کاربرد متغیرهای محیطی و تکنیک نقشه‌برداری رقومی خاک در پیش‌بینی شاخص سله ‌بستن خاک‌های استان آذربایجان‌شرقی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیئت علمی گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز

2 استاد گروه علوم و مهندسی خاک دانشکده کشاورزی دانشگاه تبریز

3 دانشیار گروه مرتع و آبخیزداری دانشکده منابع طبیعی دانشگاه ارومیه

4 دانشجوی دکتری گروه علوم و مهندسی خاک دانشکده کشاورزی دانشگاه تبریز

5 دانش آموخته کارشناسی‌ارشد گروه علوم و مهندسی خاک دانشکده کشاورزی دانشگاه تبریز

چکیده

تشکیل سله یکی از نمودهای تخریب خاک است که موجب افت کیفیت اراضی می­شود. رفع چالش­های ناشی از سله بستن خاک مستلزم شناسایی عرصه‌های تخریب یافته و بهبود مدیریت منابع خاک است. با توجه به اینکه ویژگی­های خاک دارای پیوستگی مکانی هستند، لذا تهیه نقشه­های رقومی به کمک متغیرهای محیطی می‌تواند اقدام مؤثری در مطالعات پراکنش مکانی باشد. بدین­ منظور، تعداد 107 نمونه به­طور تصادفی از سطح استان آذربایجان ‌شرقی تهیه و شاخص سله‌بندی بر اساس روش فائو محاسبه شد. به‌منظور پیش­بینی شاخص سله‌بندی خاک برای محدوده مورد مطالعه، دو مدل‌ جنگل تصادفی و رگرسیون خطی چندگانه در محیط برنامه­نویسی R و با کاربرد دو گروه از متغییرهای محیطی مشتمل بر مشتقات مدل رقومی ارتفاع (18 شاخص) و شاخص­های دورسنجی (8 شاخص) مورد ارزیابی قرار گرفت و در نهایت نقشه­های رقومی با استفاده از مدل برتر تهیه گردید. نتایج نشان داد شاخص سله‌بندی خاک‌های منطقه مورد مطالعه از 07/0 تا 25/2 متغیر می­باشد. همچنین مدل جنگل تصادفی با استفاده از داده‌های مشتقات مدل رقومی ارتفاع و مدل رگرسیون خطی چندگانه با کاربرد شاخص‌های دورسنجی به‌عنوان مدل­های برتر در پیش‌بینی شاخص سله بستن خاک شناسایی شدند. بنابراین می­توان نتیجه گرفت که انتخاب مدل برتر بستگی به نوع متغیرهای محیطی و داده­هایی دارد که در مدل استفاده قرار می‌شوند. علیرغم وجود تفاوت­های جزئی در مقادیر پیکسل­های هر دو نقشه مستخرج از مدل­های برتر معرفی شده، نقشه­های نهایی دارای روند تقریباً یکسانی هستند. نتایج نشان داد که حداکثر مقدار شاخص سله­بندی در قسمت­های غربی و مرکز استان، سپس جنوب­شرقی و شمال­شرقی استان می­باشد. نقشه­های رقومی تهیه شده نشان می­دهد که شاخص مذکور در اراضی جنگلی و مرتعی دارای حداقل مقدار بوده و اراضی زراعی و متفرقه در رتبه‌های بعدی جای داشتند که با مشاهدات میدانی نیز همخوانی دارد. این تحقیق اهمیت تکنیک نقشه­برداری رقومی خاک در مدیریت منابع خاک را بیش از پیش نمایان می­سازد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Using Environmental Covariates and Soil Digital Mapping Technique in Predicting Soil Crusting Index of East Azerbaijan Province

نویسندگان [English]

  • Hossein Rezaei 1
  • Aliasghar Jafarzadeh 2
  • Esmaeil Sheidai-Karkaj 3
  • Behzad Mohammadhosseini sagayesh 4
  • Farshid Pashaeizadeh 4
  • Saba Hasani 5
  • Farzin Shahbazi 2
1 Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz- Iran
2 Professor, Soil Science and Engineering Department, Faculty of Agriculture, University of Tabriz- Iran.
3 Associated Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University
4 Ph.D Student, Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz
5 M.Sc. Graduate, Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz
چکیده [English]

Soil crusting is one of the degradation features which causes to decrease the land quality. To fix the crises due to soil crusting, it is therefore needed to identify the degraded areas and improve soil resource management. Since the soil properties have a spatial continuity, providing the digital maps using environmental covariates could be an interesting issue to study the spatial distribution. For this, a total of 107 soil samples were randomly taken over the East Azerbaijan Province, subsequently soil crusting index was calculated based on FAO method. To predict the soil crusting index across the study area, two models i.e., random forests (RF) and multiple linear regression (MLR) within the R programming environment using the data derived from digital elevation model (DEM) (18 indices) as well as remote sensing (eight indices) were evaluated. Results showed that the calculated soil crusting index for the entire study area varied from 0.07 to 2.25. Based on the results, RF was superior to MLR when using DEM-derived data, while MLR was distinguished as a parsimonious model when using RS data. It is concluded that selection of the best-fit model mainly depends on the available soil and covariates data used in modelling. Despite somewhat differences in pixel values between provided maps by the relevant models, the final maps demonstrated a similar trend. Generally, based on the results, the highest soil crusting index was found for west and central part of province, followed by south-eastern and north-eastern areas. The provided maps show that the forest and pasture areas have low value of crusting index, while the cultivated and miscellaneous lands were in the following orders which was consistent with field observations. This research further supports the importance of the digital soil mapping (DSM) technique in soil resources management.

کلیدواژه‌ها [English]

  • Crusting index
  • Digital Soil Mapping
  • Modeling
  • Multiple Liner Regression
  • Random Forest

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  • تاریخ دریافت: 25 اسفند 1401
  • تاریخ بازنگری: 08 مهر 1402
  • تاریخ پذیرش: 16 مهر 1402

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