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.
Similar content being viewed by others
REFERENCES
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.
Allain, C. and Cloitre, M., Characterizing the lacunarity of random and deterministic fractal sets, Phys. Rev. A, 1991, vol. 44, no. 6, p. 3552.
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
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
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
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.
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.
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
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
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.
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.
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
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
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
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.
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.
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.
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.
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.
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
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
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.
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
Mandelbrot, B.B., The Fractal Geometry of Nature, New York: W.H. Freeman and Co, 1983.
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.
Orfeo ToolBox (OTB). htttps://www.orfeo-toolbox.org. Accessed January 4, 2019.
PolSARSignatures. https://github.com/ipms-sb-ras/ Pol-SARSignatures. Accessed January 4, 2019.
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.
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.
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.
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.
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.
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
Corresponding authors
Additional information
Translated by D. Zabolotny
Rights and permissions
About this article
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
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0001433819090093