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Eucalyptus growth recognition using machine learning methods and spectral variables
Forest Ecology and Management ( IF 3.7 ) Pub Date : 2021-07-09 , DOI: 10.1016/j.foreco.2021.119496
Bruno Rodrigues de Oliveira 1 , Arlindo Ananias Pereira da Silva 2 , Larissa Pereira Ribeiro Teodoro 1 , Gileno Brito de Azevedo 1 , Glauce Taís de Oliveira Sousa Azevedo 1 , Fábio Henrique Rojo Baio 1 , Renato Lustosa Sobrinho 3 , Carlos Antonio da Silva Junior 4 , Paulo Eduardo Teodoro 1, 2
Affiliation  

Growth and production models can help to simulate the growth of tree dimensions to predict forest productivity at different levels. In this context, the following questions arise: (i) is it possible to recognize the growth pattern of eucalyptus species based on spectral features using machine learning (ML) for data modeling? (ii) what spectral features provides better accuracy? and (iii) what ML algorithms are most accurate for performing this modeling? To answer these questions, the present study evaluated the use of ML techniques using breast height and total plant height to classify the growth of five species of eucalyptus and Corymbria citriodora in an unsupervised learning, and the obtained classes for induce ML algorithms to recognize the species with relation to their growth using vegetation indices (VIs) and spectral bands (SBs). It were evaluated five eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis e E. urograndis) and C. citriodora in experimental design of randomized blocks with four replicates, with 20 plants inside each experimental plot. The diameter at breast height and total plant height at stand level were obtained by measuring five trees in each experimental unit in seven measurements. During this same period, a flight was carried out using a remotely piloted aircraft for the acquisition of spectral variables (SBs and VIs). For recognition of eucalyptus species in relation to their growth two machine learning approaches were employed: supervised and unsupervised. The average accuracy obtained from 10-fold cross-validation, employing Random Forest algorithm and 24 features, was 0.76. This result shows that the proposed approach is appropriate to recognize different eucalyptus species based on their growth.



中文翻译:

使用机器学习方法和光谱变量识别桉树生长

生长和生产模型有助于模拟树木尺寸的生长,以预测不同水平的森林生产力。在这种情况下,出现了以下问题:(i) 是否可以使用机器学习 (ML) 进行数据建模,基于光谱特征识别桉树物种的生长模式?(ii) 哪些光谱特征提供了更好的准确度?(iii) 什么 ML 算法对于执行此建模最准确?为了回答这些问题,本研究评估了 ML 技术的使用,使用胸高和植物总高度对五种桉树和Corymbria citriodora的生长进行分类在无监督学习中,获得的类用于诱导 ML 算法使用植被指数 (VI) 和光谱带 (SB) 识别与其生长相关的物种。评估了五种桉树物种(E. camaldulensisE. uroplylla、E. salignaE. grandis e E. urograndis)和C. citriodora在具有四个重复的随机区组的实验设计中,每个实验小区内有 20 株植物。通过对每个实验单元中的五棵树进行七次测量,获得胸高直径和林分总株高。在同一时期,使用遥控飞机进行飞行以获取光谱变量(SB 和 VI)。为了识别与生长相关的桉树物种,采用了两种机器学习方法:有监督和无监督。使用随机森林算法和 24 个特征,从 10 倍交叉验证获得的平均准确度为 0.76。该结果表明,所提出的方法适用于根据其生长识别不同的桉树物种。

更新日期:2021-07-09
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