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A Levenberg–Marquardt Backpropagation Neural Network for Predicting Forest Growing Stock Based on the Least-Squares Equation Fitting Parameters

by Ruyi Zhou 1,2, Dasheng Wu 1,2,*, Luming Fang 1,2, Aijun Xu 1,2 and Xiongwei Lou 1,2
1
Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research of Zhejiang Province, Zhejiang A & F University, Lin’an 311300, Zhejiang, China
2
School of Information Engineering, Zhejiang A & F University, Lin’an 311300, Zhejiang, China
*
Author to whom correspondence should be addressed.
Forests 2018, 9(12), 757; https://doi.org/10.3390/f9120757
Received: 26 October 2018 / Revised: 1 December 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
(This article belongs to the Special Issue Defining, Quantifying, Observing and Modeling Forest Canopy Traits)
Traditional field surveys are expensive, time-consuming, laborious, and difficult to perform, especially in mountainous and dense forests, which imposes a burden on forest management personnel and researchers. This study focuses on predicting forest growing stock, one of the most significant parameters of a forest resource assessment. First, three schemes were designed—Scheme 1, based on the study samples with mixed tree species; Scheme 2, based on the study samples divided into dominant tree species groups; and Scheme 3, based on the study samples divided by dominant tree species groups—the evaluation factors are fitted by least-squares equations, and the non-significant fitted-factors are removed. Second, an overall evaluation indicator system with 17 factors was established. Third, remote sensing images of Landsat Thematic Mapper, digital elevation model, and the inventory for forest management planning and design were integrated in the same database. Lastly, a backpropagation neural network based on the Levenberg–Marquardt algorithm was used to predict the forest growing stock. The results showed that the group estimation precision exceeded 90%, which is the highest standard of total sampling precision of inventory for forest management planning and design in China. The prediction results for distinguishing dominant tree species were better than for mixed dominant tree species. The results also showed that the performance metrics for prediction could be improved by least-squares equation fitting and significance filtering of the evaluation factors. View Full-Text
Keywords: Levenberg–Marquardt backpropagation (LM–BP); forest growing stock; predicting; least-squares equation Levenberg–Marquardt backpropagation (LM–BP); forest growing stock; predicting; least-squares equation
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Zhou, R.; Wu, D.; Fang, L.; Xu, A.; Lou, X. A Levenberg–Marquardt Backpropagation Neural Network for Predicting Forest Growing Stock Based on the Least-Squares Equation Fitting Parameters. Forests 2018, 9, 757.

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