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

Yiran Li, Ruirui Wang (), Wei Shi, Qiang Yu, Xiuting Li and Xingwang Chen
Additional contact information
Yiran Li: College of Forestry, Beijing Forestry University, Beijing 100083, China
Ruirui Wang: College of Forestry, Beijing Forestry University, Beijing 100083, China
Wei Shi: Beijing Ocean Forestry Technology, Beijing 100083, China
Qiang Yu: College of Forestry, Beijing Forestry University, Beijing 100083, China
Xiuting Li: College of Forestry, Beijing Forestry University, Beijing 100083, China
Xingwang Chen: College of Forestry, Beijing Forestry University, Beijing 100083, China

Sustainability, 2022, vol. 14, issue 17, 1-18

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 (R 2 = 0.9346, RMSE = 8.8399) and the test set (R 2 = 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.

Keywords: LiDAR; Eucalyptus; biomass; multiple regression; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/17/10576/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/17/10576/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:17:p:10576-:d:897260

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().