• DOI 10.31509/2658-607x-2019-2-4-1-10
  • УДК 528.871

Relationship between tree crown diameter and various taxation indicators in the North-taiga forest area

© 2019

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A.P. Bogdanov1,2*, R.A. Aleshko1,2, A.S. Ilintsev1,2

1Northern research institute of forestry

Russia, 163062 Arkhangelsk, Nikitova Str., 13

2Northern (Arctic) Federal University named after M.V. Lomonosov,

Russia, 163002 Arkhangelsk, Northern Dvina emb, 17

*E-mail: a.p.bogdanov@sevniilh-arh.ru

Received 18 June 2019

To improve the accuracy of the assessment of forest area from aerial photographs by automated decoding, it is necessary to study of the degree of tightness and interrelation forms between decryption and evaluation indexes. The purpose of the study is to identify the most reliable (optimal) dependencies between these indicators and the subsequent development of equations in their calculation. The interrelations of the taxation diameter and decoding indicators of the forest stand such as: height and diameter of the crowns are revealed. As a result of the analysis of the dependencies between the fullness and closed canopy revealed a weak relationship.

Key words: decryption indicators, taxation indicators, crown width, canopy structure, taxation-decoding test plots, super detailed shooting


Bartalev S.A., Egorov V.A., Zharko V.O., Loupian E.A., Plotnikov D.E., Khvostikov S.A., Current state and development prospects of satellite mapping methods of Russia’s vegetation cover. 7-ya mezhdunarodnaya nauchno-tekhnicheskaya konferenciya, KE Ciolkovskij-160 let so dnya rozhdeniya. Kosmonavtika. Radioelektronika. Geoinformatika, 2017, pp. 74-79.

Sochilova E.N., Surkov N.V., Ershov D.V., Khamedov V.A., Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi Autonomous Okrug), Voprosy lesnoj nauki, 2018, No. 1. pp. 1-23.

Shanin V.N., Shashkov M.P., Ivanova N.V., Grabarnik P.Ya., The effect of aboveground competition on spatial structure and crown shape of the dominating canopy species of forest stands of European Russia, Russian Journal of Ecosystem Ecology, 2016, Vol. 4. No. 1, pp. 112-125.

Franklin S.E., Ahmed O.S., Williams G., Northern Conifer Forest Species Classification Using Multispectral Data Acquired from an Unmanned Aerial Vehicle, Photogrammetric Engineering & Remote Sensing, 2017, Vol. 83, No. 7, P. 501-507.

Giannetti F., Gobakken T., Chirici G., Næsset E., A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data, Remote Sensing of Environment, 2018, Vol. 213, P. 195-205.

Hüttich C, Korets M., Bartalev S., Zharko V., Schepaschenko D., Shvidenko A., Schmulliu C., Exploiting growing stock volume maps for large scale forest resource assessment: cross-comparisons of ASAR-and PALSAR-based GSV estimates with forest inventory in central Siberia, Forests, 2014, Vol. 5, No. 7, P. 1753-1776.

Valbuena R. Maltamo M., Mehtдtalo L., Packalen P., Key structural features of boreal forests may be detected directly using L-moments from airborne lidar data. Remote Sensing of Environment, 2017, Vol. 194. P. 437-446.

White J., Coops N., Wulder M., Vastaranta M., Hilker, T., Tompalski P., Remote sensing technologies for enhancing forest inventories: A review. Canadian Journal of Remote Sensing, 2016, Vol. 42, No. 5, P. 619-641.