基于无人机图像分析的树木胸径预测

Prediction of trees diameter at breast height based on unmanned aerial vehicle image analysis

树木胸径是林木资产评估中的重要参数,该文利用图像分析技术预测树木胸径可为资产评估提供参考。以银杏和法国梧桐为试验树种,通过拟合无人机正射图像中的单株树木树冠面积与胸径的关系预测树木胸径值。首先利用二型模糊聚类方法对无人机采集的纯林样地正射图像中的单株树冠进行分割,获取树冠像素面积,然后利用地面参照物计算出树冠的实际面积,并与测量的胸径值进行拟合,得出树冠面积与胸径的函数关系,林区中其他树木胸径值可基于该函数关系和其树冠面积计算得出。试验结果显示无人机正射图像中的银杏及法桐树冠面积与胸径均呈对数关系,且该文计算所得的银杏1.2 m处的胸径与实际胸径之间的平均误差约为0.31 cm,法桐1 m处的胸径与实际胸径之间的平均误差为0.27 cm,均在行业允许的1 cm误差范围内,该文提出的基于无人机正射图像分析技术预测树木胸径较为准确,可为中小尺度林地资产评估提供参考。

The diameter of tree at breast height （DTBH） is an important parameter in the evaluation of forestry assets. The traditional method of obtaining the DTBH requires the field survey by the forest workers. To explore the feasibility of the image analysis technique in the evaluation of forestry assets, this article proposes a new approach to predict the DTBH based on the remote sensing imaging technology of the unmanned aerial vehicle （UAV）. Tree species used in our experiments include Ginkgo biloba L. and Platanus orientalis Linn. which are from Tancheng District, Linyi City, Shandong Province, China. In the past few years, many studies have shown a high correlation between the DTBH and the crown diameter of tree. This paper explores the correlation between the tree crown region in the image and estimated DTBH. First, the tree crowns in the image are segmented using a Type-2 fuzzy c-means algorithm. Then, the actual area of tree crown （AATC） is calculated from the segmented image. Finally, the correlation between the AATC and measured DTBH can be established. The detail steps of this method are described as follows： 1） The individual tree crown is segmented from the ortho images taken by the UAV using a Type-2 fuzzy c-means algorithm in order to obtain the pixel area （pixel number） of tree crown （PATC） in the image. 2） A white flag with the size of 2.88 m ＆#215; 1.92 m or 30 cm ＆#215; 40 cm is used in our study. The ratio of the pixel area in ortho image to the actual area of the flag is calculated. Based on this ratio and the segmented PATC, the AATC is uniform for different flight height. 3） Multiple trees are chosen as training samples, and their measured DTBH is used to derive the correlation function between the AATC and DTBH. 4） The above correlation function is used to calculate the DTBH of the other trees for the validation, and the estimated DTBH and actually measured DTBH are compared to obtain the DTBH error. All of them are less than 1 cm which is the forestry standard. In this study, we model a small area of the forest. The reason to choose a small area is that a huge forest with different landscapes or tree ages will have an impact on deriving a precise correlation function. Hence, a small forest area of 20 m ＆#215; 20 m is selected as a sample site to establish the correlation function between the tree crown area and the measured DTBH by using multiple tree samples. The DTBH in the other areas of the forest with the same tree species can then be predicted according to this correlation function. Experimental results demonstrate that the proposed method is feasible and effective. Based on our preliminary experiments, the average error between the actually measured and calculated DTBH of Ginkgo biloba L. with 1.2 m height is 0.31 cm, and 0.27 cm for Platanus orientalis Linn. with 1 m height. Both errors are less than 1 cm which is acceptable by the forestry standard. This automatic measurement method has the advantages of simplicity, high efficiency, and low cost. The proposed method can be generalized to other species in the forestry. At the same time, this new method provides an intelligent exploration method and informative way for the forest asset evaluation.