Volume 6, Issue 1 (2018)
ECOPERSIA 2018, 6(1): 41-54 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Maleki S, Khormali F, Karimi A. Estimation of Soil Organic Carbon in a Small-Scale Loessial Hillslope Using Terrain Derivatives of Northern Iran. ECOPERSIA 2018; 6 (1) :41-54
URL: http://ecopersia.modares.ac.ir/article-24-14466-en.html
URL: http://ecopersia.modares.ac.ir/article-24-14466-en.html
1- Department of Soil Science, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2- Department of Soil Science, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran ,khormali@yahoo.com
3- Department of Soil Science, Ferdowsi University of Mashhad, Mashhad, Iran
2- Department of Soil Science, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran ,
3- Department of Soil Science, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: (7325 Views)
Aims: Soil organic carbon (SOC) is contemplated as a crucial proxy to manage soil quality, conserve natural resources, monitoring CO2 and preventing soil erosion within the landscape, regional, and global scale. Therefore, the main aims of this study were to (1) determine the impact of terrain derivatives on the SOC distribution and (2) compare the different algorithms of topographic wetness index (TWI) calculation for SOC estimation in a small-scale loess hillslope of Toshan area, Golestan province, Iran. (3) Comparison between multiple linear regression (MLR) and artificial neural networks (ANN) methods for SOC prediction.
Materials & Methods: total of 135 soil samples were taken in different slope positions, i.e., shoulder (SH), backslope (BS), footslope (FS), and toeslope (TS). Primary and secondary terrain derivatives were calculated using digital elevation model (DEM) with a spatial resolution of 10 m × 10 m. To SOC estimation (dependent variable) was applied two models, i.e., MLR and ANN with terrain derivatives as the independent variables.
Findings: The results showed significant differences using Duncan’s test in where TS position had the higher mean value of SOC (25.90 g kg−1) compared to SH (5.00 g kg−1) and BS (12.70 g kg−1) positions. The present study also revealed which SOC was more correlated with TWIMFD (Multiple-Flow-Direction) and TWIBFD (Biflow-Direction) than TWISFD (Single Flow Direction). The MLR and ANN models were validated by additional samples (25 points) that can be explain 65% and 76% of the total variability of SOC, respectively, in the study area.
Conclusion: These results indicated that the use of terrain derivatives is a beneficial method for SOC estimation. In general, an accurate understanding of TWIMFD is needed to better estimate SOC to evaluate soil and ecosystem related effects on global warming of as this hilly region at a larger scale in a future study.
Materials & Methods: total of 135 soil samples were taken in different slope positions, i.e., shoulder (SH), backslope (BS), footslope (FS), and toeslope (TS). Primary and secondary terrain derivatives were calculated using digital elevation model (DEM) with a spatial resolution of 10 m × 10 m. To SOC estimation (dependent variable) was applied two models, i.e., MLR and ANN with terrain derivatives as the independent variables.
Findings: The results showed significant differences using Duncan’s test in where TS position had the higher mean value of SOC (25.90 g kg−1) compared to SH (5.00 g kg−1) and BS (12.70 g kg−1) positions. The present study also revealed which SOC was more correlated with TWIMFD (Multiple-Flow-Direction) and TWIBFD (Biflow-Direction) than TWISFD (Single Flow Direction). The MLR and ANN models were validated by additional samples (25 points) that can be explain 65% and 76% of the total variability of SOC, respectively, in the study area.
Conclusion: These results indicated that the use of terrain derivatives is a beneficial method for SOC estimation. In general, an accurate understanding of TWIMFD is needed to better estimate SOC to evaluate soil and ecosystem related effects on global warming of as this hilly region at a larger scale in a future study.
Article Type: Original Research |
Subject:
Aquatic Ecology
Received: 2017/03/12 | Accepted: 2018/01/9 | Published: 2018/03/30
Received: 2017/03/12 | Accepted: 2018/01/9 | Published: 2018/03/30
References
1. Sharma U, Datta M, Sharma V. Soil Fertility, erosion, runoff and crop productivity affected by different farming systems. ECOPERSIA. 2014;2(3):629-50. [Link]
2. Maleki S, Khormali F, Bagheri Bodaghabadi M, Mohammadi J, Kehl M, Hoffmeister D. Geological controlling soil organic carbon and nitrogen density in a hillslope landscape, semiarid area of Golestan Province, Iran. Desert. 2017;22:221-8. [Link]
3. Khormali F, Ajami M, Ayoubi Sh, Srinivasarao Ch, Wani SP. Role of deforestation and hillslope position on soil quality attributes of loess-derived soils in Golestan province, Iran. Agr Ecosyst Environ. 2009;134(3-4):178-89. [Link] [DOI:10.1016/j.agee.2009.06.017]
4. Nadeu E, Qui-onero-Rubio JM, de Vente J, Boix-Fayos C. The influence of Catchment morphology, lithology and land use on soil organic carbon export in a Mediterranean mountain region. Catena. 2015;126:117-25. [Link] [DOI:10.1016/j.catena.2014.11.006]
5. Ajami M, Heidari A, Khormali F, Gorji M, Ayoubi Sh. Environmental factors controlling soil organic carbon storage in loess soils of a subhumid region, northern Iran. Geoderma. 2016;281:1-10. [Link] [DOI:10.1016/j.geoderma.2016.06.017]
6. Lal R. Soil carbon sequestration impacts on global climate change and food security. Science. 2004;304(5677):1623-7. [Link] [DOI:10.1126/science.1097396]
7. Mokhtari Karchegani P, Ayoubi Sh, Mosaddeghi MR, Honarju N. Soil Organic Carbon Pools in Particle-Size Fractions as Affected by Slope Gradient and Land Use Change in Hilly Regions, Western Iran. J Mt Sci. 2012;9(1):87-95. [Link] [DOI:10.1007/s11629-012-2211-2]
8. Schwanghart W, Jarmer T. Linking spatial patterns of soil organic carbon to topography- a case study from south-eastern Spain. Geomorphology. 2011;126(1-2):252-263. [Link] [DOI:10.1016/j.geomorph.2010.11.008]
9. Bameri A, Khormali F, Kiani F, Dehghani AA. Spatial variability of soil organic carbon in different hillslope positions in Toshan area, Golestan province, Iran: Geostatistical approaches. J Mt Sci. 2015;12(6):1422-33. [Link] [DOI:10.1007/s11629-014-3213-z]
10. Bou Kheir R, Greve MH, Bøcher PK, Greve MB, Larsen R, McCloy K. Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: The case study of Denmark. J Environ Manag. 2010;91(5):1150-60. [Link] [DOI:10.1016/j.jenvman.2010.01.001]
11. Bagheri Bodaghabadi M, Martínez-Casasnovas JA, Salehi MH, Mohammadi J, Esfandiarpoor Borujeni I, et al. Digital soil mapping using artificial neural networks and terrain-related attributes. Pedosphere. 2015;25(4):580-591. [Link] [DOI:10.1016/S1002-0160(15)30038-2]
12. Ebrahimi M, Masoodipour AR, Rigi M. Role of soil and topographic features in distribution of plant species (case study: Sanib Taftan Watershed). ECOPERSIA. 2015;3(1):917-32. [Link]
13. Nosrati K, Haddadchi A, Zare MR, Shirzadi L. An evaluation of the role of hillslope components and land use in soil erosion using 137Cs inventory and soil organic carbon stock. Geoderma. 2015;243-244:29-40. [Link] [DOI:10.1016/j.geoderma.2014.12.008]
14. Mueller TG, Pierce FJ. Soil carbon maps: Enhancing spatial estimates with simple terrain attributes at multiple scales. Soil Sci Soc Am J. 2003;67(1):258-67.
https://doi.org/10.2136/sssaj2003.2580 [Link] [DOI:10.2136/sssaj2003.0258]
15. Beven KJ, Kirkby MJ. A physically based variable contributing area model of basin hydrology. Hydrolog Sci Bull. 1979;24(1):43-69. [Link] [DOI:10.1080/02626667909491834]
16. Pei T, Qin C, Zhu A, Yang L, Luo M, Li B, et al. Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods. Ecol Indic. 2010;10(3):610-9. [Link] [DOI:10.1016/j.ecolind.2009.10.005]
17. Ebrahimi M, Safari Sinegani AA, Sarikhani MR, Mohammadi SA. Comparison of artificial neural network and multivariate regression models for prediction of Azotobacteria population in soil under different land uses. Comput Electron Agric. 2017;140:409-21. [Link] [DOI:10.1016/j.compag.2017.06.019]
18. Li X, McCarty GW, Karlen DL, Cambardella CA. Topographic metric predictions of soil redistribution and organic carbon in Iowa cropland fields. Catena. 2018;160:222-32. [Link] [DOI:10.1016/j.catena.2017.09.026]
19. Parvizi Y, Heshmati M, Gheituri M. Intelligent approaches to analysing the importance of land use management in soil carbon stock in a semiarid ecosystem, west of Iran. ECOPERSIA. 2017;5(1):1699-709. [Link] [DOI:10.18869/modares.ecopersia.5.1.1699]
20. Mokhtari Karchegani P, Ayoubi Sh, Honarju N, Jalalian A. Predicting soil organic matter by artificial neural network in landscape scale using remotely sensed data and topographic attributes [Internet]. Khorasgan: Islamic Azad University; 2011 [cited 2018 mar]. available from: http://meetingorganizer.copernicus.org/EGU2011/EGU2011-1075.pdf. [Link]
21. Mahmoudabadi E, Karimi AR, Haghnia GH, Sepehr A. Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran. Environ Monit Assess. 2017;189(10):500. [Link] [DOI:10.1007/s10661-017-6197-7]
22. Maleki S, Khormali F, Karimi AR. Mapping soil organic matter using topographic attributes and geostatistic approaches in Toshan area, Golestan province. Iran J Soil Res. 2014;28(2):459-68. [Persian] [Link]
23. Food and Agriculture Organization of the United Nations. Can carbon (SOC) offset the climate change? [Internet]. Rome: Food and Agriculture Organization of the United Nations; 2015 [cited 2018 Mar]. Available from: http://www.fao.org/3/a-bl100e.pdf. [Link]
24. Burt R. Soil survey laboratory method manual. Washington DC: United States Department of Agriculture, Natural Resources Conservation Service, Soil Survey Investigations; 2004. Report NO.: 42. [Link]
25. Sorensen R, Zinko U, Seibert J. On the calculation of the topographic wetness index: Evaluation of different methods based on field observation. Hydrol Earth Syst Sci. 2006;10:101-12. [Link] [DOI:10.5194/hess-10-101-2006]
26. Hass J. Soil moisture modeling using TWI and satellite imagery in the Stockholm region [Dissertation]. Stockholm: Royal Institute of Technology (KTH); 2010. p. 103. [Link]
27. O'Callaghan JF, Mark DM. The extraction of drainage networks from digital elevation data. Comput Vision Graph. 1984;28(3):323-44. [Link] [DOI:10.1016/S0734-189X(84)80011-0]
28. Fairfield J, Leymarie P. Drainage networks from grid digital elevation models. Water Resour Res. 1991;27(5):709-17. [Link] [DOI:10.1029/90WR02658]
29. Moore ID, Gessler PE, Nielson GA. Soil attributes prediction using terrain analysis. Soil Sci Soc Am J. 1993;57(2):443-52. [Link] [DOI:10.2136/sssaj1993.03615995005700020026x]
30. Liu Sh, An N, Yang J, Dong Sh, Wang C, Tin Y. Prediction of soil organic matter variability associated with different land use types in mountainous landscape in southwestern Yunnan province, China. Catena. 2015;133:137-44. [Link] [DOI:10.1016/j.catena.2015.05.010]
31. Somaratne S, Seneviratne G, Coomaraswam U. Prediction of soil organic carbon across different land-use patterns: A neural network approach. Soil Sci Soc Am J. 2005;69:1580-19. [Link] [DOI:10.2136/sssaj2003.0293]
32. Mirzaee S, Ghorbani-Dashtaki S, Mohammadi J, Asadi H, Asadzadeh F. Spatial variability of soil organic matter using remote sensing data. Catena. 2016;145:118-27. [Link] [DOI:10.1016/j.catena.2016.05.023]
33. Tiwari SK, Saha SK, Kumar S. Prediction modeling and mapping of soil carbon content using artificial neural network, hyperspectral satellite data and field spectroscopy. Adv Remote Sens. 2015;4(1):63-72. [Link] [DOI:10.4236/ars.2015.41006]
34. Bangroo SA, Najar GR, Rasool A. Effect of altitude and aspect on soil organic carbon and nitrogen stocks in the Himalayan Mawer Forest Range. Catena. 2017;158:63-8. [Link] [DOI:10.1016/j.catena.2017.06.017]
35. Mirzaee S, Ghorbani-Dashtaki S, Mohammadi J, Asadi H, Asadzadeh F. Spatial variability of soil organic matter using remote sensing data. Catena. 2016;145:118-27. [Link] [DOI:10.1016/j.catena.2016.05.023]
36. Tiwari SK, Saha SK, Kumar S. Prediction modeling and mapping of soil carbon content using artificial neural network, hyperspectral satellite data and field spectroscopy. Adv Remote Sens. 2015;4(1):63-72. [Link] [DOI:10.4236/ars.2015.41006]
37. Bangroo SA, Najar GR, Rasool A. Effect of altitude and aspect on soil organic carbon and nitrogen stocks in the Himalayan Mawer Forest Range. Catena. 2017;158:63-8. [Link] [DOI:10.1016/j.catena.2017.06.017]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |