基于BP神经网络的广东省针阔混交异龄林立地质量评价
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摘要
【目的】针阔混交异龄林的地位指数计算一直是立地质量评价中的难点,国内外对针阔混交异龄林的地位指数模型的研究较少,为建立更精确的针阔混交异龄林地位指数模型,把神经网络模型引入针阔混交异龄林的立地质量评价。【方法】以广东省针阔混交异龄林为研究对象,建立基于神经网络方法的林分优势高模型以及针阔混交林地位指数模型,除年龄因子外,加入了海拔、坡度、坡向、坡位、土壤厚度、腐殖层厚度等立地因子,另外考虑针叶树种与阔叶树种的断面积比重对针阔混交异龄林样地地位指数的影响,并建立针阔混交异龄林的地位指数的计算模型。【结果】针阔混交林的地位指数的最大值为21.4 m,最小值为6.1 m,平均值为13.7 m,中位数为13.6 m,标准差为3.2 m,地位指数的最大值与最小值的差值为15.3 m。【结论】从结果中可以反映出,广东省地貌复杂且破碎,山多平地少,立地状况差异较大;另外由于广东省林地树种及树种比例具有复杂多样性的特征,导致基准年龄的差异较大。故地位指数的变异较大。本研究在计算针阔混交林的地位指数时,加入了海拔、坡度、坡向、坡位、土壤厚度、腐殖层厚度等立地因子,提高了针阔混交林地位指数的预估精度。研究结果为针阔混交异龄林地位指数的计算提供了精度更高的方法。
[Objective] The calculation to the site index of uneven-aged coniferous and broadleaved mixed stands has always been a difficult point in the evaluation of site quality. At home and abroad, there are few studies on the site index model of uneven-aged coniferous and broadleaved mixed stands. In order to establish a more accurate site index of coniferous and broadleaved mixed stands, the model introduces the neural network model into the site quality evaluation of uneven-aged coniferous and broadleaved mixed stands. [Method] In this study, uneven-aged coniferous and broad-leaved mixed stands in Guangdong province were used as study object, the stand dominant tree height model and site index model to unevenaged coniferous and broadleaved mixed forests were estabished based on neural network. Besides the age factor, altitude, slope, slope direction, slope position, soil thickness and humus layer thickness were added.The influence of the basal area ratio of coniferous and broad-leaved species on the site index were also considered, then the site index model of coniferous and broadleaved mixed stands was estabished.[Result] The results showed that the maximum value of the site index to the uneven-aged coniferous and broadleaved mixed stands was 21.4 m, the minimum value was 6.1 m, the average value was 13.7 m, the median was 13.6 m, and the standard deviation was 3.2 m. The difference between the maximum and minimum values of the site index was 15.3 m. [Conclusion] From the results, it can be reflected that the landforms in Guangdong Province are complex and fragmented, and there are more mountains and less flat land, and the site conditions are different one another. In addition, due to the complexity and diversity of tree species and tree species ratio in Guangdong Province, then it lead to quite different in the reference age.Therefore, the site index is not the same one another. In the computation of the site index of coniferous and broadleaved mixed stands, the site factors such as altitude, slope, slope aspect, slope position, soil thickness and humus layer thickness were added to improve the prediction accuracy of the site index of coniferous and broadleaved mixed stands. The results provide a more accurate method for the calculation of the site index of uneven-aged coniferous and broadleaved mixed stands.
[Objective] The calculation to the site index of uneven-aged coniferous and broadleaved mixed stands has always been a difficult point in the evaluation of site quality. At home and abroad, there are few studies on the site index model of uneven-aged coniferous and broadleaved mixed stands. In order to establish a more accurate site index of coniferous and broadleaved mixed stands, the model introduces the neural network model into the site quality evaluation of uneven-aged coniferous and broadleaved mixed stands. [Method] In this study, uneven-aged coniferous and broad-leaved mixed stands in Guangdong province were used as study object, the stand dominant tree height model and site index model to unevenaged coniferous and broadleaved mixed forests were estabished based on neural network. Besides the age factor, altitude, slope, slope direction, slope position, soil thickness and humus layer thickness were added.The influence of the basal area ratio of coniferous and broad-leaved species on the site index were also considered, then the site index model of coniferous and broadleaved mixed stands was estabished.[Result] The results showed that the maximum value of the site index to the uneven-aged coniferous and broadleaved mixed stands was 21.4 m, the minimum value was 6.1 m, the average value was 13.7 m, the median was 13.6 m, and the standard deviation was 3.2 m. The difference between the maximum and minimum values of the site index was 15.3 m. [Conclusion] From the results, it can be reflected that the landforms in Guangdong Province are complex and fragmented, and there are more mountains and less flat land, and the site conditions are different one another. In addition, due to the complexity and diversity of tree species and tree species ratio in Guangdong Province, then it lead to quite different in the reference age.Therefore, the site index is not the same one another. In the computation of the site index of coniferous and broadleaved mixed stands, the site factors such as altitude, slope, slope aspect, slope position, soil thickness and humus layer thickness were added to improve the prediction accuracy of the site index of coniferous and broadleaved mixed stands. The results provide a more accurate method for the calculation of the site index of uneven-aged coniferous and broadleaved mixed stands.
引文
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[14]Nukala B T,Shibuya N,Rodriguez A,et al.An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network(ANN)and support vector machine(SVM)algorithms[J].Open Journal of Applied Biosensor,2015,3(4):29-39.
[15]陈永富,杨彦臣,张怀清,等.海南岛热带天然山地雨林立地质量评价研究[J].林业科学研究,2000,13(2):134-140.Chen Y F,Yang Y C,Zhang H Q,et al.A study on site quality evaluation of natural tropical mountainous rain forest in Hainan Island[J].Forest Research,2000,13(2):134-140.
[16]朱光玉,康立.森林立地生产力评价指标与方法[J].西北林学院学报,2016,31(6):275-281.Zhu G Y,Kang L.A review of forest site productivity evaluation indicators and methods[J].Journal of Northwest Forestry University,2016,31(6):275-281.
[17]翁国庆,白卫国,王维芳,等.地位指数表编制技术规程[S].北京:中国标准出版社,2015.Weng G Q,Bai W G,Wang W F,et al.Technical regulations for drafting of site index table[S].Beijing:Standards Press of China,2015.
[18]李际平,姚东和.BP模型在单木树高与胸径生长模拟中的应用[J].中南林学院学报,1996,16(3):34-36.Li J P,Yao D H.Application of BP neural network model to the simulation of breast-height diameter and tree-height growth[J].Journal of Central South Forestry University,1996,16(3):34-36.
[19]徐罗,亢新刚,郭韦韦,等.天然云冷杉针阔混交林立地质量评价[J].北京林业大学学报,2016,38(5):11-22.Xu L,Kang X G,Guo W W,et al.Site quality evaluation of natural spruce-fir and broadleaf mixed stands[J].Journal of Beijing Forestry University,2016,38(5):11-22.
[20]郭如意,韦新良,刘姗姗.天目山区针阔混交林立地质量评价研究[J].西北林学院学报,2016,31(4):233-240.Guo R Y,Wei X L,Liu S S.Site quality evaluation of coniferous and broad-leaved mixed forest in Tianmu Mountain[J].Journal of Northwest Forestry University,2016,31(4):233-240.
[21]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 X G,Zhou J C,Huang B H,et al.Study of oak growth dynamic simulation system based on artificial neural network[J].Journal of Henan Agricultural University,2015,49(2):190-194.
[22]阙泽胜.广东省三维地形场景仿真研究[J].测绘地理信息,2016,41(3):100-102.Que Z S.Study on 3D terrain scene simulation in Guangdong Province[J].Journal of Geomatic,2016,41(3):100-102.
[2]孟宪宇.测树学[M].第3版.北京:中国林业出版社,2006.Meng X Y.Forest measurements[M].Third edition.Beijing:China Forestry Publishing House,2006.
[3]张超,彭道黎,黄国胜,等.基于森林清查数据的三峡库区林地立地质量评价[J].东北林业大学学报,2015,43(11):56-61.Zhang C,Peng D L,Huang G S,et al.Site quality evaluation in Three Gorges Reservoir region based on forest inventory data[J].Journal of Northeast Forestry University,2015,43(11):56-61.
[4]黄国胜,马炜,王雪军,等.基于一类清查数据的福建省立地质量评价技术[J].北京林业大学学报,2014,36(3):1-8.Huang G S,Ma W,Wang X J,et al.Forestland site quality evaluation of Fujian Province based on continuous forest inventory data[J].Journal of Beijing Forestry University,2014,36(3):1-8.
[5]李际平,姚东和.BP模型在单木树高与胸径生长模拟中的应用[J].中南林业科技大学学报,1996,16(3):34-36.Li J P,Yao D H.Application of BP neural network model to the simulation of breast-height diameter and tree-height growth[J].Journal of Central South Forestry University,1996,16(3):34-36.
[6]马天晓,赵晓峰,黄家荣,等.基于人工神经网络的树高曲线模型研究[J].河南林业科技,2006,26(1):4-5.Ma T X,Zhao X F,Huang J R,et al.Research on the heightdiameter model of tree with artificial neural network[J].Journal of Henan Forestry Science and Technology,2006,26(1):4-5.
[7]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.Xu Z Y.Height-diameter model for Pinus massoniana based on BP neural network[J].Forest Inventory and Planning,2015,40(2):6-8.
[8]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,2014,42(7):154-156.Dong Y F,Sun Y J,Wang Y F,et al.Generalized height-diameter model for Chinese fir based on BP neural network[J].Journal of Northeast Forestry University,2014,42(7):154-156.
[9]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 J B,Lei X D,Li Y T,et al.Prediction mean height for Larix olgensis plantation based on BP neural network[J].Journal of Nanjing Forestry University(Natural Sciences Edition),2018,42(2):147-154.
[10]张德丰.MATLAB神经网络应用设计[M].北京:机械工业出版社,2009.Zhang D F.MATLAB neural network application design[M].Beijing:China Machine Press,2009.
[11]Sun J,Mao H,Liu J,et al.The research of paddy rice moisture lossless detection based on L-M BP neural network[J].Computer&Computing Technologies in Agriculture,2008,120(1):83-93.
[12]Selvamuthu D,Kumar V,Mishra A.Indian stock market prediction using artificial neural networks ontick data[J].Financial Innovation,2019,5(1):1-12.
[13]郭阳明,冉从宝,姬昕禹,等.基于组合优化BP神经网络的模拟电路故障诊断[J].西北工业大学学报,2013,31(1):44-48.Guo Y M,Ran C B,Ji X Y,et al.Fault diagnosis in analog circuits based on combined-optimization BP neural networks[J].Journal of Northwestern Polytechnical University,2013,31(1):44-48.
[14]Nukala B T,Shibuya N,Rodriguez A,et al.An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network(ANN)and support vector machine(SVM)algorithms[J].Open Journal of Applied Biosensor,2015,3(4):29-39.
[15]陈永富,杨彦臣,张怀清,等.海南岛热带天然山地雨林立地质量评价研究[J].林业科学研究,2000,13(2):134-140.Chen Y F,Yang Y C,Zhang H Q,et al.A study on site quality evaluation of natural tropical mountainous rain forest in Hainan Island[J].Forest Research,2000,13(2):134-140.
[16]朱光玉,康立.森林立地生产力评价指标与方法[J].西北林学院学报,2016,31(6):275-281.Zhu G Y,Kang L.A review of forest site productivity evaluation indicators and methods[J].Journal of Northwest Forestry University,2016,31(6):275-281.
[17]翁国庆,白卫国,王维芳,等.地位指数表编制技术规程[S].北京:中国标准出版社,2015.Weng G Q,Bai W G,Wang W F,et al.Technical regulations for drafting of site index table[S].Beijing:Standards Press of China,2015.
[18]李际平,姚东和.BP模型在单木树高与胸径生长模拟中的应用[J].中南林学院学报,1996,16(3):34-36.Li J P,Yao D H.Application of BP neural network model to the simulation of breast-height diameter and tree-height growth[J].Journal of Central South Forestry University,1996,16(3):34-36.
[19]徐罗,亢新刚,郭韦韦,等.天然云冷杉针阔混交林立地质量评价[J].北京林业大学学报,2016,38(5):11-22.Xu L,Kang X G,Guo W W,et al.Site quality evaluation of natural spruce-fir and broadleaf mixed stands[J].Journal of Beijing Forestry University,2016,38(5):11-22.
[20]郭如意,韦新良,刘姗姗.天目山区针阔混交林立地质量评价研究[J].西北林学院学报,2016,31(4):233-240.Guo R Y,Wei X L,Liu S S.Site quality evaluation of coniferous and broad-leaved mixed forest in Tianmu Mountain[J].Journal of Northwest Forestry University,2016,31(4):233-240.
[21]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 X G,Zhou J C,Huang B H,et al.Study of oak growth dynamic simulation system based on artificial neural network[J].Journal of Henan Agricultural University,2015,49(2):190-194.
[22]阙泽胜.广东省三维地形场景仿真研究[J].测绘地理信息,2016,41(3):100-102.Que Z S.Study on 3D terrain scene simulation in Guangdong Province[J].Journal of Geomatic,2016,41(3):100-102.
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