基于BP神经网络的马尾松人工林胸径-树高模型预测

卯光宪; 谭伟; 柴宗政; 赵杨; 杨深钧; 卯光宪; 谭伟; 柴宗政; 赵杨; 杨深钧

doi:10.11833/j.issn.2095-0756.20190486

[1]

MEHTÄTALO L, de MIGUEL S, GREGOIRE T G. Modeling height-diameter curves for prediction [J]. Can J For Res, 2015, 45(7): 826 − 837.

[2]

李春明, 李利学. 基于非线性混合模型的栓皮栎树高与胸径关系研究[J]. 北京林业大学学报, 2009, 31(4): 7 − 12.

LI Chunming, LI Lixue. Height-diameter relationship for Quercus variabilis Blume plantations based on nonlinear mixed model [J]. J Beijing For Univ, 2009, 31(4): 7 − 12.

[3]

张连金, 孙长忠, 辛学兵, 等. 北京九龙山不同林分树高与胸径相关生长关系分析[J]. 中南林业科技大学学报, 2014, 34(12): 66 − 70.

ZHANG Lianjin, SUN Changzhong, XIN Xuebing, et al. Allometric relationship between height and diameter at breast height of different stand in Beijing Jiulong Mountain [J]. J Cent South Univ For Technol, 2014, 34(12): 66 − 70.

[4]

CHAI Zongzheng, TAN Wei, LI Yuanyuan, et al. Generalized nonlinear height-diameter models for a Cryptomeria fortunei plantation in the Pingba region of Guizhou Province, China [J]. Web Ecol, 2018, 18(1): 29 − 35.

[5]

童洁, 石玉立. 加格达奇3种森林类型树高-胸径的曲线拟合[J]. 东北林业大学学报, 2017, 45(2): 6 − 11, 21.

DONG Jie, SHI Yuli. Tree height-diameter model in Jiagedaqi [J]. J Northeast For Univ, 2017, 45(2): 6 − 11, 21.

[6]

王冬至, 张冬燕, 张志东, 等. 基于非线性混合模型的针阔混交林树高与胸径关系[J]. 林业科学, 2016, 52(1): 30 − 36.

WANG Dongzhi, ZHANG Dongyan, ZHANG Zhidong, et al. Height-diameter relationship for conifer mixed forest based on nonlinear mixed-effects model [J]. Sci Silv Sin, 2016, 52(1): 30 − 36.

[7]

卢军, 张会儒, 雷相东, 等. 长白山云冷杉针阔混交林幼树树高-胸径模型[J]. 北京林业大学学报, 2015, 37(11): 10 − 25.

LU Jun, ZHANG Huiru, LEI Xiangdong, et al. Height-diameter models for saplings in a spruce-fir mixed forest in Changbai Mountains [J]. J Beijing For Univ, 2015, 37(11): 10 − 25.

[8]

刘鑫, 王海燕, 雷相东, 等. 基于BP神经网络的天然云冷杉针阔混交林标准树高-胸径模型[J]. 林业科学研究, 2017, 30(3): 368 − 375.

LIU Xin, WANG Haiyan, LEI Xiangdong, et al. Generalized height-diameter model for natural mixed spruce-fir coniferous and broadleaf forests based on BP neural network [J]. For Res, 2017, 30(3): 368 − 375.

[9]

董云飞, 孙玉军, 王轶夫, 等. 基于BP神经网络的杉木标准树高曲线[J]. 东北林业大学学报, 2014, 42(7): 154 − 156, 165.

DONG Yunfei, SUN Yujun, WANG Yifu, et al. Generalized height-diameter model for Chinese fir based on BP neural network [J]. J Northeast For Univ, 2014, 42(7): 154 − 156, 165.

[10]

沈剑波, 雷相东, 李玉堂, 等. 基于BP神经网络的长白落叶松人工林林分平均高预测[J]. 南京林业大学学报(自然科学版), 2018, 42(2): 147 − 154.

SHEN Jianbo, LEI Xiangdong, LI Yutang, et al. Prediction mean height for Larix olgensis plantation based on Bayesian-regularization BP neural network [J]. J Nanjing For Univ Nat Sci Ed, 2018, 42(2): 147 − 154.

[11]

徐步强, 张秋良, 弥宏卓, 等. 基于BP神经网络的油松人工林生长模型[J]. 东北林业大学学报, 2011, 39(12): 33 − 35.

XU Buqiang, ZHANG Qinliang, MI Hongzhuo, et al. Growth model of Pinus tabulaeformis plantation based on BP neural network [J]. J Northeast For Univ, 2011, 39(12): 33 − 35.

[12]

金星姬, 贾炜玮, 李凤日. 基于BP人工神经网络的兴安落叶松天然林全林分生长模型的研究[J]. 植物研究, 2008, 28(3): 370 − 374, 384.

JIN Xingji, JIA Weiwei, LI Fengri. Whole stand growth model for natural Dahurian larch forests based on BP ANN [J]. Bull Bot Res, 2008, 28(3): 370 − 374, 384.

[13]

刘建忠, 余娜. 贵州省马尾松林地质量空间评价与低效林防控措施研究[J]. 湖北农业科学, 2016, 55(20): 5202 − 5206.

LIU Jianzhong, YU Na. Quality spatial evaluation of Pinus massoniana and control measures of low-efficiency forest in Guizhou Province [J]. Hubei Agric Sci, 2016, 55(20): 5202 − 5206.

[14]

何婧, 韦小丽, 徐海. 贵州省马尾松林分树种多样性分析[J]. 西部林业科学, 2016, 45(3): 26 − 29, 37.

HE Jing, WEI Xiaoli, XU Hai. Tree species diversity of Pinus massoniana forest in Guizhou Province [J]. J West China For Sci, 2016, 45(3): 26 − 29, 37.

[15]

王科, 谭伟, 戚玉娇. 近自然经营间伐对黔中马尾松天然次生纯林生长的初期效应[J]. 浙江农林大学学报, 2019, 36(5): 886 − 893.

WANG Ke, TAN Wei, QI Yujiao. Initial effects of close-to-nature thinning on a natural secondary Pinus massoniana pure forest in central Guizhou [J]. J Zhejiang A&F Univ, 2019, 36(5): 886 − 893.

[16]

浦瑞良, 宫鹏, YANG R. 应用神经网络和多元回归技术预测森林产量[J]. 应用生态学报, 1999, 10(2): 129 − 134.

PU Ruiliang, GONG Peng, YANG R. Forest yield prediction with an artificial neural network and multiple regression [J]. Chin J Appl Ecol, 1999, 10(2): 129 − 134.

[17]

PESCHEL W. Mathematical methods for growth studies of trees and forest stands and the results of their application [J]. Tharandter Forstl Jahrb, 1938, 89: 169 − 247.

[18]

CURTIS R O. Height-diameter and height-diameter age equations for second-growth Douglas fir [J]. For Sci, 1967, 13(4): 365 − 375.

[19]

PEARL R, REED L J. On the rate of growth of the population of the United States since 1790 and its mathematical representation [J]. Proc Nati Acad Sci, 1920, 6(6): 275 − 288.

[20]

WEIBULL W. A statistical distribution function of wide applicability [J]. J Appl Mech, 1951, 18(3): 293 − 297.

[21]

GOMPERZ B. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies [J]. Phil Trans Roy Soc London, 1832, 115: 513 − 585.

[22]

LUNDQVIST B. On the height growth in cultivated stands of pine and spruce in Northern Sweden [J]. Medd Fran Statens Skogsforskningsinst, 1957, 47(2): 1 − 64.

[23]

张立明. 人工神经网络的模型及其应用[M]. 上海: 复旦大学出版社, 1993: 43 − 46.

[24]

罗恒春, 张超, 魏安超, 等. 云南松林分平均胸径生长模型及模型参数环境解释[J]. 浙江农林大学学报, 2018, 35(6): 1079 − 1087.

LUO Hengchun, ZHANG Chao, WEI Anchao, et al. Average DBH growth model of a stand with environmental parameters for Pinus yunnanensis in central Yunnan, China [J]. J Zhejiang A&F Univ, 2018, 35(6): 1079 − 1087.

[25]

HUANG S S, TITUS S J, WIENS D P. Comparison of nonlinear height-diameter functions for major Alberta tree species [J]. Can J For Res, 1992, 22(9): 1297 − 1304.

[26]

GUPTA A, SRIVASTAVA R, JABEEN N, et al. Height estimation model for Eucalyptus tereticornis grown under semiarid conditions of India [J]. Indian J Agrofor, 2008, 10(2): 34 − 39.

[27]

KALBI S, FALLAH A, BETTINGER P, et al. Mixed-effects modeling for tree height prediction models of Oriental beech in the Hyrcanian forests [J]. J For Res, 2018, 29(5): 1195 − 1204.

[28]

SHARMA R P, VACEK Z, VACEK S, et al. Modelling individual tree height-diameter relationships for multi-layered and multi-species forestsin central Europe [J]. Trees, 2019, 33(1): 103 − 119.

[29]

黄旭光, 周俊朝, 黄柏华, 等. 基于人工神经网络对栎树天然林地位指数模拟系统的研究[J]. 河南农业大学学报, 2015, 49(2): 190 − 194.

HUANG Xuguang, ZHOU Junchao, HUANG Bohua, et al. Study of oak growth dynamic simulation system based on artificial neural network [J]. J Henan Agric Univ, 2015, 49(2): 190 − 194.

[30]

徐志扬. 基于BP神经网络的马尾松树高曲线模型[J]. 林业调查规划, 2015, 40(2): 6 − 8, 73.

XU Zhiyang. Height-diameter model for Pinus massoniana based on BP neural network [J]. For Inventory Plann, 2015, 40(2): 6 − 8, 73.

[31]

杜志, 甘世书. 基于BP神经网络的杉木和马尾松树高曲线模型研究[J]. 中南林业调查规划, 2017, 36(4): 36 − 39.

DU Zhi, GAN Shishu. Height-diameter models for Cunninghamia lanceolata and Pinus massoniana based on BP neural network [J]. Cent South For Inventory Plann, 2017, 36(4): 36 − 39.

[32]

王轶夫, 孙玉军, 郭孝玉. 基于BP神经网络的马尾松立木生物量模型研究[J]. 北京林业大学学报, 2013, 35(2): 17 − 21.

WANG Yifu, SUN Yujun, GUO Xiaoyu. Single-tree biomass modeling of Pinus massoniana based on BP neural network [J]. J Beijing For Univ, 2013, 35(2): 17 − 21.

[33]

AHMADI K, ALAVI S J, TABARI M, et al. Non-linear height-diameter models for oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forests, Iran [J]. Biotechnol Agron Soc Environ, 2013, 17(3): 431 − 40.

[34]

苏崇宇, 汪毓铎. 基于改进的自适应遗传算法优化BP神经网络[J]. 工业控制计算机, 2019, 32(1): 67 − 69.

SU Chongyu, WANG Yuduo. BP neural network optimized by improved adaptive genetic algorithm computer engineering and applications [J]. Ind Control Comput, 2019, 32(1): 67 − 69.

[35]

李松, 刘力军, 翟曼. 改进粒子群算法优化BP神经网络的短时交通流预测[J]. 系统工程理论与实践, 2012, 32(9): 2045 − 2049.

LI Song, LIU Lijun, ZHAI Man. Prediction for short-term traffic flow based on modified PSO optimized BP neural net work [J]. Syst Eng-Theory Pract, 2012, 32(9): 2045 − 2049.