دوماه نامه

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 ساری

چکیده

برای درک بهتر از ترسیب یا آزادسازی کربن به اتمسفر، پهنه‌بندی کربن آلی خاک برای داشتن خط مبنایی از مقدار و ذخیره آن در خاک و همچنین امکان پایش تغییرات آن در طول زمان، بسیار حائز اهمیت است. هدف اصلی این تحقیق، شناخت تغییرپذیری مقدار و ذخیره کربن آلی خاک با استفاده از روش‌های شبکه عصبی مصنوعی و زمین‌آمار در شرق و جنوب شرق استان کهگیلویه و بویراحمد بود. نمونه‌های خاک به‌صورت مرکب و تصادفی از 204 نقطه از عمق 0-15 سانتی‌متر جمع‌آوری و مقدار کربن و ذخیره کربن آلی و برخی خصوصیات خاک اندازه‌گیری شد و از شاخص پوشش گیاهی، ارتفاع، دما، بارش و شیب به‌عنوان داده‌های کمکی استفاده شد. به‌منظور تخمین نقاط در محل‌های نمونه­برداری نشده از روش­های شبکه عصبی مصنوعی (پرسپترون چندلایه، MLP)، کوکریجینگ، کریجینگ معمولی و وزن­دهی معکوس فاصله استفاده شد و از شاخص­های آماری نظیر ضریب همبستگی (R2)، ضریب همبستگی همگام (CCC)، خطای میانگین (ME) و ریشه میانگین مربعات خطا (RMSE) برای تعیین بهترین روش استفاده شد. مقدار و ذخیره کربن آلی خاک با کاهش میانگین دما و افزایش ارتفاع، ارتقا یافت و در کاربری جنگل بیشترین مقدار بود. بهترین مدل واریوگرام برای مقدار و ذخیره کربن آلی مدل گوسی بود و روش MLP نسبت به روش­های زمین‌آماری در تخمین مقدار و ذخیره کربن آلی خاک دقت بیشتری داشت. پهنه‌بندی حاصل از روش MLP با توجه به‌دقت بالای آن (856/0= RMSE، 133/0= ME، 89/0 CCC=و 68/0= R2) و مدنظر قرار دادن عوامل زمینی، خاکی و اقلیمی، می­تواند به‌عنوان یک نقشه مبنا برای بیان وضعیت فعلی کربن آلی در منطقه معرفی گردد.

کلیدواژه‌ها

عنوان مقاله [English]

Soil Organic Carbon Mapping By Geostatistics and Artificial Neural Network Methods (Kohgiluyeh& Boyer-Ahmad Province)

نویسندگان [English]

  • Parisa Lahooti 1
  • Seyed Mostafa Emadi 1
  • mohammad ali bahmanyar 2
  • Mehdi Ghajar Sepanlou 1

1 Sari University of Agricultural Sciences and Natural Resources

2

چکیده [English]

Introduction: Predicting and mapping soil organic carbon (SOC) contents and stocks are important for C sequestration, greenhouse gas emissions and national carbon balance inventories. The SOC plays a vital role in sustaining agricultural productions in arid ecosystems. It shows very quick and direct changes with atmosphere through the photosynthesis and the SOC decomposition. The depletion of C storage not only exacerbates the risk of soil erosion but also reduces agricultural production. An accurate knowledge of regional SOC contents and stocks and their spatial distribution are essential to optimize the soil management and land-use policy for SOC sequestration. Today, digital soil mapping methods such as geostatistics and artificial neural network (ANN) have focused more on SOC contents and stocks mapping. Geostatistics is a robust tool widely applied to model and quantify soil variation and analyze the spatial variability of SOC in large scale. The ANN as a nonlinear technique has been received much less attention for modeling SOC contents and stocks. Therefore, in this study, we aimed to develop and compare the performance of ordinary Kriging, co-kriging, inverse distance weighting (IDW) and artificial neural network models in predicting and mapping the SOC contents and stocks in East and Southeast of the Kohgiluyeh and Boyer-Ahmad province, southern Iran.
Materials and Methods: The composite soil samples were collected randomly from the 0-15 cm soil depths at 204 sampling sites at different land uses in east and southeast of the Kohgiluyeh and Boyer-Ahmad province. The collected soil samples were air-dried, ground, and sieved to pass through a 2 mm mesh. Soil properties such as organic carbon contents and stocks, pH, electrical conductivity (EC), bulk density (BD) and soil texture were determined according to the standard analysis protocols. The normality tests were done according to the Kolmogrov–Smirnov method, and the variability of SOC contents and stocks were analyzed by the classical statistics (mean, maximum, minimum, standard deviation, skewness, and coefficient of variations). The digital elevation model (DEM), slope gradient, precipitation and temperature and Normalized Difference Vegetation Index (NDVI) were used as co-variables (auxiliary data). The NDVI was obtained by the remotely sensed data of LANDSAT 8. The geostatistical parameters were calculated for each soil property as a result of corresponding semivariogram analysis. The spatial prediction maps of soil properties were generated by ordinary kriging (OK), cokriging (Co-K) inverse distance weighting (IDW) with powers of  1, 2, 3, 4 and 5 as well as the Artificial Neural Network (Multilayer Perception model, MLP) methods. The mentioned interpolation methods were used to prepare the SOC spatial distribution maps by using the 80 % of data as the training datasets. The prediction results were then evaluated by the validation data set (20 % of all data). The differences between the observation and prediction values were evaluated by Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (R2) and Concordance Correlation Coefficient (CCC). The spatial distribution maps of the SOC contents and stocks in the study area were finally developed by ArcGIS 10 software.
Results and Discussion: The SOC content for all samples largely varied from 0.20 to 3.96 % .The high coefficient of variation of 53.38 % demonstrates the strong spatial variation of SOC content in the study area. The SOC stocks had also a relatively high variability compared with other soil properties. Such strong variation could be attributed to the diverse soil types, land covers and other environmental conditions across the study area. The average SOC content for forest land use was significantly higher than the other land uses. The intensive tillage in cropland soils appears to have induced the acceleration of organic carbon oxidations leading to the lowest SOC contents and stocks. By increasing the mean precipitation within our study area (in eastern and northeastern regions), the SOC contents and stocks increased significantly. The inverse trend was, however, observed for temperature implying the fact that the higher the temperature, the lower the SOC. Gaussian model was found to be the best model for parameters such as SOC contents and stocks due to the lowest RSS and R2.Overall, the results denoted the higher ability of ANN compared to geostatistical techniques (cokriging, kriging and IDW methods) in estimating both soil organic carbon contents and stocks. According to the results, ANN (MLP) method with one hidden layers with 50 neurons performed better in estimating soil organic carbon contents and stocks atunsampled points, whereas the largest errors were obtained for IDW method.
Conclusions: The good performance of ANN method can be attributed to the division of the study area and the capability of ANN to capture the nonlinear relationships between SOC and environmental factors i.e. slope, DEM, precipitation, temperature and NDVI. The results suggest that the proposed structural method for ANN can play a vital role in improving the prediction accuracy of SOC spatial variability in large scale.
 

کلیدواژه‌ها [English]

  • Carbon sesustration
  • Kohgiluye va Boyer Ahmad province spatial variability
  • Soil organic carbon
1. Abdollahi S., Delavar M.A., and Shekari P. 2013. Spatial distribution mapping of Pb, Zn and Cd and soil pollution assessment in Anguran area of Zanjan province. Journal of Water and Soil, 26(6):1410-1420. (In Persian with English abstract)
2. Afshar H., Salehi M.H., Mohammadi J., and Mehnatkesh A. 2009. Spatial variability of soil properties and irrigated wheat yield in a quantitative suitability map, a case study: Shahr-e-Kian area, Chaharmahal va Bakhtiari province. Journal of Water and Soil Conservation, 23(1):161-172. (In Persian with English abstract)
3. Akbarzadeh M., and Ghahraman B. 2013. A combined strategy of entropy and spatio-temporal kriging in determining optimal network for groundwater quality monitoring of Mashhad basin. Journal of Water and Soil Conservation, 27(3):613-629. (In Persian with English abstract)
4. Ansari H., Erfanian M., and Naderianfar M. 2011. The evaluating of drought zoning models in various time scales case study: Sistan and Balouchestan Province. Journal of Water and Soil Conservation, 18 (1):59-79. (In Persian with English abstract)
5. Ayoubi Sh., Mohammad Zamani S., and Khormali F. 2007. Prediction total N by organic matter content using some geostatistic approaches in part of farm land of Sorkhankalateh, Golestan Province. Journal of Agricultural Science and Natural resources, 14(4):1-10. (In Persian with English abstract)
6. Bameri A., Khormali F., Kiani F., and Dehghani A.A. 2012. Spatial variability of soil organic carbon in different slope positions of hilly loess lands, Towshan area, in Golestan province. Journal of Soil and Water Conservation, 19 (2):43-60. (In Persian with English abstract)
7. Blake G.R., and Hartge K.H. 1986. Methods of soil analysis. Part I: Physical and mineralogical methods, American Society of Agronomy, Inc, Madison, Wisconsin.
8. Bouyoucos G.J. 1962. Hydrometer method improved for making particle size analysis of soils. Agronomy Journal, 54:464-465.
9. Cambardella C.A., Moorman T.B., Novak J.M., Parkin T.B., Karlen D.L., Yurco R.F., and Koropaka A.E. 1994. Field scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58:1501-1511.
10. Clair T.A., and Ehrman J.M. 1996. Variations in discharge and dissolved organic carbon and nitrogen export from terrestrial basins with changes in climate: A neural network approach. Limnology and Oceanography, 4 l(5):921-927.
11. Dai F., Liu G., Zhou Q., Lv Zh., and Wang X. 2014. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators, 45:184-194.
12. Falahatkar S., Hosseini S.M., Ayoubi Sh., and Salman Mahiny A. 2013. The impact of primary terrain attributes and land cover/use on soil organic carbon density in a region of northern Iran. Journal of Water and Soil, 27(5):963-972. (In Persian with English abstract)
13. Falahatkar S., Hosseini S.M., Ayoubi Sh., and Salmanmahiny A. 2015. Predicting soil organic carbon density using auxiliary environmental variables in northern Iran. Archives of Agronomy and Soil Science, (62):375-393.
14. Gholamalizadeh Ahangar A., Sarani F., Hashemi F., and Shabani A. 2015. Comparison of linear regression methods, geostatistical and artificial neural network modeling of organic carbon in dry land of Sistan plain. Journal of Water and Soil, 28 (6):1250-1260. (In Persian with English abstract)
15. Gholipour S., Kadkhodaei A., Makkipour M., and Abadi Chalaksaraee, A.R. 2016. Comparison of artificial neural network, ΔLogR and cluster analysis for the assessment of organic carbon in hydrocarbon-bearing formations. Geoscience, 25(98):147-158. (In Persian with English abstract)
16. Hashemi M., Gholamalizadeh Ahangar A., Bameri A., Sarani F., and Hejazizadeh A. 2016. Survey and zoning of soil physical and chemical properties using geostatistical methods in GIS (case study: Miankangi Region in Sistan). Journal of Water and Soil, 30(2):443-458. (In Persian with English abstract)
17. Jafari M., Asgari H.M., Moazemi M., Beniaz M., and Tahmoures M. 2008. Investigation of spatial distribution of soil properties using geostatistical methods. Pajouhesh & Sazandegi, 80:177-191. (In Persian)
18. Jalali Gh., Tehrani M.M., Boroumand N., and Sanjari S. 2014. Comparison of land ststistics methods in the protection of spatial distribution map of some food elements in east of Mazandaran province. Soil research (Soil and Water Sciences), 27(2):195-204. (In Persian with English abstract)
19. Kuang B., Tekin Y., and Mouazen A.M. 2015. Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content. Soil and Tillage Research, 146:243-252.
20. Lakzian A., Fazeli Sangani M., Astaraei A., and Fotivat A. 2013. Estimation and mapping soil organic carbon content using Terrain analysis (Case study: Mashhad, Iran). Journal of Water and Soil, 27(1):180-192. (In Persian with English abstract)
21. Li Q., Yue T., Wang Ch., Zhang W., Yu Y., Li B., Yang J., and Bai G. 2013. Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach. Journal of Cosmology and Astroparticle Physics, 104:210-218.
22. Lin L. 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics, (45):255-268.
23. Moghimi S., Parvizi Y., Mahdian M.H., and Masih Abadi M.H. 2015. Comparative application of multiple linear regression and artificial neural networks for simulating the effects of topographic factors on organic carbon changes in soil. Journal of Watershed Engineering and Management, 6(4):312-322. (In Persian with English abstract)
24. Mohammadzadeh M J., Aghababaei H., and Naseri A. 2007. Application of neural network in estimating total organic carbon, binak oil field, Bushehr province. Geosciences, 66:60-67. (In Persian with English abstract)
25. Mokhtari Karchegani P., Ayoubi Sh., Mosaddeghi M.R., and Malekian M. 2011. Effects of land use and slope gradient on soil organic carbon pools in particle-size fractions and some soil physico-chemical properties in hilly regions, western Iran. Jornal of Soil Management and Sustainable Production, 44(2):193-202. (In Persian with English abstract)
26. Noshadi E., Bahrami H.A., and Alavipanah S.K. 2014. Study the relationship between digital number values from ETM+ satellite images and soil organic matter using artificial neural network and regression models. Environmental Erosion Researches Journal, 4 (13): 29-38. (In Persian with English abstract)
27. Nosrati K. 2011. The effect of land use and soil erosion on soil organic carbon and nitrogen stock. Environmental erosion research, 3: 127-140. (In Persian with English abstract)
28. Page M.C., Sparks D.L., Noll M.R., and Hendricks G.J. 1987. Kinetics and mechanisms of potassium release from sandy middle Atlantic coastal plain soils. Geoderma, 51:1460-1465.
29. Pilevar Shahri A.R., Ayoubi S.H., and Khademi S.H. 2011. Comparison of artificial neural network (ANN) and multivariate linear regression (MLR) models to predict soil organic carbon using digital terrain analysis (Case study: Zargham Abad Semirom, Isfahan proviance). Journal of Water and Soil, 24(6):1151-1163. (In Persian with English abstract)
30. Sarmadian F., and Taghizadeh Mehrgerdi R. 2009. Comparison of interpolation methods for mapping of soil quality characteristics (a case study: fields of agricultural college, Tehran University). Iranian Journal of Soil and Water Researches, 40(2):157-165. (In Persian with English abstract)
31. Sefidari A., Kadkhodaei A., and Sharifi M. 2012. Comparison of self-constructive neural network and cluster analysis methods for evaluating the amount of organic carbon in hydrocarbon-bearing formations using intelligent systems. Petroleum Research, 23(75):117-130.
32. Shakouri Katigari M., Shabanpour M., Davagar N., and Babazadeh SH. 2011. Evaluation efficiency spatial interpolation techniques in mapping Organic carbon and Bulk density paddy soils of Guilan. Journal of Water and Soil, 18(2):195-210. (In Persian with English abstract)
33. Sohrabi Seraj B., Kiadaliri H., Akhavan R., and Babaei Kafaki S. 2015. Investigation of spatial variations and mapping of forest contamination to the semi-parasite species (Loranthus europaeus) in Zagros forests (A case study, Ilam). Iranian Journal of Forest and Range Protection Research, 12(2):94-106.
34. Taghizadeh Mehrjardi R., Mahmoudi S.H., Zareian Jahromi M., and Heidari A. 2007. Mapping of soil texture spatial distribution with using geostatistic method and GIS, Case study (Khezrabad Yazd). Fourth national conference on watershed management and management, University of Tehran, Natural Resources University of Karaj. 1-7.
35. Taghizadeh Merjerdi R., Sarmadian F., Omid M., and Savaghebi Gh. 2012. Soil salinity maping using geostatistics technique and electromagnetic induction device in Ardakan region. Iranian Journal of Soil Research. 26 (4): 369-380. (In Persian with English abstract)
36. Vahedi S., Zare Abyane H., Taheri M., and Bahmani O. 2013. Investigation of spatial variation of some chemical and hydrological features lands surrounding the Ghezel Ozan River using geostatistics methods. Iranian Water Research Journal, (12) 141-150. (In Persian with English abstract)
37. Walkley A., and Black C.A. 1934. An examination of the degtjareff method of determining soil organic matter and a proposed modification of the chronic acid titration method. Journal of Soil Science, 37:29–38.
38. Were K., Bui D T., Dick Y B., and Singh B R. 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.
39. Wu W., Xiu D.T., and Liu H.B. 2008. Spatial variability of soil heavy metals in the three gorges area, Multivariate and geostatistical analyses. Environmental Monitoring and Assessment, 157: 63-71.
40. Yana J., Lee Ch.K., Umeda M., and Kosaki T. 2013. Spatial variability of soil chemical properties in a paddy field. Soil Science and Plant Nutrition, 46(2):473-482.
41. Zare Abyaneh H., and Bayat M. 2013. Development and application of statistical and neural, Fuzzy, Genetic algorithm models in estimation of spatial distribution of water table level. Journal of Water and Soil Conservation, 20(4): 1-25. (In Persian with English abstract)
42. Zhang H., Zhuang Sh., Qian H., Wang F., and Ji H. 2015. Spatial variability of the topsoil organic carbon in the moso Bamboo forests of southern China in association with soil properties. PlOS one, 10 (3) 1-17.
CAPTCHA Image