Research on Accurate Estimation Method of Eucalyptus Biomass Based on Airborne LiDAR Data and Aerial Images

Abstract

Forest biomass is a key index to comprehend the changes of ecosystem productivity and forest growth and development. Accurate acquisition of single tree scale biomass information is of great significance to the protection, management and monitoring of forest resources. LiDAR technology can penetrate the forest canopy and obtain information on the vertical structure of the forest. Aerial photography technology has the advantages of low cost and high speed, and can obtain information on the horizontal structure of the forest. Therefore, in this study, multispectral imagery and LiDAR data were integrated, and a part of the Zengcheng Forest Farm in Guangdong Province was selected as the study area. Large-scale and high-precision Eucalyptus biomass estimation research was gradually carried out by screening influencing factors and establishing models. This study compared and analysed the performance of multiple stepwise regression methods, random forest algorithms, support vector machine algorithms and decision tree algorithms for Eucalyptus biomass estimation to determine the best method for Eucalyptus biomass estimation. The results demonstrated that the accuracy of the model established by the machine learning method was higher than that of the linear regression model, and in the machine learning model, the random forest model had the best performance on both the training set (R2 = 0.9346, RMSE = 8.8399) and the test set (R2 = 0.8670, RMSE = 15.0377). RF was more suitable for the biomass estimation of Eucalyptus in this study. The spatial resolution of Eucalyptus biomass distribution was 0.05 m in this study, which had higher accuracy and was more accurate. It can provide data reference for the details about biomass distribution of Eucalyptus in the majority of provinces, and has certain practical reference significance.

A Note on Statistical Techniques and Biological Background in Analysis of Remote Sensed Data in Forest Inventory

“…This importance of variable is determined by summing the reduced GINI index for all nodes for and for each deciding tree in the Random Forest (Chen and Ishwaran 2012). Again, among many others, the method was used for example in (Li et al 2022). Random Forest provides the variable importance and in contrast to previously mentioned two techniques it does not produce variable weight (like in case of linear regression) but the quality of the model can be evaluated using accuracy or test the accuracy on dataset which was not used for training.…”

LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations

“…According to Dong and Chen [32], the canopy height model (CHM) forms the basis upon which other tree-level information can be derived. This is demonstrated in a study carried out by Li et al [48] in which CHM generated from a digital surface model and a digital terrain model via data pre-processing was utilized for single wood extraction of eucalyptus globulus. Analysis performed based on the extraction results was used to ascertain eucalyptus biomass estimation performance via multiple stepwise regression and machine learning algorithms such as support vector machine, random forest, and decision tree.…”

“…This is also known as the residual standard error (RSE) in other literature. RMSE represent the distance or the gap between the actual result and the target estimate, and is a preferred performance evaluation measure when conducting regression analysis [48]. We noted in the review that about 16% of the authors adopted RMSE as a model assessment criterion.…”

Predição do Incremento Médio Anual Volumétrico de Eucalyptus com Aprendizado de Máquina

“…O trabalho avaliou o desempenho pela métrica de erro RMSE (Equac ¸ão 1) e alcanc ¸ou um percentual de erro (RMSE %) por volta de 12,75% com o RF. (ii) [Li et al 2022]: usou dados de imagens multiespectrais e de georeferenciamento de uma floresta subtropical úmida da região sul da China como conjunto de dados para predic ¸ão da biomassa florestal. Foram comparados três algoritmos de aprendizado de máquina (SVM, RF e Árvore de Decisão) com modelos de regressão linear.…”