‌مدل‌سازی زیتوده جنگل‌های شاخه‌زاد بلوط غرب با استفاده از متریک‌های استخراج شده از داده‌های لایدار هوایی

محورهای موضوعی : منابع طبیعی

فرزاد یاوری ¹ , هرمز سهرابی ²

1 - دانش آموخته کارشناسی ارشد دانشکده منابع طبیعی و علوم دریایی دانشگاه تربیت مدرس
2 - دانشیار، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس، تهران، ایران. *(مسوول مکاتبات)

تاریخ دریافت : 1397/02/03 تاریخ پذیرش : 1397/06/14 تاریخ انتشار : 1400/10/01

کلید واژه: تاج پوشش, لایدار, معادلات رگرسیونی, جنگل شاخه زاد, زی‌توده روی زمین,

چکیده مقاله :

زمینه و هدف: یکی از مهمترین داده‌های فعال سنجش از دوری برای استفاده در کمی کردن ویژگی‌های مختلف توده ها جنگلی داده‌های لایدار است. از جمله حوزه های فعال تحقیقاتی سنجش از دور، امکان سنجی برآورد زیتوده درختان با استفاده از متریک‌های مختلف داده‌های لایدار هوایی است. روش بررسی: زیتوده درختان در 127 قطعه نمونه مربعی به روش منظم تصادفی در دو منطقه کم تراکم و پرتراکم به ابعاد 900 متر مربع برای اندازه گیری موجود در قطعات نمونه برداشت شد. داده های لایدار برای یافتن وحذف هر گونه خطا بررسی، DTM [1]، DSM [2]و CHM[3] از این داده ها استخراج و شاخص های آماری مختلف از ارتفاعی داده های لایدار برای هر قطعه نمونه استخراج شد. به منظور برآورد زیتوده رگرسیون گام به گام استفاده شد. یافته‌ها: نتایج دقت متوسط برای براورد زیتوده توسط داده‌های لایدار نشان داد به شکلی که مقدار ضریب تعیین و جذر میانگین مربعات خطا (بر حسب تن در هکتار) در برآورد زیتوده با داده های لایدار برای برگ، سرشاخه، شاخه، تنه وکل درخت در کل منطقه به ترتیب (58/0 و 28)، (54/0 و 23)، (68/0 و 35/1) (68/0 و 53/1) (65/0 و 69/3) بود. بحث و نتیجه گیری: به دلیل خطای بالا در مشخص کردن نوک تاج درختان در توده های پهن برگ و مخصوصا توده های شاخه زاد به دلیل ارتفاع کم و شکل غیرهندسی تر، برآورد ارتفاع و سایر مشخصه ها در این توده ها با خطای زیادی همراه است. رسیدن به دقت‌های بالا مستلزم تحقیقات بیشتر است. [1]- Digital Terrain Model 4- Digital Surface Model 5- Canopy Hight Model

چکیده انگلیسی:

Background and Objective: One of the most important active remotely sensed data for quantification of different attributes of forest stands is LiDAR data. A research hotspot in this subject is to estimate forest biomass using different LiDAR derived metrics. Material and Methodology: Forest biomass was measured in 127 systematically designed 900-meter square rectangular plots in two different sites (dense and sparse sites). LiDAR data was inspected for any possible error. DTM, DSM, and CHM were extracted from LiDAR data and different metrics at plot level were calculated. For modeling, stepwise regression was applied.  Findings: Result showed a moderate precision for biomass estimation using LiDAR data in a way that the coefficient of determination and root mean square error (Ton/ha) for biomass estimation of leaves, twigs, branches, bole, and whole tree were 0.58-28, 0.54-23, 0.68-1.35, 0.68-1.53, .65-3.69, respectively.  Discussion and Conclusion: Due to high error of determination of tree tips in broadleaves forests especially in coppice stands which have low tree height with irregular shapes; the error of the estimation of tree height using LiDar data is high. Better results demand further researches.  

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