基于深度神经网络的杉木树高-胸径模型研建
收稿日期: 2023-12-26
修回日期: 2024-02-02
网络出版日期: 2024-03-22
基金资助
贵州省科技计划项目“杉木高世代多性状新品种选育研究”(黔科合支撑合[2018]2301号)
Height-diameter Model of Cunninghamia lanceolata Based on Deep Neural Network
Received date: 2023-12-26
Revised date: 2024-02-02
Online published: 2024-03-22
利用深度神经网络(DNN)模型建立杉木的树高-胸径模型,寻求一种更加高效的杉木树高预测方法。以贵州省清镇市国有林场49块样地中杉木的胸径、树高数据为研究对象,分成不同比例的训练集和测试集,训练集占比分别为20%,30%,40%,50%,60%,70%,80%;对应的测试集占比分别为80%,70%,60%,50%,40%,30%,20%。利用DNN构建树高-胸径模型,并将其与11个传统基础模型进行比较,通过R2、RMSE和MAE对比选出预测效果最好的模型,并根据最优模型添加林木胸径与优势木平均胸径比(DDH),以提高模型的预测精度。利用DNN模型建立的树高-胸径模型在训练集占比为20%的情况下,加入DDH因子后其预测精度R2达到0.89。利用DNN构建杉木树高-胸径模型对杉木树高进行预测,在使用较小数据量的前提下加入DDH因子能够提高对杉木树高的预测效果。
关键词： 杉木; 深度神经网络; 林木胸径与优势木平均胸径比; 树高-胸径模型
王贵林 , 谭伟 , 陈波涛 . 基于深度神经网络的杉木树高-胸径模型研建[J]. 林草资源研究, 2024 , 0(1) : 82 -87 . DOI: 10.13466/j.cnki.lczyyj.2024.01.011
By using the deep Neural Network (DNN) model to establish the tree height-diameter model of Cunninghamia lanceolata, we are seeking a more efficient method for predicting the tree height of Cunninghamia lanceolata.The diameter at breast height and tree height data of Cunninghamia lanceolata in 49 plots of state-owned forest farm in Qingzhen City,Guizhou Province were studied,and divided into different proportions of training set and test set data.This consists of training set data(20%,30%,40%,50%,60%,70%,80%,respectively)and test set data(80%,70%,60%,50%,40%,30%,20%,respectively).DNN was used to build a tree height-diameter model,and the model was compared with 11 traditional basic models.Select the model with the best predictive performance by comparing the results of R2,RMSE and MAE.Adding the ratio of diameter at breast height to average diameter at breast height(DDH)of dominant trees based on the optimal model to improve the prediction accuracy.When the training set proportion of the DNN model is 20%,the prediction accuracy of the tree height-diameter model can reach more than 0.89 after adding DDH factor.DNN was used to build a height-diameter model to predict the height of Cunninghamia lanceolata,and adding DDH factor could improve the prediction accuracy of Cunninghamia lanceolata height with a smaller dataset.
Key words： Cunninghamia lanceolata; DNN; DDH; height-diameter model
| [1] | 郭卫红, 郑庆荣, 胡砚秋, 等. 山西五台山主要针叶树种树高-胸径曲线模型研究[J]. 湖南林业科技, 2022, 49(6):72-77. |
| [2] | 王景弟, 杨蕊, 田育新. 枫香和杉木树高-胸径模型的拟合与评价[J]. 湖南林业科技, 2021, 48(4):64-67. |
| [3] | 刘金义. 辽东山区人工红松枝下高模型的研建[J]. 林业科技通讯, 2022(8):95-98. |
| [4] | 李晓晶. 杂种落叶松树高-胸径模型的研究[J]. 林业科技情报, 2020, 52(4):28-30. |
| [5] | 郭嘉, 孙帅超, 田相林, 等. 引入优势木树高建立的秦岭林区松栎林树高-胸径模型[J]. 东北林业大学学报, 2019, 47(11):66-72. |
| [6] | Cheng Deng, Zhang Yiyi, Lu Jiangyan, et al. Thinning effects on the tree height-diameter allometry of Masson Pine(Pinus massoniana Lamb.)[J]. Multidisciplinary Digital Publishing Institute, 2019, 10(12):1129. |
| [7] | Mensah S, Pienaar O L, Kunneke A. Height-Diameter allometry in South Africa's indigenous high forests:Assessing generic models performance and function forms[J]. For.Ecol.Manag. 2018, 410:1-11. |
| [8] | Khamyong N, Wangpakapattanawong P, Chairuangsri S, et al. Tree species composition and height-diameter allometry of three forest types in Northern Thailand[J]. CMU J.Nat. Sci.2018, 17(4):289-306. |
| [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. |
| [10] | 卯光宪, 谭伟, 柴宗政, 等. 基于BP神经网络的马尾松人工林胸径-树高模型预测[J]. 浙江农林大学学报, 2020, 37(4):752-760. |
| [11] | Chen Xinxin, Jiang Kang, Zhu Yunshi, et al. Individual tree crown segmentation directly from UAV-Borne LiDAR data using the point net of deep learning[J]. Forests 2021, 12(2):131. |
| [12] | Ercanli I. Innovative DNN artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height[J]. Forest Ecosystems, 2020, 7(1):7-12. |
| [13] | Maler S, Miglietta F, Gobakken T, et al. Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques[J]. iForest-Biogeosciences and Forestry, 2019(3). |
| [14] | Loubota P G J, Bocko Y E, Mavoungou A Y, et al. Height-diameter allometry in African monodominant forest close to mixed forest[J]. Journal of Tropical Ecology, 2021(2):37. |
| [15] | 梁瑞婷, 孙玉军, 李芸. 深度学习和传统方法模拟杉木树高-胸径模型比较[J]. 林业科学研究, 2021, 34(6):65-72. |
| [16] | Dantas D, Calegario N, Fausto W A J, et al. Multilevel nonlinear mixed-effects model and machine learning for predicting the volume of eucalyptus spp.trees[J]. Cerne, 2020, 26(1),48-57. |
| [17] | Aguilar F J, Nemmaoui A, Aguilar M A, et al. Building tree allometry relationships based on TLS point clouds and machine learning regression[J]. Applied Sciences, 2021, 11(21):10139. |
| [18] | Casas G G, Gonzales D G E, Villanueva J R B, et al. Configuration of the deep neural network hyperparameters for the hypsometric modeling of the Guazuma crinita Mart in the Peruvian Amazon[J]. Forests 2022, 13(5):697. |
| [19] | Wang Jiamin, Chen Xinxin, Cao Lin, et al. Individual rubber tree segmentation based on ground-based LiDAR data and faster R-CNN of deep learning[J]. Forests, 2019, 10(9):793. |
| [20] | 贺梦莹. 长白落叶松-水曲柳混交林冠幅与冠长预测模型的研究[D]. 哈尔滨: 东北林业大学, 2020. |
| [21] | 曹晓梅, 苗铮, 郝元朔, 等. 基于树种分类的帽儿山阔叶混交林树高-胸径模型[J/OL]. 应用生态学报:1-17(2024-01-12)[2024-02-02].http://doi.org/10.13287/j.1001-9332.202402.016. |
| [22] | Ercanli I. Artificial intelligence with DNN algorithms to model relationships between total tree height and diameter at breast height[J]. Forest Systems, 2020, 29(2):103. |
| [23] | Corte A, Souza D V, Rex F E, et al. Forest inventory with high-density UAV-Lidar:Machine learning approaches for predicting individual tree attributes[J]. Computers and Electronics in Agriculture, 2020, 179:105815. |
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