In:
Forests, MDPI AG, Vol. 10, No. 4 ( 2019-04-15), p. 337-
Abstract:
Climate change continues to threaten forests and their ecosystem services while substantially altering natural disturbance regimes. Land cover changes and consequent management entail discrepancies in carbon sequestration provided by forest ecosystems and its accounting. Currently there is a lack of sufficient and harmonized data for Ukraine that can be used for the robust and spatially explicit assessment of forest provisioning and regulation of ecosystem services. In the frame of this research, we established an experimental polygon (area 45 km2) in Northern Ukraine aiming at estimating main forest carbon stocks and fluxes and determining the impact caused by natural disturbances and harvest for the study period of 2010–2015. Coupled field inventory and remote sensing data (RapidEye image for 2010 and SPOT 6 image for 2015) were used. Land cover classification and estimation of biomass and carbon pools were carried out using Random Forest and k-Nearest Neighbors (k-NN) method, respectively. Remote sensing data indicates a ca. 16% increase of carbon stock, while ground-based computations have shown only a ca. 1% increase. Net carbon fluxes for the study period are relatively even: 5.4 Gg C·year−1 and 5.6 Gg C C·year−1 for field and remote sensing data, respectively. Stand-replacing wildfires, as well as insect outbreaks and wind damage followed by salvage logging, and timber harvest have caused 21% of carbon emissions among all C sources within the experimental polygon during the study period. Hence, remote sensing data and non-parametric methods coupled with field data can serve as reliable tools for the precise estimation of forest carbon cycles on a regional spatial scale. However, featured land cover changes lead to unexpected biases in consistent assessment of forest biophysical parameters, while current management practices neglect natural forest dynamics and amplify negative impact of disturbances on ecosystem services.
Type of Medium:
Online Resource
ISSN:
1999-4907
Language:
English
Publisher:
MDPI AG
Publication Date:
2019
detail.hit.zdb_id:
2527081-3
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