بررسی ویژگی‌های جذبی کربن آلی خاک با روش طیف‌سنجی آزمایشگاهی در مناطق مستعد تولید ریزگرد استان خوزستان

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه علوم و مهندسی خاک، دانشگاه شهید چمران اهواز

2 استاد، گروه علوم و مهندسی خاک، دانشگاه شهید چمران اهواز و عضو مرکز پژوهشی منطقه‌ای ریزگردها، دانشگاه شهید چمران اهواز

3 دانشیار، گروه علوم و مهندسی خاک، دانشگاه شهید چمران اهواز

4 دانشیار پژوهشی، پژوهشکده حفاظت خاک و آبخیزداری

5 دانشیار، گروه خاکشناسی، دانشگاه تربیت مدرس

چکیده

سابقه و هدف: در سال‌های اخیر نواحی گسترده‌ای از استان خوزستان به دلیل عدم پوشش سطحی و مقاومت کم خاک در برابر باد فرساینده، مستعد تولید ریزگرد هستند. در بین ویژگی‌های خاک، ماده آلی با اتصال ذرات خاک نقش مهمی در مقاومت به فرسایش بادی و تولید ریزگرد دارد. با توجه با سطح گسترده‌ی کانون‌های ریزگرد استان خوزستان، استفاده از روش‌های سنتی تجزیه و تحلیل خاک پر هزینه و زمان‌بر است. روش طیف‌سنجی به دلیل مزیت سرعت عمل و سهولت جابجایی، می‌تواند هزینه و زمان اندازه‌گیری ویژگی‌های خاک را کاهش دهد. بر این اساس هدف از این پژوهش بررسی رفتار طیفی کربن آلی خاک در مناطق مرکز و جنوب استان خوزستان با استفاده از دو مدل رگرسیونی چند متغیره ماشین بردار پشتیبان (SVR) و شبکه عصبی (PLS-ANN) و تعیین طول موج کلیدی کربن آلی خاک در این مناطق است.
مواد و روش‌ها: در این پژوهش منطقه مطالعاتی به شبکه‌های 2 در 2 کیلومتری تقسیم گردید و نمونه‌برداری به روش سیستماتیک- تصادفی انجام شد. مقدار کربن آلی نمونه‌های خاک در آزمایشگاه اندازه‌گیری گردید. طیف بازتابی نمونه‌ها با استفاده از دستگاه Fildspec3 در اتاقک تاریک تعیین شد و اندازه‌گیری طیفی با سه نوع آشکارساز در محدوده مرئی تا مادون‌قرمز نزدیک (2500-350 نانومتر) صورت گرفت. به منظور حذف نویز در طیف بازتابی، طیف اصلی با 4 روش مشتق اول همراه با فیلتر ساویتزکی - گولای (FD-SG)، روش مشتق دوم به همراه فیلتر ساویتزکی - گولای (SD-SG)، واریانس نرمال استاندارد (SNV) و حذف پیوستار (CR) پیش‌پردازش شد. در ادامه عملکرد مدل‌های SVR و PLS-ANN در طیف اصلی و روش‌های پیش‌پردازش مورد مقایسه قرار گرفت.
یافته‌ها: نتایج نشان داد که مدل PLS-ANN دقت بیشتری نسبت به مدل SVR در برآورد کربن آلی خاک داشت. در مدل SVR روش پیش‌پردازش حذف پیوستار (CR) بهترین عملکرد (CR) (82/1=CAL RPD و 06/0 =CAL RMSE ،84/0 =CAL R2) و طیف اصلی (ROW) (66/1=CAL RPD و 14/0 =CAL RMSE ،74/0 =CAL R2) کمترین عملکرد را داشتند. در مدل PLS-ANN بهترین عملکرد مرتبط به روش مشتق دوم (SD-SG) (34/2=CAL RPD و 05/0 =CAL RMSE ،92/0 =CAL R2) و کمترین عملکرد در روش مشتق اول (FD-SG) (86/1=CAL RPD و 1/0 =CAL RMSE ،80/0 =CAL R2) مشاهده شد.
نتیجه‌گیری: در این پژوهش روش‌های پیش‌پردازش سبب بهبود دقت کلی مدل های SVR و PLS-ANN نسبت به طیف اصلی شدند. با توجه به عملکرد روش مشتق دوم در مدل PLS-ANN که بهترین دقت را در برآورد کربن آلی خاک داشت، طول موج‌های 800، 1800 و 2000 نانومتر به عنوان طول موج کلیدی کربن آلی خاک در برای مناطق مستعد تولید ریزگرد شناسایی شد.

کلیدواژه‌ها

عنوان مقاله [English]

Investigation of Absorbance Characteristics of Soil Organic carbon using Laboratory Spectroscopy in dust sensitive areas of Khuzestan Province

نویسندگان [English]

  • Mansour Chatrenor 1
  • Ahmad Landi 2
  • Ahmad Farrokhian Firouzi 3
  • Aliakbar Noroozi 4
  • Hosseinali Bahrami 5
1 Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Shahid Chamran University of ahvaz
3 Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
4 Conservation and Watershed Management Research Institute, Tehran, Iran
5 Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
چکیده [English]

Background and Objectives: In recent years, due to the lack of surface coating and low soil resistance to wind erosion, the large area of Khuzestan province is sensitive to dust production. Among the soil characteristics, the organic matter by collecting soil particles, has important role in soil resistance to wind erosion and dust production. Since these areas are so wide, the use of traditional methods of soil analysis is really costly and time consuming. The spectroscopy approach, due to the advantage of speed and easy movement, can reduce the cost and time of measurement. The aim of this study is to investigate the spectral behavior of soil organic carbon in central and southern regions of Khuzestan province by using tow multivariate regression, Support Vector Regression) SVR( and neural network (PLS-ANN), and key wavelength determination of soil organic matter in these areas.
Materials and Methods: In this research, the study area was divided into 2 km square grids and systematic and random sampling Methods were performed. The soil organic matter in samples was measured in the laboratory. The Reflectance spectra of soil samples were determined using FildSpect setup in dark room. And spectral measurements were carried out with three types of detectors in range of visible to near infrared (3500-2500 nm). To eliminate the noise in normal reflectance spectra, the main spectra were preprocessed by four methods, including the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variant method (SNV) and the continuum removed method (CR). Next, the performance of SVR and PLS-ANN models in main spectra preprocessed method were compared.
Results: The results showed that the PLS-ANN model had better accuracy compared to SVR model in estimating organic carbon. In SVR models, the continuum removal method (CR) had the best performance (R2CAL=0.84, RMSECAL=0.06 and RPDCAL=1.82) and the main Spectra had the worst performance (R2CAL=0.74, RMSECAL=0.14 and RPDCAL=1.66). In PLS-ANN models, the best performance belonged to the second derivative (SD-SG), (R2CAL=0.92, RMSECAL=0.05 and RPDCAL=2.34) and the worst performance was related to the first derivative (FD-SG), (R2CAL=0.80, RMSECAL=0.1 and RPDCAL=1.86).
Conclusion: In this study, the preprocessing methods improved the overall accuracy of SVR and PLS-ANN models compared to the main spectrum. According to the second derivative method, in PLS-ANN witch had the best accuracy in estimating soil organic carbon, the Wavelength ranges around 800, 1800 and 2000 nm were identified as the key wavelength of the organic carbon in sensitive centers to dust production.

کلیدواژه‌ها [English]

  • Support Vector Machine
  • Neural Network
  • Preprocessing
  • Continuum Removal Method

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مجله مدیریت خاک و تولید پایدار
دوره 10، شماره 1
خرداد 1399
صفحه 65-81

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