Neural Network Based Landscape Pattern Simulation in ChangBai Mountain, Northeast China

2009 ◽  
pp. 911-920
Author(s):  
Mingchang Wang ◽  
Shengbo Chen ◽  
Lixin Xing ◽  
Chunyan Yang ◽  
Zijun Wang
Keyword(s):  
Neural Network ◽  
Landscape Pattern ◽  
Northeast China ◽  
Changbai Mountain ◽  
Pattern Simulation

scholarly journals Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 134-143 ◽  
Author(s):  
Tuan Nguyen Thanh ◽  
Tai Dinh Tien ◽  
Hai Long Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Northeast China ◽  
Tree Height ◽  
Pinus Koraiensis ◽  
Accurate Estimation ◽  
Korean Pine ◽  
Ann Models ◽  
Artificial Neural ◽  
Forest Farm

Korean pine (Pinus koraiensis Sieb. et Zucc.) is one of the highly commercial woody species in Northeast China. In this study, six nonlinear equations and artificial neural network (ANN) models were employed to model and validate height-diameter (H-DBH) relationship in three different stand densities of one Korean pine plantation. Data were collected in 12 plots in a 43-year-old even-aged stand of P. koraiensis in Mengjiagang Forest Farm, China. The data were randomly split into two datasets for model development (9 plots) and for model validation (3 plots). All candidate models showed a good perfomance in explaining H-DBH relationship with error estimation of tree height ranging from 0.61 to 1.52 m. Especially, ANN models could reduce the root mean square error (RMSE) by the highest 40%, compared with Power function for the density level of 600 trees. In general, our results showed that ANN models were superior to other six nonlinear models. The H-DBH relationship appeared to differ between stand density levels, thus it is necessary to establish H-DBH models for specific stand densities to provide more accurate estimation of tree height.

scholarly journals Nitrogen addition impacts on the emissions of greenhouse gases depending on the forest type: a case study in Changbai Mountain, Northeast China

2016 ◽  
Vol 17 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Zhijie Chen ◽  
Heikki Setälä ◽  
Shicong Geng ◽  
Shijie Han ◽  
Shuqi Wang ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Northeast China ◽  
Forest Type ◽  
Type A ◽  
Nitrogen Addition ◽  
Changbai Mountain

Radial growth ofPinus koraiensisand carbon sequastration potential of the old growth forest in Changbai Mountain, Northeast China

2016 ◽  
Vol 36 (9) ◽  
Author(s):  
于健 YU Jian ◽  
罗春旺 LUO Chunwang ◽  
徐倩倩 XU Qianqian ◽  
孟盛旺 MENG Shengwang ◽  
李俊清 LI Junqing ◽  
...  
Keyword(s):  
Northeast China ◽  
Radial Growth ◽  
Changbai Mountain ◽  
Old Growth ◽  
Old Growth Forest

Evaluation of a sustainable forest utilization program for broadleaved Korean pine mixed forests in the Changbai Mountain region of Northeast China

2015 ◽  
Vol 35 (1) ◽  
Author(s):  
吴志军 WU Zhijun ◽  
苏东凯 SU Dongkai ◽  
牛丽君 NIU Lijun ◽  
于大炮 YU Dapao ◽  
周旺明 ZHOU Wangming ◽  
...  
Keyword(s):  
Northeast China ◽  
Changbai Mountain ◽  
Mountain Region ◽  
Korean Pine ◽  
Mixed Forests ◽  
Forest Utilization
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