نقشه‌برداری رقومی کربن آلی خاک (مطالعه موردی: مریوان، استان کردستان)

آب و خاک

مقاله 3، دوره 32، شماره 4 - شماره پیاپی 60، آبان 1397، صفحه 737-750   

نوع مقاله: مقالات پژوهشی

شناسه دیجیتال (DOI): 10.22067/jsw.v32i4.68318

نویسندگان

شلیر اسکندری¹؛ کمال نبی اللهی^{* 1}؛ روح الله تقی زاده مهرجردی²

¹دانشگاه کردستان

²دانشگاه اردکان

چکیده

کربن آلی خاک یکی از خصوصیات مهم خاک می‌باشد که اطلاعات پیرامون تغییرات مکانی آن جهت مدیریت زراعی، تخریب اراضی و مطالعات زیست محیطی حائز اهمیت می‌باشد. هدف از این پژوهش استفاده از روش شبکه عصبی مصنوعی برای تهیه نقشه کربن آلی خاک می‌باشد. بنابراین، تعداد 137 نمونه خاک از عمق 30-0 سانتی‌متری خاک‌های منطقه مریوان استان کردستان برداشت شده و خصوصیت کربن آلی خاک اندازه‌گیری شد. متغیرهای محیطی که در این پژوهش استفاده شد شامل اجزاء سرزمین و داده‌های تصویر +ETM ماهواره لندست می‌باشد. جهت ارتباط دادن بین کربن آلی خاک و متغیرهای کمکی، از مدل شبکه عصبی مصنوعی بهره گرفته شد. بر اساس نتایج انالیز حساسیت به روش رپر، برای پیش‌بینی کربن آلی خاک، متغییرهای کمکی شامل شاخص خیسی، شاخص همواری دره، فاکتور LS، شاخص NDVI و باند 3 مهم‌ترین بودند. نتایج این تحقیق نشان داد که مدل شبکه عصبی مصنوعی دارای 80/0، 01/0- و 67/0 به ترتیب برای ضریب تبیین، میانگین خطا و میانگین ریشه مربعات خطا می‌باشد. لذا پیشنهاد می‌شود که جهت تهیه نقشه رقومی خاک از مدل‌های شبکه عصبی مصنوعی در مطالعات آینده استفاده شود.

کلیدواژه‌ها

متغیرهای محیطی؛ تغییرات مکانی؛ نقشه ‌برداری رقومی

مراجع

1- Adhikari K., Minasny B., Greve B.G., and Greve M.H. 2014. Constructing a soil class map of Denmark based on the FAO legend using digital techniques. Geoderma, 214–215: 101-113.

2- Ayoubi S., Shahri A.P., Karchegani P.M., and Sahrawat K.L. 2011. Application of artificial neural network (ANN) to predict soil organic matter using remote sensing data in two ecosystems. In Tech Publication, 181-196.

3- Bonfatti B., Hartemink A.E., Giasson E., Tornquist C.G., and Adhikari K. 2016. Digital mapping of soil carbon in a viticultural region of Southern Brazil. Catena, 261: 204-221.

4- Brus D.J., Kempen B., and Heuvlink G.B.M. 2011. Sampling for validation of digital soil maps. European Journal of Soil Science, 62: 394-407.

5- Chartin C., Stevens A., Goidts E., Kruger I., Carnol M., and Wesemael B.V., 2017. Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia). Geoderma Regional, 9: 73-86.

6- Celik I. 2005. Land-use Effects on Organic Matter and Physical Properties of Soil in a Southern Mediterranean Highland of Turkey. Soil and Tillage Research, 83: 270-277.

7- Dai P.F., Qigang Z., Zhiqiang L.V., Xuemei W., and Gangcai L. 2014. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators, 45: 184-194.

8- Greve M.H., Kheir R.B., and Greve B.M. 2012. Using Digital Elevation Models as an Environmental Predictor for Soil Clay Contents. Soil Science Society of America Journal, 76: 2116-2127.

9- Gholamalizadeh Ahangar A., Sarani F., Hashemi M., 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: 1250-1260. (In Persian with English abstract)

10- Hengel T., Rossiter D.G., and Stein A. 2003. Soil sampling strategies for spatial prediction by correlation with auxiliary maps. Geoderma, 120: 75-93.

11- Heung B., Bulmer C.E., and Schmidt M.G. 2014. Predictive soil parent material mapping at a regional-scale: a random forest approach. Geoderma, 214-215: 141-154.

12- Jafari A., Finke P.A., Van deWauw J., Ayoubi S., and Khademi H. 2012. Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. European Journal of Soil Science, 63: 284-309.

13- Jafari A., Khademi H., Finke P., Wauw J.V.D., and Ayoubi S. 2014. Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma, 232-234: 148-163.

14- Kumar S., Lal R., and Liu D., 2012. A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma, 189-190: 627-634.

15- Malone B.P., McBratney A.B., Minasny B., and Laslett G.M. 2009. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154: 138- 152.

16- Manlay R., Feller C., and Swift M. 2007. Historical evolution of soil organic matter con-cepts and their relationships with the fertility and sustainability of cropping systems. Agriculture Ecosystems & Environment, 119: 217-233.

17- Marcel G.S., Feike J.L., Martinus T., and van Genuchten H. 1998. Neural Network Analysis for Hierarchical Prediction of Soil Hydraulic Properties. Soil Science Society of America Journal, 62: 847-855.

18- Mattara, M.A., Alazbab, A.A., and Zin El-Abedin T.K. 2015. Forecasting furrow irrigation infiltration using artificial neuralnetworks. Agricultural Water Managment, 148: 63–71.

19- McBratney A.B., Santos M.L.M., and Minasny B. 2003. On digital soil mapping. Geoderma, 117: 3-52.

20- Minasny B., and McBratney A. 2002. The method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66(2): 352-361.

21- Nelson D.W., and Sommers L.E. 1982. Total carbon, organic carbon, and organic matter. p. 539-594. In: Page, A.L., R.H., D.R., Keeney (Eds.), Methods of Soil Analysis, Part 2-Chemical and Microbiological Properties. ASA-SSSA, Madison, WI.

22- Nosrati H., and Eftekhari M. 2014. A new approach for variable selection using fuzzy logic. Computational Intelligence in Electrical Engineering, 4: 71-83. (In Persian with English abstract)

23- Pahlavan-Rad M.R., Toomanian N., Khormali F., Brungard C.W., Komaki C.B., and Bogaert P. 2014. Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma, 232-234: 97-106.

24- Piccini C., Marchetti A., and Francaviglia R. 2014. Estimation of soil organic matter by geostatistical methods: use of auxiliary information in agricultural and environ-mental assessment. Ecological Indicators, 36: 301-314.

25- Ratnayake R.R., Karunaratne R.B., Lessels J.S., Yogenthiran N., Rajapaksha R.P.S.K., and Gnanavelrajah N. 2016. Digital soil mapping of organic carbon concentration in paddy growing soils of Northern Sri Lanka. Geoderma Regional, 7: 167-176.

26- Rudiyanto Minasny B, Setiawan BI, Arif C, Saptomo SK, and Chadirin Y. Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands. Geoderma, 272: 20–31 (2016).

27- Sindayihebura A., Ottoy S., Dondeyne S., Meirvenne M.V., and Orshoven J.V. 2017. Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus. Catena. 156: 161-175.

28- Somaratne S., Seneviratne G., and Coomaraswamy U. 2005. Prediction of Soil Organic Carbon across Different Land-use Patterns: A Neural Network Approach. Soil Science Society of America Journal, 69: 1580-1589.

29- Taghizadeh-Mehrjardi M., Neupane R., Sood K., and Kumar S. 2017. Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA. Carbon Management, 8: 277-291

30- Taghizadeh-Mehrjardi R., Nabiollahi K., and Kerry R. 2016. Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran. Geoderma, 253-254: 67-77.

31- Taghizadeh-Mehrjardi R., Nabiollahi K., Minasny B., and Triantafilis J. 2015. Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran. Geoderma, 253-254: 67-77.

32- Vasques G.M., Dematte J.A.M., Viscarra Rossel R.A., Ramirez-Lopez L., and Terra F.S. 2014. Soil classification using visible/near-infrared diffuse reflectance spectra frommultiple depths. Geoderma, 223-225: 73-78.

33- Veronesi F., Corstanje R., and Mayr T. 2014. Landscape scale estimation of soil carbon stock using 3D modeling. Science Total Environment, 487: 578-586.

34- Were K, Bui DT, Dick QB, and Singh BR. 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.

35- Wang B., Waters C., Orgill S., Gray J., Cowie A., Clark A., and Liu D.L., 2018. High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia. Science Total Environment, 630: 367-378.

36- Wang S., Zhuang Q., Wang Q., Jin X., and Han C. 2017. Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China. Geoderma, 305: 250-263.

37- Zhao Z., Chow T.L.W., Rees H., Yang O., Xing Z., and Meng F.R. 2009. Predict soil texture distributions using an artificial neural network model. Computers and Electronics in Agriculture, , 65: 36-48.