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当前位置: 首页 > 文章 > Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm 林业研究(英文版) 2021,33 (3)
Position: Home > Articles > Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm Journal of Forestry Research 2021,33 (3)

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

作  者：
Friday Nwabueze Ogana;İlker Ercanl
单  位：
Department of Social and Environmental Forestry, Faculty of Renewable Natural Resources, University of Ibadan, IbadanOyo, Nigeria;Department of Forest Engineering, Faculty of Forestry, Çankiri Karatekin University, Çankiri, Turke
关键词：
Artificial intelligence;Height-diameter model;Mixed-effects;Nonlinear least squares;Tropical mixed forest;TREE HEIGHT;GROWTH;REGRESSION;EQUATIONS;ALLOMETRY;BIOMAS
摘  要：
Abstract Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.

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