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Open AccessArticle

UAV-Based High-Throughput Approach for Fast Growing Cunninghamia lanceolata (Lamb.) Cultivar Screening by Machine Learning

by Xiaodan Zou 1, Anjie Liang 1, Bizhi Wu 2, Jun Su 2,3,*, Renhua Zheng 4,* and Jian Li 1,*
1
College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
3
Department of Molecular, Cell & Developmental Biology, University of California, Los Angeles, CA 90095, USA
4
Fujian Academy of Forestry, the Key Laboratory of Timber Forest Breeding and Cultivation for Mountainous Areas in Southern China, State Forestry Administration Engineering Research Center of Chinese Fir, the Key Laboratory of Forest Culture and Forest Product Processing Utilization of Fujian Province, Fuzhou 350012, China
*
Authors to whom correspondence should be addressed.
Forests 2019, 10(9), 815; https://doi.org/10.3390/f10090815
Received: 14 July 2019 / Revised: 10 September 2019 / Accepted: 17 September 2019 / Published: 19 September 2019
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
Obtaining accurate measurements of tree height and diameter at breast height (DBH) in forests to evaluate the growth rate of cultivars is still a significant challenge, even when using light detection and ranging (LiDAR) and three-dimensional (3-D) modeling. As an alternative, we provide a novel high-throughput strategy for predicting the biomass of forests in the field by vegetation indices. This study proposes an integrated pipeline methodology to measure the biomass of different tree cultivars in plantation forests with high crown density, which combines unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Using a planation of Cunninghamia lanceolate, which is commonly known as Chinese fir, in Fujian, China, images were collected while using a hyperspectral camera. Vegetation indices and modeling were processed in Python using decision trees, random forests, support vector machine, and eXtreme Gradient Boosting (XGBoost) third-party libraries. The tree height and DBH of 2880 samples were manually measured and clustered into three groups—“Fast”, “median”, and “normal” growth groups—and 19 vegetation indices from 12,000 pixels were abstracted as the input of features for the modeling. After modeling and cross-validation, the classifier that was generated by random forests had the best prediction accuracy when compared to other algorithms (75%). This framework can be applied to other tree species to make management and business decisions. View Full-Text
Keywords: Cunninghamia lanceolate; UAVs; hyperspectral camera; machine learning; random forests; XGBoost Cunninghamia lanceolate; UAVs; hyperspectral camera; machine learning; random forests; XGBoost
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Zou, X.; Liang, A.; Wu, B.; Su, J.; Zheng, R.; Li, J. UAV-Based High-Throughput Approach for Fast Growing Cunninghamia lanceolata (Lamb.) Cultivar Screening by Machine Learning. Forests 2019, 10, 815.

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