Identifying non-thrive trees and predicting wood density from resistograph using temporal convolution network
Identifying non-thrive trees and predicting wood density from resistograph using temporal convolution network
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DOI：
10.1080/21580103.2022.2115561
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发表时间：
2022-09
影响因子：
1.9
通讯作者：
Rapeepan Kantavichai;E. Turnblom
中科院分区：
文献类型：
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作者：
Rapeepan Kantavichai;E. Turnblom
摘要 深度学习方法已应用于林业研究，包括树木分类和库存预测。在这项研究中，我们提出了一种深度学习方法（时间卷积网络）在径向电阻图序列上的应用，以识别不生长的树木并预测木材密度。对美国俄勒冈州马里恩县 41 年树龄花旗松林中 274 棵树的南向和西向进行的无损阻力钻孔测量被用作输入序列。非繁荣树木是根据它们自建立以来社会地位的变化来定义的。木材密度是通过 X 射线密度测定法从增量蛀虫获得的芯中得出的。数据被分割以进行交叉验证。使用训练和验证数据集对最佳模型进行微调，然后使用测试数据集运行以获得模型评估指标。结果证实，时间卷积网络在电阻记录仪剖面上的应用能够以接收算子特征曲线下面积表示的概率等于 0.823 来识别非生长树。与传统的线性（RMSE = 20.15）和非线性（RMSE = 20.33）回归方法相比，用于木材密度预测的时间卷积网络在准确性（RMSE = 18.22）方面略有提高。我们认为，使用机器学习算法可以成为分析非破坏性设备的顺序数据的一种有前途的方法。
Abstract Deep learning approaches have been adopted in Forestry research including tree classification and inventory prediction. In this study, we proposed an application of a deep learning approach, Temporal Convolution Network, on sequences of radial resistograph profiles to identify non-thrive trees and to predict wood density. Non-destructive resistance drilling measurements on South and West orientations of 274 trees in a 41-year-old Douglas-fir stand in Marion County, Oregon, USA were used as input series. Non-thrive trees were defined based on their changes in social status since establishment. Wood density was derived by X-ray densitometry from cores obtained by increment borers. Data was split for cross validation. Optimal models were fine-tuned with training and validation datasets, then run with test datasets for model evaluation metrics. Results confirmed that the application of the Temporal Convolution Network on resistograph profiles enables non-thrive tree identification with the probability, represented by the area under the Receiver Operator Characteristic curve, equal to 0.823. Temporal Convolution Network for wood density prediction showed a slight improvement in accuracy (RMSE = 18.22) compared to the traditional linear (RMSE = 20.15) and non-linear (RMSE = 20.33) regression methods. We suggest that the use of machine learning algorithms can be a promising methodology for the analysis of sequential data from non-destructive devices.