Stand biomass model of <i>Larix olgensis</i> plantations based on multi-layer perceptron networks

Xu Qigang; Lei Xiangdong; Guo Hong; Li Haikui; Li Yutang

doi:10.13332/j.1000-1522.20190035

Journal of Beijing Forestry University > 2019 > 41(5): 97-107. > DOI: 10.13332/j.1000-1522.20190035

Xu Qigang, Lei Xiangdong, Guo Hong, Li Haikui, Li Yutang. Stand biomass model of Larix olgensis plantations based on multi-layer perceptron networks[J]. Journal of Beijing Forestry University, 2019, 41(5): 97-107. DOI: 10.13332/j.1000-1522.20190035

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Stand biomass model of Larix olgensis plantations based on multi-layer perceptron networks

Xu Qigang^1,2,,
Lei Xiangdong^1,2, ,,
Guo Hong^1,2,
Li Haikui^1,2,
Li Yutang³

1.
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2.
Key Labotary of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Beijing 100091, China
3.
Jilin Forestry Inventory and Planning Institute, Changchun 130022, Jilin, China

More Information

Received Date: January 14, 2019
Revised Date: March 12, 2019
Available Online: April 29, 2019
Published Date: April 30, 2019

Graphical Abstract

Abstract

Abstract

ObjectiveNeural network model can avoid the collinearity and heteroscedasticity of variables in modeling forest stand biomass. This paper aims to apply multi-layer perceptron networks to forest biomass model to provide methods for the calculation and prediction of forest biomass and carbon stocks at forest management unit and regional levels.
MethodBased on 917 observations from the sample plots of larch plantations from national forest inventory in Jilin Province of northeastern China, the aboveground and total biomass models by log-transformed linear regression, and multi-layer perceptron networks with and without site factors were established. AIC, adjusted R², RMSE, RMSE_rand MAE were used to evaluate the models.
ResultThe model with the highest prediction accuracy was the neural network one with the input unit quadratic mean diameters (D), stand mean height (H), stand density index (S), altitude (HB), slope (PD), slope aspect (PX), slope position (PW), two hidden layers and hidden unit number of 40−20. Compared with traditional log-transformed linear regression model, the adjusted R² of the aboveground and total biomass models was increased from 0.902 1 to 0.914 1, and from 0.897 9 to 0.908 9, RMSE_r was decreased from 6.330 5% to 5.992 2%, and from 6.490 1% to 6.153 6%, respectively. The neural network model with site factors had slightly higher estimation accuracy than that without site factors. The adjusted R² of the aboveground and total biomass models was increased by 0.88% and 0.99%, and RMSE_r decreased by 5.33% and 5.46%, respectively.
ConclusionBiomass model based on multi-layer perceptron networks had similar performance in terms of model accuracy, but it could avoid treating the traditional assumptions such as the collinearity and heteroscedasticity of variables, and had the ability to calculate aboveground and total biomass models at one time.
- Larix olgensis,
- stand biomass,
- log-transformed linear regression model,
- multi-layer perceptron networks

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References (22)

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Stand biomass model of Larix olgensis plantations based on multi-layer perceptron networks