Dynamic Monitoring of Forest Volumes by a Feature Extraction Method

Dynamic Monitoring of Forest Volumes by a Feature Extraction Method

Xu Jie (College of Information technology, Heilongjiang Bayi Agricultural University, Daqing, China) and Dawei Qi (College of Science, Northeast Forestry University, Harbin, China)
DOI: 10.4018/IJSSCI.2018070104
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In this article, in order to improve tree volume calculation method, a measurement method based on tree information point feature extraction is proposed, the method based on image processing and binocular vision, according to the measurement result of information point change and tree growth model, achieve through the distance change of information point to study the tree volume change. The visual measurement method is compared with the traditional method, the feasibility and accuracy of the method are proven. From the results, tree volume changes through the information point feature extraction and the traditional breast diameter measurement is very similar, the maximal percentage increase is 2.570% and 2.546%, the minimum percentage increase is 0.092% and 0.068%, which shows that volume change is consistent with the results, confirmed the tree volume change scheme of visual measurement is feasible and the result is reliable, which can reduce the impact of environmental change in the manual measurement.
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2. Theoretical Basis

2.1. The Parallel Binocular Stereo Vision Principle

Figure 1 is the imaging schematic diagram of the simple parallel binocular 3D (Zhang,2005; Zhang,2013). B stands for the baseline of the 2 cameras.

Figure 1.

Binocular 3D imaging principle


The two cameras view the same feature P(xc,yc,zc) of the space object at the same time, and respectively obtain the image coordinates, (XL,Y)and(XR,Y), from the left image and the right image. Supposing both of the two cameras’ focal distance are f,then the following formula will be gotten by the triangular geometry relationship (Xiang, Ying, Jiang,2012; Zhang,2013):


In the formula, D is the optical parallax, and IJSSCI.2018070104.m02.

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