نوع مقاله : مقاله پژوهشی


1 استادیار گروه سنجش ازدور و GIS ، دانشکده منابع طبیعی و محیط زیست، داﻧﺸﮕﺎه آزاد اسلامی، واحد علوم و تحقیقات

2 عضو باشگاه پژوهشگران جوان، دانشگاه آزاد اسلامی،واحد سنندج، سنندج، اﯾﺮان


آماربرداری و نقشهبرداری از درختان شهری بهمنظور برنامهریزی و کمک به طراحی استراتژیهای بهینهسازی خدمات اکوسیستم شهری و سازگاری با تغییرات اقلیمی بسیار ضروری است. پیشرفتهای اخیر در فناوری سیستمهای هوایی بدون سرنشین، فناوری انعطافپذیر مکانی و زمانی دادههای سهبعدی با وضوح بالا را تسهیل کرده است. روشهای رایج آشکارسازی پایههای درختی براساس دادههای ماهوارهای با وضوح بسیار بالا یا دادههای اسکن لیزر هوایی است. با این حال، دادههای ماهوارهای اغلب با مشکل مناسب نبودن در مقیاس تکدرخت و محدودیت ابرها مواجه است و دادههای لیزر اسکن هوایی نیز از هزینههای نسبتاً بالایی برخوردار هستند. بنابراین در مطالعه حاضر با هدف آشکارسازی تاج درختان، از دو الگوریتم رشد ناحیهای و حوضه آبخیز معکوس در یک جنگل شهری با ساختارهای متفاوت از مدل ارتفاع تاج بهدست آمده از ساختار حرکت مبنا استفاده شد. به همین منظور تصویربرداری و آماربرداری زمینی درختان درتابستان 1398 در جنگل شهری باغ فاتح واقع در شهرستان کرج انجام شد. پس از پردازش تصاویر و تولید مدل ارتفاع تاج، آشکارسازی درختان در 5 اندازه پیکسل 25، 50، 75، 100 و 125 سانتیمتر و در سه ساختار ناهمگن متراکم، ناهمگن پراکنده و همگن متراکم انجام شد. نتایج نشان داد که دو الگوریتم رشد ناحیهای و حوضه آبخیز معکوس در ساختار همگن متراکم بیشترین عملکرد را دارد. همچنین الگوریتم رشد ناحیهای با میزان صحت کلی 88 درصد در سایت3 (ساختار همگن متراکم) با اندازه پیکسل 75 سانتیمتر بهترین نتیجه را در آشکارسازی درختان ارائه داد. در کل نتایج این تحقیق نشان داد که آشکارسازی پایههای درختی با استفاده از مدل ارتفاع تاج بهدست آمده از تصاویر پهپاد در سایتهای همگن دارای دقت بالایی است، در حالیکه در سایتهای ناهمگن و متراکم از کارایی بالایی برخوردار نبود. 



عنوان مقاله [English]

Assessing the accuracy of UAV-captured images used for individual trree crowns delineation in different structures of an urban forest

نویسندگان [English]

  • Zahra Azizi 1
  • Mojdeh Miraki 2

1 Asistant professor of Remote sensing and GIS , Science and Research Branch,Islamic Azad University, Tehran, Iran

2 Young researchers and elites club, Sanandaj Branch Islamic Azad University, Sanandaj, Iran

چکیده [English]

Extended Abstract
Advances in computer vision and the development of remote sensing instruments have made indirect measurement of tree features possible. Individual tree crown delineation is an important step towards information collection and mapping trees in an urban area. This information is then used to help planners design strategies for optimization of urban ecosystem services and adapt to climate changes. Common methods of Individual tree crown delineation (ITCD) were based on very high-resolution satellite or Light Detection and Ranging (LiDAR) data. However, satellite data are usually covered by clouds and thus, cannot be appropriate for the measurement of individual trees. Aerial Laser scanning is also relatively expensive. Remote sensing with unmanned aerial vehicle (UAV) captures low altitude imagery and thus, is potentially capable of mapping complex urban vegetation. Automatic delineation of trees with UAV data makes collection of detailed information from trees in large geographic and urban regions possible. Therefore, a multirotor UAV equipped with a high-resolution RGB camera was used in the present study to obtain aerial images and delineate individual trees.
Materials & Methods
The present study has compared the performance of Inverse watershed segmentation (IWS) and region growing (RG) algorithms using point clouds derived from Structure from Motion (SfM) algorithm and UAV imagery captured with the aim of tree delineation in Fateh urban forest located in Karaj. Region growing (RG) is used to separate regions and distinguish objects in an image. It starts at the initial seed points and determines whether the neighboring pixels should be added to the growing region. If the neighboring pixels are sufficiently similar to the seed pixel, they are labeled as belonging to the seed pixel. To implement the algorithm, the window size and the growing threshold were set for all resolutions. In order to obtain the most appropriate size for the search window, we examined different window sizes with a growing threshold of 0.5 for each resolution. Individual trees delineation was performed for each CHM resolution in the three different sites using "itcSegment" package of R software. Watershed segmentation algorithm is also similar to RG algorithm. The only difference is that it sets the growing seeds at the local minima. In other words, the local maxima in this algorithm change into local minima and vice versa. Inverse Watershed Segmentation (IWS) method was implemented in ArcGIS 10.3 because of its capability in delineation of distinct tree entities. In the summer of 2018, three sites with different structures including a mixed uneven-aged dense stand (site 1), a mixed uneven-aged sparse stand (site 2), and a homogeneous even-aged dense stand (site 3) were surveyed and photographed, and a 3D point cloud was extracted from the images. Then, the performance of algorithms was tested using a series of different canopy height models (CHM) with spatial resolutions of 25, 50, 75, 100, and 120 cm. To generate these models, digital surface model (DSM) was subtracted from digital terrain model (DTM). Results of individual tree delineation were validated using data collected in field observation of the aforementioned sites.
Results & Discussion
Results indicated that both RG and IWS algorithms yielded their best performance in the dense homogeneous structure. Moreover, the number of segments resulting from CHMs with low resolution was often much more than the actual number of trees. This was due to the occurrence of several peaks within an individual tree crown especially in low resolution images. With an F-score of 0.88, homogeneous even-aged dense stand (site 3) showed the highest overall accuracy in RG algorithm with a pixel size of 75 cm. Generally, results indicated that RG is an appropriate approach for individual tree delineation due to its flexibility in delineation of varying crown sizes. Furthermore, this method does not assume a circular shape for tree crowns and thus, is capable of detecting and segmenting irregular crowns. Generally, delineation of trees in urban forests using CHMs obtained from UAV-captured aerial imagery was highly accurate in homogeneous sites, while such models lacked efficiency in heterogeneous sites.

کلیدواژه‌ها [English]

  • Urban forest
  • UAV
  • Inverse watershed segmentation algorithm
  • Region-growing algorithm
  • Individual Tree Crown Delineation (ITCD)
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