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首页> 外文期刊>Silva Fennica >Measurement errors in the use of smartphones as low-cost forestry hypsometers
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Measurement errors in the use of smartphones as low-cost forestry hypsometers

机译:使用智能手机作为低成本林业湿度计的测量误差

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摘要

Various applications currently available for Android allow the estimation of tree heights by using the 3D accelerometer on smartphones. Some make the estimation using the image on the screen, while in others, by pointing with the edges of the terminal. The present study establishes the measurement errors obtained with HTC Desire and Samsung Galaxy Note compared to those from Blume Leiss and Vertex IV. Six series of 12 measurements each were made with each hypsometer (for heights of 6 m, 8 m, 10 m and 12 m). A Kruskall Wallis test is applied to the relative errors to determine whether there are significant differences between the devices. The results indicate that the errors of the uncalibrated smartphones significantly exceed those of traditional forestry apparatus. However, calibration is a very easy procedure that can be done by means of a linear regression line between real angles (obtained with a Digital Angle Finder or with a series of measurements taken independently of the experiment), and the angles of the accelerometer. With this adjustment, the smartphones achieve adequate quality levels although the bias was not totally eliminated. The relative errors when pointing with the edges of the terminal show no significant differences compared to Blume Leiss. Applications that use the screen image give better results (no significant differences were detected with Vertex). There is currently no application that offers calibration of the linear regression slope, which is an essential requirement for ensuring the accuracy of height measurements obtained with smartphones.
机译:当前可用于Android的各种应用程序都可以通过使用智能手机上的3D加速度计来估算树木的高度。有些使用屏幕上的图像进行估计,而另一些则通过指向终端的边缘进行估计。本研究确定了与从Blume Leiss和Vertex IV相比,使用HTC Desire和Samsung Galaxy Note获得的测量误差。每个湿度计分别进行了六个系列的12次测量(高度分别为6 m,8 m,10 m和12 m)。将Kruskall Wallis测试应用于相对误差,以确定设备之间是否存在显着差异。结果表明,未经校准的智能手机的误差大大超过了传统林业设备的误差。但是,校准是非常简单的过程,可以通过实际角度(通过数字角度查找器或独立于实验获得的一系列测量值)与加速度计角度之间的线性回归线来完成。通过这种调整,尽管并没有完全消除偏差,但智能手机仍可以达到足够的质量水平。与Blume Leiss相比,指向终端边缘时的相对误差没有显着差异。使用屏幕图像的应用程序可获得更好的结果(Vertex未检测到明显差异)。当前没有提供线性回归斜率校准的应用程序,这是确保使用智能手机获得的高度测量准确性的基本要求。

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