ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 3, pp. 277-282

Possibility analysis of stem volume of forests assessment using Landsat ETM data

E.N. Sochilova , D.V. Yershov 
Center for Problems of Forest Ecology and Productivity, 117997 Moscow, 84/32 Profsoyuznaya str
We studied the Landsat data using possibilities for stem volume assessment of such species as larch and aspen. Analysis of relationship was based on correlation definition between forest specie reflectance in a red band of the winter imagery and its stem volume, derived from forest inventory data. The level of relationship between those parameters is 0.83 and 0.79 for larch and aspen, accordingly
Keywords: forests stem volume, remote sensing, forestry
Full text


  1. Zamolodchikov D.G., Utkin A.I., Chestnykh O.V., Lesnaya taksatsiya i lesoustroistvo, 2003, Issue 1(32), pp. 119 – 127.
  2. Jakubauskas M.E., Price K.P., Empirical relationships between structural and spectral factors of Yellowstone lodgepole pine forests, Photogrammetric Engineering & Remote Sensing, 1997, Vol. 63 (12), pp. 1375–1381.
  3. Foody G.M., Boyd D.S., Cutler M.J., Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions, Remote Sensing of Environment, 2003, Vol. 85, pp. 463–474.
  4. Franklin J., Thematic mapper analysis of coniferous forest structure and composition, International Journal of Remote Sensing, 1986, Vol. 7 (10), pp. 1287–1301.
  5. Markham B.L., Barker J.L., Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures, MD Landsat Technical Notes, 1986, Vol. 1, No. 1, pp. 3–8.
  6. Roy P.S., Ravan S.A., Biomass estimation using satellite remote sensing data: an investigation on possible approaches for natural forest, Journal of Biosciences, 1996, Vol. 21 (4), pp. 535–561.
  7. Vohland M., Stoffels J., Hau C., Schüler G., Remote sensing techniques for forest parameter assessment: multispectral classification and linear spectral mixture analysis, Silva Fennica, 2007, Vo. 41(3), pp. 441–456.