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Customizing unmanned aircraft systems to reduce forest inventory costs: can oblique images substantially improve the 3D reconstruction of the canopy?

机译：定制无人机系统以减少森林库存成本：可以倾斜图像大大改善树冠的三维重建？

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摘要

Photogrammetry makes it possible to estimate forest canopy surface at lower costs than light detection and ranging (LiDAR), which is considered the best data source to evaluate forest structure. Recent studies even suggest that points of forest understories can be obtained by means of unmanned aerial photogrammetry. However, little is known about how the characteristics of image sets, processing workflows, and forest openness affect understory point surveying. For forest inventories, unmanned aircraft systems (UASs) of fixed-wing type are preferred because they can survey large areas. It has been shown that the accuracy of UAS photogrammetry tends to increase by adding oblique images but acquiring them with fixed-wing UASs is challenging. To address this challenge, we proposed a multi-camera array for acquiring oblique images with fixed-wing UASs. To test our idea, we built two customized UAS and surveyed an open pine plantation and a tall deciduous forest with variable overstory density. The open plantation was selected for optimizing the measurement of reference canopy surface points using a terrestrial laser scanner. In the deciduous forest, we obtained reference understory points from a leaf-off photogrammetry survey. We confirm that including oblique images in the image set is a good practice for forestry applications. Using the UASs to test whether a multi-camera system is better than a single-camera system for acquiring nadir-oblique image sets, we conclude that the advantages are (1) more efficient acquisition of oblique images and (2) better understory modelling in open canopies. The multi-camera acquisition of oblique images increases the understory point density, making the estimation of crown cover percentage and maximum canopy height more accurate, by 33% and 50%, respectively.
机译：摄影测量使得可以在低于光检测和测距（LIDAR）的成本下估计森林冠层表面，这被认为是评估森林结构的最佳数据源。最近的研究甚至建议通过无人空中摄影测量来获得森林借鉴点。然而，关于如何如何如何影响林分调查的图像集，处理工作流和森林开放性的特点知之甚少。对于森林清单，无人驾驶飞机系统（UASS）的固定翼类型是首选，因为它们可以调查大区域。已经表明，通过添加倾斜图像而是通过固定翼uass获得倾斜图像的UAS摄影测量的准确性趋于增加。为了解决这一挑战，我们提出了一种多摄像机阵列，用于使用固定翼UASS获取倾斜图像。为了测试我们的想法，我们建立了两种定制的UA，并调查了一个开放的松树种植园和一个具有可变夸张密度的高大落叶林。选择开放的种植园用于使用地面激光扫描仪优化参考冠层表面点的测量。在落叶林中，我们获得了从叶片摄影测量调查中的参考林分观察点。我们确认包括图像集中的倾斜图像是林业应用的良好做法。使用uass来测试多摄像机系统是否优于用于获取Nadir-倾斜图像集的单相机系统，我们得出结论，优势是（1）更有效地获取倾斜图像和（2）更好的林读模型打开檐篷。倾斜图像的多摄像机采集增加了林分密度，使冠盖百分比和最大冠层高度的估计分别更准确，分别为33％和50％。

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  • 来源
    《International journal of remote sensing》 |2020年第10期|3480-3510|共31页
  • 作者

    Mauro Diaz Gaston; Mohr-Bell Diego; Garrett Martin; Munoz Lucas; Daniel Lencinas Jose;

  • 作者单位

    Consejo Nacl Invest Cient & Tecn Tecnol Caba Buenos Aires Argentina|CIEFAP Area Geomat Esquel Argentina;

    CIEFAP Area Geomat Esquel Argentina;

    MapAer Soluc Aereas Area Tecn San Carlos De Bariloche Rio Negro Argentina;

    MapAer Soluc Aereas Area Tecn San Carlos De Bariloche Rio Negro Argentina;

    Consejo Nacl Invest Cient & Tecn Tecnol Caba Buenos Aires Argentina|CIEFAP Area Geomat Esquel Argentina;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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