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An investigation of tree extraction from UAV-based photogrammetric dense point cloud

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Abstract

Manually, tree detection with terrestrial field work is a nonprofit labor in terms of time, cost, and manpower. As a rapid alternative, terrestrial and airborne laser scanners are widely in use for data collecting. But this active remote sensing technology is expensive, especially in local small areas. At this point, unmanned aerial vehicles stand as a new opportunity for data collection platforms for tree detection in both large and local study areas. This study shows the usage of unmanned aerial vehicle as a platform to collect aerial images. The images are used to generate 3D dense point clouds. This point cloud is investigated with the random sample consensus algorithm in order to detect tree. The trees are assumed as or in a cylinder which is geometrically defined with respect to tree’s parameters such as radius. According to the results, the RANSAC algorithm is successful in the detection of trees from unclassified image-based dense point cloud. The 232 individual trees have managed to extract with a rate of 70.1% from 3 different study sites.

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Funding

This study is supported by Afyon Kocatepe University, project numbered 16.FEN.BIL.18.

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Authors and Affiliations

  1. Faculty of Engineering Geomatics Department, HRU (Harran University), Şanlıurfa, 63100, Turkey

    Nizar Polat

  2. Faculty of Engineering Geomatics Department, AKU, 03200, Afyonkarahisar, Turkey

    Murat Uysal

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  1. Nizar Polat
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  2. Murat Uysal
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Correspondence to Nizar Polat.

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Responsible Editor: Biswajeet Pradhan

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Polat, N., Uysal, M. An investigation of tree extraction from UAV-based photogrammetric dense point cloud. Arab J Geosci 13, 846 (2020). https://doi.org/10.1007/s12517-020-05769-x

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  • DOI: https://doi.org/10.1007/s12517-020-05769-x

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