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برآورد برخی مشخصههای کمی جنگل با استفاده از دادههای ماهواره Pleiades و الگوریتمهای ناپارامتریک در جنگلهای دارابکلای مازندران | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 8، دوره 23، شماره 3، مهر 1394، صفحه 465-477 اصل مقاله (402.43 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2015.105652 | ||
| نویسندگان | ||
| مژگان ظهریبان* 1؛ اصغر فلاح2؛ شعبان شتایی3؛ سیاوش کلبی4 | ||
| 1کارشناس ارشد جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| 2دانشیار، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| 3دانشیار، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان | ||
| 4دانشجوی دکتری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| چکیده | ||
| شناسایی وضعیت کمی جنگل برای مدیریت تودههای جنگلی از اساسیترین اطلاعات محسوب میشود. هدف از پژوهش پیشرو برآورد دادههای طیفی با قدرت تفکیک مکانی زیاد ماهواره Pleiades در برآورد دو مشخصه حجم سرپا و رویه زمینی با استفاده از الگوریتمهای ناپارامتریک در جنگل دارابکلای ساری بود. تعداد 144 قطعهنمونه 10 آری به روش تصادفی منظم پیاده شد و قطر برابر سینه کلیه درختان و ارتفاع برخی از آنها بههمراه موقعیت مراکز قطعات نمونه برداشت شد، سپس حجم سرپا و رویه زمینی درختان در هکتار محاسبه شد. پس از انجام برخی پیشپردازشها و پردازشهای مناسب، ارزشهای رقومی متناظر با قطعات نمونه زمینی از باندهای طیفی استخراج شدند و بهعنوان متغیرهای مستقل درنظر گرفته شدند. حجم سرپا و رویه زمینی در هکتار نیز به عنوان متغیر وابسته درنظر گرفته شدند. مدلسازی با روشهای k امین نزدیکترین همسایه، ماشین بردار پشتیبان و جنگل تصادفی با 70 درصد از قطعات نمونه انجام شد و نتایج با 30 درصد باقیمانده قطعات نمونه مورد ارزیابی قرار گرفت. براساس نتایج، بهترین برآوردها با روش ماشین بردار پشتیبان برای مشخصه حجم با درصد مجذور میانگین مربعات خطا برابر با 45/13 درصد و اریبی نسبی برابر با 3/21- و برای مشخصه رویه زمینی با درصد مجذور میانگین مربعات خطا برابر با 38/75 و اریبی نسبی برابر با 3/12 بهدست آمد که بین این روشها دارای بهترین عملکرد بود. نتایج این پژوهش نشان داد که با توجه به ناهمگنی و متراکم بودن جنگل دارابکلا، دادههای ماهواره Pleiades دارای قابلیت متوسطی در برآورد این دو مشخصه بودند. | ||
| کلیدواژهها | ||
| جنگل دارابکلای ساری؛ روشهای ناپارامتریک؛ ماهواره Pleiades؛ مشخصههای کمی جنگل | ||
| عنوان مقاله [English] | ||
| Estimating quantitative forest attributes using Pleiades satellite data and non-parametric algorithms in Darabkola forests, Mazandaran | ||
| نویسندگان [English] | ||
| Mojgan Zahriban1؛ Asghar Fallah2؛ Shaban Shataee3؛ Siavash Kalbi4 | ||
| 1M.Sc. Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resources University, Sari, Iran. | ||
| 2Associate Prof., Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resources University, Sari, Iran | ||
| 3Associate Prof., Department of Forestry, Faculty of Natural Resources, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, Iran | ||
| 4Ph.D. Student Forestry, Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resources University, Sari, Iran. | ||
| چکیده [English] | ||
| Knowledge on quantitative forest attributes is a prerequisite for forest stand management. The aim of this study was to evaluate high resolution Pleiades data in estimating the standing volume and basal area using non-parametric algorithms in Darabkola forest of Sari, Mazandaran province. A sampling design of 144 plots each with area of 1000 m2 was established using a systematic random sampling method. In each plot, information including as position of plot center, diameter at breast height of all trees within sample plot and height of selected trees were recorded, based on which the standing volume and basal area per ha were derived. The Pleiades data was preprocessed, and the pixel grey values corresponding to the ground samples were extracted from spectral bands. These were further considered as the independent variables to predict the standing volume and basal area per ha. Modeling was carried out based on 70% of sample plots as training set using K-Nearest Neighbor, support vector machine, and random forest methods. The predictions were cross-validated using the left-out 30% samples. Support vector machine comparatively retuned the best estimates for stand basal area with root mean square error of 38.75% and relative bias of 3.12, while it predicted the stand volume with root mean square error of 45.13% and relative bias of -3.21 as well. The results of study proved the average spectral and spatial capability of Pleiades data to estimate these two main, where the caveats are concluded to be mainly due to the heterogeneity and the density of forest stands across the study area. | ||
| کلیدواژهها [English] | ||
| Darabkola Forest of Sari, non-parametric methods, Pleiades satellite, forest quantitative characteristics | ||
| مراجع | ||
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