UDC

630*1

DOI

10.3897/issn2541-8416.2018.18.3.106

Authors

NA Demina¹, AA Karpov^1,2, VV Voronin¹, EV Lopatin¹, AP Bogdanov^1,2
1 Northern Research Institute of Forestry (Arkhangelsk, Russian Federation)
² Northern (Arctic) Federal University named after Lomonosov (Arkhangelsk, Russian Federation)
Corresponding author: Alexander A. Karpov (xxstpatrickxx@gmail.com)

Abstract

This article considers the possibility of using remote sensing to monitor reforestation as exemplified in the Severodvinsk and Onezhsk forestry districts of the Arkhangelsk region of Russia’s Arctic zone. Remote sensing makes use of medium spatial resolution satellite images and high resolution unmanned aerial vehicle (UAV) images. In the course of work on the project, a preliminary method was developed for reforesting land previously subjected to cutting, fire, or windfall. Steps include detecting a reduction in forest cover and collecting field data through the use of UAVs to create a training set, which is used to classify satellite images according to the two classes of ‘restored’ or ‘not restored’. Various data processing tools are used to perform these steps. The Tasseled Cap multi-channel satellite image transformation method is employed as a tool for detecting a reduction in forest cover and analysing reforestation. The k-nearest neighbour algorithm is employed to classify satellite images. This article provides a step-by-step algorithm for monitoring and an assessment is provided of the situation in relation to forest regeneration in the Severodvinsk and Onezhsk forestry districts. The work carried out has shown that it is possible to use UAV images to monitor forest recovery, which is of significant importance for the conditions of the Arctic zone of European Russia.

Keywords

reforestation, forest monitoring, forest cutting, forest dynamic, boreal forest, Landsat, Sentinel, remote sensing, ERS, unmanned aerial vehicle, UAV

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