Skip to main content
Log in

Spatial Anisotropy Assessment of the Forest Vegetation Heterogeneity at Different Azimuth Angles of Radar Polarimetric Sensing

  • PHYSICAL FOUNDATION OF EARTH OBSERVATION AND REMOTE SENSING
  • Published:
Izvestiya, Atmospheric and Oceanic Physics Aims and scope Submit manuscript

Abstract

The results of studies to assess the texture of L- and C-band radar polarimetric images from SIR-C and ALOS PALSAR-1 satellites for analyzing forest-vegetation characteristics using different signatures are summarized. A fractal polarization signature is proposed for the study; this signature makes it possible to estimate the spatial anisotropy of forest-vegetation heterogeneities at different azimuthal angles of radar sensing. In addition, the signature of lacunarity is suggested as a tool for the qualitative assessment of the angular distribution of tree branches. The heterogeneities of forest vegetation at the test site near Lake Baikal are estimated based on the results of an analysis of the fractal dimension and lacunarity at different states of the polarization ellipse.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

REFERENCES

  1. Aghababaee, H. and Sahebi, M.R., Model-based target scattering decomposition of polarimetric SAR tomography, IEEE Trans. Geosci. Remote Sens., 2018, vol. 56, no. 2, pp. 972–983.

    Article  Google Scholar 

  2. Allain, C. and Cloitre, M., Characterizing the lacunarity of random and deterministic fractal sets, Phys. Rev. A, 1991, vol. 44, no. 6, p. 3552.

    Article  Google Scholar 

  3. Bondur, V.G., Aerospace methods and technologies for monitoring oil and gas areas and facilities, Izv., Atmos. Ocean. Phys., 2011, vol. 47, no. 9, pp. 1007–1018.https://doi.org/10.1134/S0001433811090039

    Article  Google Scholar 

  4. Bondur, V.G., Satellite monitoring of wildfires during the anomalous heat wave of 2010 in Russia, Izv., Atmos. Ocean. Phys., 2011, vol. 47, no. 9, pp. 1039–1048.https://doi.org/10.1134/S0001433811090040

    Article  Google Scholar 

  5. Bondur, V.G., Satellite monitoring of trace gas and aerosol emissions during wildfires in Russia, Izv., Atmos. Ocean. Phys., 2016, vol. 52, no. 9, pp. 1078–1091.https://doi.org/10.1134/S0001433816090103

    Article  Google Scholar 

  6. Bondur, V.G. and Chimitdorzhiev, T.N., Texture analysis of radar images of vegetation, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2008a, no. 5, pp. 9–14.

  7. Bondur, V.G. and Chimitdorzhiev, T.N., Remote sensing of vegetation by optical microwave methods, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2008b, no. 6, pp. 64–73.

  8. Bondur, V.G. and Ginzburg, A.S., Emission of carbon-bearing gases and aerosols from natural fires on the territory of Russia based on space monitoring, Dokl. Earth Sci., 2016, vol. 466, no. 2, pp. 148–152.https://doi.org/10.1134/S1028334X16020045

    Article  Google Scholar 

  9. Bondur, V.G. and Gordo, K.A., Satellite monitoring of burnt-out areas and emissions of harmful contaminants due to forest and other wildfires in Russia, Izv., Atmos. Ocean. Phys., 2018, vol. 54, no. 9, pp. 955–965.https://doi.org/10.1134/S0001433818090104

    Article  Google Scholar 

  10. Bondur, V.G. and Savin, A.I., Design of a system to monitor the environment for purposes relating to ecology and natural resources, Sov. J. Remote Sens., 1993, vol. 10, no. 6, pp. 1078–1093.

    Google Scholar 

  11. Bondur, V.G. and Starchenkov, S.A., Methods and programs for processing and classification of aerospace images, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 2001, no. 3, pp. 118–143.

  12. Bondur, V.G. and Vorobev, V.E., Satellite monitoring of impact Arctic regions, Izv., Atmos. Ocean. Phys., 2015, vol. 51, no. 9, pp. 949–968.https://doi.org/10.1134/S0001433815090054

    Article  Google Scholar 

  13. Bondur, V.G., Vorobyev, V.E., and Lukin, A.A., Satellite monitoring of the northern territories disturbed by oil production, Izv., Atmos. Ocean. Phys., 2017a, vol. 53, no. 9, pp. 1005–1013.https://doi.org/10.1134/S0001433817090067

    Article  Google Scholar 

  14. Bondur, V.G., Gordo, K.A., and Kladov, V.L., Spacetime distributions of wildfire areas and emissions of carbon-containing gases and aerosols in Northern Eurasia according to satellite-monitoring data, Izv., Atmos. Ocean. Phys., 2017b, vol. 53, no. 9, pp. 859–874.https://doi.org/10.1134/S0001433817090055

    Article  Google Scholar 

  15. Chimitdorzhiev, T.N., Arkhincheev, V.E., Dmitirev, A.V., and Tsydypov, B.Z., Fractal analysis of polarimetric radar data, Issled. Zemli Kosmosa, 2007, no. 4, pp. 27–33.

  16. Danudirdjo, D. and Hirose, A., Local subpixel coregistration of interferometric synthetic aperture radar images based on fractal models, IEEE Trans. Geosci. Remote Sens., 2013, vol. 51, no. 7, pp. 4292–4301.

    Article  Google Scholar 

  17. Danudirdjo, D. and Hirose, A., InSAR image regularization and DEM error correction with fractal surface scattering model, IEEE Trans. Geosci. Remote Sens., 2015, vol. 53, no. 3, pp. 1427–1439.

    Article  Google Scholar 

  18. Di Martino, G., Iodice, A., Riccio, D., et al., The role of resolution in the estimation of fractal dimension maps from SAR data, Remote Sens., 2018, vol. 10, no. 1, id 9.

  19. Dmitriev, A.V., Chimitdorzhiev, T.N., and Dagurov, P.N., New type of polarization signature for radar images of earth covers with fractal properties, Optoelectron., Instrum., Data Process., 2016a, vol. 52, no. 3, pp. 245–251.

    Article  Google Scholar 

  20. Dmitriev, A.V., Chimitdorzhiev, T.N., and Dagurov, P.N., Fractal polarization signature of radar backscattering variations, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016b, pp. 4972–4975. https://doi.org/10.1109/IGARSS.2016.7730297

  21. Dmitriev, A.V., Chimitdorzhiev, T.N., and Dagurov, P.N., Polarization signature of lacunarity for heterogeneity estimation of radar backscattering from pine forest, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2018, pp. 5326–5329. https://doi.org/10.1109/IGARSS.2018.8518498

  22. Dong, P., Test of a new lacunarity estimation method for image texture analysis, Int. J. Remote Sens., 2000, vol. 21, no. 17, pp. 3369–3373.

    Article  Google Scholar 

  23. Lappalainen, H.K., Kerminen, V.-M., Petäjä, T., Bondur, V., et al., Pan-Eurasian Experiment (PEEX): Towards a holistic understanding of the feedbacks and interactions in the land–atmosphere–ocean–society continuum in the Northern Eurasian region, Atmos. Chem. Phys., 2016, vol. 16, pp. 14421–14461. https://doi.org/10.5194/acp-16-14421-2016

    Article  Google Scholar 

  24. Mandelbrot, B.B., The Fractal Geometry of Nature, New York: W.H. Freeman and Co, 1983.

    Book  Google Scholar 

  25. Myint, S.W., Mesev, V., and Lam, N., Urban textural analysis from remote sensor data: Lacunarity measurements based on the differential box counting method, Geogr. Anal., 2006, vol. 38, no. 4, pp. 371–390.

    Article  Google Scholar 

  26. Orfeo ToolBox (OTB). htttps://www.orfeo-toolbox.org. Accessed January 4, 2019.

  27. PolSARSignatures. https://github.com/ipms-sb-ras/ Pol-SARSignatures. Accessed January 4, 2019.

  28. Shang, R., Yuan, Y., Jiao, L., et al., A fast algorithm for SAR image segmentation based on key pixels, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, vol. 10, no. 12, pp. 5657–5673.

    Article  Google Scholar 

  29. Sun, W., Shi, L., Yang, J., et al., Building collapse assessment in urban areas using texture information from postevent SAR data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2016, vol. 9, no. 8, pp. 3792–3808.

    Article  Google Scholar 

  30. Van Zyl, J.J., Zebker, H.A., and Elachi, C., Imaging radar polarization signatures: Theory and observation, Radio Sci., 1987, vol. 22, no. 4, pp. 529–543.

    Article  Google Scholar 

  31. Wang, S., Jiao, L., and Yang, S., SAR images change detection based on spatial coding and nonlocal similarity pooling, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2016, vol. 9, no. 8, pp. 3452–3466.

    Article  Google Scholar 

  32. Weissgerber, F., Colin-Koeniguer, E., Trouvè, N., et al., A temporal estimation of entropy and its comparison with spatial estimations on PolSAR images, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2016, vol. 9, no. 8, pp. 3809–3820.

    Article  Google Scholar 

Download references

Funding

This study was carried out within the framework of testing methods and technologies developed according to the state assignment of the Institute of Physical Materials Science of the Siberian Branch of the Russian Academy of Sciences, and was supported by the Ministry of Science and Higher Education of Russia (project RFMEFI58317X0061).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to V. G. Bondur or T. N. Chimitdorzhiev.

Additional information

Translated by D. Zabolotny

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bondur, V.G., Chimitdorzhiev, T.N., Dmitriev, A.V. et al. Spatial Anisotropy Assessment of the Forest Vegetation Heterogeneity at Different Azimuth Angles of Radar Polarimetric Sensing. Izv. Atmos. Ocean. Phys. 55, 926–934 (2019). https://doi.org/10.1134/S0001433819090093

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0001433819090093

Keywords:

Navigation