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کاهش ابعاد دادههای سنجش از دوری به منظور برآورد کربن آلی خاک | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 4، دوره 56، شماره 5، 1403، صفحه 589-606 اصل مقاله (1.06 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2024.22053.7890 | ||
| نویسندگان | ||
| نیوشا مظفری نهر؛ حدیثه سادات حسنی؛ مرضیه جعفری* | ||
| گروه ژئودزی مهندسی نقشه برداری، دانشگاه تفرش، تفرش، ایران | ||
| چکیده | ||
| توزیع مواد آلی در خاک، بر فعالیت بیولوژیکی، در دسترس بودن مواد مغذی، ساختمان خاک و خاکدانهها و ظرفیت نگهداری آب و بر مدیریت خاک بسیار موثر است. در این پژوهش به بررسی ارتباط بازتاب طیفی خاک با استفاده از دادههای ماهواره لندست 8 و همچنین دادههای ارتفاعی SRTM و کربن آلی خاک پرداخته شده است. در روش پیشنهادی، انعکاس طیفی باندهای ماهواره لندست 8 در کنارشاخصهای گیاهی و ویژگیهای توپوگرافی با هدف تعیین شاخصهای موثر بر کربن آلی خاک، مورد بررسی قرار گرفته است. برای این منظور ، از شبکه عصبی جهت ارتباط بین دادههای سنجش از دوری و کربن آلی خاک استفاده شده است. جهت پیادهسازی روش پیشنهادی از 100 نمونه خاک در استان آذربایجان شرقی استفاده شده است. مقادیر شاخص های آماریRMSE ، R2 و RRMSE به ترتیب 0/404، 0/254 و 46/597 بدست آمده و روش شبکه عصبی در مقایسه با روشهای رگرسیون خطی و رگرسیون بردار پشتیبان خطی به دقت بالاتری دست یافت. در ادامه الگوریتم ژنتیک جهت کاهش ابعاد وتعیین شاخص های بهینه، افزایش دقت و کاهش پیچیدگی محاسبات پیشنهاد شد. این الگوریتم با حذف شاخص های اضافی، منجر به افزایش دقت مدل سازی کربن آلی خاک می شود. در این مرحله مقادیر شاخص های آماری RMSE ، R2 و RRMSE با مقادیر 0/279، 0/718 و 27/116 بهبود یافت. همچنین به منظور بررسی کارایی الگوریتم ژنتیک، الگوریتم آنالیز مولفههای اصلی نیز بر روی دادهها پیاده سازی شد و نتایج مقایسه نشان داد که الگوریتم ژنتیک در کاهش ابعاد همراه با افزایش دقت موفق بوده است. | ||
| کلیدواژهها | ||
| سنجش ازدور؛ کربن آلی خاک؛ شبکه عصبی؛ کاهش ابعاد؛ الگوریتم ژنتیک | ||
| موضوعات | ||
| آنالیز داده ها؛ محیط زیست | ||
| عنوان مقاله [English] | ||
| Dimension reduction of the remote sensing data to estimate soil organic carbon | ||
| نویسندگان [English] | ||
| Niusha Mozafari؛ Hadiseh Sadat Hasani؛ Marzieh Jafari | ||
| Department of Geodesy and Surveying Engineering, Tafresh University, Tafresh, Iran | ||
| چکیده [English] | ||
| Soil is a very complex phenomenon that includes organic materials, minerals, water, and air. The distribution of organic matter in the soil has a profound effect on biological activity, nutrient availability, soil and soil seed structure, and water holding capacity, and soil management in general. In this research, the relation between soil spectral reflectance using the Landsat 8 satellite data as well as the SRTM Elevation data and soil organic carbon has been investigated. In the proposed method, spectral reflection of data in the main bands of the Landsat 8 satellite is investigated and processed. In addition to the main bands, vegetation and lighting indices, and topographic features have been studied. In this study, a method for selecting effective indexes in increasing the accuracy of soil organic carbon modeling is presented. For this purpose, in the first step of modeling, Linear regression, Support Vector Machine regression, and Neural Network methods have been used for the connection between remote sensing data and soil organic carbon. To implement the proposed method, 100 soil samples in East Azerbaijan province have been used. According to RMSE and R2 statistical indices, which are the basis for evaluating the models, the neural network model was selected as the final model, and with the values of RMSE = 0.404, R2= 0.254, and RRMSE=46.597 is more accurate than the regression method. Due to the importance of dimensionality to increase accuracy and reduce the complexity of calculations, a genetic algorithm was proposed in this study. This efficient algorithm increases the accuracy of soil organic carbon modeling and eliminates additional indicators. After applying the genetic algorithm (GA) to the neural network model, we were able to achieve better accuracy, and the values of the baseline statistical indices were changed to RMSE = 0.279, R2 = 0.718, and RRMSE=27.116. Also, to check the efficiency of the genetic algorithm, the PCA algorithm was also implemented on the data and the comparison results showed that the genetic algorithm was successful in reducing dimensions along with increasing accuracy. | ||
| کلیدواژهها [English] | ||
| Remote Sensing, Soil Organic Carbon, Neural Network, Dimension Reduction, Genetic Algorithm | ||
| مراجع | ||
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