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پیش بینی کربن آلی خاک سطحی با تلفیق تحلیل عاملی و رگرسیون چندگانه در مراتع نیمه استپی لزور، فیروزکوه | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 19، دوره 25، شماره 3، آذر 1397، صفحه 699-712 اصل مقاله (896.39 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2018.117819 | ||
| نویسندگان | ||
| رستم خلیفه زاده1؛ رضا تمرتاش* 2؛ محمدرضا طاطیان2؛ محمدرضا سراجیان مارالان3 | ||
| 1دانشجوی دکترای علوم مرتع، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران | ||
| 2استادیار، گروه مرتعداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران | ||
| 3استاد، گروه سنجش از دور، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشگاه تهران، ایران | ||
| چکیده | ||
| کربن آلی خاک، یکی از مهمترین شاخص‌های کیفیت خاک است که تقریباً تمامی ویژگی‌های فیزیکی، شیمیایی و بیولوژیکی خاک را تحت تأثیر خود قرار داده و سبب حاصلخیزی خاک می‌شود. این شاخص نقش کلیدی در چرخة سراسری کربن دارد. هدف از انجام این پژوهش مطالعة رفتارهای طیفی و غیرطیفی خاک به منظور برآورد کربن آلی خاک سطحی با استفاده از روش‌های تحلیل عاملی و رگرسیون چندگانه در مراتع نیمه‌استپی لزور فیروزکوه است. نمونه‌برداری از خاک، با استفاده از روش نمونه‌برداری تصادفی طبقه‌بندی شده صورت گرفت. تعداد 157 سایت تعلیمی در واحدهای کاری همگن انتخاب شد. اطلاعات 127 سایت برای واسنجی مدل و اطلاعات 30 سایت برای اعتبارسنجی آن بکار گرفته شد. در هر یک از سایت‌های تعلیمی به شیوه تصادفی، یک نمونه خاک متشکل از 9 مشاهده از عمق صفر تا 20 سانتیمتری خاک سطحی برداشت شد. کربن آلی خاک با استفاده از روش تیتراسیون والکلی – بلاک اندازه‌گیری شد. نتایج نشان داد متغیرهای آلبیدو، شاخص رس، شاخص گیاهی تفاضلی بهنجار، شاخص‌های روشنایی و سبزینگی تبدیل تسلدکپ و ارتفاع نسبی، همبستگی معنی‌داری با کربن آلی خاک دارند. همچنین نتایج تحلیل عاملی به روش تجزیة مؤلفه‌های اصلی (PCA) با مقادیر ویژة بزرگتر از یک نشان داد کل واریانس تجمعی تبیین‌شده بوسیلة شش متغیر مذکور، برابر 146/81 درصد بود که این میزان واریانس بوسیلة دو عامل توضیح داده شد. معادلة رگرسیون تولید شده با دو عامل استخراج شده، از پتانسیل مناسبی برای پیش‌بینی کربن آلی خاک سطحی برخوردار بود (789/0 = R2). میانگین نسبی خطای مطلق (MARE) و ریشة متوسط مربعات خطا (RMSE) مدل پیشنهادی به ترتیب برابر 1/0 و 24/0 محاسبه شد. با توجه به ارتباط مستقیم کربن آلی خاک با عوامل حاصلخیزی و مقاومت خاک در مقابل فرسایش، مدل توزیع مکانی کربن آلی خاک می‌تواند بعنوان یک زیرمدل مهم به منظور طراحی سایر مدل‌های پیچیده همچون تولید (بایومس) اکوسیستم‌های خشکی و مدل‌های فرسایش خاک مورد استفاده واقع شود. | ||
| کلیدواژه‌ها | ||
| کربن آلی خاک؛ لندست 8؛ پارامترهای مورفومتریک؛ تحلیل عاملی؛ رگرسیون خطی چندگانه | ||
| عنوان مقاله [English] | ||
| An estimation of topsoil organic carbon by combining factor analysis and multiple regression in semi-steppe rangelands of Lazour, Firouzkooh | ||
| نویسندگان [English] | ||
| Rostam Khalifezadeh1؛ Reza Tamartash2؛ Mohammadreza Tatian2؛ Mohammadreza Sarajian Maralan3 | ||
| 1Ph.D. Candidate in Rangeland Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 2Assistant Professor, Rangeland Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 3Professor, Remote Sensing Department, School of Surveying and Geospatial Engineering, Tehran University, Tehran, Iran | ||
| چکیده [English] | ||
| Organic carbon is one of the most important soil quality indices, affecting almost all physical, chemical and biological properties of the soil. The purpose of this study was to investigate soil spectral and morphometric factors to estimate the organic carbon of topsoil, using factor analysis and multiple regression methods in semi-steppe rangelands of Lazour. Soil samples were taken with a stratified random method. For this purpose, 157 training sites were selected in homogeneous units. Of these, 127 sites were used to calibrate the model and 30 sites were used to validate the model. In each of the training site in a random manner, a soil sample including nine observations was taken from a depth of 0 to 20 cm of soil surface. Soil Organic Carbon (SOC) was measured using Walkley-Black titration method. The results showed that the variables of Albedo, Clay Index (CI), NDVI, Relative Relief and Tasseled-Cap's Brightness and Greenness indices had a significant correlation with the SOC (p<0.05). Also, the result of factor analysis by Principal Component Analysis (PCA) method with eigen-values greater than one indicated that the total cumulative variance, explained by the six variables, was equal to 81.1%.This variance was explained by two components. Using multiple regression model, an appropriate regression equation was calculated to predict SOC (R2=0.789). The Root Mean Square Error and the Mean Absolute Relative Error of the proposed model were calculated as 0.24 and 0.10, respectively. Due to the direct relationship between the SOC and the factors such as soil fertility and sustainability against erosion, a spatial distribution model of SOC could be an important sub-model to design other complex models such as the terrestrial ecosystems biomass and soil erosion models. | ||
| کلیدواژه‌ها [English] | ||
| Landsat 8, spectral factors, morphometric factors | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 605 تعداد دریافت فایل اصل مقاله: 503 |
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