مدلسازي روند تغييرات شوري خاک با استفاده از شاخصهاي پوشش گياهي، شوري و روش جنگل تصادفي (مطالعه موردي: شهرستان گميشان، استان گلستان)
محورهای موضوعی : مدیریت بهینه منابع آب و خاکصالح آرخی 1 * , سمیه عمادالدین 2 , محمد بهرامی 3
1 - دانشيار، گروه جغرافيا، دانشکده علوم انساني، دانشگاه گلستان، گرگان، ايران.
2 - دانشيار، گروه جغرافيا، دانشکده علوم انساني، دانشگاه گلستان، گرگان، ايران.
3 - دانش آموخته کارشناسي ارشد مخاطرات محيطي، دانشکده علوم انساني، دانشگاه گلستان، گرگان، ايران.
کلید واژه: شوري خاک, تخريب زمين, نقشه¬برداري, آناليز حساسيت, مدلسازي, منطقه گميشان, استان گلستان,
چکیده مقاله :
زمينه و هدف: شور شدن خاک يک فرآيند تخريب زمين است که منجر به تجمع بيش از حد نمکهاي محلول در خاک ميشود. با توجه نبود اطلاعات در رابطه با خصوصيات شوري و بويژه با رويکردهاي نقشهبرداري نوين و همچنين اهميت شوري خاک به عنوان يک ويژگي پويا و تأثيرگذار بر روي کيفيت خاک، تحقيق حاضر بر اساس مدلسازي مکاني شوري خاک با مدل جنگل تصادفي در منطقه مورد مطالعه شهرستان گميشان واقع در استان گلستان در بازه زماني 2000 تا 2024 انجام شده است.
روش پژوهش: در تحقيق حاضر 150 نقطه نمونهبرداري به روش روش اَبرمکعب لاتين انتخاب و 36 پارامتر بعنوان دادههاي محيطي در مدلسازي شامل: شاخصهاي پوشش گياهي، شاخصهاي شوري خاک و شاخصهاي باندهاي تصاوير ماهوارهاي (مانند باند آبي، سبز، قرمز و...) استفاده شده است. همچنين براي تهيه شاخصهاي مورد نظر از تصاوير ماهوارهاي لندست (سنجنده TM) سال 2000 و لندست 8 (سنجندۀ OLI) سال 2024 استفاده شد. پس از تهيه پارامترهاي محيطي و دادههاي شوري خاک، از مدل جنگل تصادفي (RF) براي مدلسازي استفاده گرديده شد. نتايج آمارههاي توصيفي براي EC سطحي خاك نشان ميدهد که مقدار EC خاك در 150 نمونه خاک از 28/0 تا 46/107 دسيزيمنس بر متر (ds/m) متغير بوده و انحراف معيار آن 58/2 است. مهمترين پارامترهاي حاصل از آناليز حساسيت مدل جنگل تصافي در مدلسازي شوري خاک، شاخصهاي پوشش گياهي مانند MNF، PCA و شاخصهاي باندهاي تصاوير ماهوارهاي ميباشند. با توجه به اينکه پوشش گياهي منطقه ضعيف است، بنابراين حضور و تجمع نمک در سطح خاک به راحتي توسط مؤلفههاي اصلي تصوير ماهواره لندست شناسايي ميشوند.
يافتهها: نتايج نشان داد كه ارتباط زيادي بين دادههاي خاك و متغيرهاي محيطي وجود دارد، به طوري كه نقشه نهايي شوري خاك را با ضريب تبيين 87/0 پيشبيني كرده است. مقايسه روند تغييرات شوري خاک از سال 2000 تا 2024 نشان ميدهد که منطقه گميشان در اين مدت به سمت شور شدن پيش ميرود. مقدار حداکثر و حداقل شوري در سال 2000 به ترتيب 93/0 و 65/54 و مقدار حداکثر و حداقل شوري در سال 2024 به ترتيب 14/1 و 87/71 دسيزيمنس بر متر (ds/m) ميباشد. نتايج مقايسه روند تغييرات کلاسهاي شوري خاک نشان داد که مساحت کلاسهاي بدون محدوديت، محدوديت کم و محدوديت زياد به ترتيب 848، 12110 و 15202 هکتار (63/0-، 95/8، 23/11- درصد) کاهش، و مساحت کلاسهاي محدوديت زياد، محدوديت خيلي زياد و محدوديت شديد به ترتيب 3256، 18906 و 5995 هکتار (41/2، 97/13 و 43/4 درصد) افزايش يافتهاند.
نتايج: روند تغييرات شوري در منطقه به سمت مرکز و جنوب افزايشي است كه با روند تغييرات مهمترين متغيرهاي كمكي تشخيص داده شده مانند PCA، همخواني دارد. از نتايج اين تحقيق ميتوان نتيجهگيري نمود که خاکهاي منطقه مورد مطالعه داراي محدوديت شوري و نيز تغييرپذيري زياد مکاني از نظر مقدار پارامترهاي خاک بخصوص شوري ميباشند. اين مسئله نشان ميدهد که براي اصلاح و بهسازي خاکهاي منطقه گميشان نياز به تهيه نقشههاي خاک ميباشد.
Introduction: Soil salinization is a land degradation process that leads to excessive accumulation of soluble salts in the soil. Given the lack of information regarding salinity characteristics, especially modern mapping approaches, and the importance of soil salinity as a dynamic and influential characteristic on soil quality, the present study was conducted based on spatial modeling of soil salinity with a random forest model in the study area of Gomishan County, Golestan Province, during the period 2000 to 2024.
Methods: In the present study, 150 sampling points were selected using the Latin hypercube method and 36 parameters were used as environmental data in modeling, including vegetation cover indices, soil salinity indices, and satellite images band indices (such as blue, green, red bands, etc.). Also, to prepare the desired indicators, Landsat (TM) satellite images from 2000 and Landsat 8 (OLI sensor) from 2024 were used. After preparing environmental parameters and soil salinity data, the Random Forest (RF) model was used for modeling. The results of descriptive statistics for soil surface EC show that the soil EC value in 150 soil samples varied from 0.28 to 107.46 deci-Siemens per meter (ds/m) and its standard deviation is 2.58. The most important parameters obtained from the sensitivity analysis of the random forest model in soil salinity modeling are vegetation cover indices such as MNF, PCA and band indices of satellite images. Given that the vegetation cover of the region is poor, the presence and accumulation of salt on the soil surface are easily identified by the main components of the Landsat satellite image.
Results: The results showed that there is a strong correlation between soil data and environmental variables, such that the final map predicted soil salinity with a coefficient of determination of 0.87. A comparison of the trend of soil salinity changes from 2000 to 2024 shows that the Gomishan region is moving towards salinization during this period. The maximum and minimum salinity values in 2000 were 0.93 and 54.65, respectively, and the maximum and minimum salinity values in 2024 were 1.14 and 71.87 deci-Siemens per meter (ds/m), respectively. The results of comparing the trends in soil salinity classes showed that the areas of the no-limitation, low-limitation, and high-limitation classes decreased by 848, 12,110, and 15,202 hectares (-0.63, 8.95, and -11.23 percent), respectively, and the areas of the high-limitation, very high-limitation, and severe-limitation classes increased by 3,256, 18,906, and 5,995 hectares (2.41, 13.97, and 4.43 percent), respectively.
Conclusion: The trend of salinity changes in the region is increasing towards the center and south, which is consistent with the trend of changes in the most important auxiliary variables identified, such as PCA. From the results of this study, it can be concluded that the soils of the studied region have limited salinity and also have high spatial variability in terms of the amount of soil parameters, especially salinity. This indicates that soil maps are needed to improve and refinement the soils of the Gomishan region.
References
Abrol, I. P., Yadav, J.S.P., & Massoud, F.I. (1988). Salt-affected soils and their management (Vol. 39). Food & Agriculture Org.
https://www.fao.org/4/x5871e/x5871e00.htm
Akpa, S.I., Odeh, I.O., Bishop, T.F., & Hartemink, A.E. (2014). Digital mapping of soil particle‐size fractions for Nigeria. Soil Science Society of America Journal, 78(6), 1953-1966.
https://doi.org/10.2136/sssaj2014.05.0202
Allouche, O., Tsoar, A., &Kadmon, R. )2006(. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of applied ecology 43, 1223-1232.
https://doi.org/10.1111/j.1365-2664.2006.01214.x
Asfaw, Engdawork., Suryabhagavan, K.V., &Argaw, Mekuria. )2018). Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia, Journal of the Saudi Society of Agricultural Sciences, Volume 17, Issue 3, Pages 250-258.
https://doi.org/10.1016/j.jssas.2016.05.003
Asgari, H. R., Rashno, A., Kokmi Bayram, C., Abdolhossein, B. 2019. Investigating the possibility of preparing a soil salinity map using Landsat-8 digital data (Case study: Dashli Borun district - Golestan province. Degradation and Restoration of Natural Lands, 1 (1): 72-81. [In Persian]
http://drnl.sanru.ac.ir/article-1-147-fa.pdf
Ayub, M.A., Ahmad, H.R., Ali, M., Rizwan, M., Ali, S., Rehman, M.Z., & Waris, A.A. (2020). Salinity and its tolerance strategies in plants. In Plant life under changing environment (pp. 47-76). Academic Press.
DOI: 10.1016/B978-0-12-818204-8.00003-5
Azabdaftari, A., & Sunar, F. (2016). Soil salinity mapping using multitemporal Landsat data. The international archives of the photogrammetry, remote sensing and spatial information sciences, 41, 3-9.
https://doi.org/10.5194/isprs-archives-XLI-B7-3-2016
Bandak, S., Movahedi-Naeini, S. A., Mehri, S., & Lotfata, A. (2024). A longitudinal analysis of soil salinity changes using remotely sensed imageries. Scientific Reports, 14(1), 10383.
doi.org/10.1038/s41598-024-60033-6
Bleam, W. F. (2016). Soil and environmental chemistry. Academic Press.
https://doi.org/10.1016/C2015-0-01022-X
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
https://doi.org/10.1023/A:1010933404324
Burt, R. (2011). Soil Survey Investigations Report, no. 45, version 2.0. Natural Resources Conservation Service.
https://www.nrcs.usda.gov/sites/default/files/2022-10/SSIR45.pdf
Csillag, F., Pásztor, L., & Biehl, L. L. (1993). Spectral band selection for the characterization of salinity status of soils. Remote sensing of environment, 43(3), 231-242.
https://doi.org/10.1016/0034-4257(93)90068-9
Cutler, D.R, , Edwards, T.C., Karen, J.R., Beard, H., Cutler, A., Kyle, T., Hess, Jacob., Gibson, J., & Lawler, J. (2007). RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY, Ecology, Pages 2783-2792.
https://doi.org/10.1890/07-0539.1
Daliakopoulos, I.N., Tsanis, I.K., Koutroulis, A., Kourgialas, N.N., Varouchakis, A.E., Karatzas, G.P.,
Ritsema, C. J. (2016). The threat of soil salinity: A European scale review. Science of the total environment, 573, 727-739.
https://doi.org/10.1016/j.scitotenv.2016.08.177
Das, A. (2000). Optimization-based simulation and design of tile drainage system. ASCE J. Irrg. Drain Eng. 126(6): 381-388.
https://doi.org/10.1061/(ASCE)0733-9437(2000)126:6(381)
De la Paix, M.J., Lanhai, L., Xi, C., Varenyam, A., Nyongesah, M.J., & Habiyaremye, G. (2013). Physicochemical properties of saline soils and aeolian dust. Land Degradation & Development, 24(6), 539-547.
https://doi.org/10.1002/ldr.1148
Dwivedi, R. S., & Sreenivas, K. (1998). Delineation of salt-affected soils and waterlogged areas in the Indo-Gangetic plains using IRS-1C LISS-III data. International Journal of Remote Sensing, 19(14), 2739-2751.
https://doi.org/10.1080/014311698214488
Fakhar, M. A., Nazari, B. 1402. Evaluation and preparation of soil salinity map using vegetation indices and multispectral images from Sentinel-2 and Landsat-8 in saline Qazvin plain. Water and Irrigation Management 13:3: 783-799. [In Persian]
https://journals.ut.ac.ir/article_93356_e5555f6209aacfbbb65dfd1d63a2c128.pdf
Fathizad, H., Ardakani, M.A.H., Sodaiezadeh, H., Kerry, R., & Taghizadeh-Mehrjardi, R. (2020). Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran. Geoderma, 365, 114233.
https://doi.org/10.1016/j.geoderma.2020.114233
Feng, D., Zongsuo, L., Xuexuan, X., Xingchang, Z., & Lun, S. (2008). Spatial heterogeneity of soil nutrients and aboveground biomass in abandoned old-fields of Loess Hilly region in Northern Shaanxi, China. Acta Ecologica Sinica, 28(1), 13-22.
https://doi.org/10.1016/S1872-2032(08)60017-7
Fernandez-Buces, N., Siebe, C., Cram, S., & Palacio, J. L. (2006). Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Journal of Arid Environments, 65(4), 644-667.
https://doi.org/10.1016/j.jaridenv.2005.08.005
Greenway, H., & Munns, R. (1980). Mechanisms of salt tolerance in nonhalophytes. Annual review of plant physiology, 31(1), 149-190.
https://www.annualreviews.org/content/journals/10.1146/annurev.pp.31.060180.001053
Han, Y., Ge, H., Xu, Y., Zhuang, L., Wang, F., Gu, Q., & Li, X. (2023). Estimating soil salinity using multiple spectral indexes and machine learning algorithm in songnen plain, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 7041-7050.
DOI: 10.1109/JSTARS.2023.3274579
Hand, D. J. (2007). Principles of data mining. Drug safety, 30, 621-622.
DOI: 10.2165/00002018-200730070-00010
Hassani, A., Azapagic, A., D'Odorico, P., Keshmiri, A., & Shokri, N. (2020). Desiccation crisis of saline lakes: A new decision-support framework for building resilience to climate change. Science of The Total Environment, 703, 134718.
https://doi.org/10.1016/j.scitotenv.2019.134718
Hasti, T. (2001). The elements of statistical learning: data mining, inference, and prediction.
doi.org/10.1007/978-0-387-84858-7
Hillel, D. (2000). Salinity management for sustainable irrigation: integrating science, environment, and economics. World Bank Publications.
http://documents.worldbank.org/curated/en/687661468741583380
Ivits, E., Cherlet, M., Tóth, T., Lewińska, K.E., & Tóth, G. (2013). Characterisation of productivity limitation of salt‐affected lands in different climatic regions of Europe using remote sensing derived productivity indicators. Land Degradation & Development, 24(5), 438-452.
https://doi.org/10.1002/ldr.1140
Kempen, B., Brus, D.J., Heuvelink, G.B., & Stoorvogel, J.J. (2009). Updating the 1: 50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach. Geoderma, 151(3-4), 311-326.
https://doi.org/10.1016/j.geoderma.2009.04.023
Kidd, D.B., Malone, B.P., McBratney, A.B., Minasny, B., & Webb, M. A. (2014). Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia. Soil Research, 52(2), 107-119.
https://doi.org/10.1071/SR13100
Kotsiantis, S., & Pintelas, P. 2004. Supervised Machine Learning, TR-02-02, Department of Mathematics, University of Patras, Hellas, Page 28.
https://www.etpe.gr/wp-content/uploads/pdfs/etpe228.pdf
Lhissou, R., Harti, A.E., & Chokmani, K. (2014). Mapping soil salinity in irrigated land using optical remote sensing data, Eurasian Journal of Soil Science 3, 82-88.
DOI: 10.18393/ejss.84540
Maas, E. V., & Grattan, S. R. (1999). Crop yields as affected by salinity. Agricultural drainage, 38, 55-108.
https://doi.org/10.2134/agronmonogr38.c3
Matinfar, H.R., Alavi Panah, S.K., Zand, F., & Khodaei, K. (2011). Detection of soil salinity changes and mapping land cover types based upon remotely sensed data. Arabian Journal of Geosciences, 6, 913-919.
https://doi.org/10.1007/s12517-011-0384-6
Metternicht, G.I., & Zinck, J.A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote sensing of environment, 85(1), 1-20.
https://doi.org/10.1016/S0034-4257(02)00188-8
Munns, R., & Tester, M. (2008). Mechanisms of salinity tolerance. Annu. Rev. Plant Biol., 59(1), 651-681.
DOI: 10.1146/annurev.arplant.59.032607.092911
Nachtergaele, F., van Velthuizen, H., Verelst, L., Wiberg, D., Henry, M., Chiozza, F., & Tramberend, S. (2023). Harmonized world soil database version 2.0. FAO.
https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/
Noori, S. (2004). Determination of the best vegetation indexes for assessing Mazandaran rangeland (Doctoral dissertation, M. Sc. Thesis, Tarbiat Modarres University, 128 p.(In Persian).
https://www.sid.ir/paper/23036/en#downloadbottom
Pannell, D.J., & Ewing, M.A. (2006). Managing secondary dryland salinity: options and challenges. Agricultural water management, 80(1-3), 41-56.
https://doi.org/10.1016/j.agwat.2005.07.003
Parihar, P., Singh, S., Singh, R., Singh, V.P., & Prasad, S.M. (2015). Effect of salinity stress on plants and its tolerance strategies: a review. Environmental science and pollution research, 22, 4056-4075.
https://doi.org/10.1007/s11356-014-3739-1
Perri, S., Suweis, S., Holmes, A., Marpu, P.R., Entekhabi, D., & Molini, A. (2020). River basin salinization as a form of aridity. Proceedings of the National Academy of Sciences, 117(30), 17635-17642.
Pishnamaz Ahmadi, M., Rezaei Moghadam, M.H., Feizizadeh, B. 2017. Investigating soil salinity indices and preparing a map using remote sensing data (case study: Ajichai Delta). Remote Sensing and Geographic Information Systems in Natural Resources, 8(1): 85-96. [In Persian]
https://journals.iau.ir/article_531276_1eab38b06889fe5d87180e72391e6839.pdf
Ranjbar, R., Oliaei, H.R., Ranjbar, H., Adhami, A. 2018. Monitoring soil salinity changes using remote sensing in the Zahid Shahr region, Fars province. Remote Sensing and Geographic Information Systems in Natural Resources, 9 (3): 115-128. [In Persian]
https://journals.iau.ir/article_544821_5bd7104f3eb34f7e6eca6d3e782a3044.pdf
Rath, K.M., & Rousk, J. (2015). Salt effects on the soil microbial decomposer community and their role in organic carbon cycling: a review. Soil Biology and Biochemistry, 81, 108-123.
https://doi.org/10.1016/j.soilbio.2014.11.001
Rengasamy, P. (2006). World salinization with emphasis on Australia. Journal of experimental botany, 57(5), 1017-1023.
https://doi.org/10.1093/jxb/erj108
Richards, L. A. (Ed.). (1954). Diagnosis and improvement of saline and alkali soils (No. 60). US Government Printing Office.
Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J.P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 67, Pages 93-104.
https://doi.org/10.1016/j.isprsjprs.2011.11.002
Sentis, I.P.L.A. (1996). Soil salinization and land desertification. Soil degradation and desertification in Mediterranean environments, 105-129.
Singh, K. (2016). Microbial and enzyme activities of saline and sodic soils. Land Degradation & Development, 27(3), 706-718.
https://doi.org/10.1002/ldr.2385
Soil Science Society of America. (1997). Glossary of soil science terms. Soil Science Society of America.
https://www.soils.org/publications/soils-glossary
Sulieman, M.M., Kaya, F., Elsheikh, M.A., Başayiğit, L., & Francaviglia, R. (2023). Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions. Land, 12(9), 1680.
https://doi.org/10.3390/land12091680
Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., & Malone, B.P. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15-28.
https://doi.org/10.1016/j.geoderma.2013.07.020
Thiam, S., Villamor, G.B., Faye, L.C., Sène, J.H.B., Diwediga, B., & Kyei-Baffour, N. (2021). Monitoring land use and soil salinity changes in coastal landscape: A case study from Senegal. Environmental Monitoring and Assessment, 193, 1-18.
https://doi.org/10.1007/s10661-021-08958-7
Vågen, T.G., Winowiecki, L.A., Tondoh, J.E., Desta, L.T., & Gumbricht, T. (2016). Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma, 263, 216-225.
https://doi.org/10.1016/j.geoderma.2015.06.023
Wang, J., Peng, J., Li, H., Yin, C., Liu, W., Wang, T., & Zhang, H. (2021). Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China. Remote Sens. 2021, 13, 305.
https://doi.org/10.3390/rs13020305
Wu, W., Zucca, C., Muhaimeed, A.S., Al‐Shafie, W.M., Fadhil Al‐Quraishi, A.M., Nangia, V., ... & Liu, G. (2018). Soil salinity prediction and mapping by machine learning regression in C entral M esopotamia, I raq. Land degradation & development, 29(11), 4005-4014.
https://doi.org/10.1002/ldr.3148
Zaman, M., Shahid, S.A., & Heng, L. (2018). Guideline for salinity assessment, mitigation and adaptation using nuclear and related techniques (p. 164). Springer Nature.
https://library.oapen.org/bitstream/id/fa591144-4b76-41b4-a297-791b27c51720/1007237.pdf
Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144, 152-171.
https://doi.org/10.1016/j.rse.2014.01.011
Ziadat, F.M. (2007). Land suitability classification using different sources of information: Soil maps and predicted soil attributes in Jordan. Geoderma, 140(1-2), 73-80.
https://doi.org/10.1016/j.geoderma.2007.03.004