人工神经网络与相容性生物量模型预测单木地上生物量的比较

doi:10.13287/j.1001-9332.202201.001

应用生态学报 ›› 2022, Vol. 33 ›› Issue (1): 9-16.doi: 10.13287/j.1001-9332.202201.001

人工神经网络与相容性生物量模型预测单木地上生物量的比较

梁瑞婷, 王轶夫^*, 邱思玉, 孙玉军, 谢运鸿

北京林业大学森林资源和环境管理国家林业和草原局重点开放性实验室, 北京 100083

收稿日期:2021-03-08 接受日期:2021-06-23 出版日期:2022-01-15 发布日期:2022-07-15
通讯作者: * E-mail: wyfbing@163.com
作者简介:梁瑞婷, 女, 1996年生, 硕士研究生。主要从事森林资源监测与模型研究。E-mail: 15600990723@163.com
基金资助:
国家自然科学基金项目(31870620)

Comparison of artificial neural network with compatible biomass model for predicting aboveground biomass of individual tree

LIANG Rui-ting, WANG Yi-fu^*, QIU Si-yu, SUN Yu-jun, XIE Yun-hong

State Forestry & Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China

Received:2021-03-08 Accepted:2021-06-23 Online:2022-01-15 Published:2022-07-15

摘要/Abstract

摘要： 森林生物量是林业生产经营和森林资源监测的重要指标,为探索高效低偏的单木生物量估测方法,引入人工神经网络。本研究采用黑龙江省东折棱河林场的101株长白落叶松地上生物量数据,基于不同变量(胸径、树高、冠幅)组合建立了4个聚合模型体系(AMS),采用加权回归消除模型的异方差。然后,基于最优的变量组合建立人工神经网络(ANN)生物量模型,并采用留一交叉验证法对模型进行检验,比较两种生物量估测方法的精度。结果表明: 仅基于胸径一个变量的生物量模型已经能较准确地估测生物量,引入树高和冠幅因子能进一步提高模型精度,最优模型体系为AMS4。通过两种方法建立的生物量模型都能较准确地进行估测,各组分生物量的决定系数(R²)均高于0.87。相比AMS4,人工神经网络模型系统中,树叶生物量模型的R²高了约0.05,其余各器官也高了0.01左右。此外,均方根误差(RMSE)等指标明显更小,树干和地上生物量的RMSE分别减小了2.135和3.908 kg,模型的检验指标如平均相对误差(MRE)等也表现更优。总体来看,人工神经网络(ANN)是一种灵活可靠的生物量估计方法,估测林木地上生物量或单独某器官生物量时,ANN模型是值得考虑的替代方法。

关键词: 人工神经网络, 相容性模型, 似乎不相关回归, 地上生物量

Abstract: Forest biomass is an important index in forest development planning and forest resource monitoring. In order to provide a more efficient and low-biased method for estimating individual tree biomass, we introduced artificial neural network here. We used the data of aboveground biomass of 101 Larix olgensis trees harvested from the Dongzhelenghe Forest Farm in Heilongjiang Province to develop four aggregation model systems (AMS), based on different combination of the variables (diameter at breast height, tree height, crown width). The weighted functions were used to eliminate heteroscedasticity. Then, we trained artificial neural network (ANN) biomass model based on the optimal combination. The models were tested by the leave-one-out cross-validation method to compare the accuracy of the two biomass estimation methods. The results showed that biomass model based on only one variable, diameter at breast height, could accurately estimate the biomass of L. olgensis. Adding two indices, tree height and crown width, could improve the fitting performance of models, with AMS4 performing the best among the four addictive model systems. The biomass models developed by the two methods both could estimate biomass at tree level accurately, with the coefficient of determination (R²) of each component was higher than 0.87. Compared with the AMS4, R² of leaf biomass model was about 0.05 higher, and that of other organs were also about 0.01 higher in artificial neural network model system. In addition, the root mean square error (RMSE) and other indicators were also significantly smaller. For example, the RMSE of tree stem and aboveground biomass were smaller by 2.135 kg and 3.908 kg, respectively. The model's validation statistics mean relative error (MRE) performed better. In general, ANN was a flexible and reliable biomass estimation method, which was worthy consideration when predicting tree component biomass or aboveground biomass.

Key words: artificial neural network, compatible model, seemingly unrelated regression, aboveground biomass

梁瑞婷, 王轶夫, 邱思玉, 孙玉军, 谢运鸿. 人工神经网络与相容性生物量模型预测单木地上生物量的比较[J]. 应用生态学报, 2022, 33(1): 9-16.

LIANG Rui-ting, WANG Yi-fu, QIU Si-yu, SUN Yu-jun, XIE Yun-hong. Comparison of artificial neural network with compatible biomass model for predicting aboveground biomass of individual tree[J]. Chinese Journal of Applied Ecology, 2022, 33(1): 9-16.

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人工神经网络与相容性生物量模型预测单木地上生物量的比较

Comparison of artificial neural network with compatible biomass model for predicting aboveground biomass of individual tree

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