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مدلسازی تغییرات کربن آلی خاک با استفاده از شاخصهای سنجش از دور در حوضه آبخیز بالیخلیچای اردبیل | ||
تحقیقات آب و خاک ایران | ||
دوره 51، شماره 9، آذر 1399، صفحه 2417-2429 اصل مقاله (1.19 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2020.299509.668542 | ||
نویسندگان | ||
سولماز فتح العلومی* 1؛ علیرضا واعظی1؛ سید کاظم علوی پناه2؛ اردوان قربانی3 | ||
1گروه علوم خاک، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران | ||
2گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران | ||
3گروه منابع طبیعی، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
چکیده | ||
مدلسازی و تهیه اطلاعات دقیق از توزیع مکانی خصوصیات خاک، یک عامل کلیدی در بسیاری از کاربردهای محیطی و کشاورزی است. از اینرو، هدف از مطالعه حاضر، مدلسازی و تهیه نقشه رقومی کربن آلی خاک با استفاده از شاخصهای سنجش از دور در حوضه آبخیز بالخلیچای بود. ابتدا خصوصیات توپوگرافی و طیفی مؤثر بر مقدار کربن آلی خاک بر اساس شاخصهای مکانی و طیفی مختلف از مدل رقومی ارتفاع و تصویر ماهوارهای لندست 8 استخراج شد. سپس بر مبنای مدل جنگل تصادفی، عملکرد مدلسازی رقومی خاک در مدلسازی کربن آلی خاک در حالتهای استفاده از 1) متغیرهای زمینی، 2) شاخصهای طیفی و 3) ترکیب متغیرهای زمینی و شاخصهای طیفی، ارزیابی و مقایسه شد. برای این منظور، مقدار ضریب همبستگی (R2) بین مقادیر برآوردی و اندازهگیری شده کربن آلی خاک و ریشه میانگین مربعات خطا (RMSE) در حالتهای مختلف محاسبه شد. نتایج نشان داد که مقدار کربن آلی در منطقه از 32/0 تا 98/6 درصد متغیر و میانگین آن در منطقه 04/3درصد بود. تغییرات کربن در منطقه عمدتاً وابسته به تغییرات شاخصهای طیفی بود. در بین خصوصیات توپوگرافی، ارتفاع و در بین شاخصهای طیفی، ضریب گسیلندگی (Emissivity)، مهمترین خصوصیت در مدلسازی کربن آلی خاک بودند. مقدار R2 در سه مدل مذکور بهترتیب 51/0 62/0 و 75/0 و مقدار RMSE بهترتیب 88/0، 67/0 و 57/0 بود که نشاندهنده کارایی بهتر مدل سوم است. استفاده از ترکیب متغیرهای زمینی و طیفی سبب افزایش قابلتوجه دقت مدلسازی کربن آلی خاک میشود. | ||
کلیدواژهها | ||
سنجش از دور؛ کربن آلی خاک؛ متغیر محیطی؛ مدل جنگل تصادفی؛ مدل رقومی ارتفاع | ||
عنوان مقاله [English] | ||
Modeling Soil Organic Carbon Variations Using Remote Sensing Indices in Ardabil Balikhli Chay Watershed | ||
نویسندگان [English] | ||
Solmaz Fathololoumi1؛ Alireza Vaezi1؛ Seyed Kazem Alavipanah2؛ Ardavan Ghorbani3 | ||
1Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
2Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran,, Tehran, Iran | ||
3Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardebili, Ardabil, Iran | ||
چکیده [English] | ||
Modeling and providing accurate information on the spatial distribution of soil properties is a key factor in many environmental and agricultural applications. Therefore, the purpose of the present study was to model and prepare a digital map of soil organic carbon using remote sensing indices in the Balikhli Chay watershed. At first, topographic and spectral characteristics affecting soil organic carbon content were extracted from digital elevation model and Landsat 8 satellite image. Then the performance of soil organic carbon modeling for different states was evaluated and compared based on random forest models. The states including 1) terrain covariates, 2) spectral indices, and 3) combination of terrain and spectral covariates, were evaluated and compared together. To this end, the correlation coefficient (R2) between the estimated and measured soil organic carbon and root mean square error (RMSE) were calculated for the different states. The results showed that the amount of organic carbon in the study area varied from 0.32 to 6.98 and the mean value was 3.04%. Carbon changes in the study area mostly dependent on changes in spectral indices. Elevation and Emissivity were respectively the most important terrain and spectral covariates in soil organic carbon modeling. The R2 values in the three models were 0.61, 0.62 and 0.75 and the RMSE values were 0.88, 0.67 and 0.57, respectively, which indicates the better performance of the third model. The use of a combination of terrestrial and spectral variables significantly increases the accuracy of soil organic carbon modeling. | ||
کلیدواژهها [English] | ||
Digital soil map, Environmental covariates, Random forest model, Remote sensing, Soil organic carbon | ||
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