Comparison vegetation indices and tasseled cap transformation for estimates of soil organic carbon using Landsat-8 OLI images in a semi-steppe rangelands
Subject Areas : Geospatial systems developmentMasoumeh Aghababaie 1 , Ataollah Ebrahimi 2 , Pejman Tahmasebi 3
1 - PhD Student of Rangeland Sciences, Department of Natural Resources and Earth Sciences, Shahrekord University
2 - Assoc. Prof. College of Range and Watershed Management, Department of Natural Resources and Earth Sciences, Shahrekord University
3 - Assoc. Prof. College of Range and Watershed Management, Department of Natural Resources and Earth Sciences, Shahrekord University
Keywords: Vegetation index, Soil organic carbon, Karsank area, Tasseled cap transformation,
Abstract :
In this research, the capability of Landsat-8 OLI data for generating a soil organic carbon (SOC) map is investigated in a semi-steppe rangeland of Chaharmahal-va- Bakhtiari province. To do so, in the June 2013 ground sampling was performed based on a systematic-random scheme in 24 sampling sites within each site 3 transects was established and along each transect 5 soil samples were chosen and collected from 0 to 20 cm depth and SOC content of the samples was measured. In order to compare, on ground sampled values of SOC with the corresponding and Landsat-8 OLI data (June 2013), vegetation indices and tasseled cap transformation bands were calculated and extracted from the study area. The values of vegetation indices and tasseled cap transformation bands (dependent variable) were regressed against organic carbon values (independent variable) at site level in SPSS software. Finally, the SOC map was drawn based on the best-fitted model between the independent and dependent variable. The results showed that amongst vegetation indices, PVI and Brightness band have the most significant correlation with SOC. Finally, the SOC maps of the study area were drawn by the quadratic linear regression after finding the best regression fit between SOC and vegetation index as well as the tasseled cap. The results of the validation test show that between vegetation indices the PVI index (R=0.53) and tasseled cap transformation bands (R=0.63) showed the highest correlation with soil organic carbon (SOC). Finally, by calculating the fitting of binary linear regression, organic carbon maps were prepared. The validation results of the model indicate that there is no significant difference between ground sampled SOC and extracted values of vegetation indices and tasseled cap. Therefore, the spectral data of the Landsat-8 satellite images (OLI) are a valuable source for determining the soil organic carbon changes in such areas.
اخضری، د. و ا. اسدی میآبادی. 1395. تهیۀ نقشه شوری خاک با استفاده از تحلیل طیفی دادههای سنجنده OLI و دادههای میدانی(مطالعۀ موردی: جنوب دشت ملایر). سنجشازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 7(2): 87-100.
پیشنماز احمدی، م.، م. ح. رضائی مقدم و ب. فیضیزاده. 1396. بررسی شاخصها و تهیه نقشه شوری خاک با استفاده از دادههای سنجشازدور (مطالعۀ موردی: دلتای آجی چای). سنجشازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 8(1): 85-96.
پیلهورشهری، ا. ر.، ش. ایوبی و ح. خادمی. 1389. مقایسۀ مدل شبکۀ عصبی مصنوعی و رگرسیون خطی چند متغیره در پیشبینی کربن آلی خاک به کمک دادههای آنالیز سطح زمین (مطالعۀ موردی: منطقه ضرغام آباد سمیرم). نشریه آب و خاک. 24(6): 1151-1163.
عباسنژاد، ب. و س. ج. خواجهالدین. 1393. بررسی تأثیر جنگلکاری شهری در مناطق خشک بر میزان کربن ترسیب شده در خاک با استفاده از فناوری سنجشازدور. سنجشازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 5(2): 75-88.
Agbu PA, Fehrenbacher DJ, Jansen IJ. 1990. Soil property relationships with SPOT satellite digital data in east central Illinois. Soil Science Society of America Journal, 54(3): 807-812.
Bajwa SG, Tian L, Bullock D, Sudduth K, Kitchen N, Palm H. 1998. Soil characterization in agricultural fields using hyperspectral image data. In: 2001 ASAE Annual Meeting. American Society of Agricultural and Biological Engineers, pp 1-8.
Bongiovanni MD, Lobartini JC. 2006. Particulate organic matter, carbohydrate, humic acid contents in soil macro-and microaggregates as affected by cultivation. Geoderma, 136(3-4): 660-665.
Chris HW, Caughlin TT, Rifai SW, Boughton EH, Mack MC, Flory SL. 2017. Multi‐decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland. Ecological Applications, 27(5): 1646-1656.
Demattê JAM, Epiphanio JCN, Formaggio AR. 2003. Influence of organic matter and iron oxides on the spectral reflectance of tropical soils. Bragantia, 62(3): 451-464.
Dinakaran J, Krishnayya N. 2008. Variations in type of vegetal cover and heterogeneity of soil organic carbon in affecting sink capacity of tropical soils. Current Science: 1144-1150.
Hendrickson O, Kubiseski T. 1991. Soil microbial activity at high levels of carbon monoxide. Journal of environmental quality, 20(3): 675-678.
Hummel J, Sudduth K, Hollinger S. 2001. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and electronics in agriculture, 32(2): 149-165.
Julius SGG, Cerri CC, Herpin U, Bernoux M. 2011. Assessing soil carbon stocks under pastures through orbital remote sensing. Scientia Agricola, 68(5): 574-581.
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(2): 283-293.
López-Granados F, Jurado-Expósito M, Peña-Barragán JM, García-Torres L. 2005. Using geostatistical and remote sensing approaches for mapping soil properties. European Journal of Agronomy, 23(3): 279-289.
Muchena R. 2017. Estimating soil carbon stocks in a dry miombo ecosystem using remote sensing. Chesa Forest Research Station, 6: 2-6.
Muhaimeed SA, Auras MT. 2017. Using remote sensing and GIS techniques for predicting soil organic carbon in southern Iraq. Global Symposium on Soil Organic Carbon. 21-23 March, Rome, Italy, 1-5.
Podwojewski P, Poulenard J, Nguyet ML, De Rouw A, Pham QH, Tran DT. 2011. Climate and vegetation determine soil organic matter status in an alpine inner-tropical soil catena in the Fan Si Pan Mountain, Vietnam. Catena, 87(2): 226-239.
Senthilkumar S, Kravchenko A, Robertson G. 2009. Topography influences management system effects on total soil carbon and nitrogen. Soil Science Society of America Journal, 73(6): 2059-2067.
Simbahan GC, Dobermann A, Goovaerts P, Ping J, Haddix ML. 2006. Fine-resolution mapping of soil organic carbon based on multivariate secondary data. Geoderma, 132(3-4): 471-489.
Sørensen L, Dalsgaard S. 2005. Determination of clay and other soil properties by near infrared spectroscopy. Soil Science Society of America Journal, 69(1): 159-167.
Stockmann U, Adams MA, Crawford JW, Field DJ, Henakaarchchi N, Jenkins M, Minasny B, McBratney AB, De Courcelles VdR, Singh K. 2013. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems & Environment, 164: 80-99.
Sudheer TK, Saha SK, Kumar S. 2015. Prediction modeling and mapping of soil carbon content using artificial neural network, hyperspectral satellite data and field spectroscopy. Advances in Remote Sensing, 4(01): 63.
Wang B, Waters C, Orgill S, Cowie A, Clark A, Li 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: 425-438.
Winowiecki L, Vagen T-G, Massawe B, Jelinski NA, Lyamchai C, Sayula G, Msoka E. 2016. Landscape-scale variability of soil health indicators: effects of cultivation on soil organic carbon in the Usambara Mountains of Tanzania. Nutrient Cycling in Agroecosystems, 105(3): 263-274.
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