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برآورد مشخصههای کمی جنگل با استفاده از دادههای سنجنده ASTER و الگوریتم CART (منطقه موردمطالعه: جنگل شصتکلاته گرگان) | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 8، دوره 22، شماره 3، آذر 1393، صفحه 434-446 اصل مقاله (515.15 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2014.12424 | ||
| نویسندگان | ||
| نورالدین نوریان* 1؛ شعبان شتایی جویباری2؛ جهانگیر محمدی3؛ سلام یزدانی4 | ||
| 1دانشجوی دکتری جنگلداری، دانشگاه گرگان | ||
| 2دانشیار، دانشگاه گرگان | ||
| 3دکتری جنگلداری، دانشگاه گرگان | ||
| 4کارشناسی ارشد جنگلداری، دانشگاه گرگان | ||
| چکیده | ||
| برآورد مشخصههای کمی جنگل در سطوح وسیع بااستفاده از دادههای ماهوارهای اهمیت فراوانی در مدیریت پایدار جنگل دارد. هدف از این پژوهش، بررسی امکان برآورد برخی مشخصههای کمی جنگل بااستفاده از تصاویر سنجنده ASTER در جنگل شصتکلاته گرگان است. برای این منظور تعداد 112 قطعهنمونه زمینی مربعی بهمساحت 09/0 هکتار بهروش خوشهای تصادفی در واحدهای همگن ازنظر تیپ درختی و جهت دامنه پیاده شد و مشخصههای حجم، رویه زمینی و تعداد درختان در هکتار در هر قطعهنمونه محاسبه شد. مرکز هر یک از قطعاتنمونه نیز با GPS تفاضلی ثبت شد. تصحیح هندسی و رادیومتری روی تصویر انجام شد. بااستفاده از روشهای مختلف نسبتگیری، تجزیه مؤلفههای اصلی، تحلیل بافت، تبدیل تسلدکپ بههمراه باندهای اصلی، مجموعهای از باندهای مناسب برای تجزیهوتحلیلهای همبستگی و مدلسازی فراهم شد. بررسی رابطه بین دادههای زمینی و طیفی بااستفاده از آنالیز رگرسیون درختی (CART) انجام گرفت. ارزیابی اعتبار مدلها با معیارهای میانگین مجذور مربعات خطا و اریبی با استفاده از قطعاتنمونه استفادهنشده در مدل انجام شد. نتایج نشان داد که بهترین مدل برای مشخصههای حجم سرپا، رویه زمینی و تعداد درختان در هکتار بهترتیب دارای ضریبتبیین تعدیلیافته 76/0، 73/0 و 80/0 است. در مدل یادشده مقادیر RMSE و اریبی به درصد بهترتیب 22/40 و 5/17 درصد برای حجم سرپا، 67/38 و هشت درصد برای رویه زمینی و 68/58 و 72/2 درصد برای تعداد درختان در هکتار بهدست آمد. نتایج بهطور کلی نشان داد که دادههای طیفی این سنجنده برای مشخصههای کمی موردبررسی دارای قابلیت متوسطی هستند. | ||
| کلیدواژهها | ||
| مشخصههای کمی جنگل؛ ASTER؛ الگوریتم CART؛ جنگل شصتکلاته | ||
| عنوان مقاله [English] | ||
| Estimating forest structural attributes by means of ASTER imagery and CART algorithm (Case study: Shastkolateh forest, Gorgan) | ||
| نویسندگان [English] | ||
| Nuroddin Noorian1؛ Shaban Shataee2؛ Jahangir Mohammadi3؛ Salam Yazdani4 | ||
| 1Ph. D. Student of Forestry, Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, I.R. Iran. | ||
| 2Associate Prof., Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, I.R. Iran. | ||
| 3Ph. D. Forestry, Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, I.R. Iran. | ||
| 4M. Sc. Forestry, Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, I.R. Iran. | ||
| چکیده [English] | ||
| Large-area estimation of forest structural attributes by remotely-sensed data is crucial for cost effective inventory of the stands, and in turn for sustainable forest management. The objective of this research was to investigate the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery for predicting forest structural attributes over Shastkolateh experimental forest in Gorgan. By means of random cluster sampling method, 112 DGPS-established square plots with an area of 0.09 ha were inventoried which were also homogenous by type and aspect. In those plots, the stand volume, basal area and tree stem density were measured. The image data was geometrically and atmospherically corrected. Moreover, information within the data was used to create additional band ratios, principal components, texture indices, and tasseled cap components, which were then added to the original datasets. Classification and Regression Trees (CART) algorithm was applied for modeling the ground inventory data. The models were assessed for their performance by means of root mean square error (RMSE) and Bias using hold-out samples. The results showed the best values of adjusted R-squared to be 76, 73 and 80% for stand volume, basal area and tree stem density, respectively. Whereas the models of standing volume, basal area and stem density retuned RMSE vauues of 40.22, 38.67, and 58.68, the models were associated with bias values of 17.5 %, 8% and 2.72%, respectively. Results therefore indicate the moderate potential of ASTER imagery for sample plot-based estimation of forest structural attributes. | ||
| کلیدواژهها [English] | ||
| Forest structural attributes, ASTER imagery, CART algorithm, Shastkolateh forest | ||
| مراجع | ||
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