Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods

SONG Xiaodong; LIU Feng; JU Bing; ZHI Junjun; LI Decheng; ZHAO Yuguo; ZHANG Ganlin

doi:10.1007/s11769-017-0869-7

Volume 27 Issue 4

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Article Navigation > Chinese Geographical Science > 2017 > 27(4): 516-528

SONG Xiaodong, LIU Feng, JU Bing, ZHI Junjun, LI Decheng, ZHAO Yuguo, ZHANG Ganlin. Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods[J]. Chinese Geographical Science, 2017, 27(4): 516-528. doi: 10.1007/s11769-017-0869-7

Citation:

SONG Xiaodong, LIU Feng, JU Bing, ZHI Junjun, LI Decheng, ZHAO Yuguo, ZHANG Ganlin. Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods[J]. Chinese Geographical Science, 2017, 27(4): 516-528. doi: 10.1007/s11769-017-0869-7

Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods

doi: 10.1007/s11769-017-0869-7

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China

Funds: Under the auspices of Basic Project of State Commission of Science Technology of China (No. 2008FY110600), National Natural Science Foundation of China (No. 91325301, 41401237, 41571212, 41371224), Field Frontier Program of Institute of Soil Science, Chinese Academy of Sciences (No. ISSASIP1624)

More Information

Corresponding author: ZHANG Ganlin.E-mail:glzhang@issas.ac.cn
Received Date: 2016-07-21
Rev Recd Date: 2016-11-05
Publish Date: 2017-08-27

Abstract

The main aim of this paper was to calculate soil organic carbon stock (SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China. A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons (MCV). The model-then-calculate with fixed-thickness (MCF), soil profile statistics (SPS), pedological professional knowledge-based (PKB) and vegetation type-based (Veg) methods were carried out for comparison. With respect to the similar pedological information, nine common layers from topsoil to bedrock were grouped in the MCV. Validation results suggested that the MCV method generated better performance than the other methods considered. For the comparison of polygon based approaches, the Veg method generated better accuracy than both SPS and PKB, as limited soil data were incorporated. Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions. The intermediate product, that is, horizon thickness maps were fluctuant enough and reflected many details in space. The linear mixed model indicated that mean annual air temperature (MAAT) was the most important predictor for the SOCS simulation. The minimal residual of the linear mixed models was achieved in the vegetation type-based model, whereas the maximal residual was fitted in the soil type-based model. About 95% of SOCS could be found in Argosols, Cambosols and Isohumosols. The largest SOCS was found in the croplands with vegetation of Triticum aestivum L., Sorghum bicolor (L.) Moench, Glycine max (L.) Merr., Zea mays L. and Setaria italica (L.) P. Beauv.
- soil organic carbon stock,
- model-then-calculate,
- random forest,
- linear mixed model,
- northeastern China

References

[1]	Ahrens R J, Eswaran H, Rice T J, 2003. Soil classification: past and present. In: Eswaran H et al. (eds.). Soil Classification: A Global Desk Reference. Boca Raton: CRC Press.
[2]	Aitkenhead M J, Coull M C, 2016. Mapping soil carbon stocks across Scotland using a neural network model. Geoderma, 262: 187-198. doi: 10.1016/j.geoderma.2015.08.034
[3]	Bapat R B, 2012. Linear Mixed Models. Heidelberg: Springer. doi: 10.1007/978-1-4471-2739-0
[4]	Bishop T F A, McBratney A B, Laslett G M, 1999. Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91(1-2): 27-45. doi: 10.1016/S0016-7061(99)00003-8
[5]	Blake G R, 1965. Bulk density. In: Black, C A (eds.). Methods of Soil Analysis, Part1. Physical and Mineralogical Properties, including Statistics of Measurement and Sampling. Madison: American Society of Agronomy, Soil Science Society of America.
[6]	Bockheim J G, 2014. Soil Geography of the USA: A Diagnostic-Horizon Approach. Heidelberg: Springer.
[7]	Boehner J, Koethe R, Conrad O et al., 2002. Soil regionalisation by means of terrain analysis and process parameterisation. In: Micheli E et al. (eds.). Soil Classification 2001. Luxembourg: European Soil Bureau, 213-222.
[8]	Bourennane H, Salvador-Blanes S, Couturier A et al., 2014. Geostatistical approach for identifying scale-specific correlations between soil thickness and topographic attributes. Geomorphology, 220: 58-67. doi: 10.1016/j.geomorph.2014.05.026
[9]	Breiman L, Friedman, J H, Olshen R A et al., 1984. Classification and Regression Trees. New York: Chapman and Hall.
[10]	Cardinael R, Chevallier T, Barthès B G et al., 2015. Impact of alley cropping agroforestry on stocks, forms and spatial distribution of soil organic carbon: a case study in a Mediterranean context. Geoderma, 259-260: 288-299. doi: 10.1016/j.geoderma.2015.06.015
[11]	Chaplot V, Lorentz S, Podwojewski P et al., 2010. Digital mapping of A-horizon thickness using the correlation between various soil properties and soil apparent electrical resistivity. Geoderma, 157(3-4): 154-164. doi: 10.1016/j.geoderma.2010.04.006
[12]	CMA (China Meteorological Administration), 2011. China Meteorological Data Daily Value. Beijing: China Meteorological Data Sharing Service System.
[13]	Cooperative Research Group on Chinese Soil Taxonomy, 2001. Chinese Soil Taxonomy. Beijing: Science Press.
[14]	Crouvi O, Pelletier J D, Rasmussen C, 2013. Predicting the thickness and aeolian fraction of soils in upland watersheds of the Mojave Desert. Geoderma, 195-196: 94-110. doi: 10.1016/j.geoderma.2012.11.015
[15]	de Gruijter J, Brus D J, Bierkens M F P et al., 2006. Sampling for Natural Resource Monitoring. Berlin: Springer. doi: 10.1007/3-540-33161-1
[16]	Ding F, Hu Y L, Li L J et al., 2013. Changes in soil organic carbon and total nitrogen stocks after conversion of meadow to cropland in Northeast China. Plant & Soil, 373(1-2): 659-672. doi: 10.1007/s11104-013-1827-5
[17]	Dorji T, Odeh I O A, Field D J et al., 2014. Digital soil mapping of soil organic carbon stocks under different land use and land cover types in montane ecosystems, Eastern Himalayas. Forest Ecology and Management, 318: 91-102. doi:10.1016/j.foreco. 2014.01.003
[18]	Editorial board of Series of Chinese Soil Taxonomy Classification, 1993. Progress of the Chinese Soil Taxonomy Classification. Beijing: Science Press.
[19]	Gallant J C, Dowling T I, 2003. A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research, 39(12): 1347-1359. doi:10.1029/2002 WR001426
[20]	Grunwald S, 2009. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152(3-4): 195-207. doi: 10.1016/j.geoderma.2009.06.003
[21]	Jenny H, 1941. Factors of Soil Formation: A System of Quantitative Pedology. New York: McGraw Hill.
[22]	Kempen B, Brus D J, Stoorvogel J J, 2011. Three-dimensional mapping of soil organic matter content using soil type-specific depth functions. Geoderma, 162(1-2): 107-123. doi: 10.1016/j.geoderma.2011.01.010
[23]	Kosmas C, Gerontidis S, Marathianou M, 2000. The effect of land use change on soils and vegetation over various lithological formations on Lesvos (Greece). Catena, 40(1): 51-68. doi: 10.1016/S0341-8162(99)00064-8
[24]	Lacoste M, Minasny B, McBratney A et al., 2014. High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape. Geoderma, 213: 296-311. doi: 10.1016/j.geoderma.2013.07.002
[25]	Ließ M, Glaser B, Huwe B, 2012. Making use of the World Reference Base diagnostic horizons for the systematic description of the soil continuum: application to the tropical mountain soil-landscape of southern Ecuador. Catena, 97: 20-30. doi:1 0.1016/j.catena.2012.05.002
[26]	Lin L I K, 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1): 255-268. doi: 10.2307/2532051
[27]	Liu F, Zhang G L, Sun Y J et al., 2013. Mapping the three-dimensional distribution of soil organic matter across a subtropical hilly landscape. Soil Science Society of America Journal, 77(4): 1241-1253. doi: 10.2136/sssaj2012.0317
[28]	Liu X, Burras L, Kravchenko Y S et al., 2012. Overview of Mollisols in the world: Distribution, land use and management. Canadian Journal of Soil Science, 92(3): 383-402. doi: 10.4141/cjss2010-058
[29]	Mao D H, Wang Z M, Li L et al., 2015. Soil organic carbon in the Sanjiang Plain of China: storage, distribution and controlling factors. Biogeosciences, 12(6): 1635-1645. doi: 10.5194/bg-12-1635-2015
[30]	Martin M P, Orton T G, Lacarce E et al., 2014. Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma, 223-225: 97-107. doi: 10.1016/j.geoderma.2014.01.005
[31]	Martin M P, Wattenbach M, Smith P et al., 2011. Spatial distribution of soil organic carbon stocks in France. Biogeosciences, 8: 1053-1065. doi: 10.5194/bg-8-1053-2011
[32]	Nelson D W, Sommers L E, 1982. Total carbon, organic carbon and organic matter. In: Page A L et al. (eds.). Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties. Madison: Agronomy Monograph, 539-579.
[33]	Ottoy S, Beckers V, Jacxsens P et al., 2015. Multi-level statistical soil profiles for assessing regional soil organic carbon stocks. Geoderma, 253-254: 12-20. doi:10.1016/j.geoderma.2015. 04.001
[34]	Parras-Alcántara L, Lozano-García B, Brevik E C et al., 2015. Soil organic carbon stocks assessment in Mediterranean natural areas: a comparison of entire soil profiles and soil control sections. Journal of Environmental Management, 155: 219-228. doi: 10.1016/j.jenvman.2015.03.039
[35]	Qi Guang, Chen Hua, Zhou Li et al., 2016. Carbon stock of larch plantations and its comparison with an old-growth forest in northeast China. Chinese Geographical Science, 26(1): 10-21. doi: 10.1007/s11769-015-0772-z
[36]	Qin Falyu, Shi Xuezheng, Xu Shengxiang et al., 2016. Zonal differences in correlation patterns between soil organic carbon and climate factors at multi-extent. Chinese Geographical Science, 26(5): 670-678. doi: 10.1007/s11769-015-0736-3
[37]	Song X D, Brus D J, Liu F et al., 2016. Mapping soil organic carbon content by geographically weighted regression: a case study in the Heihe River Basin, China. Geoderma, 261: 11-22. doi: 10.1016/j.geoderma.2015.06.024
[38]	Vanwalleghem T, Poesen J, McBratney A et al., 2010. Spatial variability of soil horizon depth in natural loess-derived soils. Geoderma, 157(1-2): 37-45. doi:10.1016/j.geoderma.2010. 03.013
[39]	Vasenev V I, Stoorvogel J J, Vasenev I I et al., 2014. How to map soil organic carbon stocks in highly urbanized regions? Geoderma, 226-227: 103-115. doi:10.1016/j.geoderma.2014. 03.007
[40]	Webster R, Oliver M A, 2001. Geostatistics for Environmental Scientists. Chichester: John Wiley & Sons.
[41]	Wei Yawei, Yu Dapao, Lewis Bernard Joseph et al., 2014. Forest carbon storage and tree carbon pool dynamics under natural forest protection program in northeastern China. Chinese Geographical Science, 24(4): 397-405. doi: 10.1007/s11769-014-0703-4
[42]	Were K, Bui D T, Dick Ø B et al., 2015. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52: 394-403. doi: 10.1016/j.ecolind.2014.12.028
[43]	Xiong Yi, 1987. Chinese Soils (Second Edition). Beijing: Science Press, 20-38. (in Chinese)
[44]	Yu P, Li Q, Jia H et al., 2013. Carbon stocks and storage potential as affected by vegetation in the Songnen grassland of northeast China. Quaternary International, 306(450): 114-120. doi: 10.1016/j.quaint.2013.05.053
[45]	Zhang Dan, Zheng Haifeng, Ren Zhibin et al., 2015. Effects of forest type and urbanization on carbon storage of urban forests in Changchun, Northeast China. Chinese Geographical Science, 25(2): 147-158. doi: 10.1007/s11769-015-0743-4
[46]	Zhang Y, Zhao Y C, Shi X Z et al., 2008. Variation of soil organic carbon estimates in mountain regions: a case study from Southwest China. Geoderma, 146(3-4): 449-456. doi: 10.1016/j.geoderma.2008.06.015
[47]	Zhi J, Jing C, Lin S et al., 2014. Estimating soil organic carbon stocks and spatial patterns with statistical and GIS-based methods. Plos One, 9(5): e97757. doi:10.1371/journal.pone. 0097757

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Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China

Corresponding author: ZHANG Ganlin.E-mail:glzhang@issas.ac.cn

Received Date: 2016-07-21

Rev Recd Date: 2016-11-05

Publish Date: 2017-08-27

Key words:

Abstract: The main aim of this paper was to calculate soil organic carbon stock (SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China. A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons (MCV). The model-then-calculate with fixed-thickness (MCF), soil profile statistics (SPS), pedological professional knowledge-based (PKB) and vegetation type-based (Veg) methods were carried out for comparison. With respect to the similar pedological information, nine common layers from topsoil to bedrock were grouped in the MCV. Validation results suggested that the MCV method generated better performance than the other methods considered. For the comparison of polygon based approaches, the Veg method generated better accuracy than both SPS and PKB, as limited soil data were incorporated. Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions. The intermediate product, that is, horizon thickness maps were fluctuant enough and reflected many details in space. The linear mixed model indicated that mean annual air temperature (MAAT) was the most important predictor for the SOCS simulation. The minimal residual of the linear mixed models was achieved in the vegetation type-based model, whereas the maximal residual was fitted in the soil type-based model. About 95% of SOCS could be found in Argosols, Cambosols and Isohumosols. The largest SOCS was found in the croplands with vegetation of Triticum aestivum L., Sorghum bicolor (L.) Moench, Glycine max (L.) Merr., Zea mays L. and Setaria italica (L.) P. Beauv.

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Reference (47)

[1]	Ahrens R J, Eswaran H, Rice T J, 2003. Soil classification: past and present. In: Eswaran H et al. (eds.). Soil Classification: A Global Desk Reference. Boca Raton: CRC Press.
[2]	Aitkenhead M J, Coull M C, 2016. Mapping soil carbon stocks across Scotland using a neural network model. Geoderma, 262: 187-198. doi:10.1016/j.geoderma.2015.08.034
[3]	Bapat R B, 2012. Linear Mixed Models. Heidelberg: Springer. doi:10.1007/978-1-4471-2739-0
[4]	Bishop T F A, McBratney A B, Laslett G M, 1999. Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91(1-2): 27-45. doi:10.1016/S0016-7061(99)00003-8
[5]	Blake G R, 1965. Bulk density. In: Black, C A (eds.). Methods of Soil Analysis, Part1. Physical and Mineralogical Properties, including Statistics of Measurement and Sampling. Madison: American Society of Agronomy, Soil Science Society of America.
[6]	Bockheim J G, 2014. Soil Geography of the USA: A Diagnostic-Horizon Approach. Heidelberg: Springer.
[7]	Boehner J, Koethe R, Conrad O et al., 2002. Soil regionalisation by means of terrain analysis and process parameterisation. In: Micheli E et al. (eds.). Soil Classification 2001. Luxembourg: European Soil Bureau, 213-222.
[8]	Bourennane H, Salvador-Blanes S, Couturier A et al., 2014. Geostatistical approach for identifying scale-specific correlations between soil thickness and topographic attributes. Geomorphology, 220: 58-67. doi:10.1016/j.geomorph.2014.05.026
[9]	Breiman L, Friedman, J H, Olshen R A et al., 1984. Classification and Regression Trees. New York: Chapman and Hall.
[10]	Cardinael R, Chevallier T, Barthès B G et al., 2015. Impact of alley cropping agroforestry on stocks, forms and spatial distribution of soil organic carbon: a case study in a Mediterranean context. Geoderma, 259-260: 288-299. doi:10.1016/j.geoderma.2015.06.015
[11]	Chaplot V, Lorentz S, Podwojewski P et al., 2010. Digital mapping of A-horizon thickness using the correlation between various soil properties and soil apparent electrical resistivity. Geoderma, 157(3-4): 154-164. doi:10.1016/j.geoderma.2010.04.006
[12]	CMA (China Meteorological Administration), 2011. China Meteorological Data Daily Value. Beijing: China Meteorological Data Sharing Service System.
[13]	Cooperative Research Group on Chinese Soil Taxonomy, 2001. Chinese Soil Taxonomy. Beijing: Science Press.
[14]	Crouvi O, Pelletier J D, Rasmussen C, 2013. Predicting the thickness and aeolian fraction of soils in upland watersheds of the Mojave Desert. Geoderma, 195-196: 94-110. doi:10.1016/j.geoderma.2012.11.015
[15]	de Gruijter J, Brus D J, Bierkens M F P et al., 2006. Sampling for Natural Resource Monitoring. Berlin: Springer. doi:10.1007/3-540-33161-1
[16]	Ding F, Hu Y L, Li L J et al., 2013. Changes in soil organic carbon and total nitrogen stocks after conversion of meadow to cropland in Northeast China. Plant & Soil, 373(1-2): 659-672. doi:10.1007/s11104-013-1827-5
[17]	Dorji T, Odeh I O A, Field D J et al., 2014. Digital soil mapping of soil organic carbon stocks under different land use and land cover types in montane ecosystems, Eastern Himalayas. Forest Ecology and Management, 318: 91-102. doi:10.1016/j.foreco. 2014.01.003
[18]	Editorial board of Series of Chinese Soil Taxonomy Classification, 1993. Progress of the Chinese Soil Taxonomy Classification. Beijing: Science Press.
[19]	Gallant J C, Dowling T I, 2003. A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research, 39(12): 1347-1359. doi:10.1029/2002 WR001426
[20]	Grunwald S, 2009. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152(3-4): 195-207. doi:10.1016/j.geoderma.2009.06.003
[21]	Jenny H, 1941. Factors of Soil Formation: A System of Quantitative Pedology. New York: McGraw Hill.
[22]	Kempen B, Brus D J, Stoorvogel J J, 2011. Three-dimensional mapping of soil organic matter content using soil type-specific depth functions. Geoderma, 162(1-2): 107-123. doi:10.1016/j.geoderma.2011.01.010
[23]	Kosmas C, Gerontidis S, Marathianou M, 2000. The effect of land use change on soils and vegetation over various lithological formations on Lesvos (Greece). Catena, 40(1): 51-68. doi:10.1016/S0341-8162(99)00064-8
[24]	Lacoste M, Minasny B, McBratney A et al., 2014. High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape. Geoderma, 213: 296-311. doi:10.1016/j.geoderma.2013.07.002
[25]	Ließ M, Glaser B, Huwe B, 2012. Making use of the World Reference Base diagnostic horizons for the systematic description of the soil continuum: application to the tropical mountain soil-landscape of southern Ecuador. Catena, 97: 20-30. doi:10.1016/j.catena.2012.05.002
[26]	Lin L I K, 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1): 255-268. doi:10.2307/2532051
[27]	Liu F, Zhang G L, Sun Y J et al., 2013. Mapping the three-dimensional distribution of soil organic matter across a subtropical hilly landscape. Soil Science Society of America Journal, 77(4): 1241-1253. doi:10.2136/sssaj2012.0317
[28]	Liu X, Burras L, Kravchenko Y S et al., 2012. Overview of Mollisols in the world: Distribution, land use and management. Canadian Journal of Soil Science, 92(3): 383-402. doi:10.4141/cjss2010-058
[29]	Mao D H, Wang Z M, Li L et al., 2015. Soil organic carbon in the Sanjiang Plain of China: storage, distribution and controlling factors. Biogeosciences, 12(6): 1635-1645. doi:10.5194/bg-12-1635-2015
[30]	Martin M P, Orton T G, Lacarce E et al., 2014. Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma, 223-225: 97-107. doi:10.1016/j.geoderma.2014.01.005
[31]	Martin M P, Wattenbach M, Smith P et al., 2011. Spatial distribution of soil organic carbon stocks in France. Biogeosciences, 8: 1053-1065. doi:10.5194/bg-8-1053-2011
[32]	Nelson D W, Sommers L E, 1982. Total carbon, organic carbon and organic matter. In: Page A L et al. (eds.). Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties. Madison: Agronomy Monograph, 539-579.
[33]	Ottoy S, Beckers V, Jacxsens P et al., 2015. Multi-level statistical soil profiles for assessing regional soil organic carbon stocks. Geoderma, 253-254: 12-20. doi:10.1016/j.geoderma.2015. 04.001
[34]	Parras-Alcántara L, Lozano-García B, Brevik E C et al., 2015. Soil organic carbon stocks assessment in Mediterranean natural areas: a comparison of entire soil profiles and soil control sections. Journal of Environmental Management, 155: 219-228. doi:10.1016/j.jenvman.2015.03.039
[35]	Qi Guang, Chen Hua, Zhou Li et al., 2016. Carbon stock of larch plantations and its comparison with an old-growth forest in northeast China. Chinese Geographical Science, 26(1): 10-21. doi:10.1007/s11769-015-0772-z
[36]	Qin Falyu, Shi Xuezheng, Xu Shengxiang et al., 2016. Zonal differences in correlation patterns between soil organic carbon and climate factors at multi-extent. Chinese Geographical Science, 26(5): 670-678. doi:10.1007/s11769-015-0736-3
[37]	Song X D, Brus D J, Liu F et al., 2016. Mapping soil organic carbon content by geographically weighted regression: a case study in the Heihe River Basin, China. Geoderma, 261: 11-22. doi:10.1016/j.geoderma.2015.06.024
[38]	Vanwalleghem T, Poesen J, McBratney A et al., 2010. Spatial variability of soil horizon depth in natural loess-derived soils. Geoderma, 157(1-2): 37-45. doi:10.1016/j.geoderma.2010. 03.013
[39]	Vasenev V I, Stoorvogel J J, Vasenev I I et al., 2014. How to map soil organic carbon stocks in highly urbanized regions? Geoderma, 226-227: 103-115. doi:10.1016/j.geoderma.2014. 03.007
[40]	Webster R, Oliver M A, 2001. Geostatistics for Environmental Scientists. Chichester: John Wiley & Sons.
[41]	Wei Yawei, Yu Dapao, Lewis Bernard Joseph et al., 2014. Forest carbon storage and tree carbon pool dynamics under natural forest protection program in northeastern China. Chinese Geographical Science, 24(4): 397-405. doi:10.1007/s11769-014-0703-4
[42]	Were K, Bui D T, Dick Ø B et al., 2015. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52: 394-403. doi:10.1016/j.ecolind.2014.12.028
[43]	Xiong Yi, 1987. Chinese Soils (Second Edition). Beijing: Science Press, 20-38. (in Chinese)
[44]	Yu P, Li Q, Jia H et al., 2013. Carbon stocks and storage potential as affected by vegetation in the Songnen grassland of northeast China. Quaternary International, 306(450): 114-120. doi:10.1016/j.quaint.2013.05.053
[45]	Zhang Dan, Zheng Haifeng, Ren Zhibin et al., 2015. Effects of forest type and urbanization on carbon storage of urban forests in Changchun, Northeast China. Chinese Geographical Science, 25(2): 147-158. doi:10.1007/s11769-015-0743-4
[46]	Zhang Y, Zhao Y C, Shi X Z et al., 2008. Variation of soil organic carbon estimates in mountain regions: a case study from Southwest China. Geoderma, 146(3-4): 449-456. doi:10.1016/j.geoderma.2008.06.015
[47]	Zhi J, Jing C, Lin S et al., 2014. Estimating soil organic carbon stocks and spatial patterns with statistical and GIS-based methods. Plos One, 9(5): e97757. doi:10.1371/journal.pone. 0097757

Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods

doi: 10.1007/s11769-017-0869-7

Abstract

References

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