استفاده از روش طیف‌سنجی مرئی-مادون قرمز نزدیک در مدل‌سازی شوری خاک اراضی مستعد تولید ریزگرد استان خوزستان

نوع مقاله : مقاله پژوهشی

نویسندگان

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

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

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

4 دانشیار، هیئت علمی پژوهشی، پژوهشکده حفاظت خاک و آبخیزداری، تهران، ایران

5 دانشیار، گروه خاکشناسی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

چکیده

سطح وسیعی از اراضی شور و نیمه شور استان خوزستان به علت عدم پوشش سطحی و مقاومت کم خاک در برابر باد فرساینده به کانون‌های مستعد تولید ریزگرد تبدیل‌شده‌اند. هدف از این پژوهش مدل‌سازی شوری خاک مناطق حساس به تولید ریزگرد استان خوزستان با روش طیف‌سنجی امواج مرئی و مادون‌قرمز نزدیک (2500-350 نانومتر) بود. از مدل‌های چند متغیره رگرسیون حداقل مربعات جزئی، شبکه عصبی مصنوعی و مدل جنگل تصادفی برای مدل‌سازی شوری خاک به کار گرفته شد. طیف بازتابی خاک با دستگاه طیف­سنج زمینی (FieldSpec) تعیین شد. همچنین روش‌های پیش‌پردازش فیلتر ساویتزی گولای، مشتق اول به همراه فیلتر ساویتزی گولای (FD-SG)، مشتق دوم به همراه فیلتر ساویتزی گولای (SD-SG)، روش نرمال‌سازی استاندارد (SNV) و روش حذف پیوستار (CR)، جهت حذف نویز و افزایش دقت مدل‌های چند متغیره مورد استفاده قرار گرفت. نتایج نشان داد که مدل ترکیبی حداقل مربعات جزئی- شبکه عصبی مصنوعی با معیارهای ارزیابی (65/2 - 40/3 =(RPDcal در برآورد شوری خاک دقت مناسبی دارد. در مقابل مدل ترکیبی حداقل مربعات – جنگل تصادفی نیز کمترین دقت (98/1-85/0= (RPDcal را نشان داد. پیش‌پردازش طیف اصلی در دو مدل شبکه عصبی و رگرسیون حداقل مربعات جزئی سبب افزایش نسبی دقت مدل شد درحالی‌که در مدل جنگل تصادفی پیش‌پردازش سبب کاهش دقت برآورد مدل، نسبت به طیف اصلی شد. محدوده 1800،1900، 2000، 2300 و 1500 نانومتر به عنوان طول موج کلیدی متأثر از شوری خاک شناسایی شد. از طول موج‌‌های کلیدی به‌دست آمده، می‌توان در مطالعات دورسنجی و تهیه نقشه شوری مناطق حساس به تولید گرد و غبار استان خوزستان استفاده کرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling Soil Salinity in Khuzestan Lands Susceptible for Dust Production Using Visible-Near Infrared Spectroscopic Method

نویسندگان [English]

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

A broad area of saline and semi-saline lands of Khuzestan province have changed into centers susceptible to dust production due to eroded wind and lack of surface coating and low soil resistance. The objective of this study was to model the soil salinity of sensitive areas to dust production in Khuzestan Provenience usin spectrometry method of visible and near-infrared wavelengths (2500-350 nm). The least square multivariate regression model, artificial neural network and random forest model were used to estimate soil salinity. The main soil spectrum was determined using the FieldSpect machine. Also, preprocessing methods including Savitzky-Golay filter, the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normalization method (SNV), and the continuum remove method (CR) were used to eliminate the noise and to increase the accuracy of the multivariate model. The results showed that the combined model partial least squares-artificial neural network model with assessment criteria (RPDcal = 3.40-2.65) has high accuracy for salinity estimation. In contrast, the combined model of least squares - random forest showed the lowest accuracy (RPDcal = 0.85-1.98). Preprocess of the main spectrum in two models (neural network and partial least squares regression) increased the relative accuracy of the model; while in the random forest model, preprocess reduced the accuracy of the model compared to the main spectrum. The ranges of 1800, 1900, 2000, 2300 and 1500 nm were recognized as "the key wavelengths" impressed by soil salinity. The key wavelengths can be used in remote sensing studies and mapping of soil salinity in areas sensitive to dust production in Khuzestan province.

کلیدواژه‌ها [English]

  • Partial least squares regression
  • Preprocessing
  • Savitzky-Golay filter
  • Key wavelengths
  • Random forest model

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تحقیقات آب و خاک ایران
دوره 50، شماره 8
دی 1398
صفحه 1951-1962

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