برآورد ویژگی‌‌های کمی و کیفی بنه (.Pistacia atlantica Desf) و بادام (.Amygdalus spp) روی ابر نقاط تصاویر پهپاد

نوع مقاله : علمی- پژوهشی

نویسندگان

1 نویسنده مسئول، دانشیار، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران

2 استادیار، گروه ژئوماتیک، مؤسسه تحقیقات جنگل، ورشو، لهستان

چکیده

برآورد ویژگی‌‌های کمی و کیفی گیاهان روی تصاویر پهپاد، یکی از چالش‌‌های اخیر در سنجش‌ازدور است، بنابراین پژوهش پیش‌‌رو با هدف معرفی روشی برای برآورد مساحت تاج، ارتفاع و نوع گونه در یک توده آمیخته بنه (Pistacia atlantica Desf.) و بادام (Amygdalus spp.) روی ابر نقاط تصاویر پهپاد انجام شد. در یک محدوده 64 هکتاری از جنگل تحقیقاتی بنه استان فارس، 100 درخت بنه و 100 درختچه بادام به‌طور تصادفی انتخاب و ابر نقاط آن‌ها از تصاویر پهپاد با تراکم 50 نقطه در متر مربع تهیه شد. مساحت تاج (91/0R2=، 7/4 %=PRMSE) و ارتفاع بنه (83/0=R2، 2/3 %=PRMSE) و مساحت تاج (89/0R2=،            1/22 %=PRMSE) و ارتفاع بادام (47/0R2=، 5/21 %=PRMSE) از روی ابر نقاط برآورد شدند. همچنین، نوع گونه با استفاده از الگوریتم جنگل تصادفی و 37 مشخصه کمی از ابر نقاط، مدل ارتفاعی تاج و ارتوفتو پیش‌‌بینی شد. صحت و ضریب کاپا در تعیین نوع گونه به‌ترتیب 92/0 و 98/0 محاسبه شد. به‌طور کلی، نتایج نشان داد که ابر نقاط تصاویر هوایی پهپاد، کارایی مناسبی در برآورد ویژگی‌‌های کمی و کیفی بنه و بادام در منطقه مورد مطالعه داشتند، اگرچه دقت و صحت قابل‌‌قبولی در برآورد ارتفاع بادام به‌دست نیامد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of qualitative and quantitative characteristics of Pistacia atlantica Desf. and Amygdalus spp. in of UAV point clouds

نویسندگان [English]

  • Y. Erfanifard 1
  • B. Kraszewski 2
1 Corresponding author, Associate Prof., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
2 Assistant Prof., Department of Geomatics, Forest Research Institute, Warsaw, Poland
چکیده [English]

    Estimation of qualitative and quantitative characteristics of plants on UAV images is considered a challenge in remote sensing. Therefore, this study aimed to present a method to estimate crown area, height, and species in a mixed Pistacia-Amygdalus stand in UAV-derived point clouds. To this aim, 100 Pistacia atlantica Desf. trees and 100 Amygdalus spp < em>. shrubs were randomly selected. Point cloud was obtained by UAV-derived imagery with 50 points per m2 in a 64-ha study area in Baneh Research Forest, Fars province. The quantitative characteristics were then estimated on the point cloud. Additionally, species type was classified using random forest and 37 quantitative attributes measured on point cloud, canopy height model, and orthomosaic. Crown area and height of Pistacia (R2= 0.91 and 0.83, PRMSE=4.7% and 3.2%, respectively) and Amygdalus (R2= 0.89 and 0.47, PRMSE=22.1% and 21.5%, respectively) were also estimated. By application of quantitative attributes and random forest, species type was classified with an accuracy of 0.92 and κ of 0.98. All in all, results indicated that UAV point clouds can be efficiently applied to estimate a set of qualitative and quantitative attributes of Pistacia and Amygdalus within the study area. However, inaccurate and imprecise results were observed for estimated heights of Amygdalus.

کلیدواژه‌ها [English]

  • Crown height model
  • digital surface model
  • Gini coefficient
  • structure from motion

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