ارزیابی دقت نقشه‌برداری رقومی خاک با محدودیت داده در بخشی از فلات لسی استان گلستان

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانش آموخته دکتری گروه علوم خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

2 استاد گروه علوم خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

3 استادیار موسسه تحقیقات خاک و آب کرج

4 استادیار گروه علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

چکیده

روش‌های نقشه‌برداری رقومی خاک نیازمند داده‌های کمی برای تخمین هستند که به‌کارگیری آن‌ها در مناطق با تعداد داده خاک کم، و عدم دسترسی مناسب بسیار سخت می‌باشد. انجام عملیات میدانی به‌ویژه در سطح وسیع کاری زمان‌بر و همراه با هزینه زیاد است که عملیات نقشه‌برداری را مشکل‌ساز می‌نماید. از دیگر سو، باور در نقشه‌برداری رقومی بر این است که وجود یک خاک منحصر به فرد در منطقه وابستگی زیادی به متغیرهای محیطی آن منطقه و شناسایی دقیق آن‌ها دارد. در این مطالعه از الگوریتم جنگل تصادفی (RF) به همراه اطلاعات طبقه‌بندی 64 خاکرخ و 19 متغیر محیطی، شامل خصوصیات توپوگرافی، واحدهای ژئومورفولوژی، کاربری اراضی و شاخص پوشش گیاهی، برای تهیه نقشه کلاس خاک در بخشی از فلات لسی استان گلستان استفاده گردید. ژئومورفولوژی، ارتفاع، جهت شیب و کاربری اراضی تقریبا در همه سطوح رده‌بندی دارای بیش‌ترین اهمیت در تخمین کلاس‌های خاک بودند. نتایج ارزیابی دقت الگوریتم RF با متغیرهای ورودی مختلف نشان داد شاخص‌های صحت مدل از جمله صحت کلی و کاپا به ترتیب از 91/0 و 83/0 برای گروه بزرگ، به 78/0 و 56/0 برای زیرگروه، و 50/0 و 32/0 برای فامیل کاهش می‌یابد. کم‌ترین و بیش‌ترین مقدار خطای تخمین نمونه‌های اعتبارسنجی در مدل‌سازی 69/32 و 38/65 درصد به ترتیب برای سطح گروه بزرگ و فامیل خاک دست آمد و کلاس‌های دارای تعداد نمونه بیش‌تر، خطای کم‌تری داشتند. مطالعه حاضر نشان داد که در مناطق با محدودیت داده از ایران، نقشه‌برداری رقومی خاک، استفاده از متغیرهای محیطی با دقت بالا همراه با تعداد نمونه خاک کم می‌تواند نتایج مطلوبی را در سطوح بالای رده‌بندی ارائه دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of Accuracy of Digital Soil Mapping with Limited Data in a Part of Loess Plateau, Golestan Province

نویسندگان [English]

  • Sedigheh Maleki 1
  • Farhad Khormali 2
  • Mohsen Bagheri Bodaghabadi 3
  • Jahangir Mohammadi 4
1 PhD student, Department of Soil Science, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2 Professor, Department of Soil Science, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
3 Assistant Professor, Soil and Water Research Institute, Karaj, Iran
4 Assistant Professor, Department of Forestry Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
چکیده [English]

Abstract
Digital soil mapping approaches that require quantitative data for prediction are difficult to implement in countries with limited data on soil and auxiliary variables. Extensive field sampling is very labor intensive and costly that is problematic for mapping missions. On the other side, it is believed in digital soil mapping approaches that unique soil conditions (soil types or soil properties) can be associated with unique combination and configuration of environmental variables. In this study we used a Random Forest (RF) algorithm combined with classification information of 64 soil profiles and 19 environmental variables (including terrain attribute, geomorphology units, land use and vegetation cover index) to map soil classes in the part of loess plateau, Golestan Province Iran. Geomorphology, elevation, slope aspect and land use were the most important parameters in prediction of soil map in different taxonomic level. The results of accuracy assessment of RF with different entrance variables revealed that accuracy of model including overall accuracy and kappa index respectively decreased of 0.91 and 0.83 for great group, 0.78 and 0.56 for subgroup, 0.50 and 0.32 for family. The minimum and maximum Out of bag (OOB) estimate error rate in modeling were 32.69% and 65.38% for great group and soil family, respectively and the soil classes with higher frequency had the lower OOB error. The present study showed that in regions of Iran with limited data, digital soil mapping and high resolution ancillary data with smaller sample size can be led to an effective result in higher taxonomic levels. 

کلیدواژه‌ها [English]

  • Environmental variables
  • Limited data
  • Map accuracy
  • Random Forest
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