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Article

Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3
School of Nuclear Resource Engineering, University of South China, Hengyang 421001, China
*
Author to whom correspondence should be addressed.
Forests 2019, 10(9), 818; https://doi.org/10.3390/f10090818
Received: 9 August 2019 / Revised: 9 September 2019 / Accepted: 16 September 2019 / Published: 19 September 2019
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future. View Full-Text
Keywords: deep learning; convolutional neural network; tree species classification; random forest; OHS-1 hyperspectral image deep learning; convolutional neural network; tree species classification; random forest; OHS-1 hyperspectral image
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MDPI and ACS Style

Xi, Y.; Ren, C.; Wang, Z.; Wei, S.; Bai, J.; Zhang, B.; Xiang, H.; Chen, L. Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China. Forests 2019, 10, 818. https://doi.org/10.3390/f10090818

AMA Style

Xi Y, Ren C, Wang Z, Wei S, Bai J, Zhang B, Xiang H, Chen L. Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China. Forests. 2019; 10(9):818. https://doi.org/10.3390/f10090818

Chicago/Turabian Style

Xi, Yanbiao, Chunying Ren, Zongming Wang, Shiqing Wei, Jialing Bai, Bai Zhang, Hengxing Xiang, and Lin Chen. 2019. "Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China" Forests 10, no. 9: 818. https://doi.org/10.3390/f10090818

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