ارزیابی ذخایر کربن آلی خاک در کاربری‌های گوناگون با استفاده از روش‌های آماری حداقل مربعات جزئی، زمین‌آمار، مدل درختی M5 و تصاویر لندست 8

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه علوم و مهندسی خاک، دانشکدة کشاورزی، دانشگاه لرستان، خرم‌آباد

2 دانشیار گروه علوم و مهندسی خاک، دانشکدة کشاورزی، دانشگاه لرستان، خرم‌آباد

3 دانشیار گروه علوم و مهندسی خاک، دانشگاه تربیت مدرس

چکیده

کربن آلی نقشی حیاتی در پایداری زیست‌محیطی, شاخص کیفیت و سلامت خاک دارد؛ بنابراین شناسایی توزیع مکانی ترسیب کربن از الزامات برنامه‌ریزی محیطی و مدیریت خاک است. پژوهش حاضر به‌منظور بررسی میزان ترسیب کربن در کاربری‏های کشت و صنعت نیشکر، کشاورزی سنتی و بایر انجام شد. در هر کاربری، شصت نمونه خاک برداشت و کربن آلی، شوری، آهک، واکنش خاک و سدیم محلول خاک اندازه‌گیری شد. با استفاده از داده‏های طیفی سنجندة OLI و TIRS ماهوارة لندست 8، مقادیر باندها و شاخص‏های خاکی و پوشش گیاهی شامل NDVI، SAVI، TSAVI، OSAVI، MSAVI، SOCI، WDVI، PVI، RVI و BI در نقاط نمونه‌برداری به‌دست آمد و رابطة بین آنها و مقدار مادة آلی خاک محاسبه شد. نتایج نشان می‌دهد، بیشترین همبستگی را با مقدار مادة آلی خاک به خود اختصاص داده‌اند: در کاربری کشت و صنعت، شاخص SOCI با 30/50% و باند 3 با 82/53%؛ در کشاورزی سنتی، شاخص PVI با همبستگی 35/60% و باند 7 با 63/60%؛ در اراضی بایر، شاخص RVI با همبستگی 27/34% و باند 2 با 67/36%. نتایج تحلیل آماری به‌روش برازش حداقل مربعات جزئی نشان داد میانگین نتایج واسنجی و اعتبارسنجی به‌ترتیب 48/43 و 08/39% است. نتایج برآورد مادة آلی خاک به‌روش کریجینگ و مدل درخت M5 نشان می‏دهد که همبستگی مقادیر مادة آلی اندازه‏گیری و پیش‌بینی‌شده به‌ترتیب 20/66 و 00/82% بود. طبق این نتایج، بین مقادیر مادة آلی خاک و شاخص‏ها و باندهای ماهوارة لندست 8 همبستگی معنی‌داری وجود دارد و می‌توان مقادیر مادة آلی خاک منطقة مورد مطالعه و سایر مناطق دارای شرایط مشابه را با احتمال مورد قبولی تخمین زد.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of Soil Organic Carbon Storage in Different Land Uses Using Partial Least Squares, Geostatistics, M5 Tree Model and Landsat 8 Statistical Methods

نویسندگان [English]

  • Alireza Zahirnia 1
  • Hamid Reza Matinfar 2
  • Hossianali Bahrami 3
1 Ph.D. Student of Soil Science, Agriculture Faculty, Lorestan University
2 Associate Prof. of Soil Science, Agriculture Faculty, Lorestan University
3 Associate Prof. of Soil Science, Agriculture Faculty, Tarbiat ModaRres University
چکیده [English]

Organic carbon plays a activate role in environmental sustainability, soil quality and health index, so identifying the spatial distribution of carbon sequestration is a requirement of environmental planning and soil management. The purpose of this study is to investigate the amount of carbon sequestration in sugarcane and traditional uses of sugarcane, traditional agriculture and barren. In each land use, 60 soil samples were taken and organic carbon, salinity, lime, soil reaction and solution sodium were measured. Using Landsat 8 satellite OLI and TIRS spectral data, the variable ​​of soil and vegetation indices including: NDVI, SAVI, TSAVI, OSAVI, MSAVI, SOCI, WDVI, PVI, RVI and BI in the sample points was obtained and the relationship between them and the amount of soil organic matter was calculated. The results show that in agro-industrial use, SOCI index with 50.30% and band 3 with 53.82% have the highest correlation, in traditional agriculture, PVI index with a correlation of 60.35% and band 7 with 60.63% and in Barren lands ,RVI index with a correlation of 34.27% and band 2 with 36.67% have the highest correlation with the amount of soil organic matter. The results of statistical analysis by partial least squares fitting method showed that the average of calibration and validation results are 43.48 and 39.08%, respectively. The results of estimating soil organic matter by kriging method and M5 tree model show that the correlation between measured and predicted organic matter was 66.20% and 82.00%, respectively. The results show that there is a significant correlation between soil organic matter and Landsat 8 satellite indices and bands, and it is possible to estimate the soil organic matter levels of the study area and other areas with similar conditions with acceptable probability.

کلیدواژه‌ها [English]

  • Soil organic matter
  • Southwest Khuzestan
  • PLSR statistical method
  • Kriging method
  • M5 tree model

مراجع

Agbu, P.A., Donald, J., Fehrenbacher, I. & Jansen, J., 1990, Soil Property Relationships with SPOT Satellite Digital Data in East Central Illinois, Soil Science Society of America Journal, 54, PP. 807-12.
Bajwa, S.G. & Tian, F., 2005, Soil Fertility Characterization in Agricultural Fields Using Hyperspectral Remote Sensing, Transactions of the ASAE, 48, PP. 2399-2406.
Benni, T. & Amal, A., 2010, Monitoring and Evaluation of Soil Salinity in Term of Spectral Response Using Landsat Images and GIS in Mesopotamian Plain/Iraq, Iraqi Journal of Desert Studies, 2, PP. 19-32.
Bongiovanni, M.D. & Lobartini, J.C., 2006, Particulate Organic Matter, Carbohydrate, Humic Acid Contents in Soil Macro-and Microaggregates as Affected by Cultivation, Geoderma, 136, PP. 660-65.
Cambou, A., Rémi, C., Ernest, K., Manon, V., Céline, D. & Bernard, G.B., 2016, Prediction of Soil Organic Carbon Stock Using Visible and Near Infrared Reflectance Spectroscopy (VNIRS) in the Field, Geoderma, 261, PP. 151-59.
Chen, L., ZHOU, S., Wu, S., Zhu, Q. & Dang, Q., 2014, Spectral Response of Different Eroded Soils in Subtropical China: A Case Study in Changting County, China, China Journal of Mountain Science, 11, PP. 132-149.
Christensen, B., 2001, Physical Fractionation of Soil and Structural and Functional Complexity in Organic Matter Turnover, European Journal of Soil Science, 52, PP. 345-353.
Ghorbani, Kh., Sohrabian E., and Salarijazi M., 2016. Evaluation Of Hydrological And Data Mining Models In Monthly River Discharge Simulation And Prediction (Case Study: Araz-Kouseh Watershed), Journal of water and soil conservation (Journal of agricultural sciences and natural resoureces),  23 (1); 203-217.
Guo, Z., Kabindra, A., Menaka, C., Mette, B.G., Phillip, R.O. & Mogens, H.G., 2019, Selection of Terrain Attributes and Its Scale Dependency on Soil Organic Carbon Prediction, Geoderma, 340, PP. 303-12.
Jiang, Q., Yiyun, C., Long, G., Teng, F. & Kun, Q., 2016, Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy, Remote Sensing, 8, P. 755.
Katsuhisa, N., Yokobori, J., Hongo, C. & Nagata, O., 2011, Estimating Soil Carbon Stocks in an Upland Area of Tokachi District, Hokkaido, Japan, by Satellite Remote Sensing, Soil Science and Plant Nutrition, 57, PP. 283-93.
 
 
Khayamim, F., Khademi, H., Stenberg, B. & Wetterlind, J., 2015, Capability of Vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province, JWSS-Isfahan University of Technology, 19:, PP. 81-92.
Lal, R., 1997, Degradation and Resilience of Soils, Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 352., PP. 997-1010.
Liu, Y., Qinghu, J., Teng, F., Junjie, W., Tiezhu, S., Kai, G., Xiran, L. & Yiyun, C., 2014, Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes, Remote Sensing, 6, PP. 4305-22.
Lohani, A.K., Rakesh, K. & Singh, R.D., 2012, Hydrological time Series Modeling: A Comparison between Adaptive Neuro-Fuzzy, Neural Network and Autoregressive Techniques, Journal of Hydrology, 442, PP. 23-35.
López, F., Montserrat, J., José M. & Luis, G., 2005, Using geostatistical and Remote Sensing Approaches for Mapping Soil Properties, European Journal of Agronomy, 3, PP. 279-289.
Lorenz, K., Lal, R. & Shipitalo, M.J., 2008, Chemical Stabilization of Organic Carbon Pools in Particle Size Fractions in no-till and Meadow Soils, Biology and Fertility of Soils, 44, PP. 1043-51.
Mahmoudzadeh, H., Matinfar, H.R., Taghizadeh-Mehrjardi, R. & Kerry, R., 2020, Spatial Prediction of Soil Organic Carbon Using Machine Learning Techniques in Western Iran, Geoderma Regional, 21, PP. 243-260.
McDowell, M., Gregory, L., Bruland, L., Jonathan, L., Deenik, S. & Nichola, M., 2012, Soil Total Carbon Analysis in Hawaiian Soils with Visible, Near-Infrared and Mid-Infrared Diffuse Reflectance Spectroscopy, Geoderma, 189, PP. 312-320.
Mehrabi Gohari, E.,. Matinfar H. R.,   Jafari. A.,  Taghizadeh Mehrjardi. A., and Khayamim, F., 2020. Comparing Different Statistical Models And Pre-Processing Techniques For Estimation Several Chemical Properties Of The Soil Using Vnir/Swir Spectrum, Remote Sensing & GIS, 11, (4): 47-60.
Minasny, B., Alfred, E. & Hartemink, E., 2011, Predicting Soil Properties in the Tropics, Earth-Science Reviews, 106, PP. 52-62.
Mulder, V.L., Bruin, S., Michael, E., Schaepman, F. & Mayr, T.R., 2011, The Use of Remote Sensing in Soil and Terrain Mapping—A Review, Geoderma, 162, PP. 1-19.
Niwa, K., Yokobori, J., Hongo, C. & Nagata, O., 2011, Estimating Soil Carbon Stocks in an Upland Area of Tokachi District, Hokkaido, Japan, by Satellite Remote Sensing, Soil Science and Plant Nutrition, 57, PP. 283-293.
Quinlan, J., 1992, Learning with Continuous Classes, In ‘AI’92: Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, PP. 343-348, In: World Scientific: Singapore.
Rahmati, M., Neyshabouri, M.R., Mohammady Oskouei, M., Fard, A.F. & Ahmadi, A., 2016, Soil organic Carbon Prediction Using Remotely Sensed Data at Lighvan Watershed, Northwest of Iran, Azarian Journal of Agriculture, 3, PP. 45-49.
Senthilkumar, S., Kravchenko, A.N. & Robertson, G.P., 2009, Topography Influences Management System Effects on Total Soil Carbon and Nitrogen, Soil Science Society of America Journal, 73, PP. 2059-67.
Simbahan, C., Dobermann, A., Goovaerts, P., Ping, J. & Haddix, M., 2006, Fine-Resolution Mapping of Soil Organic Carbon Based on Multivariate Secondary Data, Geoderma, 132, PP. 486-497.
Thaler, A., Larsen, I. & Yu, Q., 2019, A New Index for Remote Sensing of Soil Organic Carbon Based Solely on Visible Wavelengths, Soil Science Society of America Journal, 83, PP. 1443-1450.
Vasques, G.M., Grunwald, S., Comerford, N.B. & Sickman, J.O., 2010, Regional Modelling of Soil Carbon at Multiple Depths within a Subtropical Watershed, Geoderma, 156, PP. 326-336.
Walkley, A. & Armstrong Black, I., 1934, An Examination of the Degtjareff Method for Determining Soil Organic Matter, and a Proposed Modification of the Chromic Acid Titration Method, Soil Science, 37, PP. 29-38.
Wang, B., Waters, C., Orgill, S., Cowie, A., Clark, A., Liu, D., Simpson, M., McGowen, I. & Sides, T., 2018, Estimating Soil Organic Carbon Stocks Using Different Modelling Techniques in the Semi-Arid Rangelands of Eastern Australia, Ecological Indicators, 88, PP. 425-438.
Wiesmeier, M., Barthold, F., Blank, B. & Kögel-Knabner, I., 2011, Digital Mapping of Soil Organic Matter Stocks Using Random Forest Modeling in a Semi-Arid Steppe Ecosystem, Plant and Soil, 340, PP. 7-24.
Zahirnia, A., and Matinfar, H.R., 2019. Determination of the Land Suitability of Sugarcane Fields Based on Soil Quality Index Using a Geographic Information System, Journal of water and soil science, 23 (2) :173-188.
Zahirnia, A., Matinfar, H. R., Bahrami, H., 2019, Investigation of parameters affecting soil quality under saline and semi-saline conditions (Case study of south and southwest of Khuzestan province), Journal of Agricultural Engineering, Shahid Chamran University of Ahvaz, Volume 43, Number 3.

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