![]() |
ИСТИНА |
Войти в систему Регистрация |
ИПМех РАН |
||
We develop and apply new technologies, based on airborne and spaceborne imaging and ground surveys, climate measurements and modelling, to understand changes in the forests of northern Russia since 2000. Northern forests are extremely sensitive to climate change, and are important not just as indicators but also because of their contribution to global biosystems and biodiversity, supporting traditional land management, and as economic resources. New possibilities have been created by research on climate data from northern Russia combined with long datasets of coarse-resolution satellite data, high-resolution imaging systems, airborne imaging (‘drone’) technologies and in situ measurements of the tree stands. We aim to describe and explain changes in forest distribution since 2000 and predict future impacts. In this study, we present results of forest stand modelling for 28 test sites in the central Kola Peninsula, NW Russia, surveyed in the field and from UAVs in summer 2018, and of upscaling these data to MODIS scale. Test sites cover pine-, spruce- and birch-dominated forests. We derive stand parameters: tree height, canopy cover, crown sizes, leaf area index (LAI) and growing stock volume (GSV), utilising DTMs and dense point clouds generated from UAV surveys taken from 50 m and 100 m altitude, and validated with ground measurements. These parameters are then statistically upscaled to MODIS-derived LAI and GSV products. Their trends are followed since 2000 and explained on the basis of stand structure in the test sites, as well as information on climate change and human-induced impacts in the region. It is envisaged that further work will extend to test sites in larch forests in Eastern Siberia, and will include ground photogrammetry to improve stand characterization. This work is funded by the British Council (Institutional Links grant no. 352397111) and the Ministry of Science and Higher Education of the Russian Federation (grant agreement no. 14.616.21.0099 dated 27 February 2018, project RFMEFI61618X0099).