Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape
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 region...
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2022-02-01
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| Series: | Soil and Water Research |
| Subjects: | |
| Online Access: | https://swr.agriculturejournals.cz/artkey/swr-202202-0001_predictors-for-digital-mapping-of-forest-soil-organic-carbon-stocks-in-different-types-of-landscape.php |
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| author | Luboš Borůvka Radim Vašát Vít Šrámek Kateřina Neudertová Hellebrandová Věra Fadrhonsová Milan Sáňka Lenka Pavlů Ondřej Sáňka Oldřich Vacek Karel Němeček Shahin Nozari Vincent Yaw Oppong Sarkodie |
| author_facet | Luboš Borůvka Radim Vašát Vít Šrámek Kateřina Neudertová Hellebrandová Věra Fadrhonsová Milan Sáňka Lenka Pavlů Ondřej Sáňka Oldřich Vacek Karel Němeček Shahin Nozari Vincent Yaw Oppong Sarkodie |
| author_sort | Luboš Borůvka |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T08:04:01Z |
| format | Article |
| id | doaj.art-5cd76c674233419faea85ef5d574cabe |
| institution | Directory Open Access Journal |
| issn | 1801-5395 1805-9384 |
| language | English |
| last_indexed | 2024-04-10T08:04:01Z |
| publishDate | 2022-02-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Soil and Water Research |
| spelling | doaj.art-5cd76c674233419faea85ef5d574cabe2023-02-23T03:48:50ZengCzech Academy of Agricultural SciencesSoil and Water Research1801-53951805-93842022-02-01172697910.17221/4/2022-SWRswr-202202-0001Predictors for digital mapping of forest soil organic carbon stocks in different types of landscapeLuboš Borůvka0Radim Vašát1Vít Šrámek2Kateřina Neudertová Hellebrandová3Věra Fadrhonsová4Milan Sáňka5Lenka Pavlů6Ondřej Sáňka7Oldřich Vacek8Karel Němeček9Shahin Nozari10Vincent Yaw Oppong Sarkodie11Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech RepublicForestry and Game Management Research Institute, Jíloviště-Strnady, Czech RepublicForestry and Game Management Research Institute, Jíloviště-Strnady, Czech RepublicForestry and Game Management Research Institute, Jíloviště-Strnady, Czech RepublicRECETOX, Masaryk University, Brno, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech RepublicRECETOX, Masaryk University, Brno, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech RepublicDepartment of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech RepublicForest 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.https://swr.agriculturejournals.cz/artkey/swr-202202-0001_predictors-for-digital-mapping-of-forest-soil-organic-carbon-stocks-in-different-types-of-landscape.phpcarbon stocksdigital soil mappingenvironmental covariatesrandom forestsspatial distributionterrain attributes |
| spellingShingle | Luboš Borůvka Radim Vašát Vít Šrámek Kateřina Neudertová Hellebrandová Věra Fadrhonsová Milan Sáňka Lenka Pavlů Ondřej Sáňka Oldřich Vacek Karel Němeček Shahin Nozari Vincent Yaw Oppong Sarkodie Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape Soil and Water Research carbon stocks digital soil mapping environmental covariates random forests spatial distribution terrain attributes |
| title | Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape |
| title_full | Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape |
| title_fullStr | Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape |
| title_full_unstemmed | Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape |
| title_short | Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape |
| title_sort | predictors for digital mapping of forest soil organic carbon stocks in different types of landscape |
| topic | carbon stocks digital soil mapping environmental covariates random forests spatial distribution terrain attributes |
| url | https://swr.agriculturejournals.cz/artkey/swr-202202-0001_predictors-for-digital-mapping-of-forest-soil-organic-carbon-stocks-in-different-types-of-landscape.php |
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