پیش بینی کربن آلی خاک سطحی با تلفیق تحلیل عاملی و رگرسیون چندگانه در مراتع نیمه استپی لزور، فیروزکوه

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای علوم مرتع، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران

2 استادیار، گروه مرتعداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران

3 استاد، گروه سنجش از دور، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشگاه تهران، ایران

چکیده

کربن آلی خاک، یکی از مهمترین شاخص‌های کیفیت خاک است که تقریباً تمامی ویژگی‌های فیزیکی، شیمیایی و بیولوژیکی خاک را تحت تأثیر خود قرار داده و سبب حاصلخیزی خاک می‌شود. این شاخص نقش کلیدی در چرخة سراسری کربن دارد. هدف از انجام این پژوهش مطالعة رفتارهای طیفی و غیرطیفی خاک به منظور برآورد کربن آلی خاک سطحی با استفاده از روش‌های تحلیل عاملی و رگرسیون چندگانه در مراتع نیمه‌استپی لزور فیروزکوه است. نمونه‌برداری از خاک، با استفاده از روش نمونه‌برداری تصادفی طبقه‌بندی شده صورت گرفت. تعداد 157 سایت تعلیمی در واحدهای کاری همگن انتخاب شد. اطلاعات 127 سایت برای واسنجی مدل و اطلاعات 30 سایت برای اعتبارسنجی آن بکار گرفته شد. در هر یک از سایت‌های تعلیمی به شیوه تصادفی، یک نمونه خاک متشکل از 9 مشاهده از عمق صفر تا 20 سانتیمتری خاک سطحی برداشت شد. کربن آلی خاک با استفاده از روش تیتراسیون والکلی – بلاک اندازه‌گیری شد. نتایج نشان داد متغیرهای آلبیدو، شاخص رس، شاخص گیاهی تفاضلی بهنجار، شاخص‌های روشنایی و سبزینگی تبدیل تسلدکپ و ارتفاع نسبی، همبستگی معنی‌داری با کربن آلی خاک دارند. همچنین نتایج تحلیل عاملی به روش تجزیة مؤلفه‌های اصلی (PCA) با مقادیر ویژة بزرگتر از یک نشان داد کل واریانس تجمعی تبیین‌شده بوسیلة شش متغیر مذکور، برابر 146/81 درصد بود که این میزان واریانس بوسیلة دو عامل توضیح داده شد. معادلة رگرسیون تولید شده با دو عامل استخراج شده، از پتانسیل مناسبی برای پیش‌بینی کربن آلی خاک سطحی برخوردار بود (789/0 = R2). میانگین نسبی خطای مطلق (MARE) و ریشة متوسط مربعات خطا (RMSE) مدل پیشنهادی به ترتیب برابر 1/0 و 24/0 محاسبه شد. با توجه به ارتباط مستقیم کربن آلی خاک با عوامل حاصلخیزی و مقاومت خاک در مقابل فرسایش، مدل توزیع مکانی کربن آلی خاک می‌تواند بعنوان یک زیرمدل مهم به منظور طراحی سایر مدل‌های پیچیده‌ همچون تولید (بایومس) اکوسیستم‌های خشکی و مدل‌های فرسایش خاک مورد استفاده واقع شود.

کلیدواژه‌ها


عنوان مقاله [English]

An estimation of topsoil organic carbon by combining factor analysis and multiple regression in semi-steppe rangelands of Lazour, Firouzkooh

نویسندگان [English]

  • Rostam Khalifezadeh 1
  • Reza Tamartash 2
  • Mohammadreza Tatian 2
  • Mohammadreza Sarajian Maralan 3
1 Ph.D. Candidate in Rangeland Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
2 Assistant Professor, Rangeland Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
3 Professor, Remote Sensing Department, School of Surveying and Geospatial Engineering, Tehran University, Tehran, Iran
چکیده [English]

   Organic carbon is one of the most important soil quality indices, affecting almost all physical, chemical and biological properties of the soil. The purpose of this study was to investigate soil spectral and morphometric factors to estimate the organic carbon of topsoil, using factor analysis and multiple regression methods in semi-steppe rangelands of Lazour. Soil samples were taken with a stratified random method. For this purpose, 157 training sites were selected in homogeneous units. Of these, 127 sites were used to calibrate the model and 30 sites were used to validate the model. In each of the training site in a random manner, a soil sample including nine observations was taken from a depth of 0 to 20 cm of soil surface. Soil Organic Carbon (SOC) was measured using Walkley-Black titration method. The results showed that the variables of Albedo, Clay Index (CI), NDVI, Relative Relief and Tasseled-Cap's Brightness and Greenness indices had a significant correlation with the SOC (p<0.05). Also, the result of factor analysis by Principal Component Analysis (PCA) method with eigen-values greater than one indicated that the total cumulative variance, explained by the six variables, was equal to 81.1%.This variance was explained by two components. Using multiple regression model, an appropriate regression equation was calculated to predict SOC (R2=0.789). The Root Mean Square Error and the Mean Absolute Relative Error of the proposed model were calculated as 0.24 and 0.10, respectively. Due to the direct relationship between the SOC and the factors such as soil fertility and sustainability against erosion, a spatial distribution model of SOC could be an important sub-model to design other complex models such as the terrestrial ecosystems biomass and soil erosion models.

کلیدواژه‌ها [English]

  • Landsat 8
  • spectral factors
  • morphometric factors

-  Abbas Nejad, B. and Khajedin, S. J., 2013. Effect of urban reforestation on carbon sequestration in arid soils using remote sensing technology. Journal of Applied RS & GIS Techniques in Natural Resource Science, 3(4): 57-71.

-  Bouma, J. and McBratney, A. B., 2013. Framing soils as an actor when dealing with wicked environmental problems. Geoderma 200–201:130–139.

-  Bidwell, O. W., 1989. Soil Fertility and Organic Matter as Critical Components of Production Systems. American Journal of Alternative Agriculture, 4: 91-92.

-  Baumgardner, M. F., Silva, L. F., Biehl, L. L. and Stoner, E. R., 1986. Reflectance properties of soils, in: Brady,  N. C. (Ed.), Advances in Agronomy, Academic press, pp. 1-44.

-  Boettinger, J. L., Ramsey, R. D., Bodily, J. M., Cole, N. J., Kienast_Brown, S., Nield, S. J., Saundes, A.M. and Stum, A. K., 2008. Landsat spectral data for digital soil mapping. 193–203. In: Hartemink, A. E., McBratney, A. B., Mendonca Santos, M. L. (Eds.), Digital Soil Mapping With Limited Data. Springer science, Australia.

-  Baig, M. H. A., Zhang, L., Shuai, T. and Tong, Q., 2014. Derivation of a tasseled cap transformation based on Landsat 8 at satellite reflectance. Remote Sensing Letters. 5 (5): 423-431.

-  Beven, K. and Kirkby, N., 1979. A physically based, variable contributing area model of basin hydrology. Hydrolog. Sci. Bull. 24 n (1), 43–69.

-  Dadgar, M., Mahmoudi, S., Mahdian, M. H., Masih Abadi, M. H. and Sokooti Oskouie, R., 2014. Estimating soil organic carbon using pedotransfer functions in Damavand Rangelands. Iranian Journal of Range and Desert Reseach, 21 (3):409–415.

-  Department of the interior U.S. Geological Survey, (2016). LANDSAT 8 (L8) DATA USERS HANDBOOK. Version 2.0, 98 p.

-  Esadafal, R., Girard, M. C. and Courault, D., 1989. Munsell soil color and soil reflectance in the visible spectral bands of landsat MSS and TM data. Remote Sensing of Environment, 27 (1): 37-46.

-  Fang, X., Xue, Z., Li, B. and An. S., 2012. Soil Organic Carbon distribution in relation to land use and its storage in a small watershed of the Loess Plateau, China. Catena, 88: 6-13.

-  Fergusen, E. and Cox, T., 1993. Exploratory factor analysis: A users’ guide, International Journal of Selection and Assessment, 1: 84-94.

-  Franklin, J., McCullough, P. and Gray, C., 2000. Terrain variables used for predictive mapping of vegetation communities in Southern California. In Wilson J, Gallant J (Eds,) Terrain analysis: principles and applications. Wiley, New York, Chichester, Torono and Brisbane, 331-353.

-  Goldasteh, A., Agha Mir Karimi, S., Khoda Rahmi, M., Torabi, M. and Asghari, R., 2000. User guide of SPSS 6.0 for windows. Hami press, 533 p.

-  Hartemink, A. E. and McSweeney, K., 2014. Soil Carbon, Springer Publication, 506p.

-  Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M. and Fernandez, L., 2000. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principal component analysis. Water Res., 34, 807-816.

-  Hengle, T., 2009. A Practical Guide to Geostatistical Mapping. Amesterdam University Press, 293 p.

-  Huang, C. H. and Kronrad, G. D., 2001. The cost of Sequestration Carbon on Private Forest Lands. Forest Policy and Economics. 2:133-142.

-  Howard, M. C., 2016. A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?, International Journal of Human-Computer Interaction, 32 (1): 51-62.

-  Jafarian, Z., Tayefeh Seyyed Alikhani, L. and Tamartash, R., 2012. Investigation of Carbon Storage Potential of Artemisia Aucheri, Agropyron elongatum, Stipa barbata in Semi-arid Rangelands of Iran (Case study: Peshert Region, Kiasar). Journal of Range and Watershed Management, Iranian Journal of Natural Resources, 65 (2): 191-202.

-  Kasel, S., Singh, S., Sanders, G. J. and Bennett, L. T., 2011. Species-specific effects of native trees on soil organic carbon in biodiverse plantings across north-central Victoria. Geoderma, 161: 95–106.

-  Liang, S., Shuey, C. J., Russ, A. L., Fang, H., Chen, M., Walthall, C. L. and Hunt, R., 2003. Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sensisng of Environment, 84 (1): 25-41.

-  Liu, Q., Liu, G., Huang, C. and Xie, C., 2015. Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images. International Journal of Remote Sensing, 36(2): 417-441.

-  Mahmoudi, Sh. and Hakimian, M., 2006. Fundamentals of soil sciences. Tehran university press, 700p.

-  Marchant, P., Villanneaue, J., Arrouays, D., Sabyn, P. A. and Rawlins, B. G., 2015. Quantifying and mapping topsoil inorganic carbon concentrations and stocks: approaches tested in France. Soil Use Manag. 31, 29–38.

-  Mirza Shahi, K. and Bazargan, K., 2015. Management of soil organic matter. Soil and Water Research Institute (SWRI) press, Technical Journal No. 535, 20 p.

-  Mobasheri, M. R., 2010. Fundamentals of Physics in Remote Sensing and Satellite Technology. Khajeh Nasir Toosi University of Technology Press, The Second edition, 348p.

-  Mohan, S. and Arumugam, N., 1996. Relative importance of meteorological variables in evapotranspiration: Factor Analysis approach, Water Resources Management, 10: 1-20.

-  Mondal, A., Khare, D., Kundu, S., Mondal, S., Mukherjee, S. and Mukhopadhyay, A., 2017. Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data. The Egyptian Journal of Remote Sensing and Space Sciences, 20(1): 61-70.

-  Montgomery, O. L., 1976. An investigation of the relationship between spectral reflectance and the chemical, physical and genetic characteristics of soil Purde University, West Lafayette Indiana. Ph.D thesis, LCCN: 79-32236.

-  Olaya, V., 2009. Basic Land-Surface Parameters. Geomorphometry Concepts, Software, Applications. Developments in Soil Science. Elsevier B.V, Amsterdam, 33 p.

-  Piccini, C., Marchetti, A. and Francaviglia, R., 2014. Estimation of soil organic matter by geostatistical methods: use of auxiliary information in agriculture and environment assessment. Ecol. Ind. 36: 301-314.

-  Rouse, J. w., Haas, R. H., Schell, J. A., Deering, D. W. and Harlan, J. C., 1974. Monitoring the vernal advancement and retrogradation (greenwave effect) of natalral vegetation, NASA/GSFC Type III Final Report, Greenbelt, Maryland.

-  Saha, D., Kukal, S. and Sharma, S., 2011. Land use impacts on SOC fractions and aggregate stability in typic ustochrepts of Northwest India. Plant Soil 339, 457– 470.

-  Seif, A. and Ebrahimi, B., 2012. Evaluation of the accuracy of the SRTM and GDEM Digital Elevation models using the NIDEM. Iranian Remote Sensing & GIS journal, 4(3): 81-98.

-  Sheidaye Karkaj, A., Sepehri, A., Barani, H. and Motamedi, J., 2017. Soil organic carbon reserve relationship with some soil properties in East Azerbaijan. Journal of Rangeland, 11(2): 125-138.

-  Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., Sofoniou, M. and Kouimtzis, Th., 2003. Assessment of the surface water quality in Northern Greece. Water Resources, 37, 4119-4124.

-  Tamartash, R., Tatian, M. R. and Yousefian, M., 2012. The effect of the different vegetative species on the carbon sequestration in Miankaleh Plain Rangelands. Journal of Environmental Studies, 38 (62): 45-54.

-  Troeh, F. R. and Thompson, L. M., 2005. Soils and soil fertility. Blackwell Publication., 462 p.

-  United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS), 2003. Managing Soil organic matter. Technical note No. 5, 113p.

-  United States Geological Survey (USGS)., 2016. LANDSAT 8 (L8) DATA USERS HANDBOOK, version 2.0, 106 p.

-  Walkley, A. and Black, I. A., 1934. An examination of the digeston method for determining soil organic matter, and a proposed modification of the chromic acid thtration method. Soil Sciences, 37: 29-38.

-  Wu C., Wu J., Luo Y., Zhang L. and DeGloria S. D., 2009. Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society of America Journal, 73(4): 1202-1208.

-  Xiao, J., Shen, Y., Tateishi, R. and Bayaer, W., 2006. Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. International Journal of Remote Sensing, 12(27): 2411–2422.