Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

SHI Yong-jun; XU Xiao-jun; DU Hua-qiang; ZHOU Guo-mo; JIN Wei; ZHOU Yu-feng

Volume 25 Issue 4

Aug. 2008

Turn off MathJax

Article Contents

Article Navigation > Journal of Zhejiang A&F University > 2008 > 25(4): 417-421

SHI Yong-jun, XU Xiao-jun, DU Hua-qiang, ZHOU Guo-mo, JIN Wei, ZHOU Yu-feng. Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network[J]. Journal of Zhejiang A&F University, 2008, 25(4): 417-421.

Citation:

SHI Yong-jun, XU Xiao-jun, DU Hua-qiang, ZHOU Guo-mo, JIN Wei, ZHOU Yu-feng. Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network[J]. Journal of Zhejiang A&F University, 2008, 25(4): 417-421.

Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

1.
School of Environmental Sciences and Technology，Zhejiang Forestry College，Lin’an 311300，Zhejiang，China

More Information

Corresponding author: ZHOU Guo-mo
Publish Date: 2008-08-20

Abstract

To estimate the carbon content of bamboo forest based on remote sensing，highly accurate data acquisition is necessary to reduce estimation errors. In this study，enhanced thematic mapper plus（ETM+） remote sensing data was used to extract bamboo forest data using a back propagation（BP） neural network. Matlab program language（Version 7.1） was used to compile the classification algorithm with algorithms of three training functions being compared；namely，Traingda-gradient descent backpropagation with adaptive learning rate backpropagation；Trainlm-levenberg-marquardt backpropagation；and Traingdm-gradient descent with momentum backpropagation. Results showed that for bamboo forest the BP neural network had a high classification accuracy with a producer accuracy of 84.0% and a user accuracy of 98.7%. Meanwhile，of the three different training functions Traingda had the highest classification accuracy，whereas Trainlm had the shortest training time. ［Ch，3 fig. 3 tab. 17 ref.］
- forest management,
- back propagation（BP） neural network,
- bamboo forest,
- classification,
- remote sensing,
- enhanced thematic mapper plus （ETM+）

Relative Article

[1]	LUAN Jingran, FENG Guohong, ZHU Yujie. Visible/near infrared spectrum wood identification based on SPA-GA-BP neural network . Journal of Zhejiang A&F University, 2022, 39(3): 671-678. doi: 10.11833/j.issn.2095-0756.20210377
[2]	ZHANG Yu, CHEN Cunyou, HU Xijun. Evaluation of forest ecological function based on projection pursuit classification . Journal of Zhejiang A&F University, 2020, 37(2): 243-250. doi: 10.11833/j.issn.2095-0756.2020.02.007
[3]	WU Lizhou, WANG Xiaohui, WANG Zhihui, FANG Xin, ZHU Tingyu, DING Lixia. Crops identification based on hyperspectral data and random forest method . Journal of Zhejiang A&F University, 2020, 37(1): 136-142. doi: 10.11833/j.issn.2095-0756.2020.01.018
[4]	MAO Guangxian, TAN Wei, CHAI Zongzheng, ZHAO Yang, YANG Shenjun. Diameter-height model for Pinus massoniana plantations based on BP neural network . Journal of Zhejiang A&F University, 2020, 37(4): 752-760. doi: 10.11833/j.issn.2095-0756.20190486
[5]	TONG Liang, LI Pingheng, ZHOU Guomo, ZHOU Yufeng, LI Chong. A review of research about rhizome-root system in bamboo forest . Journal of Zhejiang A&F University, 2019, 36(1): 183-192. doi: 10.11833/j.issn.2095-0756.2019.01.023
[6]	WANG Pengcheng, YANG Guodong, ZHANG Xiaochen, ZHONG Yuqian, ZHAI Feifei, ZHANG Guibao, QIU Jing, CHEN Lin, WANG Xianrong, YI Xiangui. Classification and ordination of a Magnolia zenii community based on a self-organizing feature map . Journal of Zhejiang A&F University, 2019, 36(2): 255-263. doi: 10.11833/j.issn.2095-0756.2019.02.006
[7]	ZHAO Jiujia, ZHANG Xiaoli. Interannual variation of forest cover and albedo in Northern China . Journal of Zhejiang A&F University, 2015, 32(5): 683-690. doi: 10.11833/j.issn.2095-0756.2015.05.005
[8]	YE Lisha, CHEN Shuanglin, GUO Ziwu. Research on nitrogen circulation and management of bamboo: a review . Journal of Zhejiang A&F University, 2015, 32(4): 635-642. doi: 10.11833/j.issn.2095-0756.2015.04.021
[9]	YAO Zhi, ZHANG Xiaoli. Based on WebGIS platform developing remote sensing information systems of forest canopy density inversion . Journal of Zhejiang A&F University, 2015, 32(3): 392-398. doi: 10.11833/j.issn.2095-0756.2015.03.009
[10]	ZHANG Qianqian, CHEN Jian, JIANG Hong, TANG Minzhong. Classification of Hyperion hyperspectral imagery data using texture . Journal of Zhejiang A&F University, 2013, 30(6): 880-886. doi: 10.11833/j.issn.2095-0756.2013.06.012
[11]	WANG Ni, PENG Shi-kui, LI Ming-shi. High-resolution remote sensing of textural images for tree species classification . Journal of Zhejiang A&F University, 2012, 29(2): 210-217. doi: 10.11833/j.issn.2095-0756.2012.02.010
[12]	LIU Wei-yi, FAN Shao-hui, SU Wen-hui, LIU Guang-lu, YU Lin. Research on hydrological effect in different interfaces under bamboo forests . Journal of Zhejiang A&F University, 2011, 28(3): 486-493. doi: 10.11833/j.issn.2095-0756.2011.03.022
[13]	CUI Rui-rui, DU Hua-qiang, ZHOU Guo-mo, XU Xiao-jun, DONG De-jin, Lü Yu-long. Remote sensing-based dynamic monitoring of moso bamboo forest and its carbon stock change in Anji County . Journal of Zhejiang A&F University, 2011, 28(3): 422-431. doi: 10.11833/j.issn.2095-0756.2011.03.012
[14]	GUO Zheng, JIANG Hong, CHEN Jian, CHENG Miao-miao, JIANG Zi-shan, YU Shu-quan, LI Tu-sheng. Forest landscape analysis with remote sensing data for Yuhang，Zhejiang Province . Journal of Zhejiang A&F University, 2010, 27(1): 36-43. doi: 10.11833/j.issn.2095-0756.2010.01.006
[15]	BAI Jiang-li, PENG Dao-li, YANG Fu-ning. Information classification and code of forest resources . Journal of Zhejiang A&F University, 2007, 24(3): 326-330.
[16]	GE Jing-ru, QIN An-chen, ZHAO Xiong-wei, SUN Qiu-hua, LI Ming, ZHANG Rui, JIA Zhe. Overlay classification and its application in the remote sensing interpretation of land-use types in Taihangshan area, Hebei . Journal of Zhejiang A&F University, 2007, 24(6): 681-685.
[17]	LOU Chong, ZHU Guo-min. Ecological regionalization for bamboo in Zhejiang Province . Journal of Zhejiang A&F University, 2007, 24(6): 741-746.
[18]	DING Li-xia, WANG Zu-liang, ZHOU Guo-mo, DU Qing-zhou. Monitoring Phyllostachys pubescens stands expansion in National Nature Reserve of Mount Tianmu by remote sensing . Journal of Zhejiang A&F University, 2006, 23(3): 297-300.
[19]	Zhou Guomo, Wu Yanxiong, Chen Meilan, Zhang Yunong. Warning affair classification of regional forest resources. . Journal of Zhejiang A&F University, 1999, 16(1): 20-23.
[20]	Lou Luhuan, Wang Caixia, Wang hui. Study on the Classif ication of Fruit Forms. . Journal of Zhejiang A&F University, 1998, 15(1): 85-90.

References
Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Get Citation

PDF

XML

Article views(5318) PDF downloads(243) Cited by()

Proportional views

Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

1. School of Environmental Sciences and Technology，Zhejiang Forestry College，Lin’an 311300，Zhejiang，China

Corresponding author: ZHOU Guo-mo

Publish Date: 2008-08-20

Key words:

Abstract: To estimate the carbon content of bamboo forest based on remote sensing，highly accurate data acquisition is necessary to reduce estimation errors. In this study，enhanced thematic mapper plus（ETM+） remote sensing data was used to extract bamboo forest data using a back propagation（BP） neural network. Matlab program language（Version 7.1） was used to compile the classification algorithm with algorithms of three training functions being compared；namely，Traingda-gradient descent backpropagation with adaptive learning rate backpropagation；Trainlm-levenberg-marquardt backpropagation；and Traingdm-gradient descent with momentum backpropagation. Results showed that for bamboo forest the BP neural network had a high classification accuracy with a producer accuracy of 84.0% and a user accuracy of 98.7%. Meanwhile，of the three different training functions Traingda had the highest classification accuracy，whereas Trainlm had the shortest training time. ［Ch，3 fig. 3 tab. 17 ref.］

Remote sensing image based bamboo forest monitoring with a back propagation（BP） neural network

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Related

Proportional views