Izvestiya, Atmospheric and Oceanic Physics

, Volume 53, Issue 9, pp 1019–1028 | Cite as

Influence of Forest-Canopy Morphology and Relief on Spectral Characteristics of Taiga Forests

The Use of Space Information about the Earth

Abstract

The article deals with the results of a statistical analysis reflecting tendencies (trends) of the relationship between spectral characteristics of taiga forests, indicators of the morphological structure of forest canopy and illumination of the territory. The study was carried out on the example of the model forest territory of the Priangarskiy taiga region of Eastern Siberia (Krasnoyarsk krai) using historical data (forest inventory 1992, Landsat 5 TM 16.06.1989) and the digital elevation model. This article describes a method for determining the quantitative indicator of morphological structure of forest canopy based on taxation data, and the authors propose to subdivide the morphological structure into high complexity, medium complexity, and relatively simple. As a result of the research, dependences of average values of spectral brightness in near and short-wave infrared channels of a Landsat 5 TM image for dark-coniferous, light-coniferous and deciduous forests from the degree of complexity of the forest-canopy structure are received. A high level of variance and maximum brightness average values are marked in green moss (hilocominosa) dark-coniferous and various-grass (larioherbosa) dark-coniferous forests and light-coniferous forests with a complex structure of canopy. The parvifoliate forests are characterized by high values of brightness in stands with a relatively simple structure of the canopy and by a small variance in brightness of any degree of the structure of the canopy complexity. The increase in brightness for the lit slopes in comparison with shaded ones in all stands with a difficult morphological canopy structure is revealed. However, the brightness values of the lit and shaded slopes do not differ for stands with a medium complexity of the structure. It is noted that, in addition to the indicator of the forest-canopy structure, the possible impact on increasing the variance of spectral brightness for the taxation plot has a variability of the slope ratio of “microslopes” inside the forest plot if it exceeds 60%.

Keywords

space images spectral brightness morphological structure of the forest canopy slope illumination groups of forest types 

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References

  1. Aerokosmicheskie metody v okhrane prirody i v lesnom khozyaistve (Aerospace Methods in Environmental Protection and Forestry), Sukhikh, V.I., Sinitsyn, S.G., Apostolov, Yu.S., Danyulis, E.P., Zhirin, V.M., Moroz, P.I., Rukosuev, G.N., and El’man, R.I., Eds., Moscow: Lesnaya promyshlennost’, 1979.Google Scholar
  2. Cartus, O., Kellndorfer, J., Rombach, M., and Walker, W., Mapping canopy height and growing stock volume using airborne lidar, ALOS PALSAR and Landsat ETM+, Remote Sens., 2012, vol. 4, no. 11, p. 3320–3345. doi 10.3390/rs4113320CrossRefGoogle Scholar
  3. Daliakopoulos, I.N., Grillakis, E.G., Koutroulis, A.G., and Tsanis, I.K., Tree crown detection on multispectral VHR satellite imagery, Photogram. Eng. Remote Sens., 2009, vol. 75, no. 10, pp. 1201–1211.CrossRefGoogle Scholar
  4. Danilin, I.M. and Favorskaya, M.N., Description of program modules of the use of laser location and digital aerial photos of forest areas, Issled. Zemli Kosmosa, 2013, no. 2, pp. 62–73.Google Scholar
  5. Elsakov, V.V. and Marushchak, I.O., Satellite images in the analysis of qualitative characteristics of forest phytocenoses of the Pechero-Ilych natural reserve, Komi Republic, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2011, vol. 8, no. 4, pp. 303–309.Google Scholar
  6. Elsakov, V.V. and Teteryuk, L.V., The role of topography in the formation of vegetation of karst landscapes in northeastern Europe, Issled. Zemli Kosmosa, 2012, no. 3, pp. 78–93.Google Scholar
  7. Ioffe, A.I., A method for assessing the inhomogeneities of the topography of a given area, Issled. Zemli Kosmosa, 2013, no. 3, pp. 91–94.Google Scholar
  8. Karantzalos, K.G. and Argialas, D.P., Towards automatic olive tree extraction from satellite imagery, Commis. III, WG4, 2010.Google Scholar
  9. Knyazeva, S.V., GIS of forest biogeocenotic cover of river basins, in Metodicheskie podkhody k ekologicheskoi otsenke lesnogo pokrova v basseine maloi reki (Methodical Approaches to Ecological Assessment of Forest Cover in the Basin of Small Rivers), Zaugol’nova, L.B. and Braslavskaya, T.Yu., Eds., Moscow: Tsentr po problemam ekologii i produktivnosti lesov RAN, 2010, pp. 208–226.Google Scholar
  10. Kolobov, A.N., Modeling the spatial and temporal dynamics of wood communities: An individual-oriented approach, Lesovedenie, 2014, no. 5, pp. 72–82.Google Scholar
  11. Kozoderov, V.V., Kondranin, T.V., Dmitriev, E.V., Egorov, V.D., and Borzyak, V.V., Innovation technology of processing of multispectral space images of the Earth’s surface, Issled. Zemli Kosmosa, 2008, no. 1, pp. 56–72.Google Scholar
  12. Lymburner, L., Beggs, P.J., and Jacobson, C.R., Estimation of canopy-average surface-specific leaf area using Landsat TM data, Photogram. Eng. Remote Sens., 2000, vol. 66, no. 2, pp. 183–191.Google Scholar
  13. Marushchak, O.I. snd Elsakov, V.V., Satellite monitoring materials in analyzing the density of forest phytocenoses in the near-polar Urals, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2010, vol. 7, no. 1, pp. 310–318.Google Scholar
  14. Monitoring biologicheskogo raznoobraziya lesov Rossii: metodologiya i metody (Monitoring of Forest Biodiversity in Russia: Methodology and Methods), Isaev, A.S., Ed., Moscow: Nauka, 2008.Google Scholar
  15. Novichikhin, A.E. and Tutubalina, O.V., Integration of algorithms of processing of space images of very high spatial resolution for automatic decoding of forest vegetation, Zemlya Kosmosa, 2009, no. 3, pp. 40–42.Google Scholar
  16. Palace, M., Keller, M., Asner, G., Hagen, S., and Braswell, B., Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties, Biotropica, 2008, vol. 40, no. 2, pp. 141–150.CrossRefGoogle Scholar
  17. Rysin, L.P., The past and present of forest typology in Russia, in Produktsionnyi protsess i struktura lesnykh biogeotsenozov: teoriya i eksperiment (Pamyati Utkina A.I.) (The Production Process and Structure of Forest Biogeocenoses: Theory and Experiment (Commemorating A.I. Utkin)), Moscow: Inst. lesovedeniya RAN, Tovarishchestvo nauch. izd. KMK, 2009, pp. 161–183.Google Scholar
  18. Samoilovich, G.G., Primenenie aerofotos"emki i aviatsii v lesnom khozyaistve (The Use of Aerial Photo Images in Forestry), Moscow: Lesnaya promyshlennost', 1964.Google Scholar
  19. Savorskii, V.P., Zakharov, A.I., Zakharova, L.N., Maklakov, S.M., Panova, O.Yu., and Chumachenko, S.I., Integrated analysis of the results of optical and radar observations of forest canopies, Sovrem. Probl. Distan tsionnogo Zondirovaniya Zemli Kosmosa, 2013, vol. 10, no. 4, pp. 213–223.Google Scholar
  20. Skorobogat’ko, N.D., The structure of decoding indicators of the canopy of modal spruce stands in the plain the Kama region and pine plants of the Kursk region for automated interpretation of aerial photographs, in Aktual’nye problemy lesnogo kompleksa (Important Problems of Forestry), Pamfilov, E.A., Ed., Bryansk: BGITA, 2004, vol. 9, pp. 42–50.Google Scholar
  21. Tyukavina, A.Yu., Determination of the density of the crowns of rare Taimyr larch trees from space images of various resolutions, Issled. Zemli Kosmosa, 2012, no. 5, pp. 64–74.Google Scholar
  22. Vasilevich, M.I., Elsakov, V.V., and Shchanov, V.M., Using satellite methods of research in monitoring of the state of forest phytocenoses in the zone of industrial releases, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2014, vol. 11, no. 1, pp. 30–42.Google Scholar
  23. Vohland, M., Stoffels, J., Hau, C., and Schuler, G., Remote sensing techniques for forest parameter assessment: Multispectral classification and linear spectral mixture analysis, Silva Fennica, 2007, vol. 41, no. 3, pp. 441–456.CrossRefGoogle Scholar
  24. Wulger, M.A., Han, T., White, J.C., Sweda, T., and Tsuzuki, H., Integration profiling lidar with Landsat data for regional boreal forest canopy attribute estimation and change characterization, Remote Sens. Environ., 2007, vol. 110, no. 1, pp. 123–137.CrossRefGoogle Scholar
  25. Zhirin, V.M. and Knyazeva, S.V., Specific features of the forest canopy ion the basis of vegetation indices calculated from MSU-E space surveying data, Issled. Zemli Kosmosa, 2003, no. 2, 73–79.Google Scholar
  26. Zhirin, V.M., Knyazeva, S.V., and Eydlina, S.P., Remote tracking of forest formation in post-felling taiga forests of the Russian plain, Lesovedenie, 2011, no. 6, pp. 29–38.Google Scholar
  27. Zhirin, V.M., Knyazeva, S.V., and Eydlina, S.P., Peculiarities of the recovery of damaged forest cover in taiga forest of the Russian plain, in Raznoobrazie i monitoring lesnykh ekosistem Rossii (Diversity and Monitoring of Forest Ecosystems in Russia), Isaev, A.S., Ed., Moscow: KMK, 2012, pp. 352–381.Google Scholar
  28. Zhirin, V.M., Knyazeva, S.V., and Eydlina, S.P., Dynamics of spectral brightness of the age–species structure of forest type groups on Landsat images, Lesovedenie, 2014, no. 5, pp. 3–12.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • V. M. Zhirin
    • 1
  • S. V. Knyazeva
    • 1
  • S. P. Eydlina
    • 1
  1. 1.Center of Forest Ecology and Productivity ProblemsRussian Academy of SciencesMoscowRussia

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