Spatial-temporal changes of soil organic carbon content in Wafangdian, China

Sustainability (Switzerland)

Volumn 8, Issue 11, 2016, Pages

  Wang, Shuai ^a   Wang, Qiubing ^a   Adhikari, Kabindra ^b   Jia, Shuhai ^a   Jin, Xinxin ^a   Liu, Hongbin ^a  

^a SHENYANG AGRICULTURAL UNIVERSITY   (China)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Boosted regression trees; Digital soil mapping; Land use change; soil organic carbon; Topography

Indexed keywords

ACCURACY ASSESSMENT; CARBON CYCLE; DIGITAL MAPPING; LAND USE CHANGE; MODEL VALIDATION; NDVI; ORGANIC CARBON; PERFORMANCE ASSESSMENT; SOIL CARBON; SOIL EMISSION; SOIL FERTILITY; SOIL MANAGEMENT; SPATIOTEMPORAL ANALYSIS; TOPOGRAPHY; TOPSOIL; URBANIZATION;

CHINA; LIAONING; WAFANGDIAN;

EID: 85000981991     PISSN: None     EISSN: 20711050     Source Type: Journal    
DOI: 10.3390/su8111154     Document Type: Article

References (66)

1. Batjes, N.H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 1996, 47, 151-163.
2. Bohn, H.L. Estimate of organic carbon in world soils: II. Soil Sci. Soc Am. J. 1982, 46, 1118-1119.
3. Grace, J. Understanding and managing the global carbon cycle. J. Ecol. 2004, 92, 189-202.
4. Lal, R. Soil carbon sequestration to mitigate climate change. Geoderma 2004, 123, 1-22.
5. Gal, A.; Vyn, T.J.; Micheli, E.; Kladivko, E.J.; McFee, W.W. Soil carbon and nitrogen accumulation with long-term no-till versus moldboard plowing overestimated with tilled-zone sampling depths. Soil Tillage Res. 2007, 96, 42-51.
6. Wiesmeier, M.; Hübner, R.; Spörlein, P.; Geuß, U.; Hangen, E.; Reischl, A.; Schilling, B.; Lützow, M.; Kögel-Knabner, I. Carbon sequestration potential of soils in southeast Germany derived from stable soil organic carbon saturation. Glob. Chang. Biol. 2014, 20, 653-665.
7. Zhao, M.S.; Zhang, G.L.; Wu, Y.J.; Li, D.C.; Zhao, Y.G. Driving forces of soil organic matter change in Jiangsu Province of China. Soil Use Manag. 2015, 31, 440-449.
8. Mann, L.K. Changes in soil carbon storage after cultivation. Soil Sci. 1986, 142, 279-288.
9. 2 emissions. Soil Use Manag. 1997, 13, 230-244.
10. Cruz, F.T.; Pitman, A.J.; McGregor, J.L. Probabilistic simulations of the impact of increasing leaf-level atmospheric carbon dioxide on the global land surface. Clim. Dyn. 2010, 34, 361-379.
11. Zhang, F.; Li, C.; Wang, Z.; Wu, H. Modeling impacts of management alternatives on soil carbon storage of farmland in Northwest China. Biogeosciences 2006, 3, 451-466.
12. Yin, K.; Lu, D.S.; Tian, Y.C.; Zhao, Q.J.; Yuan, C. Evaluation of Carbon and Oxygen Balances in Urban Ecosystems Using Land Use/Land Cover and Statistical Data. Sustainability 2014, 7, 195-221.
13. Qiu, L.F.; Zhu, J.X.;Wang, K.; Hu, W. Land Use Changes Induced County-Scale Carbon Consequences in Southeast China 1979-2020, Evidence from Fuyang, Zhejiang Province. Sustainability 2015, 8, 38.
14. McBratney, A.B.; Mendonça Santos, M.L.; Minasny, B. On digital soil mapping. Geoderma 2003, 117, 3-52.
15. Jenny, H. Factors of Soil Formation: A System of Quantitative Pedology; Beth, R., Ed.; Soil Science: Philadelphia, PA, USA, 1941; Volume 42, p. 415.
16. Zhao, M.S.; Rossiter, D.G.; Li, D.C.; Zhao, Y.G.; Yang, J.L.; Liu, F.; Zhang, G.L. Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index. Ecol. Indic. 2014, 39, 120-133.
17. Brus, D.J.; Yang, R.M.; Zhang, G.L. Three-dimensional geostatistical modeling of soil organic carbon: A case study in the Qilian Mountains, China. Catena 2016, 141, 46-55.
18. Marvin, D.C.; Asner, G.P.; Schnitzer, S.A. Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy. Remote Sens. Environ. 2016, 176, 98-106.
19. Grimm, R.; Behrens, T.; Märker, M.; Elsenbeer, H. Soil organic carbon concentrations and stocks on Barro Colorado Island-digital soil mapping using Random Forests analysis. Geoderma 2008, 146, 102-113.
20. Yang, R.M.; Zhang, G.L.; Liu, F.; Lu, Y.Y.; Yang, F.; Yang, F.; Yang, M.; Zhao, Y.G.; Li, D.C. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Indic. 2016, 60, 870-878.
21. Wiesmeier, M.; Barthold, F.; Blank, B.; Kögel-Knabner, I. Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem. Plant Soil 2011, 340, 7-24.
22. Were, K.; Bui, D.T.; Dick, Ø.B.; Singh, B.R. 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. Ecol. Indic. 2015, 52, 394-403.
23. Minasny, B.; Setiawan, B.I.; Arif, C.; Saptomo, S.K.; Chadirin, Y. Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands. Geoderma 2016, 272, 20-31.
24. Froeschke, J.T.; Froeschke, B.F. Spatio-temporal predictive model based on environmental factors for juvenile spotted seatrout in Texas estuaries using boosted regression trees. Fish. Res. 2011, 111, 131-138.
25. Adhikari, K.; Kheir, R.B.; Greve, M.B.; Greve, M.H. Comparing kriging and regression approaches for mapping soil clay content in a diverse Danish landscape. Soil Sci. 2013, 178, 505-517.
26. Skurichina, M.; Duin, R.P. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 2002, 5, 121-135.
27. Elith, J.; Leathwick, J.R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77, 802-813.
28. Friedman, J.H.; Meulman, J.J. Multiple additive regression trees with application in epidemiology. Stat. Med. 2003, 22, 1365-1381.
29. Minasny, B.; McBratney, A.B.; Mendonça-Santos, M.L.; Odeh, I.O.A.; Guyon, B. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Aust. J. Soil Res. 2006, 44, 233-244.
30. Pouteau, R.; Rambal, S.; Ratte, J.P.; Gogé, F.; Joffre, R.;Winkel, T. Downscaling MODIS-derived maps using GIS and boosted regression trees: the case of frost occurrence over the arid Andean highlands of Bolivia. Remote Sens. Environ. 2011, 115, 117-129.
31. Carslaw, D.C.; Taylor, P.J. Analysis of air pollution data at a mixed source location using boosted regression trees. Atmos. Environ. 2009, 43, 3563-3570.
32. Dhingra, M.S.; Dissanayake, R.; Negi, A.B.; Oberoi, M.; Castellan, D.; Thrusfield, M.; Linard, C.; Gilbert, M. Spatio-temporal epidemiology of highly pathogenic avian influenza (subtype H5N1) in poultry in eastern India. Spat. Spatio-Tempor. Epidemiol. 2014, 11, 45-57.
33. Schad, P.; Van Huyssteen, C.; Micheli, E. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Reference Base for Soil Resources: Rome, Italy, 2014; Volume 106, pp. 246-385.
34. Zhu, A.X.; Yang, L.; Li, B.; Qin, C.; English, E.; Burt, J.E.; Zhou, C.H. Purposive sampling for digital soil mapping for areas with limited data. In Digital Soil Mapping with Limited Data; Hartemink, A.E., McBratney, A.B., de Lourdes, M.-S., Eds.; Springer: Amsterdam, The Netherlands, 2008; Volume 8, pp. 233-245.
35. Nelson, D.W.; Sommers, L.E. Methods of soil analysis. In Total Carbon, Organic Carbon, and Organic Matter, 2nd ed.; Miller, R.H., Keeney, D.R., Eds.; The Alliance of Crop, Soil, and Environmental Science Societies: Madison, WI, USA, 1982; Volume 2, pp. 539-579.
36. Olaya, V.F. A Gentle Introduction to Saga GIS; The SAGA User Group eV: Göttingen, Germany, 2004.
37. Gao, Y.Y.; Hua, H.M.;Wang, S.H.; Li, B. A fast image dehazing algorithm based on negative correction. Signal Process. 2014, 103, 380-398.
38. Malone, B.P.; McBratney, A.B.; Minasny, B.; Laslett, G.M. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 2009, 154, 138-152.
39. Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: a statistical view of boosting. Ann. Stat. 2000, 28, 337-407.
40. Liu, J.T.; Sui, C.H.; Deng, D.W.;Wang, J.W.; Feng, B.; Liu, W.Y.;Wu, C.H. Representing conditional preference by boosted regression trees for recommendation. Inf. Sci. 2016, 327, 1-20.
41. Saha, D.; Alluri, P.; Gan, A. Prioritizing Highway Safety Manual's crash prediction variables using boosted regression trees. Accid. Anal. Prev. 2015, 79, 133-144.
42. Martin, M.P.; Wattenbach, M.; Smith, P.; Meersmans, J.; Jolivet, C.; Boulonne, L.; Arrouays, D. Spatial distribution of soil organic carbon stocks in France. Biogeosciences 2011, 8, 1053-1065.
43. Müller, D.; Leitão, P.J.; Sikor, T. Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees. Agric. Syst. 2013, 117, 66-77.
44. Dismo: Species Distribution Modeling, R Package Version 0.8-17. Available online: http://dssm.unipa.it/CRAN/web/packages/dismo/(accessed on 31 December 2013).
45. Gbm: Generalized Boosted Regression Models, R Package Version 1.6-3. Available online: http://127.0.0.1: 31000/library/gbm/html/gbm-package.html (accessed on 26 November 2007).
46. Yoon, L.C.; Pedro, J.; Leitão, T.L. Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees. Spat. Spatio-Tempor. Epidemiol. 2014, 10, 75-84.
47. Oksanen, J.; Kindt, R.; Legendre, P.; Oksanen, M.J. Vegan: Community Ecology Package, version 1.15-4; R Foundation for Statistical Computing: Vienna, Austria, 2014; Volume 3, pp. 1-13.
48. Lin, L. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989, 45, 255-268.
49. Ließ, M.; Glaser, B.; Huwe, B. Uncertainty in the spatial prediction of soil texture: comparison of regression tree and Random Forest models. Geoderma 2012, 170, 70-79.
50. Razakamanarivo, R.H.; Grinand, C.; Razafindrakoto, M.A.; Bernoux, M.; Albrecht, A. Mapping organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: A multiple regression approach. Geoderma 2011, 162, 335-346.
51. Guo, L.B.; Gifford, R.M. Soil carbon stocks and land use change: A meta-analysis. Glob. Chang. Biol. 2002, 8, 345-360.
52. Bowman, R.A.; Reeder, J.D.;Wienhold, B.J. Quantifying laboratory and field variability to assess potential for carbon sequestration. Commun. Soil Sci. Plant Anal. 2002, 33, 1629-1642.
53. Goidts, E.; Van Wesemael, B.; Crucifix, M. Magnitude and sources of uncertainties in soil organic carbon (SOC) stock assessments at various scales. Eur. J. Soil Sci. 2009, 60, 723-739.
54. Krishnan, P.; Bourgeon, G.; Lo Seen, D.; Nair, K.M.; Prasanna, R.; Srinivas, S.; Muthusankar, G.; Dufy, L.; Ramesh, B.R. Organic carbon stock map for soils of southern India: a multifactorial approach. Curr. Sci. 2007, 93, 706-710.
55. Yang, Y.H.; Fang, J.Y.; Tang, Y.H.; Ji, C.J.; Zheng, C.Y.; He, J.S.; Zhu, B. Storage, patterns and controls of soil organic carbon in the Tibetan grasslands. Glob. Chang. Biolog. 2008, 14, 1592-1599.
56. Shi, Y.; Baumann, F.; Ma, Y.; Song, C.; Kühn, P.; Scholten, T.; He, J.S. Organic and inorganic carbon in the topsoil of the Mongolian and Tibetan grasslands: pattern, control and implications. Biogeosciences 2012, 9, 2287-2299.
57. Riano, D.; Chuvieco, E.; Salas, J.; Aguados, I. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1056-1061.
58. Geerken, R.; Zaitchik, B.; Evans, J.P. Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. Int. J. Remote Sens. 2005, 26, 5535-5554.
59. Minasny, B.; McBratney, A.B.; Malone, B.P.; Wheeler, I. Digital mapping of soil carbon. Adv. Agron. 2013, 118, 1-47.
60. Romano, N.; Palladino, M. Prediction of soil water retention using soil physical data and terrain attributes. J. Hydrol. 2002, 265, 56-75.
61. Thompson, J.A.; Bell, J.C.; Butler, C.A. Quantitative Soil-Landscape Modeling for Estimating the Areal Extent of Hydromorphic Soils. Soil Sci. Soc. Am. J. 1997, 61, 971-980.
62. Adhikari, K.; Hartemink, A.E.; Minasny, B.; Bou Kheir, R.; Greve, M.B.; Greve, M.H. Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark. PLoS ONE 2014, 9, e105519.
63. Moore, I.D.; Gessler, P.E.; Nielsen, G.A.; Peterson, G.A. Soil Attribute Prediction Using Terrain Analysis. Soil Sci. Soc. Am. J. 1993, 57, 443-452.
64. Wiesmeier, M.; Barthold, F.; Spörlein, P.; Geuß, U.; Hangen, E.; Reischl, A.; Schilling, B.; Angst, G.; Von Lützow, M.; Kögel-Knabner, I. Estimation of total organic carbon storage and its driving factors in soils of Bavaria (southeast Germany). Geod. Reg. 2014, 1, 67-78.
65. Jobbágy, E.G.; Jackson, R.B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 2000, 10, 423-436.
66. Jin, X.M.; Wan, L.; Zhang, Y.K.; Hu, G.C.; Schaepman, M.E.; Clevers, J.G.P.W.; Su, Z. Quantification of spatial distribution of vegetation in the Qilian Mountain area with MODIS NDVI. Int. J. Remote Sens. 2009, 30, 5751-5766.