| 光谱学与光谱分析 |
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| Study on Artificial Neural Network Combined with Multispectral Remote Sensing Imagery for Forest Site Evaluation |
| GONG Yin-xi1, HE Cheng2*, YAN Fei3, FENG Zhong-ke3, CAO Meng-lei3, GAO Yuan3, MIAO Jie3, ZHAO Jin-long4 |
1. The First Institute of Photo-Grammetry and Remote Sensing, State Bureau of Surveying and Mapping, Xi’an 710054, China
2. Nanjing Forest Police College, Nanjing 210046, China
3. Institute of GIS, RS＆GPS, Beijing Forestry University, Beijing 100083, China
4. The Research Center for Forest Ecology, Beijing Forestry University, Beijing 100083, China |
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Abstract Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.
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Received: 2013-02-26
Accepted: 2013-05-21
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Corresponding Authors:
HE Cheng
E-mail: Hecheng@126.com
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[1] ZHANG Wan-ru, SHENG Wei-tong, JIANG You-xu, et al(张万儒，盛炜彤，蒋有绪，等). Forest Research(林业科学研究), 1992, 5(3): 251.
[2] Skovsgaard J P, Vanclay J K. Forestry, 2008, 81(1): 13.
[3] ZHANG Xiao-li, YOU Xian-xiang(张晓丽，游先祥). Journal of Remote Sensing(遥感学报), 1998, 2(4): 292.
[4] GUO Yan-rong, WU Bao-guo, LIU Yang, et al(郭艳荣，吴保国，刘 洋，等). World Forestry Research(世界林业研究), 2012, 25(5): 47.
[5] Louwa J H. Scholes M. Forest Ecology and Management, 2002, 171(1-2): 153.
[6] Carmean W H. Sife index curves for Northern Hardwoods in Northern Wisconsin and Upper Michigan, Northern Central Forest Experimental Station, Forest Service, U.S. Dept. of Agriculture, 1978.
[7] Curt T, Bouchaud M, Agrech G. Forest Ecology and Management, 2001, 149(1): 61.
[8] Swenson J J, Waring R H, Fan W, et al. Candian Journal of Forest Research, 2005, 35(7): 1697.
[9] HUANG Jia-rong, MA Tian-xiao, WANG Yan-mei, et al(黄家荣，马天晓，王艳梅，等). Journal of Mountain Agriculture and Biology(山地农业生物学报), 2006, 25(6): 479.
[10] Waring R H, Milner K S, Jolly W M, et al. Forest Ecology and Management, 2006, 228(1-3): 285.
[11] Mutlu A C, Boyaci I H, Genis H E, et al. European Food Research and Technology, 2012, 233(2): 267.
[12] LUAN Zhao-ping(栾兆平). Shandong Forestry Science and Technology(山东林业科技), 2011, 4: 11.
[13] LI De-ren (李德仁). Editorial Board of Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2003, 28(2): 127.
[14 ] Swenson J J, Waring R H, Fan Weihong, et al. Canadian Journal of Forest Research, 2005, 35(7): 1697.
[15] MA Ming-dong, JIANG Hong, LIU Shi-rong, et al(马明东，江 洪，刘世荣，等). Acta Ecologic Sinica(生态学报), 2006, 26(9): 2810.
[16] HE Zhong-hua, CHEN Xiao-xiang, LIANG Hong, et al(贺中华，陈晓翔，梁 虹，等). Territory & Natural Resources Study(国土与自然资源研究), 2012, 4: 48.
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