رقومی‌سازی کربن آلی خاک (مطالعه موردی: شهرستان کامیاران، استان کردستان)

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانش‌آموخته دکتری ، گروه علوم خاک، دانشگاه لرستان

2 دانشیار، گروه علوم خاک، دانشگاه لرستان،

3 دانشیار ،گروه علوم کشاورزی و منابع طبیعی، دانشگاه اردکان

چکیده

چکیده
سابقه و هدف: کربن آلی خاک نقشی حیاتی درکنترل اقلیم و پایداری محیط زیست دارد. همچنین کربن آلی تاثیر کلیدی بر خصوصیات فیزیکو شیمیایی و بیولوژیکی خاک دارد به نحوی که از آن به عنوان شاخص سلامت خاک نام برده می‌شود. به‌همین جهت، بررسی توزیع مکانی کربن آلی خاک از الزامات برنامه‌ریزی مدیریت اقلیم و خاک می‌باشد. روش‌های مرسوم برآورد کربن آلی خاک پر هزینه و زمان‌بر بوده و قابلیت تکرار و تعمیم به نقاط مشابه را ندارد. در سال‌های اخیر با پیشرفت تکنولوژی و نیاز روز افزون بشر برای دستیابی به اطلاعات زودیافت و صرفه جویی در هزینه، از طریق داده‌کاوی و به کمک تصاویر ماهواره‌ای و پارامترهای محیطی توپوگرافی، رقومی سازی ویژگی‌های خاک از جمله کربن آلی امکانپذیر شده است. نقشه برداری رقومی خاک در واقع توسعه یک مدل عددی یا آماری از رابطه بین متغییرهای محیطی و خصوصیات خاک است که برای داده‌های جغرافیایی زیادی به منظور تولید نقشه رقومی بکار می‌رود. سه هدف اصلی نقشه برداری رقومی خاک عبارت است از: 1) استنباط رابطه بین متغییرهای محیطی و خصوصیات خاک، 2) تولید و ارائه داده‌هایی که پیوستگی خاک-زمین‌نما را بهتر نمایش می‌دهند و 3) بکارگیری صریح دانش کارشناس در طراحی مدل می‌باش همچنین نقشه برداری رقومی با ایجاد بینشی در مورد فرآیندهای خاکسازی، باعث پیشرفت بالقوه پدولوژی و جغرافیای خاک می‌شود.
مواد و روش‌ها: در این پژوهش 110 نمونه خاک به‌همراه 101 پارامتر کمکی جهت پیش بینی کربن آلی خاک در شهرستان کامیاران (استان کردستان) استفاده گردید. با دو مدل رگرسیون خطی چند متغییره و شبکه عصبی مصنوعی به کمک نرم افزار JMP مدلسازی انجام شد.
یافته‌ها: نتایج نشان داد مقدار کربن آلی خاک در بخش‌های غربی و شمال غربی منطقه مورد مطالعه بیشترین مقدار است که شامل مناطق با پوشش جنگلی و مرتعی می‌باشد. متغیرهای کمکی سطح پایه شبکه کانال (40%)، باند 4 (23%)، مقدار آب برگ (20%)، زبری زمین (19%)، فاصله عمودی تا شبکه کانال (18%)، شیب حوزه (18%)، شاخص تفاضل نرمال شده پوشش گیاهی (17%)، سطح حوزه (16%)، جهت شیب (16%)، ارتفاع (16%)، باند 3 (15%)، شاخص جذب انعکاسی (14%)، باند 1 (14%)، باران (13%)، باند 5 (13%)، دمای هوا (12%)، شاخص پوشش گیاهی (11%)، شاخص خیسی توپوگرافی (10%)، شاخص تفاضل پوشش گیاهی (10%) و غیره بیشترین اثر را روی مدل‌سازی کربن آلی خاک در مدل شبکه عصبی مصنوعی داشته‌اند. مدلسازی توزیع کربن آلی خاک توسط شبکه عصبی مصنوعی (97/0 R2=) نتیجه بهتری نسبت به رگرسیون خطی چند متغییره (59/0 R2=) داشته است.
نتیجه‌گیری: نتایج این پژوهش نشان داد که پراکنش کربن آلی بیشتر تحت تأثیر فاکتورهای توپوگرافی، پوشش گیاهی و اقلیم می‌باشد. در مناطقی که به هر دلیل امکان نمونه‌برداری در کل منطقه وجود ندارد، می‌توان از طریق داده‌های محیطی مانند پارامترهای توپوگرافی، اقلیمی و پوشش گیاهی و با روش‌های نوین داده‌کاوی برای تخمین کربن آلی خاک بهره گرفت.

کلیدواژه‌ها

عنوان مقاله [English]

Soil Organic Carbon Digital Mapping (Case Study: Kamyaran County, Kurdistan Province)

نویسندگان [English]

  • Hamid Mahmoudzadeh 1
  • Hamid Reza Matinfar 2
  • Ruhollah Taghizadeh-Mehrjardi 3
1 Department of soil science, Agricultural faculty, Lorestan University, Khorram-Abad
2 associated prof. of Lorestan University
3 Department of natural resource, Faculty of natural resource, Ardakan University, Ardakan, Iran
چکیده [English]

Abstract
Background and Objectives: Soil organic carbon plays a vital role in climate control and environmental sustainability. It also has a key impact on the physico-chemical and biological properties of the soil as it is considered an indicator of soil health. Therefore, investigating the spatial distribution of soil organic carbon is one of the requirements of climate and soil management planning. Traditional methods of estimating soil organic carbon are costly and time consuming and cannot be replicated and extended to similar locations. With the advancement of technology and the ever-increasing need for cost-effective information, data mining and satellite imagery and land parameters have been digitized by soil features. Digital soil mapping is the development of a numerical or statistical model of the relationship between environmental variables and soil properties that is used for large geographic data to generate a digital map. The three main goals of soil digital mapping are: 1) inferring the relationship between environmental variables and soil characteristics, 2) producing and presenting data that better demonstrate soil-geography coherence, and 3) applying expert knowledge in model design. Digital mapping also develops the potential of pedology and soil geography by creating insights into burial processes.
Materials and Methods: In this study 110 soil samples along with 101 auxiliary parameters were used to predict soil organic carbon in Kamyaran city (Kurdistan province). Multivariate linear regression models and artificial neural networks were modeled using JMP software.
Results: The results showed that soil organic carbon content was highest in the western and northwestern parts of the study area and was related to forest cover and pasture areas. On the other hand, higher altitudes have higher estimated organic carbon. Auxiliary variables of the channel network base level (40%), band 4 (23%), leaf water content (20%), vector terrain roughness (19%), vertical distance to channel network (18%), catchment slope (18%), Normalized vegetation difference index (17%), catchment area (16%), aspect (16%), dem (16%), band 3 (15%), reflectance absorption index (14%), band 1 (14) %), Rain (13%), band 5 (13%), air temperature (12%), vegetation index (11%), topographic wetness index (10%), vegetation index (10%) and so on had the greatest effect on soil organic carbon modeling, in the artificial neural network model. Modeling soil organic carbon distribution by artificial neural network (R2 = 0.97) performed better than multivariate linear regression (R2 = 0.59). Conclusion: The results of this study showed that the distribution of organic carbon is more influenced by topographic, vegetation and climate factors. In areas where sampling is not possible in the whole area for any reason, it can be used through environmental data such as topographic, climatic and vegetation parameters and with new data mining methods to estimate soil organic carbon.

کلیدواژه‌ها [English]

  • Data Mining
  • Digital Soil Mapping
  • Soil Organic Carbon Zoning
  • Kamyaran County

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مجله مدیریت خاک و تولید پایدار
دوره 10، شماره 4
اسفند 1399
صفحه 77-98

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