Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition
Predicted global warming will be most pronounced in the Arctic and will severely affect permafrost environments. Due to its large spatial extent and large stocks of soil organic carbon, changes to organic matter decomposition rates and associated carbon fluxes in Arctic permafrost soils will signifi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Applied and Environmental Soil Science |
| Online Access: | http://dx.doi.org/10.1155/2012/241535 |
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| _version_ | 1832561804403802112 |
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| author | Harm Bartholomeus Gabriela Schaepman-Strub Daan Blok Roman Sofronov Sergey Udaltsov |
| author_facet | Harm Bartholomeus Gabriela Schaepman-Strub Daan Blok Roman Sofronov Sergey Udaltsov |
| author_sort | Harm Bartholomeus |
| collection | DOAJ |
| description | Predicted global warming will be most pronounced in the Arctic and will severely affect permafrost environments. Due to its large spatial extent and large stocks of soil organic carbon, changes to organic matter decomposition rates and associated carbon fluxes in Arctic permafrost soils will significantly impact the global carbon cycle. We explore the potential of soil spectroscopy to estimate soil carbon properties and investigate the relation between soil properties and vegetation composition. Soil samples are collected in Siberia, and vegetation descriptions are made at each sample point. First, laboratory-determined soil properties are related to the spectral reflectance of wet and dried samples using partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR). SMLR, using selected wavelengths related with C and N, yields high calibration accuracies for C and N. PLSR yields a good prediction model for K and a moderate model for pH. Using these models, soil properties are determined for a larger number of samples, and soil properties are related to plant species composition. This analysis shows that variation of soil properties is large within vegetation classes, but vegetation composition can be used for qualitative estimation of soil properties. |
| format | Article |
| id | doaj-art-daec20a6383347da975c4d8d40dfc5d5 |
| institution | Kabale University |
| issn | 1687-7667 1687-7675 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied and Environmental Soil Science |
| spelling | doaj-art-daec20a6383347da975c4d8d40dfc5d52025-02-03T01:24:18ZengWileyApplied and Environmental Soil Science1687-76671687-76752012-01-01201210.1155/2012/241535241535Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species CompositionHarm Bartholomeus0Gabriela Schaepman-Strub1Daan Blok2Roman Sofronov3Sergey Udaltsov4Centre for Geo-Information, Wageningen University, 6708 PB Wageningen, The NetherlandsInstitute of Evolutionary Biology and Environmental Studies, University of Zürich, 8006 Zurich, SwitzerlandNature Conservation and Plant Ecology, Wageningen University, 6708 PB Wageningen, The NetherlandsInstitute of Biological Problems of the Cryolithozone, 677980 Yakutsk, RussiaInstitute of Physicochemical and Biological Problems of Soil Science, 142290 Pushchino, RussiaPredicted global warming will be most pronounced in the Arctic and will severely affect permafrost environments. Due to its large spatial extent and large stocks of soil organic carbon, changes to organic matter decomposition rates and associated carbon fluxes in Arctic permafrost soils will significantly impact the global carbon cycle. We explore the potential of soil spectroscopy to estimate soil carbon properties and investigate the relation between soil properties and vegetation composition. Soil samples are collected in Siberia, and vegetation descriptions are made at each sample point. First, laboratory-determined soil properties are related to the spectral reflectance of wet and dried samples using partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR). SMLR, using selected wavelengths related with C and N, yields high calibration accuracies for C and N. PLSR yields a good prediction model for K and a moderate model for pH. Using these models, soil properties are determined for a larger number of samples, and soil properties are related to plant species composition. This analysis shows that variation of soil properties is large within vegetation classes, but vegetation composition can be used for qualitative estimation of soil properties.http://dx.doi.org/10.1155/2012/241535 |
| spellingShingle | Harm Bartholomeus Gabriela Schaepman-Strub Daan Blok Roman Sofronov Sergey Udaltsov Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition Applied and Environmental Soil Science |
| title | Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition |
| title_full | Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition |
| title_fullStr | Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition |
| title_full_unstemmed | Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition |
| title_short | Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition |
| title_sort | spectral estimation of soil properties in siberian tundra soils and relations with plant species composition |
| url | http://dx.doi.org/10.1155/2012/241535 |
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