ارزیابی و مدل سازی تراکم کربن آلی خاک با استفاده از داده های زمینی و سنجش از دور در دو نوع کاربری در منطقه فندوقلو استان اردبیل

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت منابع خاک دانشگاه شهرکرد، شهرکرد، ایران.

2 دانشیار گروه علوم و مهندسی خاک، دانشگاه شهرکرد، شهرکرد، ایران.

3 استادیار، گروه علوم و مهندسی خاک، دانشگاه شهرکرد، شهرکرد، ایران

4 استاد، گروه علوم و مهندسی خاک، دانشگاه ولی‌‌عصر رفسنجان، رفسنجان، ایران

چکیده

سابقه و هدف: کـربن آلـی خـاک نقـش بسـیار مهمـی در افزایش تولید محصولات کشاورزی، کاهش فرسایش خاک و کـاهش انتشار گازهای گلخانه‌ای دارد. درک بهتر از مقدار و ذخیره کربن آلی خاک برای استفاده از خاک و حفظ بهره‌وری آن مؤثر و ضروری می-باشد. مراتع گسترده منطقه فندوقلو در استان اردبیل به‌طور بالقوه می‌توانند مقادیر قابل توجهی از کربن را حفظ و ترسیب کند اما به‌دلیل افزایش جمعیت در معرض تغییر کاربری اراضی قرار دارند. چنین تنش‌هایی منجر به تخریب زمین و کاهش ترسیب کربن در برخی قسمت‌ها خواهد شد. بنابراین هدف این مطالعه شناسایی متغیرهای طبیعی مؤثر در ترسیب و تراکم کربن آلی خاک انجام شده است که می‎توان از متغیرهای شناسایی شده برای اصلاح و کاهش تخریب مراتع و اراضی زراعی در منطقه فندوقلو استفاده کرد.
مواد و روش‌ها: با استفاده از تکنیک اَبَر مکعب لاتین و متغیرهای مکانی مانند نقشه‌های خاک، کاربری اراضی و زمین‌شناسی، محل نقاط نمونه‌برداری تعیین شد. برای هر واحد کاربری (مرتع یا زراعی) 70 نمونه خاک سطحی مرکب (در کل 140 نمونه) از عمق صفر تا 15 سانتی‌متری جمع‌آوری شد. پس از آماده‌سازی نمونه‌های خاک، خصوصیات فیزیکی و شیمیایی خاک‌ها با توجه به روش‌های استاندارد اندازه‌گیری شد. مدل رقومی ارتفاعی از منطقه مورد مطالعه با استفاده از خطوط هم‌تراز نقشه توپوگرافی تشکیل و مشتقات اولیه و ثانویه ساخته شد. باندهای لندست هشت از درگاه زمین‌شناسی ایالات متحده استخراج و شاخص تفاضل نرمال شده پوشش گیاهی و نقشه حرارتی منطقه مورد مطالعه محاسبه شد. از مدل‌های رگرسیون خطی چندمتغیره و مدل شبکه عصبی مصنوعی با استفاده از داده‌های خاک، مدل رقومی ارتفاعی و مشتقات آن، شاخص تفاضل نرمال شده پوشش گیاهی و نقشه حرارتی به عنوان متغیرهای مستقل برای پیش‌بینی تراکم کربن آلی خاک استفاده شد. برای ارزیابی نتایج مدل‌ها، از ضریب تبیین و جذر میانگین مربعات خطا استفاده شد.
یافته‌ها: استفاده از مدل رگرسیون خطی چندمتغیره نشان داد که نیتروژن کل خاک، شاخص تفاضل نرمال شده پوشش گیاهی و میانگین وزنی قطر خاکدانه، 53 درصد تراکم کربن آلی خاک را در مراتع توجیه نمود در حالی که 46 درصد از تراکم کربن آلی خاک در کاربری زراعی توسط نیتروژن کل خاک و انحنای سطح توجیه گردید. نتایج آزمون تی جفتی، تفاوت معنی‌داری (01/0>P) بین تراکم کربن آلی خاک در کاربری مرتع و زراعی نشان داد. شبکه عصبی مصنوعی با آرایش 24-10-1 و تابع انتقال تانژانت سیگموییدی در لایه پنهان به-ترتیب 71 درصد و 82 درصد از تراکم کربن آلی خاک را در کاربری مرتع و زراعی توجیه نمود. جذر میانگین مربعات خطا مدل‌های رگرسیون خطی چندمتغیره برای پیش‌بینی تراکم کربن آلی خاک در کاربری مرتع و زراعی به‌ترتیب 07/1 و 04/1 بود، اما همین مقادیر در مدل شبکه عصبی مصنوعی به‌ترتیب برای کاربری مرتع و زراعی به 85/0 و 58/0 کاهش یافت. تحلیل حساسیت نشان داد که مدل شبکه عصبی مصنوعی به‌شدت به مدل رقومی ارتفاعی و رطوبت خاک در نقطه پژمردگی حساس هستند.
نتیجه‌گیری: نتیجه این مطالعه حاکی از معنی‌داری اثر پوشش ‌گیاهی و میانگین وزنی قطر خاکدانه روی ترسیب کربن در کاربری مرتع بود. نتایج شبکه‌های عصبی مصنوعی که برای شبیه‌سازی تغییرات تراکم کربن آلی خاک مورد استفاده قرار گرفت، نشان دادند که این شبکه-ها می‌توانند با شناسایی اثرات این متغیرها در ردیابی بخش مهمی از تغییرات تراکم کربن آلی خاک در کاربری‌های مرتع و زراعی موفق عمل کنند. نتایج همچنین نشان داد که توابع انتقالی به‌دست آمده توسط پارامترهای توپوگرافیکی اولیه و ثانویه برای تخمین تراکم کربن آلی خاک می‌تواند در منطقه، مورد استفاده قرار گیرد.

کلیدواژه‌ها

عنوان مقاله [English]

Assessment and Modeling of Soil Organic Carbon Density using Ground Data and Remote Sensing in Two Types of Land Use in the Fandoqloo of Ardabil Province

نویسندگان [English]

  • Mehran Behtari 1
  • Mehdi Naderi Khorasgani 2
  • Ahmad Karimi 3
  • Hussien Shirani 4
1 دانشگاه شهرکرد
2 Associate Professor, Dept. of Soil Science and Engineering, University of Shahr-e-Kord
3 Assistant Professor, Dept. of Soil Science and Engineering, University of Shahr-e-Kord
4 Professor, Dept. of Soil Science and Engineering, University of Vali-e-Asr of Rafsanjan
چکیده [English]

Background and Objectives: Soil organic carbon is very important in increasing the production of agricultural products and reducing soil erosion and greenhouse gas emissions. A better understanding of the amount and storage of soil organic carbon is essential to the use of soil and maintaining its productivity. The extended pastures of Fandoqloo region in Ardabil province potentially could support and sequestrate considerable amounts of carbon but they are subjected to land use change, high population and tourism. Such tensions led to land degradation and low carbon sequestration in some parts. This study aimed to find effective natural variables on soil carbon density and sequestration. Such variables could be applied for remediation and resilience of degraded pastures and croplands in Fandoghloo region.
Materials and Methods: By application of Latin Hypercube techniques and spatial variables like soil, land use and geological maps the location of sample points was determined. For each land use unit (pasture or cropland) 70 composite surface soil samples (in total 140 samples) were collected from zero to 15 cm deep. After pretreatments of soil samples, soil's physical and chemical properties were measured according to standard protocols. A digital elevation model of the study area was formed using digital isolines of topographic maps and primary and secondary derivates were constructed. Landsat 8 bands were extracted from the Geology Department site of the USA and Normalized difference vegetation index and thermal map of the study area were calculated. Multiple linear regression and artificial neural network models were applied and soil data, digital elevation model and its derivates, normalized difference vegetation index and land surface temperature map have entered the models as independent variables for the prediction of soil organic carbon density. To evaluate the results of the models, the coefficients of determination and root mean square error was used.
Results: Application of multiple linear regression model indicated that total soil nitrogen, normalized difference vegetation index and mean weight diameter of aggregates justified 53% of soil organic carbon density in the pasture while 46% of soil organic carbon density in croplands land use was explained by total soil nitrogen and plane curvature. The results of the paired t-test showed a significant difference (P< 0.01) between soil organic carbon density of the pasture and cropland land use. An artificial neural network with an arrangement of 1-10-24 and tan-sigmoid transfer function in the hidden layer justified 71% and 82% of the soil organic carbon density in the pasture and croplands, respectively. Root mean square error of multiple linear regression models for predicting soil organic carbon density in the pasture and croplands were 1.07 and 1.04, respectively but of artificial neural network model was decreased to 0.85 and 0.58 for pasture and cropland, respectively. Sensitivity analysis revealed that the artificial neural network model was severely sensed to digital elevation model and soil moisture content at wilting point.
Conclusion: The result of this study indicated the significance of natural vegetation cover and the mean weight diameter of soil aggregates for carbon sequestration in the pastures. Artificial neural networks performed to simulate changes in soil organic carbon density showed that these networks are successful in tracking an important part of soil organic carbon density changes at pasture and cropland land uses. In other words, artificial neural networks were able to identify the effects of these variables on soil organic carbon density using tracing and identifying the effects and interactions between an independent variable series with organic carbon density status. The results showed that transmission functions can be used by the data of primary and secondary topographic parameters for estimating soil organic carbon density in the region.

کلیدواژه‌ها [English]

  • Multivariate linear regression model
  • Artificial neural network
  • Sensitivity analysis

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مجله مدیریت خاک و تولید پایدار
دوره 12، شماره 4
دی 1401
صفحه 1-27

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