تعیین مناسب‌ترین درجه وضوح پیکسل و اندازه پنجره مدل رقومی ارتفاع برای تعیین پراکنش مکانی درصد رس خاک

نوع مقاله : مقاله پژوهشی

10.22092/ijsr.2016.106323

چکیده

اگر چه درک بهتر و انتخاب مناسب­تر مقیاس مدل رقومی ارتفاع به بهبود پیش­بینی­های خاک کمک خواهد کرد، اما اثرات تعاملات بین اندازه پیکسل و پنجره به تفصیل بررسی نشده است. در این تحقیق، سعی شده است تا نقش مقیاس مکانی بر روی کارایی پیش­بینی درصد رس خاک از طریق آزمودن تجربی تعاملات بین درجه وضوح پیکسل و اندازه پنجره با استفاده از مدل رگرسیون درختی ارزیابی شد. بدین منظور، در دو منطقه متفاوت از لحاظ ژئومورفولوژی و خاک (منطقه 1، میبد در استان یزد با مساحت 400 کیلومتر مربع و منطقه 2، یاسوکند در استان کردستان با مساحت 400 کیلومتر مربع) 120 نمونه خاک سطحی (30-0 سانتی­متری) نمونه­برداری و درصد رس خاک آن­ها اندازه­گیری شد. از 121 مدل رقومی ارتفاع با مقیاس­های متفاوت، 22 خصوصیت ژئومورفومتری استخراج و جهت پیش­بینی درصد رس خاک استفاده شدند. نتایج نشان داد منطقه میبد دارای حداقل میانگین ریشه مربعات خطا (0/9)، حداکثر ضریب تبیین (47/0) بوده و وابستگی مدل درختی جهت پیش­بینی درصد رس خاک به ابعاد پیکسل بیشتر می­باشد[H1] ، ولی منطقه یاسوکند دارای کمترین ریشه مربعات خطا (65/5)، بیشترین ضریب تبیین (77/0) و وابستگی مدل درختی جهت پیش­بینی درصد رس خاک به ابعاد پنجره بیشتر می­باشد.



 [H1]لطفا کنترل شود.

کلیدواژه‌ها

عنوان مقاله [English]

Determination of the Best Pixel Resolution and Window Size of DEM for Digital Mapping of Soil Clay Content

چکیده [English]

Although a better understanding and quantitative knowledge of digital elevation model scale will help to improve soil predictions, the influence of pixel size has not been investigated in detail. The aim of this study was to investigate the role of spatial scale on soil clay content prediction by empirically testing the interaction between pixel resolution and window size with regression tree model. In two different areas in terms of their geomorphology and soil (area 1, Maybod located in Yazd province covered 400 km2; area 2, Iasokand located in Kurdistan province covered 400km2), 120 surface soil samples (0-30 cm) were taken and their clay contents were measured. From 121 digital elevation models representing different scales, 22 attribute were extracted and used for soil clay content prediction. Results showed that Maybod area had the minimum RMSE (9.0%) and maximum R2 (0.47) and dependence of tree model on pixel size was significant for clay prediction[H1] ; however, in Iasokand area, the minimum RMSE (5.65%) and maximum R2 (0.77) were obtained and window size was significant for clay prediction.



 [H1]از چکیده فارسی مطلب این گونه ترجمه شده. لطفا کنترل شود.

کلیدواژه‌ها [English]

  • Regression tree
  • Wrapper algorithm
  • Geomorphometry

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پژوهش های خاک
دوره 30، شماره 1
خرداد 1395
صفحه 95-104

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