| BORUVKA, Lubos, Radim VASAT, Vit SRAMEK, Katerina Neudertova HELLEBRANDOVA, Vera FADRHONSOVA, Milan SÁŇKA, Lenka PAVLU, Ondřej SÁŇKA, Oldrich VACEK, Karel NEMECEK, Shahin NOZARI and Vincent Yaw Oppong SARKODIE. Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape. SOIL AND WATER RESEARCH. CZECH REPUBLIC: CZECH ACADEMY AGRICULTURAL SCIENCES, 2022, vol. 17, No 2, p. 69-79. ISSN 1801-5395. Available from: https://dx.doi.org/10.17221/4/2022-SWR. |
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@article{1861578,
author = {Boruvka, Lubos and Vasat, Radim and Sramek, Vit and Hellebrandova, Katerina Neudertova and Fadrhonsova, Vera and Sáňka, Milan and Pavlu, Lenka and Sáňka, Ondřej and Vacek, Oldrich and Nemecek, Karel and Nozari, Shahin and Sarkodie, Vincent Yaw Oppong},
article_location = {CZECH REPUBLIC},
article_number = {2},
doi = {http://dx.doi.org/10.17221/4/2022-SWR},
keywords = {carbon stocks; digital soil mapping; environmental covariates; random forests; spatial distribution; terrain attributes},
language = {eng},
issn = {1801-5395},
journal = {SOIL AND WATER RESEARCH},
title = {Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape},
url = {https://www.agriculturejournals.cz/web/swr.htm?type=article&id=4_2022-SWR},
volume = {17},
year = {2022}
}
TY - JOUR
ID - 1861578
AU - Boruvka, Lubos - Vasat, Radim - Sramek, Vit - Hellebrandova, Katerina Neudertova - Fadrhonsova, Vera - Sáňka, Milan - Pavlu, Lenka - Sáňka, Ondřej - Vacek, Oldrich - Nemecek, Karel - Nozari, Shahin - Sarkodie, Vincent Yaw Oppong
PY - 2022
TI - Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape
JF - SOIL AND WATER RESEARCH
VL - 17
IS - 2
SP - 69-79
EP - 69-79
PB - CZECH ACADEMY AGRICULTURAL SCIENCES
SN - 18015395
KW - carbon stocks
KW - digital soil mapping
KW - environmental covariates
KW - random forests
KW - spatial distribution
KW - terrain attributes
UR - https://www.agriculturejournals.cz/web/swr.htm?type=article&id=4_2022-SWR
N2 - Forest soils have a high potential to store carbon and thus mitigate climate change. The information on spatial distribution of soil organic carbon (SOC) stocks is thus very important. This study aims to analyse the importance of environmental predictors for forest SOC stock prediction at the regional and national scale in the Czech Republic. A big database of forest soil data for more than 7 000 sites was compiled from several surveys. SOC stocks were calculated from SOC content and bulk density for the topsoil mineral layer 0-30 cm. Spatial prediction models were developed separately for individual natural forest areas and for four subsets with different altitude range, using random forest method. The importance of environmental predictors in the models strongly differs between regions and altitudes. At lower altitudes, forest edaphic series and soil classes are strong predictors, while at higher altitudes the predictors related to topography become more important. The importance of soil classes depends on the pedodiversity level and on the difference in SOC stock between the classes. The contribution of forest types as predictors is limited when one (mostly coniferous) type dominates. Better prediction results can be obtained in smaller, but consistent regions, like some natural forest areas.
ER -
BORUVKA, Lubos, Radim VASAT, Vit SRAMEK, Katerina Neudertova HELLEBRANDOVA, Vera FADRHONSOVA, Milan SÁŇKA, Lenka PAVLU, Ondřej SÁŇKA, Oldrich VACEK, Karel NEMECEK, Shahin NOZARI and Vincent Yaw Oppong SARKODIE. Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape. \textit{SOIL AND WATER RESEARCH}. CZECH REPUBLIC: CZECH ACADEMY AGRICULTURAL SCIENCES, 2022, vol.~17, No~2, p.~69-79. ISSN~1801-5395. Available from: https://dx.doi.org/10.17221/4/2022-SWR.
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