu, Q.S., Huang, C., Liu, G.H., Yu, B.W.: Comparison of CBERS-04, GF-1, and GF-2 satellite panchromatic images for mapping quasi-circular vegetation patches in the Yellow River Delta. China Sens. 18, 2733 (2018)CrossRefGoogle Scholar
  9. 9.
    Sonnenschein, R., Kuemmerle, T., Udelhoven, T., Stellmes, M., Hostert, P.: Differences in landsat-based trend analyses in drylands due to the choice of vegetation estimate. Remote Sens. Environ. 115, 1408–1420 (2011)CrossRefGoogle Scholar
  10. 10.
    Dymond, C.C., Mladenoff, D.J., Radeloff, V.C.: Phenological differences in tasseled cap indices improve deciduous forest classification. Remote Sens. Environ. 80, 460–472 (2002)CrossRefGoogle Scholar
  11. 11.
    Jin, S.M., Sader, S.A.: Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens. Environ. 94, 364–372 (2005)CrossRefGoogle Scholar
  12. 12.
    Czerwinski, C.J., King, D.J., Mitchell, S.W.: Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987-2010. Remote Sens. Environ. 141, 188–200 (2014)CrossRefGoogle Scholar
  13. 13.
    Li, Y.Y., Liu, Q.S., Liu, G.H., Huang, C.: Detect quasi-circular vegetation community patches using images of different spatial resolutions. In: Proceedings of the 2013 6th International Congress on Image and Signal Processing (CISP 2013), pp. 824–829 (2013)Google Scholar
  14. 14.
    Liu, Q.S., Liu, G.H, Huang, C., Shi, L., Zhao, J.: Monitoring vegetation recovery at abandoned land. In: Proceedings of the 8th International congress on Image and Signal Processing (CISP 2015), pp. 88–92 (2015)Google Scholar
  15. 15.
    Huang, C.Q., Wylie, B., Yang, L.M., Zylstra, G.: A tasseled cap transformation for landsat 7 ETM+ at-satellite reflectance. Int. J. Remote Sens. 23(8), 1741–1748 (2002)CrossRefGoogle Scholar
  16. 16.
    Van der Meer, F.: Analysis of spectral absorption features in hyperspectral imagery. Int. J. Appl. Earth Observation Geoinf. 5, 55–68 (2004)CrossRefGoogle Scholar
  17. 17.
    Van Ruitenbeek, F.J.A., Bakker, W.H., Van der Werff, H.M.A., Zegers, T.E., Oosthoek, J.H.P., Omer, Z.A., Marsh, S.H., Van der Meer, F.D.: Mapping the wavelength position of deepest absorption features to explore mineral diversity in hyperspectral images. Planet. Space Sci. 101, 108–117 (2014)CrossRefGoogle Scholar
  18. 18.
    Sanches, L.D, Filho, C.R. S., Kokaly, R.F.: Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal. ISPRS J. Photogrammetry Remote Sens. 97, 111–122 (2014)Google Scholar
  19. 19.
    Tschinkel, W.R.: The life cycle and life span of Namibian fairy circles. PLoS ONE 7(6), e38056 (2012)CrossRefGoogle Scholar
  20. 20.
    Johnstone, J.F., Kokelj, S.V.: Environmental conditions and vegetation recovery at abandoned drilling mud sumps in the Mackenzie Delta Region, Northwest Territories. Canada Arctic 61(2), 199–211 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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