Practicality and Robustness of Tree Species Identification Using UAV RGB Image and Deep Learning in Temperate Forest in Japan
Practicality and Robustness of Tree Species Identification Using UAV RGB Image and Deep Learning in Temperate Forest in Japan
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DOI：
10.3390/rs14071710
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
M. Onishi;Shuntaro Watanabe;T. Nakashima;T. Ise
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文献类型：
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作者：
M. Onishi;Shuntaro Watanabe;T. Nakashima;T. Ise
森林管理长期以来一直希望从空气中识别树种。最近，无人机RGB图像与深度学习的结合在有限条件下的树木识别中表现出了高性能。在本研究中，我们评估了使用无人机和深度学习的树木识别系统的实用性和鲁棒性。我们从日本温带森林的三个地点采样了训练和测试数据。目标树种涵盖 56 种，包括死树和间隙。当我们评估从相同时间和相同树冠获得的数据集作为训练数据集的模型性能时，对于从相同时间但不同树冠获得的数据集的性能，Kappa 得分分别为 0.97 和 0.72。当我们评估训练数据集中不同时间和地点获得的数据集（与实际数据集相同的条件）时，Kappa 分数下降至 0.47。尽管针叶树和林分代表性树种在识别方面表现出一定的稳定表现，但在以下树种之间发生了一些错误分类：（1）属于系统发育相近物种的树种，（2）叶形相似的树种，（3）喜欢相同环境的树种。此外，诸如针叶树和阔叶树或常绿树和落叶树之类的树类型并不总是保证属于该树类型的不同树之间的共同特征。我们的研究结果促进了使用无人机 RGB 图像和深度学习的识别系统的实用化。
Identifying tree species from the air has long been desired for forest management. Recently, combination of UAV RGB image and deep learning has shown high performance for tree identification in limited conditions. In this study, we evaluated the practicality and robustness of the tree identification system using UAVs and deep learning. We sampled training and test data from three sites in temperate forests in Japan. The objective tree species ranged across 56 species, including dead trees and gaps. When we evaluated the model performance on the dataset obtained from the same time and same tree crowns as the training dataset, it yielded a Kappa score of 0.97, and 0.72, respectively, for the performance on the dataset obtained from the same time but with different tree crowns. When we evaluated the dataset obtained from different times and sites from the training dataset, which is the same condition as the practical one, the Kappa scores decreased to 0.47. Though coniferous trees and representative species of stands showed a certain stable performance regarding identification, some misclassifications occurred between: (1) trees that belong to phylogenetically close species, (2) tree species with similar leaf shapes, and (3) tree species that prefer the same environment. Furthermore, tree types such as coniferous and broadleaved or evergreen and deciduous do not always guarantee common features between the different trees belonging to the tree type. Our findings promote the practicalization of identification systems using UAV RGB images and deep learning.