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تأثیر اندازة همسایگی بر متغیرهای مرفومتریک و رابطة آن‌ها با پوشش گیاهی در سه زیر حوزة آبخیز متفاوت از منظر ژئومرفولوژیکی و اقلیمی در جنوب غرب ایران | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 1، فروردین 1401، صفحه 1-13 اصل مقاله (1.79 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.334244.669136 | ||
| نویسندگان | ||
| جواد خنیفر1؛ عطااله خادم الرسول* 2 | ||
| 1دانشجوی دکتری گروه خاکشناسی دانشکده کشاورزی دانشگاه شهید چمران اهواز اهواز ایران | ||
| 2استادیار گروه خاکشناسی، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، ایران | ||
| چکیده | ||
| هدف این پژوهش بررسی اهمیت مقیاس همسایگی در مدل‌سازی رابطة پوشش گیاهی و متغیرهای مرفومتریک به کمک الگوریتم درخت رگرسیونی و طبقه‌بندی (CART) در جنوب غرب ایران است. برای این هدف، شاخص پوشش گیاهی اصلاح شده (MSAVI2) از یک تصویر لندست 8 محاسبه گردید و استخراج هشت متغیر مرفومتریک با به‌کارگیری روش Wood در چهار مقیاس همسایگی (90×90، 150×150، 210×210 و 270×270 متر) از یک مدل رقومی ارتفاع SRTM با وضوح مکانی 30 متر انجام پذیرفت. نتایج آزمون کروسکال - والیس تأیید کرد که در برخی از زیر حوزه‌های آبخیز تغییر مقیاس همسایگی می‌تواند تأثیری معنادار بر گرادیان شیب، انحنای پروفیل، سطح ویژة آبخیز، عامل LS و شاخص خیسی توپوگرافیک بگذارد. نتایج این مطالعه نشان داد که در هر زیر حوزة آبخیز متغیرهای مرفومتریک متفاوتی با توزیع مکانی شاخص MSAVI2 بیش‌ترین ارتباط را دارند و مقدار ضریب همبستگی اسپیرمن بین آن‌ها به میزان کمی تحت‌تأثیر مقیاس همسایگی می‌باشد. مدل‌های CART مبتنی بر شاخص MSAVI2 و متغیرهای مرفومتریک محاسبه شده در مقیاس همسایگی 270×270 متر به ترتیب با میزان ضریب کاپای 55/0 و 78/0 دارای بهترین عملکرد در طبقه‌بندی تیپ‌های گیاهی بودند. ارتفاع هموار شده که کم‌ترین تأثیر را از مقیاس همسایگی دارد، به‌عنوان مهم‌ترین پیش‌بینی‌کننده در مدل CART شناسایی شد ولی افزایش مقیاس همسایگی منجر به بیشتر شدن اهمیت دیگر متغیرهای مرفومتریک به‌ویژه گرادیان شیب در طبقه‌بندی تیپ‌های گیاهی و نهایتاً ارتقاء دقّت مدل CART گردید. نتایج کلی این پژوهش بیانگر آن می‌باشد که کاربرد آنالیز چند مقیاسی ژئومرفومتریک باتوجه‌به ژئومرفولوژی منطقة مطالعاتی می‌تواند عملکرد مدل‌های پیش‌بینی مرتبط با پوشش گیاهی را به میزان مناسبی افزایش دهد. | ||
| کلیدواژه‌ها | ||
| پوشش گیاهی؛ ژئومرفومتری؛ مقیاس همسایگی؛ درخت رگرسیونی و طبقه‌بندی (CART) | ||
| عنوان مقاله [English] | ||
| Effect of Neighborhood Size on Morphometric Variables and Their Relationship with Vegetation Cover within Three Geomorphologically and Climatically Different Sub-Watersheds in Southwest Iran | ||
| نویسندگان [English] | ||
| Javad Khanifar1؛ Ataallah Khademalrasoul2 | ||
| 1Department of soil science, ّFaculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| 2Assistant Professor of Soil Science Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| چکیده [English] | ||
| The aim of this research was to study the importance of the neighborhood scale in modeling the relationship between vegetation cover and morphometric variables using the classification and regression trees algorithm (CART) in southwestern of Iran. For this purpose, the second Modified Soil-Adjusted Vegetation Index (MSAVI2) was calculated from a Landsat 8 image, and eight morphometric variables were derived using the Wood method in four neighborhood scales (90×90, 150×150, 210×210, and 270×270 m) from a 30 m SRTM digital elevation model. The results of the Kruskal-Wallis test confirmed that in some sub-watersheds, neighborhood-scale change can have a significant effect on slope gradient, profile curvature, specific catchment area, LS factor, and topographic wetness index. The results showed that in each sub-watershed different morphometric variables are most related to the spatial distribution of the MSAVI2 index and the value of the Spearman correlation coefficient between them is slightly affected by the neighborhood scale. CART models based on the MSAVI2 index and 270×270 m morphometric variables with a kappa coefficient of 0.55 and 0.78, respectively, had the best performance in classifying vegetation types. The elevation smoothed, which is the least affected by the neighborhood scale, was recognized as the most important predictor in the CART model. However upscaling led to the increasing importance of other morphometric variables, especially slope gradient, in classifying vegetation types and finally improving the accuracy of the CART model. Overall, the present results indicate that the application of multi-scale geomorphometric analysis with respect to the geomorphology of the study area can improve the performance of prediction models related to vegetation cover to an appropriate extent. | ||
| کلیدواژه‌ها [English] | ||
| Vegetation cover, Geomorphometry, Neighborhood scale, Classification and Regression Trees (CART) | ||
| مراجع | ||
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