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استفاده از الگوریتم نا پارامتریک k نزدیکترین همسایه (k-NN) بهعنوان روشی مناسب جهت تهیۀ فاکتور پوشش گیاهی و مدیریت مدل RUSLE در حوضۀ سد شیرین دره، شمال خراسان | ||
نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
دوره 73، شماره 4، اسفند 1399، صفحه 753-770 اصل مقاله (2.57 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jrwm.2021.247783.1199 | ||
نویسندگان | ||
عماد ذاکری* 1؛ حمیدرضا کریم زاده2؛ سید علیرضا موسوی3 | ||
1دکتری علوم مرتع ، ادارۀ کل منابع طبیعی و آبخیزداری خراسان شمالی، ایران. | ||
2دانشیار دانشکدۀ منابع طبیعی، دانشگاه صنعتی اصفهان، ایران. | ||
3استادیار دانشکدۀ منابع طبیعی، دانشگاه صنعتی اصفهان، ایران. | ||
چکیده | ||
از میان فاکتورهای مدل اصلاحشده جهانی فرسایش خاک (RUSLE)، فاکتور پوشش و مدیریت (فاکتور C) یکی از عوامل مهم و اثرگذار بر میزان فرسایش خاک است. تعیین فاکتور C بر اساس روشهای اصلی معرفیشده با توجه به فقدان اطلاعات دقیق در بسیاری از مناطق مشکل است. در این روش نقشۀ پوشش گیاهی میتواند در جهت برآورد فاکتور C مورد استفاده قرار گیرد، اما تهیۀ نقشۀ مناسب از درصد پوشش گیاهی در بسیاری از شرایط یک چالش است. درنتیجه در این مطالعه نقشۀ درصد تاج پوشش گیاهی تهیه شده با استفاده از الگوریتم نا پارامتریک k-NN، رگرسیون خطی و رگرسیون خطی گامبهگام در حوضۀ آبخیز شیرین درۀ خراسان شمالی تهیه و مورد مقایسه قرار گرفت. در روشهای رگرسیونی 17 شاخص گیاهی و محیطی تهیه و روابط آنها بررسی شد. نتایج مقایسۀ نقشههای حاصل از 3 روش نشان داد که روش k-NN به دلیل دارا بودن بالاترین درصد صحت کلی (3/83 درصد) و ضریب کاپا (9/75 درصد) نسبت به دو روش رگرسیونی دیگر از نتایج مناسبتری برخوردار است، ازاینرو جهت تهیۀ فاکتور مدیریت و پوشش (C) مورداستفاده قرار گرفت. نتایج مطالعه نشان داد که روش نا پارامتریک k-NN دارای نتایج امیدوارکنندهای در جهت تهیۀ نقشههای درصد تاج پوشش گیاهی مراتع مناطق خشک و نیمهخشک است. در میان شاخصهای گیاهی شاخص گیاهی NDVI بیشترین همبستگی (82/0) را با درصد پوشش گیاهی دارد. همچنین درروش k-NN معیار فاصلۀ اقلیدسی در نقطۀ 9=k نسبت به دو معیار دیگر ماهالانوبیس و فازی نتایج مناسبتری دارد و میتواند نقشه درصد پوشش گیاهی را با دقت بالاتری برآورد نماید. | ||
کلیدواژهها | ||
فرسایش خاک؛ فاکتور پوشش و مدیریت؛ شاخص گیاهی؛ الگوریتم ناپارامتریک؛ k نزدیکترین همسایه | ||
مراجع | ||
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