自组织特征映射网络在芦芽山自然保护区青杄林分类和排序中的应用

doi:10.11707/j.1001-7488.20140501

Abstract

Abstract:

The self-organizing feature map (SOM) was applied to the classification and ordination of Picea wilsonii forests in the Luya Mountain Nature Reserve. It was shown that 60 quadrats were classified into 8 groups, representing 8 types of plant communities. These communities had obvious differences in species composition and structure. Each type had its boundary and distributional range on the ordination map. The SOM clustering showed significant ecological meanings and revealed the relationships among communities. Additionally, by using a visualizing method of environmental factor gradient on topology map, SOM indicated that elevation, slope position and slope were the main environmental factors affecting the distribution of P. wilsonii forests. The SOM ordination clearly showed ecological gradients and revealed the relationships among communities and environmental factors. This study suggests that SOM is a very effective quantitative technique in plant ecology, and it is applicable to describe community characteristics and to analyze relationship between communities and environmental variables.

Key words: Luya Mountain Nature Reserve, Picea wilsonii forest, self-organizing feature map, classification

CLC Number:

S759.92

Li Linfeng, Zhang Jintun, Zhou Lan, Shao Dan. Application of Self-Organizing Feature Map to Classification and Ordination of Picea wilsonii Forest in Luya Mountain Nature Reserve[J]. Scientia Silvae Sinicae, 2014, 50(5): 1-7.

References

程占红,牛莉芹,张峰. 2012. 旅游开发影响下景观特征变化研究——以芦芽山自然保护区为例. 林业经济,(8): 121-125.
(Cheng Z H, Niu L Q, Zhang F. 2012. Change of landscapes characteristics with tourism development——a case study of Luya Mountain nature reserve. Problems of Forestry Economics, (8): 121-125. [in Chinese] )
韩利群. 2007. 人工神经网络理论、设计及应用. 北京: 化学工业出版社.
李宝堂. 2010. 芦芽山自然保护区生物多样性特点及保护对策. 山西林业科技,39 (3): 49-52.
李素清,张金屯,上官铁梁. 2005. 芦芽山亚高山草甸的数量分类与排序研究. 西北植物学报,25 (10):2062-2067.
(Li S Q, Zhang J T, Shangguan T L. 2005. Quantitative classification and ordination of the subalpine meadows in Luyashan Mountain. Acta Botanica Boreali-occidentalia Sinica, 25 (10):2062-2067. [in Chinese] )
苏日古嘎,张金屯,田世广,等. 2010. 自组织特征映射网络在北京松山自然保护区山地草甸数量分析中的应用. 植物生态学报,34 (7):811-818.
(Suri G, Zhang J T, Tian S G, et al. 2010. Application of self-organizing map to quantitative analysis of mountain meadow in the Songshan Nature Reserve of Beijing,China. Chinese Journal of Plant Ecology, 34 (7): 811-818. [in Chinese] )
武秀娟,常建国,于吉祥,等. 2010. 芦芽山阴坡华北落叶松-云杉天然次生林群落特征的海拔梯度格局. 东北林业大学学报,38 (11): 10-14.
(Wu X J, Chang J G, Yu J X, et al. 2010. Spatial distribution of Larix principis-rupprechtii-Picea spp. secondary forests along elevation gradients on north facing slope of Luya Mountain, Shanxi. Journal Of Northeast Forestry University, 38 (11): 10-14. [in Chinese] )
张金屯. 2004. 数量生态学. 北京: 科学出版社.
Céréghino R,Giraudel J L,Compin A. 2001. Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps. Ecological Modelling, 146 (1):167-180.
Chon T S,Park Y S,Moon K H,et al. 1996. Patternizing communities by using an artificial neural network. Ecological Modelling, 90 (2): 69-78.
Guisan A,Zimmermann N E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135 (2): 147-186.
Kohonen T. 2001. Self-organizing maps. 3^rd ed. Berlin: Springer.
Lee J,Kwak I S,Lee E,et al. 2007. Classification of breeding bird communities along an urbanization gradient using an unsupervised artificial neural network. Ecological Modelling, 203 (1): 62-71.
Lek-Ang S,Park Y S,Ait-Mouloud S,et al. 2007. Collembolan communities in a peat bog versus surrounding forest analyzed by using self-organizing map. Ecological Modelling, 203 (1): 9-17.
Mi X C,Ma K P,Zou Y B. 2005. Artificial neural network and its application in agricultural and ecological research. Acta Phytoecologica Sinica, 29 (5):863-870.
Obach M,Wagner R,Werner H,et al. 2001. Modelling population dynamics of aquatic insects with artificial neural networks. Ecological Modelling, 146 (1): 207-217.
Park Y S,Kwak I S,Chon T S,et al. 2001. Implementation of artificial neural networks in patterning and prediction of exergy in response to temporal dynamics of benthic macroinvertebrate communities in streams. Ecological Modelling, 146 (1): 143-157.
Park Y S,Céréghino R,Compin A,et al. 2003. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling, 160 (3): 265-280.
Park Y S,Tison J,Lek S,et al. 2006. Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France. Ecological Informatics, 1 (3): 247-257.
Schultz A,Wieland R,Lutze G. 2000. Neurall networks in agroecological modeling-stylish application or helpful tool. Computers and Electronics in Agriculture, 29 (2):73-97.
Song M Y,Hwang H J,Kwak I S,et al. 2007. Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation. Ecological Modelling, 203 (1): 18-25.
Terbraak C J,Fand L P. 2002. Canoco reference manual and user's guide to Canoco for Windows : software for canonical community ordination ( Version 4.5 ). Wageningen: Centre for Biometry Wageningen and Microcomputer Power.
Wu P Q,Huang M S. 2005. The application of SOM on the classification of Fujian cities' functions. Economic Geography, 25 (1): 68-70.
Yuan Z R. 2000. The artificial neural network and its application. Beijing: Tsinghua University Press.
Zhang L X,Zhang F, Shangguan T L. 2001. Quantity analysis of plant communities on Mountain Luya, Shanxi. Chinese Bulletin of Botany, 18 (2):231-239;176-176.
Zhang J T,Dong Y R,Xi Y X. 2008a. A comparison of SOFM ordination with DCA and PCA in gradient analysis of plant communities in the midst of Taihang Mountains, China. Ecological Informatics, 3 (6): 367-374.
Zhang J T,Meng D P,Xi Y X. 2008b. Ordination of self-organizing feature map neural network and its application to the study of plant communities. Journal of Beijing Forestry University, 30 (1):1-5.
Zhang J T,Zhang F. 2007. Diversity and composition of plant functional groups in mountain forests of the Lishan Nature Reserve, North China. Botanical Studies, 48 (3): 339-348.

[1]	Gao Wenwen, Zeng Yuan, Liu Yu, Yi Haiyan, Wu Bingfang, Ju Hongbo. Forest Dynamic Monitoring by Remote Sensing from 2000 to 2015 in the Water Source Area of the South-to-North Water Diversion Project [J]. Scientia Silvae Sinicae, 2019, 55(4): 97-107.
[2]	Zhou Wei, Wang Wenjie, He Xingyuan, Zhang Bo, Xiao Lu, Wang Qiong, Lü Hailiang, Wei Chenhui. Soil Fertility and Spatial Variability of Urban Green Land in Harbin [J]. Scientia Silvae Sinicae, 2018, 54(9): 9-17.
[3]	Zhang Ying, Zhang Xiaoli, Li Hongzhi, Li Liangcai. A Comparison of Landcover Classification Based on the Improved Transformed Divergence Analysis [J]. Scientia Silvae Sinicae, 2018, 54(8): 88-98.
[4]	Li Xiaohong, Chen Erxue, Li Zengyuan, Li Shiming. Object Based Land Cover Classification Method Integrating Multi-Source Remote Sensing Data [J]. Scientia Silvae Sinicae, 2018, 54(2): 68-80.
[5]	Liu Chuanze, Wang Xiao, Chen Longxian, Guo Hui, Luo Rui, Zhou Yucheng. Surface Defect Recognition of Fibreboard Based on Random Forest [J]. Scientia Silvae Sinicae, 2018, 54(11): 121-126.
[6]	Qiu Shuai, Shen Baichun, Li Tingting, Guo Juan, Wang Ji, Sun Lina, Chen Xuping, Hu Shaoqing. A Method of Osmanthus fragrans Cultivars Identification Based on Random Forest Algorithm and SRAP Molecular Markers [J]. Scientia Silvae Sinicae, 2018, 54(1): 32-45.
[7]	Zhao Han, Lei Yuancai, Fu Liyong. Biomass and Uncertainty Estimates of Pinus massoniana Forest for Different Site Classes in Jiangxi Province [J]. Scientia Silvae Sinicae, 2017, 53(8): 81-93.
[8]	Li Mingze, Kang Xiangrui, Fan Wenyi. Burned Area Extraction in Huzhong Forests Based on Remote Sensing and the Spatial Analysis of the Burned Severity [J]. Scientia Silvae Sinicae, 2017, 53(3): 163-174.
[9]	Tian Jing, Xing Yanqiu, Yao Songtao, Zeng Xujing, Jiao Yitao. Comparison of Landsat-TM Image Forest Type Classification Based on Cellular Automata and BP Neural Network Algorithm [J]. Scientia Silvae Sinicae, 2017, 53(2): 26-34.
[10]	Mao Xuegang, Hou Jiyu, Fan Wenyi. Object-Based Automatic Recognition for Forest Gaps Using Aerial Image and LiDAR Data [J]. Scientia Silvae Sinicae, 2017, 53(11): 94-103.
[11]	Luo Han, Zhao Tingning, Xie Yongsheng. Site Classification of Highway Slopes in the East of North China [J]. Scientia Silvae Sinicae, 2017, 53(1): 108-118.
[12]	Du Jian, Liang Kunnan, Zhou Zaizhi, Huang Guihua, Li Bijun, Ma Huaming. Site Classification and Evaluation of Teak Plantation in Xishuangbanna, Yunnan Province, China [J]. Scientia Silvae Sinicae, 2016, 52(9): 1-10.
[13]	Ren Chong, Ju Hongbo, Zhang Huaiqing, Huang Jianwen, Zheng Yingxuan. Multi-Source Data for Forest Land Type Precise Classification [J]. Scientia Silvae Sinicae, 2016, 52(6): 54-65.
[14]	Chen Yongping, Guo Wenjing, Wang Zheng. An Improved Algorithm of Veneer Knot Image Recognition Based on Mathematical Morphology [J]. Scientia Silvae Sinicae, 2015, 51(9): 90-95.
[15]	Le Tongchao, Zhang Huiru, Tan Fanglin. Application of Automatic Classification Method to Measuring Leaf Parameters of Mangrove [J]. Scientia Silvae Sinicae, 2014, 50(5): 34-40.

Application of Self-Organizing Feature Map to Classification and Ordination of Picea wilsonii Forest in Luya Mountain Nature Reserve

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments